diff --git a/contrib/delta/dev/diffs/4.1.0.diff b/contrib/delta/dev/diffs/4.1.0.diff new file mode 100644 index 0000000000..692dd5bd3f --- /dev/null +++ b/contrib/delta/dev/diffs/4.1.0.diff @@ -0,0 +1,232 @@ +diff --git a/build.sbt b/build.sbt +index 6f16864..e623a08 100644 +--- a/build.sbt ++++ b/build.sbt +@@ -50,6 +50,9 @@ val internalModuleNames = settingKey[Set[String]]("Internal module artifact name + + // Spark version to delta-spark and its dependent modules + // For more information see CrossSparkVersions.scala ++// Comet regression testing (added by dev/diffs/delta/4.1.0.diff in Apache DataFusion Comet) ++val cometVersion = "0.17.0-SNAPSHOT" ++ThisBuild / resolvers += Resolver.mavenLocal + val sparkVersion = settingKey[String]("Spark version") + + // Dependent library versions +@@ -617,6 +620,19 @@ lazy val spark = (project in file("spark-unified")) + "org.apache.spark" %% "spark-sql" % sparkVersion.value % "test" classifier "tests", + "org.apache.spark" %% "spark-hive" % sparkVersion.value % "test" classifier "tests", + "org.mockito" % "mockito-inline" % "4.11.0" % "test", ++ // Comet regression testing — pulls comet-spark for Spark 4.1 + Scala 2.13 from ++ // the user's mavenLocal repo. The published artifact is what `mvn install ++ // -Pspark-4.1 -Pcontrib-delta` produces; the contrib's Delta wiring is bundled ++ // into that JAR. No separate Delta-specific Comet artifact. ++ // ++ // `exclude(comet-contrib-delta-deps)`: the published comet-spark pom lists this ++ // as a transitive (advertising `delta-spark` provided-scope to end users), but ++ // its own pom has un-interpolated `${spark.version.short}` / `${scala.binary.version}` ++ // in `` and ``. Maven re-interpolates from filename, SBT ++ // doesn't — so SBT fails resolving the parent. Delta's own build already ++ // supplies delta-spark on the test classpath, so the exclude is safe here. ++ ("org.apache.datafusion" % s"comet-spark-spark4.1_${scalaBinaryVersion.value}" % cometVersion % "test") ++ .exclude("org.apache.datafusion", s"comet-contrib-delta-deps-spark4.1_${scalaBinaryVersion.value}"), + ), + + Test / testOptions += Tests.Argument("-oDF"), +@@ -636,7 +652,15 @@ lazy val spark = (project in file("spark-unified")) + "-Ddelta.log.cacheSize=3", + "-Dspark.databricks.delta.delta.log.cacheSize=3", + "-Dspark.sql.sources.parallelPartitionDiscovery.parallelism=5", +- "-Xmx1024m" ++ // Bumped from 1024m: 1GB is too tight for Spark + Delta + parquet I/O + Comet ++ // native library + offheap. Caused GC thrashing on memory-heavy suites (DV, ++ // Merge, CDC) under the 3.3.2 regression sweep — keeping 4g for 4.1.0. ++ "-Xmx4g", ++ // Match stock Delta CI: run on UTC so Delta's force-verify-all-files-in-CRC ++ // path (triggered by non-UTC user.timezone) stays disabled. Otherwise ++ // ChecksumSuite tests fail because `TimeZone.setDefault(UTC)` doesn't ++ // update `System.getProperty("user.timezone")`. ++ "-Duser.timezone=UTC" + ), + + // Required for testing table features see https://github.com/delta-io/delta/issues/1602 +diff --git a/build/sbt-config/repositories b/build/sbt-config/repositories +index 2853417..62ff4b6 100644 +--- a/build/sbt-config/repositories ++++ b/build/sbt-config/repositories +@@ -13,3 +13,12 @@ + typesafe-releases: https://repo.typesafe.com/typesafe/releases/ + apache-snapshot: https://repository.apache.org/content/groups/snapshots/ + jitpack: https://jitpack.io ++ # Comet regression testing: dev/run-delta-regression.sh publishes Comet's just-built ++ # artifacts to an ISOLATED dir under $TMPDIR rather than ~/.m2/repository/. Pointing ++ # SBT directly at ~/.m2 triggers a coursier sticky-resolver bug: when an unrelated ++ # transitive (parquet/guava/azure/...) has an orphan pom-only entry in ~/.m2 from a ++ # prior `mvn` run, coursier resolves the POM at local-m2, then refuses to fall ++ # through to maven-central for the JAR — failing the build on artifacts that have ++ # nothing to do with Comet. The isolated dir contains only `org.apache.datafusion:*` ++ # so there are no unrelated POMs to mistakenly match. ++ local-comet: file:///tmp/comet-published-4.1/ +diff --git a/spark/src/test/scala/org/apache/spark/sql/delta/CometSmokeTest.scala b/spark/src/test/scala/org/apache/spark/sql/delta/CometSmokeTest.scala +new file mode 100644 +index 0000000..013ee98 +--- /dev/null ++++ b/spark/src/test/scala/org/apache/spark/sql/delta/CometSmokeTest.scala +@@ -0,0 +1,82 @@ ++/* ++ * Copyright (2021) The Delta Lake Project Authors. ++ * ++ * Licensed under the Apache License, Version 2.0 (the "License"); ++ * you may not use this file except in compliance with the License. ++ * You may obtain a copy of the License at ++ * ++ * http://www.apache.org/licenses/LICENSE-2.0 ++ * ++ * Unless required by applicable law or agreed to in writing, software ++ * distributed under the License is distributed on an "AS IS" BASIS, ++ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ++ * See the License for the specific language governing permissions and ++ * limitations under the License. ++ */ ++ ++package org.apache.spark.sql.delta ++ ++import org.apache.spark.sql.{QueryTest, Row} ++import org.apache.spark.sql.delta.test.DeltaSQLCommandTest ++import org.apache.spark.sql.test.SharedSparkSession ++ ++/** ++ * Smoke test asserting that Comet is actually loaded and executing queries when the ++ * Delta regression diff is applied. Catches silent configuration drift where Comet ++ * is on the classpath but not wired into the physical plan (e.g. a typo in ++ * `spark.plugins` that Spark silently ignores). ++ * ++ * Added by the Comet regression diff at `dev/diffs/delta/4.1.0.diff` in ++ * Apache DataFusion Comet. ++ */ ++class CometSmokeTest extends QueryTest with SharedSparkSession with DeltaSQLCommandTest { ++ ++ test("Comet plugin is registered in SparkConf") { ++ val plugins = spark.conf.get("spark.plugins") ++ assert(plugins.contains("CometPlugin"), ++ s"Comet plugin not registered. spark.plugins=$plugins") ++ } ++ ++ test("Delta streaming source read returns the micro-batch AddFiles") { ++ // Regression guard for the pre-materialized FileIndex path: Delta's streaming ++ // micro-batch gives us an exact `TahoeBatchFileIndex` with the AddFiles for ++ // [startOffset, endOffset]. The contrib's native Delta scan must honour that ++ // list instead of re-running kernel log replay against the snapshot root ++ // (which would return an empty or different set for a streaming batch). ++ val src = "comet_stream_src" ++ try { ++ spark.sql(s"CREATE TABLE $src (key INT, value INT) USING DELTA") ++ var collected: Seq[org.apache.spark.sql.Row] = Seq.empty ++ val sw = spark.readStream.table(src).writeStream ++ .format("console") ++ .foreachBatch { (df: org.apache.spark.sql.DataFrame, _: Long) => ++ collected = df.collect().toSeq ++ } ++ .outputMode("append") ++ .start() ++ spark.sql(s"INSERT INTO $src(key, value) VALUES(0, 42)") ++ sw.processAllAvailable() ++ sw.stop() ++ assert(collected.nonEmpty, "Streaming Delta source produced no rows") ++ assert(collected.map(r => (r.getInt(0), r.getInt(1))) == Seq((0, 42))) ++ } finally { ++ spark.sql(s"DROP TABLE IF EXISTS $src") ++ } ++ } ++ ++ test("Comet operators appear in Delta query physical plan") { ++ withTempDir { dir => ++ val path = dir.getCanonicalPath ++ spark.range(10).toDF("id") ++ .write.format("delta").save(path) ++ ++ val df = spark.read.format("delta").load(path).filter("id > 2") ++ val planString = df.queryExecution.executedPlan.toString ++ ++ assert(planString.contains("Comet"), ++ s"No Comet operators found in physical plan. Is Comet actually wired in?\n$planString") ++ ++ checkAnswer(df, (3 until 10).map(i => Row(i))) ++ } ++ } ++} +diff --git a/spark/src/test/scala/org/apache/spark/sql/delta/test/DeltaSQLCommandTest.scala b/spark/src/test/scala/org/apache/spark/sql/delta/test/DeltaSQLCommandTest.scala +index cb28a4f..44a2a37 100644 +--- a/spark/src/test/scala/org/apache/spark/sql/delta/test/DeltaSQLCommandTest.scala ++++ b/spark/src/test/scala/org/apache/spark/sql/delta/test/DeltaSQLCommandTest.scala +@@ -35,5 +35,22 @@ trait DeltaSQLCommandTest extends SharedSparkSession { + classOf[DeltaSparkSessionExtension].getName) + .set(SQLConf.V2_SESSION_CATALOG_IMPLEMENTATION.key, + classOf[DeltaCatalog].getName) ++ // --- Comet regression wiring (added by dev/diffs/delta/4.1.0.diff) --- ++ // CometPlugin registers the native lib + memory manager. ServiceLoader pulls ++ // the Delta contrib's DeltaScanRuleExtension / DeltaOperatorSerdeExtension out ++ // of META-INF/services in comet-spark.jar at first use. ++ .set("spark.plugins", "org.apache.spark.CometPlugin") ++ .set("spark.shuffle.manager", ++ "org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager") ++ .set("spark.comet.enabled", "true") ++ .set("spark.comet.exec.enabled", "true") ++ .set("spark.comet.exec.shuffle.enabled", "true") ++ .set("spark.comet.scan.enabled", "true") ++ // Enable the contrib's native Delta scan path. Key is defined in ++ // org.apache.comet.contrib.delta.DeltaConf. ++ .set("spark.comet.scan.deltaNative.enabled", "true") ++ .set("spark.comet.explainFallback.enabled", "true") ++ .set("spark.memory.offHeap.enabled", "true") ++ .set("spark.memory.offHeap.size", "10g") + } + } +diff --git a/spark/src/test/scala/org/apache/spark/sql/delta/test/ScanReportHelper.scala b/spark/src/test/scala/org/apache/spark/sql/delta/test/ScanReportHelper.scala +index e7c4b90..32246a2 100644 +--- a/spark/src/test/scala/org/apache/spark/sql/delta/test/ScanReportHelper.scala ++++ b/spark/src/test/scala/org/apache/spark/sql/delta/test/ScanReportHelper.scala +@@ -44,6 +44,11 @@ trait ScanReportHelper extends SharedSparkSession with AdaptiveSparkPlanHelper { + collectWithSubqueries(plan)({ + case fs: FileSourceScanExec => Seq(fs) + case cached: InMemoryTableScanExec => collectScans(cached.relation.cacheBuilder.cachedPlan) ++ // Comet regression: Comet rewrites `FileSourceScanExec` into its own leaf ++ // variants. Tests that inspect scan metrics would otherwise see an empty ++ // list and fail with `MatchError: ArrayBuffer()`. ++ case c: org.apache.spark.sql.comet.CometScanExec => Seq(c.wrapped) ++ case c: org.apache.spark.sql.comet.CometDeltaNativeScanExec => Seq(c.originalPlan) + }).flatten + } + +diff --git a/spark/src/test/scala/org/apache/spark/sql/delta/test/TestsStatistics.scala b/spark/src/test/scala/org/apache/spark/sql/delta/test/TestsStatistics.scala +index 93c7da9..755fae9 100644 +--- a/spark/src/test/scala/org/apache/spark/sql/delta/test/TestsStatistics.scala ++++ b/spark/src/test/scala/org/apache/spark/sql/delta/test/TestsStatistics.scala +@@ -79,6 +79,13 @@ trait TestsStatistics { self: DeltaSQLTestUtils => + case f: FileSourceScanExec => Some(f) + case InputAdapter(f: FileSourceScanExec) => Some(f) + case ColumnarToRowExec(InputAdapter(f: FileSourceScanExec)) => Some(f) ++ // Comet regression: unwrap the Comet scan variants Comet's rules produce in ++ // place of Spark's `FileSourceScanExec`. Tests that search for the scan in ++ // the executed plan (e.g. to read its metrics) are otherwise blind to them. ++ case c: org.apache.spark.sql.comet.CometScanExec => Some(c.wrapped) ++ case c: org.apache.spark.sql.comet.CometDeltaNativeScanExec => Some(c.originalPlan) ++ case org.apache.spark.sql.comet.CometNativeColumnarToRowExec(inner) => ++ unapply(inner) + case _ => None + } + } +diff --git a/spark/src/test/scala/org/apache/spark/sql/delta/DeltaSuite.scala b/spark/src/test/scala/org/apache/spark/sql/delta/DeltaSuite.scala +--- a/spark/src/test/scala/org/apache/spark/sql/delta/DeltaSuite.scala ++++ b/spark/src/test/scala/org/apache/spark/sql/delta/DeltaSuite.scala +@@ -118,6 +118,11 @@ + val query = spark.read.format("delta").load(testPath).where("part = 1") + val fileScans = query.queryExecution.executedPlan.collect { + case f: FileSourceScanExec => f ++ // Comet regression: Comet's planner replaces `FileSourceScanExec` with ++ // `CometDeltaNativeScanExec`. Its `metrics` map aliases `numFiles` to the ++ // post-pruning task count so this assertion exercises the same partition-skip ++ // semantics from the Comet path. ++ case c: org.apache.spark.sql.comet.CometDeltaNativeScanExec => c + } + + // Force the query to read files and generate metrics diff --git a/contrib/delta/dev/run-regression.sh b/contrib/delta/dev/run-regression.sh new file mode 100755 index 0000000000..c064c2f786 --- /dev/null +++ b/contrib/delta/dev/run-regression.sh @@ -0,0 +1,206 @@ +#!/usr/bin/env bash +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Run Delta Lake's own test suite with Comet enabled as a regression check. +# Mirrors what .github/workflows/delta_regression_test.yml does in CI. +# +# This is the PR2 (contrib) variant: the install step bundles the Delta +# contrib via `-Pcontrib-delta` so the comet-spark JAR being installed +# carries DeltaScanRuleExtension/DeltaOperatorSerdeExtension and the +# matching JNI symbols (built into libcomet via `--features contrib-delta` +# on the native crate). Without `-Pcontrib-delta` the installed comet-spark +# JAR has no Delta wiring and Delta tests would just exercise vanilla Spark. +# +# Usage: +# dev/run-delta-regression.sh [DELTA_VERSION] [TEST_FILTER] +# +# Examples: +# dev/run-delta-regression.sh # smoke on default (4.1.0) +# dev/run-delta-regression.sh 4.1.0 # smoke on Delta 4.1.0 +# dev/run-delta-regression.sh 4.1.0 full # full Delta test suite +# dev/run-delta-regression.sh 4.1.0 DeltaTimeTravelSuite # one specific test class +# DELTA_WORKDIR=/tmp/my-delta dev/run-delta-regression.sh # reuse a checkout + +set -euo pipefail + +DELTA_VERSION="${1:-4.1.0}" +TEST_FILTER="${2:-smoke}" + +# Map Delta version -> Spark short version -> SBT module +case "$DELTA_VERSION" in + 2.4.0) SPARK_SHORT="3.4"; SBT_MODULE="core" ;; + 3.3.2) SPARK_SHORT="3.5"; SBT_MODULE="spark" ;; + 4.0.0) SPARK_SHORT="4.0"; SBT_MODULE="spark" ;; + 4.1.0) SPARK_SHORT="4.1"; SBT_MODULE="spark" ;; + *) + echo "Error: unsupported Delta version '$DELTA_VERSION'" + echo "Supported: 2.4.0 (Spark 3.4), 3.3.2 (Spark 3.5), 4.0.0 (Spark 4.0), 4.1.0 (Spark 4.1)" + exit 1 + ;; +esac + +# Script lives at contrib/delta/dev/run-regression.sh, so COMET_ROOT is three levels up. +COMET_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)" +DIFF_FILE="$COMET_ROOT/contrib/delta/dev/diffs/${DELTA_VERSION}.diff" +DELTA_WORKDIR="${DELTA_WORKDIR:-${TMPDIR:-/tmp}/delta-regression-${DELTA_VERSION}}" + +if [[ ! -f "$DIFF_FILE" ]]; then + echo "Error: diff file not found: $DIFF_FILE" + exit 1 +fi + +echo "==========================================" +echo "Delta regression run (contrib variant)" +echo " Delta version : $DELTA_VERSION" +echo " Spark profile : spark-$SPARK_SHORT" +echo " SBT module : $SBT_MODULE" +echo " Test filter : $TEST_FILTER" +echo " Work dir : $DELTA_WORKDIR" +echo " Comet root : $COMET_ROOT" +echo "==========================================" + +# Step 1: build + install Comet to local Maven repo for the target Spark profile, +# with the Delta contrib bundled into comet-spark.jar. +# +# `FAST=1` skips plugin checks that aren't relevant during iteration: +# - drop `-Prelease` (no source/javadoc/scaladoc jars, no GPG prep) +# - skip spotless check (run `mvn spotless:apply` manually before commit) +# - skip Apache RAT license header check +# - skip javadoc / scaladoc generation +# - skip source jar packaging +# Together these save ~60-120s per iteration. The canonical (no-FAST) invocation +# still runs the full lifecycle so CI parity is preserved. +echo +echo "[1/4] Building and installing Comet (spark-$SPARK_SHORT, contrib-delta)..." +cd "$COMET_ROOT" +# Spark 4.1 requires Java 17 (java.lang.Record). Comet's parent pom defaults +# java.version=11 — overriding here so the install works regardless of which JDK +# is on JAVA_HOME, as long as that JDK is ≥17. +JAVA_OVERRIDE=( + -Djava.version=17 + -Dmaven.compiler.source=17 + -Dmaven.compiler.target=17 +) +if [[ -n "${FAST:-}" ]]; then + echo " FAST=1: skipping spotless/RAT/javadoc/source-jar plugins" + # Override `jni.dir` -> `native/target/release` because Comet's parent pom defaults it + # to `native/target/debug`. The non-FAST path implicitly fixes this via `-Prelease`, + # but FAST=1 drops that profile (it pulls in shade/javadoc), so without this override + # mvn bundles a stale debug-tree dylib and contrib-delta's `#[ctor]` planner registration + # silently goes missing -- every Delta scan then fails with "No contrib planner + # registered for ContribOp.kind=delta-scan" at runtime. + ./mvnw install -DskipTests -Pspark-"$SPARK_SHORT" -Pcontrib-delta \ + "${JAVA_OVERRIDE[@]}" \ + -Djni.dir="$COMET_ROOT/native/target/release" \ + -Dspotless.check.skip=true \ + -Drat.skip=true \ + -Dmaven.javadoc.skip=true \ + -Dmaven.source.skip=true +else + ./mvnw install -Prelease -DskipTests -Pspark-"$SPARK_SHORT" -Pcontrib-delta \ + "${JAVA_OVERRIDE[@]}" +fi + +# Sync Comet's just-installed artifacts to an ISOLATED publish dir. Pointing SBT +# directly at ~/.m2/repository/ triggers coursier's sticky-resolver: orphan +# pom-only entries left over from `mvn dependency:resolve` runs make it look for +# unrelated transitive JARs (parquet, guava, azure, ...) at local-m2 and refuse +# to fall through to maven-central. Isolating Comet's artifacts in a dedicated +# directory means local-comet only matches `org.apache.datafusion:*` -- no +# orphans to mistake. +# +# Hard-coded under /tmp (not $TMPDIR) because the path is also referenced in +# dev/diffs/delta/.diff (build/sbt-config/repositories), which +# the diff applies into the Delta checkout. macOS's $TMPDIR is per-user under +# /var/folders/..., so substituting it here would diverge from the diff's +# literal path. +COMET_PUBLISH_DIR="${COMET_PUBLISH_DIR:-/tmp/comet-published-${SPARK_SHORT}}" +echo +echo "[1.5/4] Syncing Comet artifacts to $COMET_PUBLISH_DIR..." +rm -rf "$COMET_PUBLISH_DIR" +mkdir -p "$COMET_PUBLISH_DIR/org/apache/datafusion" +rsync -a "$HOME/.m2/repository/org/apache/datafusion/" "$COMET_PUBLISH_DIR/org/apache/datafusion/" +echo " Published: $(ls -1 "$COMET_PUBLISH_DIR/org/apache/datafusion/" | wc -l | tr -d ' ') Comet modules" + +# Step 2: clone Delta (or reuse existing checkout). +# +# `git clean -fd` here is intentional and cheap (sub-second): it removes +# untracked files left from the previous diff apply but respects gitignore, +# so Delta's `target/` (and SBT's zinc cache inside it) is preserved. +echo +echo "[2/4] Cloning Delta $DELTA_VERSION..." +if [[ -d "$DELTA_WORKDIR/.git" ]]; then + echo " Reusing existing checkout at $DELTA_WORKDIR" + cd "$DELTA_WORKDIR" + git fetch --depth 1 origin "refs/tags/v$DELTA_VERSION:refs/tags/v$DELTA_VERSION" 2>/dev/null || true + git checkout -f "v$DELTA_VERSION" + git clean -fd + rm -rf spark/spark-warehouse +else + rm -rf "$DELTA_WORKDIR" + git clone --depth 1 --branch "v$DELTA_VERSION" https://github.com/delta-io/delta.git "$DELTA_WORKDIR" + cd "$DELTA_WORKDIR" +fi + +# Step 3: apply the Comet diff. +echo +echo "[3/4] Applying diff $DIFF_FILE..." +git apply "$DIFF_FILE" + +# Step 4: run tests. +echo +echo "[4/4] Running tests..." +export SPARK_LOCAL_IP="${SPARK_LOCAL_IP:-localhost}" +# Skip Delta's javaunidoc generation. Delta's `configureUnidoc` wires +# `(Test / test) := (Test / test) dependsOn (Compile / unidoc)`, and the +# javaunidoc step compiles auto-generated Java stubs from Scala test sources +# that fail to resolve `org.apache.spark.sql.test.SQLTestData` etc. -- Delta's +# own gap, not ours. Setting DISABLE_UNIDOC=1 short-circuits the helper +# (Unidoc.scala line 52) so the test target runs directly. +export DISABLE_UNIDOC=1 + +# Delta 4.1.0 mandates Java 17; Comet itself builds fine on 17+. If the user +# is iterating with a newer JDK on Comet, point this at a JDK 17 install for +# SBT. Typical usage: `DELTA_JAVA_HOME=$(/usr/libexec/java_home -v 17)`. +if [[ -n "${DELTA_JAVA_HOME:-}" ]]; then + echo " Using DELTA_JAVA_HOME=$DELTA_JAVA_HOME for SBT" + export JAVA_HOME="$DELTA_JAVA_HOME" + export PATH="$DELTA_JAVA_HOME/bin:$PATH" +fi + +# Reset Gradle daemon + script cache. A daemon started with an older JDK +# sticks around and will be reused by Delta's `./gradlew` inside +# `icebergShaded/assembly`, and Gradle's compiled-build-script cache stores +# classfiles whose major version matches the JDK of the earlier run. +pkill -f 'GradleDaemon' 2>/dev/null || true +rm -rf ~/.gradle/caches/7.5.1/scripts ~/.gradle/caches/7.6.3/scripts 2>/dev/null || true + +case "$TEST_FILTER" in + smoke) + build/sbt "$SBT_MODULE/testOnly org.apache.spark.sql.delta.CometSmokeTest" + ;; + full) + build/sbt "$SBT_MODULE/test" + ;; + *) + build/sbt "$SBT_MODULE/testOnly $TEST_FILTER" + ;; +esac + +echo +echo "Done." diff --git a/contrib/delta/dev/run-test.sh b/contrib/delta/dev/run-test.sh new file mode 100755 index 0000000000..9b8c67731d --- /dev/null +++ b/contrib/delta/dev/run-test.sh @@ -0,0 +1,51 @@ +#!/bin/bash +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +# Run one or more Delta scalatest test selectors via build/sbt in the extracted +# Delta regression checkout (Delta 4.1.0 / Spark 4.1 by default; override with +# DELTA_VERSION). +# +# Usage: dev/run-delta-test.sh 'org.apache.spark.sql.delta.SomeSuite -- -z "test substring"' [...more testOnly selectors] +# +# Each argument is passed as a separate `spark/testOnly` command. Output goes to +# target/delta-regression-logs/test-.log (relative to this repo). +set -euo pipefail +REPO_ROOT="$(cd "$(dirname "$0")/../../.." && pwd)" +DELTA_VERSION="${DELTA_VERSION:-4.1.0}" +DELTA_DIR="${DELTA_DIR:-${DELTA_WORKDIR:-${TMPDIR:-/tmp}/delta-regression-${DELTA_VERSION}}}" +export JAVA_HOME="${JAVA_HOME:-$HOME/jdks/jdk-17.0.18+8/Contents/Home}" +export SPARK_LOCAL_IP=127.0.0.1 +export RUST_BACKTRACE=1 + +if [ $# -lt 1 ]; then + echo "usage: $0 'SuiteClass -- -z \"name\"' [...]" + exit 2 +fi + +LOG="$REPO_ROOT/target/delta-regression-logs/test-$(date +%Y%m%d-%H%M%S).log" +mkdir -p "$(dirname "$LOG")" + +cmds=() +for sel in "$@"; do + cmds+=("spark/testOnly $sel") +done + +cd "$DELTA_DIR" +echo "==> logging to $LOG" +build/sbt "${cmds[@]}" 2>&1 | tee "$LOG" diff --git a/contrib/delta/docs/01-overview.md b/contrib/delta/docs/01-overview.md new file mode 100644 index 0000000000..1cff30e188 --- /dev/null +++ b/contrib/delta/docs/01-overview.md @@ -0,0 +1,179 @@ + + +# Comet Delta Contrib — Design Overview + +## Who this is for + +You know Spark's DataSource V2 (`TableProvider`, `Scan`, `Batch`, `InputPartition`). +You may not know Comet, may not know `delta-kernel-rs`, and may not have read +Spark's `FileSourceScanExec` internals. This document explains the shape of +the integration in those terms and points to deeper docs for each subsystem. + +## What problem this solves + +Apache Spark reads Delta tables today by going through Delta's Scala +`DeltaParquetFileFormat` → Spark's vectorised parquet reader → JVM rows. Comet +already replaces step 2 (the vectorised parquet reader) with a native +DataFusion-based reader for plain parquet scans, and the integration with +Spark for that path is well-trodden. The Delta path was the gap: Delta wraps +its parquet reads in JVM-side projections, filters, and Deletion-Vector logic +that Spark's `FileSourceScanExec` doesn't expose cleanly enough for Comet to +slot in underneath. + +This contrib bypasses that wrapping. It plans Delta scans with +`delta-kernel-rs` (the official Rust kernel maintained by the Delta team), +hands the resolved file list to DataFusion's parquet reader, and synthesises +Delta's "virtual" columns (`row_id`, `__delta_internal_is_row_deleted`, etc.) +in native code. The result is end-to-end native execution for Delta reads, +with no Spark-side parquet decoding on the hot path. + +## Mental model: a DSv2 substitute scan that fires before DSv2 binding + +If you've written a DataSource V2 connector, the natural way to integrate +would be a `TableProvider` returning a custom `Scan`. The reason this PR +doesn't do that: + +1. **Delta is a V1 source on the Spark side.** `DeltaTableV2` exposes a V2 + facade but its read path resolves to a V1 `HadoopFsRelation` carrying a + `DeltaParquetFileFormat`. By the time DSv2 binding would run, the V1 plan + is already built. +2. **We want to replace the entire scan node, not just the reader.** Delta + inserts post-scan projections and filters to implement column mapping, + row-tracking materialisation, and DV filtering. Those need to be + *recognised* and *eliminated*, then their semantics re-emitted natively. +3. **Comet's existing plan-rewrite infrastructure already does this for + plain parquet.** Hooking in at the same layer (`CometScanRule` / + `CometExecRule`) gives us the same lifecycle, the same fallback + surface, and the same metric/error wiring. + +So instead of a DSv2 scan, this contrib is a **rule that recognises Delta +relations in the logical plan and substitutes them with a native scan node**. +From a black-box viewpoint, the substitute behaves like a DSv2 `Scan`: it +exposes a schema, partitioning, and per-partition work units (file lists), and +it produces Spark `ColumnarBatch`es. Internally those columnar batches are +produced by DataFusion in Rust and shipped to the JVM as Arrow record batches. + +## End-to-end flow + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ SPARK DRIVER (JVM) │ +│ │ +│ Catalyst logical plan │ +│ │ │ +│ │ DeltaScanRule.transformV1IfDelta (extension) │ +│ ▼ │ +│ Plan with CometDeltaNativeScanExec in place of FileSourceScanExec │ +│ │ │ +│ │ CometDeltaNativeScan.convert (proto serde) │ +│ │ 1. delta-kernel-rs resolves snapshot │ +│ │ 2. Returns AddFile list + DV info + base row IDs │ +│ │ 3. Encode into DeltaScan proto (common + per-task) │ +│ ▼ │ +│ Per-partition byte arrays (DeltaScanTask) via PlanDataInjector │ +└───────────────────┬─────────────────────────────────────────────────┘ + │ shipped to executors via Spark task serialisation + ▼ +┌─────────────────────────────────────────────────────────────────────┐ +│ SPARK EXECUTOR (JVM + Rust via JNI) │ +│ │ +│ CometNativeScanExec.compute │ +│ │ │ +│ │ JNI → planDeltaScan(proto bytes) │ +│ ▼ │ +│ contrib_delta_scan::build_plan (Rust) │ +│ │ │ +│ │ Builds DataFusion ExecutionPlan tree: │ +│ │ ParquetSource (with field_id resolution) │ +│ │ ↓ │ +│ │ ProjectionExec (CM rename, if physical != logical names) │ +│ │ ↓ │ +│ │ DeltaSyntheticColumnsExec if any emit_* flag │ +│ │ OR │ +│ │ DeltaDvFilterExec else if any task has DV │ +│ │ ↓ │ +│ │ ProjectionExec (reorder if synthetics not a suffix) │ +│ ▼ │ +│ Arrow RecordBatch stream → Comet's existing Arrow→JVM bridge │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +The two non-obvious pieces are **the proto split** (a single "common" block +plus per-partition task arrays) and the **native wrapping stack** (DV / +rename / synthetics / reorder). Both are covered in +[02-planning.md](02-planning.md) and +[03-native-execution.md](03-native-execution.md). + +## Two deployment modes from the same codebase + +Default builds (no `-Pcontrib-delta` Maven profile, no `contrib-delta` Cargo +feature) ship with zero Delta surface area: + +- The reflection bridge in `DeltaIntegration.scala` returns `None` at the + first classpath lookup and stays that way for the JVM lifetime +- The `contrib_delta_scan` arm in `native/core/src/execution/planner/` is + `#[cfg(feature = "contrib-delta")]`-gated and compiles out of the dylib +- The proto variant `delta_scan = 117` is present in the schema but never + emitted + +Delta-enabled builds (`-Pcontrib-delta` + `contrib-delta` Cargo feature): + +- `contrib/delta/src/main/scala/...` lands on the classpath, including the + Spark extension that registers `DeltaScanRule` +- `contrib/delta/native/` is linked into `libcomet` as a static lib via + re-export, contributing the `Java_…_planDeltaScan` JNI symbol +- The reflection bridge resolves on first call and caches the result + +This is the same shape as the Iceberg contrib in this repo. The motivation is +explained in [04-design-decisions.md](04-design-decisions.md); the +operational implications are in +[05-build-and-deploy.md](05-build-and-deploy.md). + +## What this contrib does NOT touch + +To keep the integration scope tight, this PR deliberately avoids: + +- **Writes.** Delta writes still go through Delta's Scala writer. The native + path is read-only. (`Delete`, `Update`, `Merge` *use* this read path via + Spark's regenerated plans, but the write back to `_delta_log` is Delta's + code.) +- **Delta transaction protocol.** We do not parse `_delta_log` ourselves — + kernel-rs does that. +- **Catalog logic.** Path-based and metastore-registered tables both work + because we plug in below the `LogicalRelation`, after Spark / Delta have + already resolved the table. + +## Where to read next + +| Topic | Document | +|---|---| +| The planning rule, proto layout, kernel-rs interaction | [02-planning.md](02-planning.md) | +| The native execution plan tree and synthetic columns | [03-native-execution.md](03-native-execution.md) | +| Why an extension rule (not DSv2), why contrib (not core), why kernel-rs | [04-design-decisions.md](04-design-decisions.md) | +| Maven profile, Cargo feature, the publishing dance | [05-build-and-deploy.md](05-build-and-deploy.md) | +| Failure handling, Spark fallback, observability | [06-fallback-and-ops.md](06-fallback-and-ops.md) | + +If you only have time for one more document, read +[04-design-decisions.md](04-design-decisions.md) — it answers the "why didn't +you just…" questions that come up first in review. + +--- + +**Navigation** · [↑ Index](README.md) · Next → [02 Planning](02-planning.md) diff --git a/contrib/delta/docs/02-planning.md b/contrib/delta/docs/02-planning.md new file mode 100644 index 0000000000..5b56c23069 --- /dev/null +++ b/contrib/delta/docs/02-planning.md @@ -0,0 +1,208 @@ + + +# Planning: from logical plan to per-partition tasks + +## The hook point + +Spark's logical plan after analysis contains a `LogicalRelation` wrapping a +`HadoopFsRelation` whose `fileFormat` is a `DeltaParquetFileFormat`. Comet's +existing `CometScanRule` runs as a strategy rewrite: it looks at each scan +node and decides whether to replace it with a native equivalent. + +The Delta contrib adds one arm to that rule via reflection. From +`spark/.../CometScanRule.scala`, inside the per-`FileSourceScanExec` match +on `HadoopFsRelation`: + +```scala +DeltaIntegration.transformV1IfDelta(plan, session, scanExec, r) match { + case Some(handled) => return handled + case None => // proceed with vanilla logic +} +``` + +`DeltaIntegration` is a thin Scala object in *core* that: + +1. On first call, reflectively looks up + `org.apache.comet.contrib.delta.DeltaScanRule#transformV1IfDelta` + with signature `(SparkPlan, SparkSession, FileSourceScanExec, HadoopFsRelation)` +2. Caches the resolved `Method` handle in a `@volatile var` +3. Invokes it and returns `Option[SparkPlan]` — `Some(handled)` if the + contrib either claimed the scan or declined it via its own `withInfo` + fallback marker; `None` if the relation isn't a Delta relation at all + +If the classpath lookup fails (default build, no contrib), the cached value +is `Some(None)` — a "definitely not present" marker — and subsequent calls +short-circuit. This is the only point in core that knows about Delta. + +`DeltaScanRule.transformV1IfDelta` in the contrib first checks whether the +relation's `fileFormat` is `DeltaParquetFileFormat` (via reflection, no +compile-time delta-spark dependency), then applies a series of gates +(covered in [06-fallback-and-ops.md](06-fallback-and-ops.md)). If all gates pass, it returns a +`CometScanExec` marker that flows through the standard +`CometExecRule.convertToComet` path and ultimately routes through +`CometDeltaNativeScan` for proto serialisation. + +## What `CometDeltaNativeScanExec` looks like + +It's a `LeafExecNode` with these responsibilities: + +- Hold the original `relation`, `output`, `dataFilters`, `partitionFilters`, + plus a `dppFilters: Seq[Expression]` list for DPP +- Lazily produce `commonBytes` (the common proto block) and `allTasks` (the + resolved kernel-rs task list) — both `@transient lazy val`s +- At `doExecuteColumnar()` time, apply any now-resolved DPP filters against + `allTasks`, serialise per-partition task bytes, and route through Comet's + existing native exec path + +The kernel-rs scan runs once on first access of `allTasks`; the result is +reused. DPP filters are deliberately applied *after* that lazy val (inside +`doExecuteColumnar`) rather than baked into `allTasks` — at planning time +the DPP subquery is still a `SubqueryAdaptiveBroadcastExec` placeholder, +so the actual partition values are not known yet. + +## kernel-rs scan resolution + +`delta-kernel-rs` is the Delta team's official Rust crate for parsing Delta +metadata. The interaction looks like: + +```rust +let engine = engine_cache.get_or_create(table_root, storage_config)?; +let table = Table::try_from_uri(table_root)?; +let snapshot = table.snapshot(engine.as_ref(), version_hint)?; +let scan = snapshot.scan_builder() + .with_schema(read_schema) + .with_predicate(pushable_filters) + .build()?; + +let mut acc = RawEntryAcc { entries: vec![], row_tracking: …, next_idx: 0 }; +scan.scan_data(engine.as_ref())?.for_each(|batch| { + visit_scan_files(batch, &mut acc, |entry, ctx| { ctx.entries.push(entry); }); +}); +``` + +The `RawEntry` we accumulate carries everything needed to build a per-file +task: the parquet path, partition values, column-mapping maps, optional DV +descriptor, and (newly) the `baseRowId` and `defaultRowCommitVersion` for +row tracking. These are extracted from kernel's `fileConstantValues` by +downcasting the underlying `RecordBatch` (kernel's `ScanFile` doesn't expose +them directly). + +The `engine_cache` is keyed by `(scheme, authority, DeltaStorageConfig)`. +Caching matters because `DefaultEngine` spawns one OS +thread per executor on creation; without a cache, hundreds of scans per minute +leaked threads faster than tokio reaped them, eventually tripping +`pthread_create EAGAIN`. + +## The proto split: common + per-task + +A naive serialisation would embed the entire `Vec` into a single +proto and have Catalyst's plan-closure capture include it in every partition. +For a 5000-file scan with 200 partitions, that's 5000 × 200 = 1M task records +shipped around even though each partition only needs its own slice. + +The split: + +- **`DeltaScanCommon`** (fields shared across all partitions): table root, + read schema, partition schema, pushable filters, column mapping mode, the + set of "emit" flags (which synthetic columns are wanted), and + `final_output_indices` for reorder +- **`DeltaScanTask[]`** (per partition): the list of files this partition + reads, with path, partition values, optional DV descriptor, optional + baseRowId, optional defaultRowCommitVersion + +The common block goes into the `OpStruct::DeltaScan` variant of the operator +proto. The per-partition task arrays ride alongside via Comet's existing +`PlanDataInjector` mechanism: at task scheduling time, the executor receives +its partition index and looks up the matching byte array. This is the same +mechanism the plain parquet scan uses for its `FilePartition` payload. + +## Synthetic columns: detection and emit flags + +After all gates pass, the planner looks at `scan.requiredSchema` and detects +Delta's "synthetic" columns by name: + +- `__delta_internal_row_index` (UInt64): per-file physical row index +- `__delta_internal_is_row_deleted` (Int32, 0/1): whether the row is masked + by a DV +- `row_id` (Int64): Delta row-tracking row ID +- `row_commit_version` (Int64): Delta row-tracking commit version + +For each detected synthetic, an `emit_*` flag goes into `DeltaScanCommon`. The +native side reads these flags to decide whether to wrap the parquet output +with `DeltaSyntheticColumnsExec` (see [03-native-execution.md](03-native-execution.md)). + +When synthetics ARE detected but are NOT a contiguous suffix of +`requiredSchema` (e.g. caller wants `[col_a, row_id, col_b]` not +`[col_a, col_b, row_id]`), we compute `final_output_indices` — a permutation +that the native side applies via a final `ProjectionExec` reorder. This lets +callers project synthetics in any position without forcing a JVM-side +projection. + +## Column mapping + +Delta supports three column-mapping modes: + +- `none`: physical parquet column names match logical +- `name`: physical names are random strings (`col-1a2b…`); a JSON map in + the table schema maps logical → physical +- `id`: physical names are random strings; parquet field IDs identify columns + +The contrib handles both `name` and `id`: + +- **`name` mode**: we rewrite the logical names into a `physicalSchema`, + pass that to kernel-rs and the parquet reader. After the parquet read, + a `ProjectionExec` renames physical → logical for the synthetic-column + detection downstream. The rename projection runs BEFORE the synthetic + wrap so name lookups by logical name work. +- **`id` mode**: we walk the logical `StructType` and on every `StructField` + (including nested struct/array/map element fields) we translate the + `delta.columnMapping.id` metadata key to `PARQUET:field_id`. The parquet + reader's `ParquetField` resolution then matches by ID, not name. + +The translator is recursive and handles `StructType`, `ArrayType`, `MapType` +element types because Delta annotates IDs on every field, not just top-level +columns. + +## DPP and partition pruning + +If the plan above includes a `DynamicPruningExpression`, the actual partition +values aren't known at plan time — they arrive after the broadcast side of a +join finishes. `CometDeltaNativeScanExec` carries `dppFilters` as a +constructor field, and re-applies them against `allTasks` inside +`doExecuteColumnar` (not in a `lazy val`, so the broadcast result is fresh +on each execution). The resulting per-partition task list is then proto- +serialised through the same encoding path as the static case. + +## What's serialised vs computed at execute time + +| Computed at plan time (driver) | Computed at execute time (driver, lazy) | +|---|---| +| Schema resolution | DPP-resolved partition values | +| Gate evaluation | kernel-rs file list | +| Column-mapping translation | per-partition task byte arrays | +| Synthetic-column detection | task packing into partitions | +| Emit flags + `final_output_indices` | proto encoding | + +Nothing in this list happens per-batch on the executor; the executor's only +job is to deserialise the proto and run the resulting DataFusion plan. + +--- + +**Navigation** · [← 01 Overview](01-overview.md) · [↑ Index](README.md) · Next → [03 Native execution](03-native-execution.md) diff --git a/contrib/delta/docs/03-native-execution.md b/contrib/delta/docs/03-native-execution.md new file mode 100644 index 0000000000..02ec87bd10 --- /dev/null +++ b/contrib/delta/docs/03-native-execution.md @@ -0,0 +1,221 @@ + + +# Native execution: from proto bytes to Arrow batches + +## Entry point + +When a Spark executor processes its partition, it calls into JNI with the +encoded proto. The relevant symbol is + +``` +Java_org_apache_comet_Native_planDeltaScan +``` + +declared in `contrib/delta/native/src/jni.rs`. The function: + +1. Decodes the `DeltaScan` proto into `(DeltaScanCommon, Vec)` +2. Calls `build_plan` in `native/core/src/execution/planner/contrib_delta_scan.rs` +3. Returns a pointer to the `Arc` to be wired into Comet's + existing native executor framework + +`build_plan` is where the wrapping stack gets assembled. The rest of this +document walks through that stack. + +## The wrapping stack + +Conceptually: + +``` +DataSourceExec(ParquetSource over the file list) + ↓ (optional, if column mapping mode requires it AND physical/logical names differ) +ProjectionExec (physical → logical rename) + ↓ (exactly one of the two, depending on what the surrounding plan asks for) +DeltaSyntheticColumnsExec ── if any emit_* flag is set + ── OR ── +DeltaDvFilterExec ── else if any task has a DV + ↓ (optional, if synthetics are present and not a suffix of required_schema) +ProjectionExec (reorder via final_output_indices) +``` + +Each layer is added only when needed; the simplest case (no DV, no CM, no +synthetics) is just `DataSourceExec`. + +The synthetic-columns exec and the DV-filter exec are **mutually exclusive**. +This is intentional: when synthetics are emitted, the surrounding Delta plan +(UPDATE/DELETE/MERGE rewrite) wants `is_row_deleted` populated and ALL rows +kept so it can decide what to do with each row itself. When synthetics are +NOT emitted, the standard read path wants deleted rows dropped inline. The +two needs never coincide. + +### Layer 1: `ParquetSource` + +This is DataFusion's existing parquet reader. We build a `FileScanConfig` +from the per-task file lists, passing: + +- Partition values as `wrap_partition_value_in_dict` columns +- Pushable filters as `PhysicalExpr` (already translated by the planner) +- `with_field_id(true)` when `common.use_field_id` is set, so the reader + matches by `PARQUET:field_id` rather than by name + +**FileGroup layout**. When any `emit_*` flag is on (or any task carries a +DV), every file gets its own `FileGroup`. This matters because both +`DeltaSyntheticColumnsExec` and `DeltaDvFilterExec` index per-partition +state vectors (deleted-row indexes, base row IDs, commit versions) by the +DataFusion partition index passed to `execute()`. Each `FileGroup` becomes +one partition, so one-file-per-group is what makes "partition index = file +index" hold. + +When neither synthetics nor DVs are involved, files can pack into shared +groups for better parallelism. + +### Layer 2: column-mapping rename projection (runs BEFORE either layer 3 or 4) + +When `column_mapping_mode = "name"` (or `id` if physical names still differ), +the parquet read produced columns under their physical names (e.g. +`col-1a2b3c`). The downstream layers expect *logical* names. We insert a +`ProjectionExec` that renames physical → logical right after the parquet +source. + +The rename runs before synthetics for two reasons: + +- Synthetic columns have fixed names (`row_id`, `__delta_internal_*`) + that are never CM-renamed; we want the parquet output already in logical + form when we append synthetics +- The synthetic exec's input-schema check uses logical names; running rename + first makes that check correct + +### Layer 3a: `DeltaDvFilterExec` (when no synthetics are requested) + +If any task in the partition has a non-empty `deleted_row_indexes` (computed +by kernel-rs on the driver from the DV file) AND no `emit_*` flag is set, we +wrap with this filter exec. It: + +1. Maintains a `current_row_offset: u64` across batches (assumes + physical-order input) +2. For each incoming batch, walks the sorted `deleted_row_indexes` and builds + a `BooleanArray` mask +3. Returns the masked batch (or skips empty batches entirely) + +Two safeguards: + +- `maintains_input_order() = [true]` — declares to optimisers that we depend + on input order +- `benefits_from_input_partitioning() = [false]` — declares that we don't + want a `RepartitionExec` inserted upstream + +Without these, a future optimiser rule that inserts a repartition above the +parquet source would silently reshuffle rows and the offset-based filter +would produce garbage. The DV filter would still "work" without errors — +it'd just delete the wrong rows. + +### Layer 3b: `DeltaSyntheticColumnsExec` (when any emit flag is set) + +This is the most Delta-specific piece. Source: `contrib/delta/native/src/synthetic_columns.rs`. + +The exec appends up to four columns onto the parquet output: + +| Column | Type | How it's computed | +|---|---|---| +| `__delta_internal_row_index` | UInt64 | per-file row counter, starts at 0, increments by batch size | +| `__delta_internal_is_row_deleted` | Int32 (0/1) | walks the per-task DV sorted indexes against the current row offset | +| `row_id` | Int64 | `task.base_row_id + physical_row_index` (per file) | +| `row_commit_version` | Int64 | `task.default_row_commit_version` (constant per file) | + +State is per **DataFusion partition**, not per file. Each `FileGroup` becomes +its own DataFusion partition (because `need_per_file_groups = true` whenever +any `emit_*` flag is set — see Layer 1), so each file gets its own +`execute(partition_idx, ...)` call with a fresh `DeltaSyntheticColumnsStream`. +Inside that stream the state is `{current_row_offset, next_delete_idx, +base_row_id, default_row_commit_version}`, all looked up by `partition_idx` +from the per-partition vectors threaded down by the planner. After each +batch: + +- `current_row_offset += batch.num_rows()` +- `next_delete_idx` advances past any DV indexes consumed in this batch + (this writeback was a review-fix — earlier versions re-walked from 0 each + batch) + +There is no explicit "task finished" reset. State doesn't need to reset +because each partition's stream object only ever sees rows from one file. + +**Why we synthesise rather than read from a materialised column**. Delta +*can* materialise `row_id` / `row_commit_version` into the parquet files at +write time, in which case we'd just read them directly. But Delta only +materialises them when row tracking has been on since the file was written — +files written before row tracking was enabled have a `baseRowId` table-level +constant and we must compute `row_id` arithmetically. Our path covers both +cases uniformly: the planner sets `emit_row_id = true` only when materialisation +is NOT available; when materialisation IS available, the column comes through +the parquet read like any other column. + +### Layer 4: reorder projection + +`required_schema` might want synthetics in non-suffix positions. The driver +computed `final_output_indices` (a permutation of `[0..n]`) and put it in the +proto. If the indices aren't the identity, we wrap with a final +`ProjectionExec` that reorders columns. Identity → skip. + +The driver's assertion `assert(emitIdx >= 0)` ensures we never compute an +out-of-bounds permutation; if a synthetic is in `required_schema` but its +emit flag wasn't set (somehow), we fail fast on the JVM side rather than +producing wrong output natively. + +## The output stream + +What leaves the topmost exec is an `Arc` whose +`execute()` returns an Arrow `RecordBatchStream`. Comet's existing native +executor framework consumes that stream, moves the batches across JNI into +the JVM via the Arrow C Data Interface, and hands them to Spark as +`ColumnarBatch`es. + +There is nothing Delta-specific in the cross-JNI machinery. As far as the +JVM is concerned, the result looks like any other Comet native scan. + +## Error handling at the native edge + +Failures at any layer (parquet decode error, DV file checksum mismatch, +schema-adaptor mismatch) propagate up as DataFusion `DataFusionError`s and +are converted to Java `RuntimeException`s by the JNI shim. The JVM-side +wrapper in `ShimSparkErrorConverter.wrapNativeParquetError` recognises +parquet-flavoured errors and wraps them with `FAILED_READ_FILE.NO_HINT` +including the file path — this matches Spark's standard error surface for +parquet read failures. + +If the failure happens at the kernel-rs layer on the driver (during plan +construction), we never get to native execution. The planner catches the +error, calls `withInfo(plan, "delta-kernel-rs error: …")`, and falls back +to Spark's Delta reader. See [06-fallback-and-ops.md](06-fallback-and-ops.md) for the full +catalogue. + +## What this stack does NOT do + +- **No vectorised expression evaluation here.** Filters that get pushed + into `ParquetSource` use DataFusion's PhysicalExpr, but anything above + the scan (joins, aggregates, post-scan projections from the user's + query) goes through Comet's regular operator stack, not this contrib. +- **No write-side anything.** No commit logic, no `_delta_log` writes, + no protocol upgrade checks. Reads only. +- **No streaming-source semantics.** Each plan invocation resolves to a + single Delta snapshot version. Structured Streaming's + `DeltaSource`/`DeltaSink` paths fall back to Spark. + +--- + +**Navigation** · [← 02 Planning](02-planning.md) · [↑ Index](README.md) · Next → [04 Design decisions](04-design-decisions.md) diff --git a/contrib/delta/docs/04-design-decisions.md b/contrib/delta/docs/04-design-decisions.md new file mode 100644 index 0000000000..a7f9d71f64 --- /dev/null +++ b/contrib/delta/docs/04-design-decisions.md @@ -0,0 +1,281 @@ + + +# Design decisions and rejected alternatives + +This document captures the "why didn't you just…" questions. Each section +states the decision, the alternatives we considered, and the reason we chose +what we chose. + +## Why a Spark extension rule, not a DataSource V2 scan? + +**Decision.** Hook into `CometScanRule` (Spark `SparkSessionExtensions`) +and substitute the scan node in Catalyst's plan tree, rather than +implementing `TableProvider` / `Scan` against DSv2. + +**Alternative.** Register a DSv2 source that takes over Delta reads. + +**Why not.** Delta's public surface is a V2 facade (`DeltaTableV2`), but +its actual read path resolves to a V1 `HadoopFsRelation` carrying a +`DeltaParquetFileFormat`. The DSv2 binding has already been done by the +time we see a Delta plan. To intercept earlier would mean either patching +Delta or re-implementing its catalog logic — both of which would extend +this PR's blast radius dramatically. + +Hooking at the same layer as the existing Comet parquet rule also reuses +the existing fallback / metric / error mechanics rather than building +parallel ones for DSv2. + +## Why a "contrib" tree, not a core module? + +**Decision.** Code lives under `contrib/delta/`, gated by the +`-Pcontrib-delta` Maven profile and `contrib-delta` Cargo feature. +Default builds are unaware of the contrib. + +**Alternative A.** Make Delta integration first-class — always built, +always on the classpath. + +**Why not.** Delta is one of several table-format integrations Comet +will need (Iceberg already in tree, Hudi likely). Each has heavy +transitive deps (`delta-spark`, `delta-kernel-rs`, kernel's own arrow / +object_store pins). Forcing all consumers to take those deps even when +they only want plain parquet is a regression vs the current state. + +**Alternative B.** Ship as a separate Maven artifact in a separate repo. + +**Why not.** This contrib needs a *small* set of core touchpoints that +must evolve in lockstep with the contrib (`PlanDataInjector.opStructCase`, +the `OpStruct::DeltaScan` variant, the `contrib_delta_scan` dispatcher +arm). Splitting repos would version-couple them anyway; same-repo is +strictly simpler. + +## Why kernel-rs, not parsing `_delta_log` ourselves? + +**Decision.** Use `delta-kernel-rs` for snapshot resolution, file +listing, DV materialisation, and column-mapping metadata. + +**Alternative.** Hand-roll log replay in Rust. + +**Why not.** Delta's transaction protocol is a moving target (DVs, row +tracking, type widening, identity columns, …). Maintaining a +hand-rolled parser would be an ongoing tax and a source of subtle +divergence from Delta's own semantics. kernel-rs is the Delta team's +official Rust kernel, tracks the protocol, and is what the Delta team +will direct integrators to use going forward. The cost is a couple of +heavy transitive deps and an arrow-version pin, both of which we +isolate (see below). + +## Why a typed proto variant, not a generic envelope? + +**Decision.** `OpStruct::DeltaScan` is a first-class variant of the +operator proto, with typed `DeltaScanCommon` and `DeltaScanTask` +messages. + +**Alternative.** Add a generic `OpStruct::ContribOp { kind: string, +payload: bytes }` envelope and decode `payload` based on `kind`. + +**Why not.** PR #3932 prototyped the envelope approach. Two problems: + +1. Every dispatch becomes a string compare + dynamic payload decode. + With a typed variant, `match` on the `OpStructCase` enum gives + O(1) dispatch, and the planner gets to use the proto-generated Rust + struct directly. +2. Loss of schema-level documentation. The proto file becomes + self-describing for typed variants; the envelope variant requires + every consumer to maintain its own out-of-band decoder catalogue. + +The downside of the typed-variant approach is that adding a new contrib +requires a one-line addition to the proto. That's a one-time cost per +contrib and an obvious place to do code review. + +## Why split-mode serialisation (common + per-task)? + +**Decision.** The `DeltaScan` proto carries a `DeltaScanCommon` block; +each partition's `DeltaScanTask[]` rides in a per-partition byte array +via `PlanDataInjector`. + +**Alternative.** Embed all tasks for all partitions in the operator +proto. + +**Why not.** Spark serialises plan closures to every executor as part +of task scheduling. A 5000-file scan over 200 partitions would push +1M task records through that path. The split keeps the common payload +small (KB) and ships only relevant tasks to each partition. + +## Why reflection bridges, not abstract types in core? + +**Decision.** `DeltaIntegration` in core is a thin reflection bridge. +The actual Delta logic lives entirely in `contrib/delta/...`. + +**Alternative.** Define a trait `ContribDeltaSupport` in core, have the +contrib implement it, wire via `ServiceLoader` or similar. + +**Why not.** A trait in core would either: + +- Force the trait to be in core's compile classpath (creating a + compile-time dependency from core to contrib, the wrong direction), OR +- Force the contrib classes into the same JAR as core (defeating the + point of contrib being optional) + +Reflection bridges achieve the same dispatch with zero compile-time +coupling. The cost is one `Method` handle lookup per JVM, which we +cache in a `@volatile var`. + +## Why cache reflection method handles? + +**Decision.** Every reflective bridge resolves `Class.forName` and +`getMethod` exactly once per JVM, caches the result, and reuses it. + +**Why.** Reflection lookup is slow enough that doing it per call would +show up in hot paths. Driver-side bridges fire once per query plan; +executor-side bridges fire per task. Per-task is hot enough to matter, +and the cache pattern is cheap. + +This applies to the S3A credential bridge as well — we resolve +`S3AUtils.createAWSCredentialProviderList` once and reuse the +`Method` handle for every kernel-rs engine creation. + +## Why does each FileGroup hold one file when synthetics emit? + +**Decision.** When any `emit_*` flag is set, the parquet +`FileScanConfig` is built with one `FileGroup` per file. + +**Alternative.** Let the parquet reader pack files into shared groups +for better parallelism. + +**Why not.** `DeltaSyntheticColumnsExec` indexes per-partition state +vectors `(deleted_row_indexes, base_row_ids, default_row_commit_versions)` +by the DataFusion partition index. One file per FileGroup means +"partition index = file index", which is what makes the index lookup +correct. With shared groups, multiple files would map to one partition +index and the lookup would return the wrong file's metadata. + +We pay a parallelism cost only when synthetics are emitted (which is a +minority of queries — primarily MERGE/UPDATE/DELETE rewrite plans and +queries that explicitly select `row_id`). + +## Why a `DeltaDvFilterExec` and not a Spark filter on top? + +**Decision.** Filter deleted rows in the native plan, between +`ParquetSource` and the synthetic-column exec. + +**Alternative.** Read all rows natively, ship to the JVM, filter there. + +**Why not.** DVs can mark significant fractions of a file as deleted +(MERGE-heavy workloads can easily hit 30%+). Filtering natively avoids +serialising and crossing JNI with rows that are about to be dropped. + +It also keeps the synthetic-column logic correct: `is_row_deleted` is +populated by walking the same DV index list, so emitting that column +naturally falls out of the same exec we built for filtering. + +## Why an engine cache keyed on storage config? + +**Decision.** kernel-rs `DefaultEngine` instances are cached on the +driver, keyed by `(scheme, authority, DeltaStorageConfig)`. + +**Why.** `DefaultEngine` spawns one OS thread +per executor on creation. In ad-hoc query workloads (notebook users +hitting many tables, MERGE-heavy ETL jobs), driver-side engine creation +was happening dozens of times per minute. Without a cache, tokio's +thread-reaper couldn't keep up and ~2h into regression the driver +tripped `pthread_create EAGAIN`. + +The key includes the storage config because two queries against the +same bucket with different IAM credentials must NOT share an engine — +the cached engine has those credentials baked in. The +`DeltaStorageConfig` hash captures the relevant credential identity. + +## Why S3A credential resolution Scala-side, not Rust-side? + +**Decision.** Walk Hadoop's S3A credential chain +(`SimpleAWS` / `TemporaryAWS` / `AssumedRole` / `IAMInstance`) on the +JVM driver, materialise concrete credentials, and pass them into +kernel-rs's engine config. + +**Alternative.** Have kernel-rs's object_store resolve the credential +chain itself. + +**Why not.** object_store's credential model is its own type system +(`CredentialProvider`); kernel-rs ships with object_store-0.12 pinned +internally. To bridge Hadoop's `AWSCredentialProviderList` into +object_store 0.12 from Rust would require either rewriting Hadoop's +chain in Rust or fragile FFI. Doing it Scala-side, where +`S3AUtils.createAWSCredentialProviderList` is a known entry point, is +mechanical reflection. + +The downside is that long-lived sessions with STS-rotating credentials +would not see rotation events until the engine cache evicts. We accept +this for now because (a) the cache is keyed on a snapshot of the config, +which is sufficient for short-lived sessions and (b) eviction-on-401 is +a follow-up. + +## Why row-tracking *synthesis*, not "fall back if not materialised"? + +**Decision.** When Delta hasn't materialised `row_id` / +`row_commit_version`, synthesise them natively from `baseRowId + +physical_row_index`. + +**Alternative.** Fall back to Spark for any plan that wants row tracking +columns on a non-materialised file. + +**Why not.** Row tracking on Delta tables that pre-date the row-tracking +feature flag is exactly the case that needs `baseRowId`-based synthesis. +Falling back in that case means tables in mixed-state (some files +materialised, some not — i.e. tables that pre-date row tracking but have +been touched since) would always fall back. The user-visible result is +"row tracking acceleration only works on tables you wrote from scratch +after enabling row tracking", which is a sharp edge. + +Synthesising covers both cases uniformly. The cost is the per-task +`base_row_id` field in the proto and the per-batch arithmetic in the +synthetic-columns exec. + +## Why a standalone Cargo manifest in `contrib/delta/native`? + +**Decision.** `contrib/delta/native/Cargo.toml` is its own manifest, NOT +part of the workspace. + +**Alternative.** Add the crate to the workspace `Cargo.toml`. + +**Why not.** kernel-rs internally pins arrow-57. Comet core pins +arrow-58. Putting both in the same Cargo workspace forces resolution +through the workspace's resolver, which would force one or the other. +A separate manifest lets the contrib build against its own arrow version +and surface only Arrow C Data Interface pointers across the boundary. + +The cost is that you can't `cargo build` from the root and get the +contrib; you build core (with `--features contrib-delta`) and it +re-exports the contrib via static linking. See [05-build-and-deploy.md](05-build-and-deploy.md). + +## Why is `CometCreateArray` declined for type mismatches? + +**Decision.** When `CreateArray` is asked to build an array from +elements of different concrete types, decline in the planner and let +Spark do it. + +**Why.** Upstream DataFusion's `make_array` is strict about element-type +agreement (`apache/datafusion#22366`). Without the decline, valid +Spark queries that build mixed-type arrays would crash native execution. + +We will remove this decline once the upstream issue lands. + +--- + +**Navigation** · [← 03 Native execution](03-native-execution.md) · [↑ Index](README.md) · Next → [05 Build and deploy](05-build-and-deploy.md) diff --git a/contrib/delta/docs/05-build-and-deploy.md b/contrib/delta/docs/05-build-and-deploy.md new file mode 100644 index 0000000000..1fa85c28ad --- /dev/null +++ b/contrib/delta/docs/05-build-and-deploy.md @@ -0,0 +1,231 @@ + + +# Build, packaging, and deployment + +## The two switches + +Two things must be enabled together to get Delta acceleration: + +| Switch | What it controls | +|---|---| +| Maven: `-Pcontrib-delta` | Scala/Java contrib classes are compiled and packaged into `comet-spark` JAR. The Spark extension is registered. | +| Cargo: `--features contrib-delta` (on `native/core`) | The contrib Rust crate is linked into `libcomet`. The JNI symbol `Java_…_planDeltaScan` is exported. | + +Mismatched switches produce a clear failure: + +- JAR with contrib, dylib without → first Delta query: `UnsatisfiedLinkError: planDeltaScan` +- JAR without contrib, dylib with → contrib classes simply absent; `DeltaIntegration.transformV1IfDelta` returns `None`; all Delta queries go through Spark's reader + +The Maven `verify` phase has no cross-language assertion; getting both +switches set is on the operator. + +## Cargo manifest structure + +``` +native/ +├── core/ # Comet core native code (workspace member) +│ ├── Cargo.toml # arrow = "58", with feature contrib-delta = ["delta-contrib-impl"] +│ └── src/execution/planner/contrib_delta_scan.rs +└── proto/ # Comet proto definitions + └── Cargo.toml + +contrib/delta/native/ # Standalone, NOT a workspace member +├── Cargo.toml # arrow = "57" (kernel-rs's pin) +└── src/ + ├── lib.rs + ├── engine.rs + ├── scan.rs + ├── planner.rs + ├── dv_filter.rs + ├── synthetic_columns.rs + └── jni.rs +``` + +The contrib crate is referenced from `native/core/Cargo.toml` as a path +dependency gated by the `contrib-delta` feature: + +```toml +[features] +contrib-delta = ["delta-contrib-impl"] + +[dependencies] +delta-contrib-impl = { + package = "comet-delta-contrib", + path = "../../contrib/delta/native", + optional = true, +} +``` + +`native/core` and `contrib/delta/native` are NOT in the same workspace, so +Cargo resolves their dependencies independently. This is the only way to +keep arrow-57 (kernel-rs's pin) and arrow-58 (Comet core's pin) in the same +final binary without cross-contamination — they end up as distinct crate +graphs and the boundary between them is the Arrow C Data Interface +(stable across versions). + +If you `cargo build` directly in `contrib/delta/native/`, you get a `.rlib` +that does nothing useful — there's no JNI entry compiled in until the +parent `native/core` crate enables the `contrib-delta` feature and pulls +this crate in. Always build from `native/core` (or via the `make` +targets / Maven invocations that do so). + +## Maven profile + +`spark/pom.xml` declares the `contrib-delta` profile, which does two things: + +1. Adds `io.delta:delta-spark` at provided scope so the contrib's + reflective helpers and tests have the Delta types on the classpath at + compile time +2. Adds `contrib/delta/src/main/scala/` as an extra source directory via + `build-helper-maven-plugin` so its sources compile into `comet-spark.jar` + +```xml + + contrib-delta + + 4.1.0 + + + + io.delta + delta-spark_${scala.binary.version} + ${delta.version} + provided + + + + + + build-helper-maven-plugin + + + add-contrib-delta-source + generate-sources + add-source + + + ${project.parent.basedir}/contrib/delta/src/main/scala + + + + + + + + +``` + +The Maven profile does *not* trigger the native Cargo build — that is a +separate invocation. Operators must remember to pass +`--features contrib-delta` to the Cargo command (or set the equivalent +environment variable used by `make release`) so the dylib and the JAR end +up consistent. The build invariants section below covers the supported +combinations. + +The contrib registers its Spark extension reflectively from +`DeltaIntegration` rather than via +`META-INF/services/SparkSessionExtensionsProvider`, so no service file is +required. + +## What the `comet-spark` JAR looks like + +| File or class | Default build | `-Pcontrib-delta` build | +|---|---|---| +| `org.apache.comet.rules.CometScanRule` | yes | yes | +| `org.apache.comet.rules.DeltaIntegration` | yes (reflective bridge, returns None at runtime) | yes | +| `org.apache.comet.contrib.delta.DeltaScanRule` | absent | present | +| `org.apache.comet.contrib.delta.CometDeltaNativeScan` | absent | present | +| `org.apache.comet.contrib.delta.DeltaPlanDataInjector` | absent | present | +A `default` consumer is therefore entirely free of Delta classes. Running +the default JAR against a Delta workload simply means +`DeltaIntegration.transformV1IfDelta` returns `None` and Spark's +unaccelerated path runs. + +## What `libcomet` looks like + +The dylib produced by `cargo build --release -p comet --features contrib-delta` +contains: + +- All of Comet core +- The contrib Rust code, statically linked +- `Java_org_apache_comet_Native_planDeltaScan` exported + +Default build (no feature) omits the contrib code entirely; the dispatcher +in `native/core/src/execution/planner/mod.rs` has a `#[cfg(not(feature = +"contrib-delta"))]` arm that returns a clear error if a `DeltaScan` proto +somehow arrives: + +```rust +#[cfg(not(feature = "contrib-delta"))] +OpStruct::DeltaScan(_) => Err(DataFusionError::Plan( + "DeltaScan operator received but native build does not include contrib-delta feature".into(), +)), +``` + +In practice this can't fire because the JVM side wouldn't have produced a +`DeltaScan` proto without the contrib classpath, but defense-in-depth. + +## How to build and ship + +For a Comet binary that supports Delta: + +```bash +# Build the native dylib with the contrib feature +cargo build -p comet --features contrib-delta --release + +# Build and install the comet-spark JAR with the contrib profile +mvn -Pspark-4.1 -Pcontrib-delta -DskipTests install +``` + +The two commands are independent — the Maven build doesn't drive the +Cargo build. The regression script `contrib/delta/dev/run-regression.sh` +runs both in the right order, which is the easiest way to keep them in +sync during iteration. + +For default (no Delta) builds, omit both switches: + +```bash +cargo build -p comet --release +mvn -Pspark-4.1 -DskipTests install +``` + +## CI matrix expectation + +CI should exercise both build paths. Adding a `-Pcontrib-delta` matrix +entry to the existing Spark profile axis is sufficient — the regression +suite then runs against the Delta test diff (`dev/diffs/delta/4.1.0.diff`) +under that matrix entry. + +## Local iteration tips + +- **Iterate on Scala only**: `mvn -Pspark-4.1 -Pcontrib-delta -DskipTests + -pl spark -am install` — skips the native build, reuses your existing + dylib +- **Iterate on Rust only**: build native (`cargo build -p comet --features + contrib-delta`), then `cp target/release/libcomet.dylib + spark/target/...` if you want to skip the JAR repack — the contrib + classes are still wired the same way + +The regression script `contrib/delta/dev/run-regression.sh` handles all of +this from scratch but is slow (full install + sbt + JVM forks). + +--- + +**Navigation** · [← 04 Design decisions](04-design-decisions.md) · [↑ Index](README.md) · Next → [06 Fallback and ops](06-fallback-and-ops.md) diff --git a/contrib/delta/docs/06-fallback-and-ops.md b/contrib/delta/docs/06-fallback-and-ops.md new file mode 100644 index 0000000000..abcaa4df86 --- /dev/null +++ b/contrib/delta/docs/06-fallback-and-ops.md @@ -0,0 +1,185 @@ + + +# Fallback paths, observability, and operations + +## Design philosophy: fail safe, surface why + +Anywhere this contrib cannot confidently produce identical results to +Spark's Delta reader, the planner declines and lets Spark execute the +scan unchanged. The decline path is uniform: + +```scala +withInfo(plan, "delta-contrib: ") +return plan // unchanged — Spark runs it +``` + +`withInfo` attaches the reason to the plan's `extraMetadata`, which Comet's +explain-fallback rendering picks up. Users running +`EXPLAIN EXTENDED` on a fallback-affected query see: + +``` +== Comet Native Plan Info == +- CometDeltaNativeScan rejected: delta-contrib: DV materialisation failed for + file s3://bucket/_delta_log/00000000000000000123.dv (read error) +``` + +This is the primary observability surface. Operators investigating "why +didn't Comet take over this query" should look at this section first. + +## Decline catalogue + +The current set of decline points, grouped by reason class. Each entry +documents WHY the decline exists and what would need to change to remove it. + +### Correctness — load-bearing, do not remove + +| Decline | Reason | Removal path | +|---|---|---| +| DV materialisation failure | Kernel-rs couldn't read or parse a DV file → we don't know which rows are deleted → we cannot safely return data | Cannot be removed; this is "kernel errored, defer to Spark" | +| Reflective AddFile extraction failure | Couldn't get the file list from the Delta relation → nothing to scan | Cannot be removed; this is "Delta's reflection surface changed shape" | +| Kernel-rs log-replay error | Kernel returned an error during snapshot resolution → we don't have an authoritative file list | Cannot be removed; same class as above | +| Phase 6 reader-feature gate | Currently an empty list; future kernel-rs versions may return reader-feature names we don't yet understand | Per-feature evaluation as kernel-rs evolves | + +### Shared Comet limits (apply to any native scan, not Delta-specific) + +| Decline | Reason | Removal path | +|---|---|---| +| Unsupported encryption KMS config | Comet core's `CometParquetUtils` rejects | Implement KMS bridge in Comet core | +| Custom Hadoop FS schemes (`fake://`) | `object_store` has no Hadoop FS plugin layer | Bridge Hadoop `FileSystem` to `object_store` in Rust | +| `ShortType` under default config | `CometScanTypeChecker` rejects | Flip the default after coverage testing | +| String collation in schema | Comet core can't evaluate collation-aware ops yet | Implement in core expression evaluators | +| Variant struct in schema | Arrow-rs has `parquet-variant` but Comet hasn't integrated | Integrate `parquet-variant` in Comet core | + +### External + +| Decline | Reason | Removal path | +|---|---|---| +| `TahoeLogFileIndexWithCloudFetch` | Databricks-proprietary file index; not in OSS Delta | Wouldn't ship in this PR; DBR-specific | + +### Workaround, tracked upstream + +| Decline | Reason | Removal path | +|---|---|---| +| `CreateArray` mixed element types | `apache/datafusion#22366` (`make_array` strict on types) | Remove this decline when upstream lands | + +### User off-switches + +| Switch | Effect | +|---|---| +| `spark.comet.scan.deltaNative.enabled=false` | Decline all Delta scans → Spark's reader | +| `spark.comet.exec.enabled=false` | Disable Comet entirely → Spark for everything | + +## Removed decline gates (post-PR2) + +Earlier versions of this contrib declined on broader cases; sweeps during +gate-unblock work brought them under native execution. Removed gates: + +- **Column-mapping `id` mode** — implemented via Delta-ID → parquet-field-ID + translation in the planner +- **General Parquet field-ID matching** — proto now carries `use_field_id` +- **Synthetic columns (`__delta_internal_*`)** — emit flags + native + synthesis +- **`outputHasIsRowDeleted` DV fallback** — handled by `DeltaDvFilterExec` +- **`TahoeBatchFileIndex` DV fallback** — handled by + `buildTaskListFromAddFiles` + `deletedRowIndexesByPath` path +- **`enableRowTracking=false` for `row_id` queries** — synthesis from + `baseRowId` +- **Synthetic columns NOT a suffix** — `final_output_indices` reorder +- **`checkLatestSchemaOnRead=false`** — our snapshot is pinned via + `extractSnapshotVersion(relation)` so the at-read check doesn't apply +- **TahoeBatchFileIndex with DVs** — handled the same as `TahoeBatchFileIndex` non-DV + +Each removed gate has its own commit (P7s-P7y series) documenting the +mechanism. + +## Operational signals + +### Per-query + +- `EXPLAIN EXTENDED` — see "Comet Native Plan Info" section for fallback + reasons (covered above) +- Comet's existing scan metrics (`scan_time_ms`, `output_rows`, + `output_batches`) work unchanged for `CometDeltaNativeScan` — they're + reported through the same DataFusion metric mechanism + +### Per-driver + +- Driver-side engine cache size: not currently exposed; would be a useful + follow-up metric. The cache lives behind `engine::engine_cache()` (a + `OnceLock>>>` static) +- kernel-rs scan-planning time: implicit in `CometDeltaNativeScanExec`'s + driver-side latency, not separately reported + +### Cluster-wide + +- `pthread_create EAGAIN` in driver logs would indicate the engine cache + is leaking — the fix that landed in this PR addresses the known cause + (per-scan engine creation without caching). If it returns, investigate + cache eviction policy (currently no TTL) vs. legitimately high storage + diversity +- `ServiceConfigurationError` in executor logs typically means the + `comet-spark` JAR being used by the JVM doesn't match the `libcomet` + dylib being loaded — usually caused by partial upgrades or stale + classpaths during iteration. The build invariants in + [05-build-and-deploy.md](05-build-and-deploy.md) cover the correct combinations + +## Known-safe configuration changes operators can make + +| Config | Default | Notes | +|---|---|---| +| `spark.comet.scan.deltaNative.enabled` | `true` (when contrib loaded) | Per-query off-switch via SET | +| `spark.comet.parquet.read.io.threadPoolSize` | (Comet default) | Same setting as plain Comet parquet | +| `spark.comet.batchSize` | (Comet default) | Same setting; controls Arrow batch size | + +There is currently no Delta-specific tuning beyond the on/off switch. The +contrib reuses Comet's parquet tuning surface because the read path IS +Comet's parquet reader. + +## Debug entry points + +For investigating contrib behaviour locally: + +1. **Decline reasons**: `EXPLAIN EXTENDED` against the affected query +2. **Native plan shape**: enable DataFusion explain via Comet's + `spark.comet.debug.enabled` — the resulting trace shows the wrapping + stack actually built for each partition +3. **kernel-rs interaction**: `RUST_LOG=delta_kernel=debug` on the + executor surfaces snapshot resolution and DV reads +4. **JVM↔Native bridge**: existing Comet log levels; nothing + Delta-specific + +For production investigation: + +1. Check the `Comet Native Plan Info` section first +2. Check driver logs for kernel-rs errors (they bubble up as warnings + before triggering decline) +3. Check the engine cache hasn't been exhausted (driver logs for + `pthread_create`) +4. Compare a Comet-on vs Comet-off run of the same query if a + correctness issue is suspected + +The regression diff in `contrib/delta/dev/diffs/delta/4.1.0.diff` is the +canonical reference for "what should work" — if a Delta upstream test +isn't in the diff and isn't passing with the contrib enabled, that's +either a missed decline gate or a real bug. + +--- + +**Navigation** · [← 05 Build and deploy](05-build-and-deploy.md) · [↑ Index](README.md) diff --git a/contrib/delta/docs/README.md b/contrib/delta/docs/README.md new file mode 100644 index 0000000000..f17aa80765 --- /dev/null +++ b/contrib/delta/docs/README.md @@ -0,0 +1,138 @@ + + +# Comet Delta Contrib — Design Documentation + +This directory contains the design documentation for the native Delta Lake +scan integration in Comet. It is written for engineers who: + +- Know Spark's DataSource V2 interfaces (TableProvider / Scan / Batch / + InputPartition) at a working level +- Have *not* read Comet's internals before +- Have *not* worked with `delta-kernel-rs` before +- Want to understand the *design*, not just the *code* + +If you only have ten minutes, read [01-overview.md](01-overview.md). + +## Reading order + +| # | Document | Audience | +|---|---|---| +| 01 | [Overview](01-overview.md) | Everyone — start here | +| 02 | [Planning](02-planning.md) | Engineers reviewing the Scala-side planning rule and proto serde | +| 03 | [Native execution](03-native-execution.md) | Engineers reviewing the Rust-side execution plan tree | +| 04 | [Design decisions](04-design-decisions.md) | Reviewers asking "why didn't you just…" — read after 01 | +| 05 | [Build and deploy](05-build-and-deploy.md) | Operators packaging and deploying Comet with Delta support | +| 06 | [Fallback and ops](06-fallback-and-ops.md) | Operators investigating fallbacks and observability | + +## One-paragraph summary + +This contrib makes Apache Comet read Delta Lake tables natively in Rust +without going through Spark's `DeltaParquetFileFormat`. It plugs into +Comet's existing plan-rewrite rule (`CometScanRule`) via reflection, +recognises Delta `LogicalRelation`s, and substitutes them with a native +scan node. Driver-side, `delta-kernel-rs` resolves the snapshot and +produces a file list; the result is encoded into a typed proto variant +and shipped to executors. Executor-side, a DataFusion `ExecutionPlan` +tree handles parquet reads (with field-ID matching for column mapping), +deletion-vector filtering, column-mapping rename, and synthesis of +Delta's "virtual" columns (`row_id`, `__delta_internal_is_row_deleted`, +etc.). The contrib is gated behind a Maven profile and a Cargo feature; +default Comet builds are unaware of it. + +## Conceptual model in one diagram + +``` +Catalyst plan with Delta LogicalRelation + │ + ▼ (driver, JVM) +[DeltaScanRule] ──→ decline? ──yes──→ Spark's Delta reader (fallback) + │ + │ no + ▼ +delta-kernel-rs scan resolution + │ + ▼ +DeltaScan proto (common block + per-task arrays) + │ + ▼ (executor, JNI boundary) +contrib_delta_scan::build_plan (Rust) + │ + ▼ builds DataFusion ExecutionPlan tree: +ParquetSource + → ProjectionExec rename [optional, if CM physical != logical names] + → DeltaSyntheticColumnsExec [if any emit_*] + OR (the two are mutually exclusive) + DeltaDvFilterExec [else if any task has DV] + → ProjectionExec reorder [optional, if synthetics not a suffix] + │ + ▼ +Arrow RecordBatch stream → Spark ColumnarBatch (via Comet's Arrow bridge) +``` + +## Glossary + +- **kernel-rs** — `delta-kernel-rs`, the Delta team's official Rust crate + for Delta protocol parsing, snapshot resolution, and DV materialisation +- **DV** — Deletion Vector, Delta's mechanism for soft-deletes (a bitmap + of deleted row indexes stored alongside the parquet files) +- **CM** — Column Mapping, Delta's mechanism for renaming columns without + rewriting parquet (`name` or `id` mode) +- **Synthetic columns** — Delta's internal "virtual" columns that aren't + stored in parquet but are computed at read time: + `__delta_internal_row_index`, `__delta_internal_is_row_deleted`, + `row_id`, `row_commit_version` +- **Contrib** — A Maven-profile-gated extension to Comet, with no compile + or runtime impact on default builds (model follows the Iceberg contrib + in this repo) +- **Plan-rewrite rule** — A Spark `SparkSessionExtensions` strategy that + pattern-matches against logical plan nodes and produces physical plans; + Comet's existing pattern, extended here for Delta + +## Where the code lives + +``` +spark/src/main/scala/org/apache/comet/rules/DeltaIntegration.scala # reflection bridge in core +spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala # one arm calling DeltaIntegration +native/proto/src/proto/operator.proto # DeltaScan proto variant +native/core/src/execution/planner/contrib_delta_scan.rs # native dispatcher arm + +contrib/delta/src/main/scala/org/apache/comet/contrib/delta/... # all contrib Scala +contrib/delta/native/src/... # all contrib Rust +contrib/delta/dev/diffs/delta/4.1.0.diff # regression diff vs Delta 4.1 +contrib/delta/dev/run-regression.sh # regression driver +``` + +The Scala side has roughly 1500 lines of contrib code; the Rust side has +roughly 2500 lines. Core touchpoints (default-build code) total ~40 +lines of net new logic plus the proto variant. + +## Reviewing this PR + +If you are reviewing the PR that introduces this contrib (PR #4366), the +suggested reading sequence is: + +1. [01-overview.md](01-overview.md) here +2. [04-design-decisions.md](04-design-decisions.md) here — answers most "why" questions +3. The Code review strategy in the PR body +4. 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See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Standalone Cargo.toml -- this crate is outside the `native/` workspace root, so it +# cannot use `{ workspace = true }` inheritance. Versions are kept in sync with the +# rest of the repo by convention; the path deps anchor against the same on-disk crates. + +[package] +name = "comet-contrib-delta" +description = "delta-kernel-rs integration for Comet. Reads Delta tables via kernel-rs's log replay and DataFusion's parquet scan, with DV / column-mapping / row-tracking support. Linked into libcomet via core's `contrib-delta` Cargo feature flag." +version = "0.17.0" +edition = "2021" +rust-version = "1.86.0" +publish = false +license = "Apache-2.0" + +[lib] +# rlib: linked INTO `libcomet` when `contrib-delta` is enabled on core. Never a cdylib +# on its own — there's no separate Delta library to ship. +crate-type = ["rlib"] + +[dependencies] +# Typed Delta proto messages live in core's proto crate (alongside IcebergScan, ...) +# so the dispatcher arm has direct access. We re-export them as `crate::proto::*`. +datafusion-comet-proto = { path = "../../../native/proto" } +# JNI helpers (CometError, CometResult, try_unwrap_or_throw). jni-bridge is a leaf +# crate -- depending on it doesn't drag any Comet logic into the contrib. +datafusion-comet-jni-bridge = { path = "../../../native/jni-bridge" } +# Heavy Delta deps -- intentionally live ONLY in this contrib, never in core. delta_kernel +# 0.19 pins arrow-57 / object_store-0.12 internally; that subtree never exchanges typed +# values with Comet's arrow-58 / object_store-0.13 -- only plain Rust types cross the +# boundary (ScanFile, HashMap, etc.). +delta_kernel = { version = "0.19", default-features = false, features = ["default-engine-rustls", "arrow"] } +# Second object_store version required by delta_kernel 0.19 (kernel's engine uses 0.12). +# Renamed so it doesn't collide with Comet's `object_store = "0.13.1"`. +object_store_kernel = { package = "object_store", version = "0.12", features = ["aws", "azure"] } +# Roaring bitmap decoder for Delta deletion vectors (inline + on-disk). +roaring = "0.10" + +# DataFusion / Arrow / parquet versions chosen to match core's pinned values. +datafusion = { version = "53.1.0", default-features = false, features = ["parquet"] } +arrow = { version = "58.1.0", features = ["prettyprint", "ffi", "chrono-tz"] } +object_store = { version = "0.13.1" } +url = "2.5.4" +parquet = { version = "58.1.0", default-features = false, features = ["experimental"] } +futures = "0.3" +thiserror = "2" +prost = "0.14.3" +jni = "0.22.4" +# Used by parse_delta_partition_scalar for timestamp parsing across the JVM's TZ shapes +# (IANA names, GMT+/-HH:MM, etc). +chrono = "0.4" +chrono-tz = "0.10" +log = "0.4" + +[dev-dependencies] +# Used by unit tests under #[cfg(test)] in scan.rs to materialise a Delta table +# in a tempdir without polluting the real filesystem. +tempfile = "3" +tokio = { version = "1.39.0", features = ["macros", "rt-multi-thread"] } diff --git a/contrib/delta/native/src/dv_filter.rs b/contrib/delta/native/src/dv_filter.rs new file mode 100644 index 0000000000..7ad6cf3083 --- /dev/null +++ b/contrib/delta/native/src/dv_filter.rs @@ -0,0 +1,500 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Delta Lake deletion-vector filter operator. +//! +//! Wraps a child `ExecutionPlan` (produced by `init_datasource_exec` over the +//! list of Delta parquet files) and applies Delta deletion vectors at the +//! batch level. One `Vec` of deleted row indexes per partition drives +//! the filter. +//! +//! Design notes: +//! +//! - **One file per partition.** The planner match arm places each DV'd +//! file in its own `FileGroup`, so when this operator sees partition +//! `i`, it knows the full set of rows that `ParquetSource` is going to +//! emit for that partition is exactly the physical rows of one file +//! in physical order. That's the only assumption we rely on for the +//! "subtract deleted indexes by tracking a running row offset" strategy +//! to be correct. +//! +//! - **Indexes are pre-materialized.** Kernel already turned the DV +//! (inline bitmap / on-disk file / UUID reference) into a sorted +//! `Vec` on the driver via `DvInfo::get_row_indexes`. That's what +//! `plan_delta_scan` returns. We don't touch DV bytes on the executor +//! side at all. +//! +//! - **Filter uses arrow `filter_record_batch`.** Builds a per-batch +//! `BooleanArray` mask where `true` means "keep". One mask per batch, +//! allocated fresh — the batch sizes are small and allocation overhead +//! is negligible compared with decoding parquet. + +use std::any::Any; +use std::fmt; +use std::pin::Pin; +use std::sync::Arc; +use std::task::{Context, Poll}; + +use arrow::array::{BooleanArray, RecordBatch}; +use arrow::compute::filter_record_batch; +use arrow::datatypes::SchemaRef; +use datafusion::common::{DataFusionError, Result as DFResult}; +use datafusion::execution::{RecordBatchStream, SendableRecordBatchStream, TaskContext}; +use datafusion::physical_expr::EquivalenceProperties; +use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType}; +use datafusion::physical_plan::metrics::{ + BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricBuilder, MetricsSet, +}; +use datafusion::physical_plan::{DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties}; +use futures::{Stream, StreamExt}; + +/// Execution-plan wrapper that applies per-partition deletion-vector filters +/// to the output of a child parquet scan. +/// +/// `deleted_row_indexes_by_partition[i]` is the sorted list of physical row +/// indexes to drop from partition `i`'s output. An empty vec means "no DV +/// for this partition — pass through untouched". +#[derive(Debug)] +pub struct DeltaDvFilterExec { + input: Arc, + /// One entry per output partition. Length must match the input's + /// partition count. + deleted_row_indexes_by_partition: Vec>, + plan_properties: Arc, + metrics: ExecutionPlanMetricsSet, +} + +impl DeltaDvFilterExec { + pub fn new( + input: Arc, + deleted_row_indexes_by_partition: Vec>, + ) -> DFResult { + let input_props = input.properties(); + let num_partitions = input_props.output_partitioning().partition_count(); + if deleted_row_indexes_by_partition.len() != num_partitions { + return Err(DataFusionError::Internal(format!( + "DeltaDvFilterExec: got {} DV entries for {} partitions", + deleted_row_indexes_by_partition.len(), + num_partitions + ))); + } + let plan_properties = Arc::new(PlanProperties::new( + EquivalenceProperties::new(input.schema()), + input_props.output_partitioning().clone(), + EmissionType::Incremental, + Boundedness::Bounded, + )); + Ok(Self { + input, + deleted_row_indexes_by_partition, + plan_properties, + metrics: ExecutionPlanMetricsSet::new(), + }) + } +} + +impl DisplayAs for DeltaDvFilterExec { + fn fmt_as(&self, _t: DisplayFormatType, f: &mut fmt::Formatter) -> fmt::Result { + let total_dv: usize = self + .deleted_row_indexes_by_partition + .iter() + .map(|v| v.len()) + .sum(); + let dv_partitions = self + .deleted_row_indexes_by_partition + .iter() + .filter(|v| !v.is_empty()) + .count(); + write!( + f, + "DeltaDvFilterExec: {dv_partitions} partitions with DVs, \ + {total_dv} total deleted rows" + ) + } +} + +impl ExecutionPlan for DeltaDvFilterExec { + fn name(&self) -> &str { + "DeltaDvFilterExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn properties(&self) -> &Arc { + &self.plan_properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.input] + } + + // DV filtering relies on `current_row_offset` matching the child's physical row + // index. That invariant only holds if (a) the child preserves its input order and + // (b) DataFusion doesn't slip in a RepartitionExec / SortPreservingMergeExec that + // interleaves rows between the parquet scan and this exec. Override both to pin + // the contract: if either ever stops being true the optimizer is forced to bail + // rather than silently re-order rows. + fn maintains_input_order(&self) -> Vec { + vec![true] + } + + fn benefits_from_input_partitioning(&self) -> Vec { + vec![false] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> DFResult> { + if children.len() != 1 { + return Err(DataFusionError::Internal(format!( + "DeltaDvFilterExec takes exactly one child, got {}", + children.len() + ))); + } + Ok(Arc::new(Self::new( + Arc::clone(&children[0]), + self.deleted_row_indexes_by_partition.clone(), + )?)) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> DFResult { + let child_stream = self.input.execute(partition, context)?; + let deleted = self + .deleted_row_indexes_by_partition + .get(partition) + .cloned() + .unwrap_or_default(); + let metrics = DeltaDvFilterMetrics::new(&self.metrics, partition); + metrics.num_deleted.add(deleted.len()); + Ok(Box::pin(DeltaDvFilterStream { + inner: child_stream, + deleted, + current_row_offset: 0, + next_delete_idx: 0, + schema: self.input.schema(), + baseline_metrics: metrics.baseline, + rows_dropped_metric: metrics.rows_dropped, + })) + } + + fn metrics(&self) -> Option { + Some(self.metrics.clone_inner()) + } +} + +struct DeltaDvFilterMetrics { + baseline: BaselineMetrics, + num_deleted: Count, + rows_dropped: Count, +} + +impl DeltaDvFilterMetrics { + fn new(metrics: &ExecutionPlanMetricsSet, partition: usize) -> Self { + Self { + baseline: BaselineMetrics::new(metrics, partition), + num_deleted: MetricBuilder::new(metrics).counter("dv_rows_scheduled_delete", partition), + rows_dropped: MetricBuilder::new(metrics).counter("dv_rows_dropped", partition), + } + } +} + +struct DeltaDvFilterStream { + inner: SendableRecordBatchStream, + /// Sorted deleted row indexes for this partition. + deleted: Vec, + /// Physical row offset into the file that the NEXT batch starts at. + current_row_offset: u64, + /// Index into `deleted` of the first entry that hasn't been applied yet. + /// `deleted[..next_delete_idx]` are all strictly less than + /// `current_row_offset`. + next_delete_idx: usize, + schema: SchemaRef, + baseline_metrics: BaselineMetrics, + rows_dropped_metric: Count, +} + +impl DeltaDvFilterStream { + /// Drop rows from `batch` whose physical row index is in the DV. Returns + /// the filtered batch (possibly empty) and advances `current_row_offset`. + fn apply(&mut self, batch: RecordBatch) -> DFResult { + let batch_rows = batch.num_rows() as u64; + if batch_rows == 0 || self.deleted.is_empty() { + self.current_row_offset += batch_rows; + return Ok(batch); + } + + let batch_start = self.current_row_offset; + let batch_end = batch_start + batch_rows; + + // Fast-path: if no remaining deletes fall into this batch's row + // range, pass it through untouched. + if self.next_delete_idx >= self.deleted.len() + || self.deleted[self.next_delete_idx] >= batch_end + { + self.current_row_offset = batch_end; + return Ok(batch); + } + + // Build the keep-mask. Walk forward through `deleted` popping entries + // that fall inside [batch_start, batch_end). + let mut mask_buf: Vec = vec![true; batch_rows as usize]; + let mut dropped: usize = 0; + // Loop is safe: next_delete_idx < deleted.len() is checked by the while + // condition, and deleted is sorted ascending by the kernel contract. + while self.next_delete_idx < self.deleted.len() { + let d = self.deleted[self.next_delete_idx]; + if d >= batch_end { + break; + } + if d < batch_start { + return Err(DataFusionError::Internal(format!( + "DV index {d} predates batch start {batch_start}" + ))); + } + let local = (d - batch_start) as usize; + if local < mask_buf.len() && mask_buf[local] { + mask_buf[local] = false; + dropped += 1; + } + self.next_delete_idx += 1; + } + + self.current_row_offset = batch_end; + self.rows_dropped_metric.add(dropped); + + if dropped == 0 { + return Ok(batch); + } + let mask = BooleanArray::from(mask_buf); + filter_record_batch(&batch, &mask).map_err(DataFusionError::from) + } +} + +impl Stream for DeltaDvFilterStream { + type Item = DFResult; + + fn poll_next(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll> { + let poll = self.inner.poll_next_unpin(cx); + let result = match poll { + Poll::Ready(Some(Ok(batch))) => Poll::Ready(Some(self.apply(batch))), + other => other, + }; + self.baseline_metrics.record_poll(result) + } +} + +impl RecordBatchStream for DeltaDvFilterStream { + fn schema(&self) -> SchemaRef { + Arc::clone(&self.schema) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use arrow::array::{ArrayRef, Int64Array}; + use arrow::datatypes::{DataType, Field, Schema}; + use datafusion::physical_plan::metrics::ExecutionPlanMetricsSet; + use std::sync::Arc as StdArc; + + fn schema() -> SchemaRef { + StdArc::new(Schema::new(vec![Field::new("id", DataType::Int64, false)])) + } + + fn batch(rows: &[i64]) -> RecordBatch { + let arr: ArrayRef = StdArc::new(Int64Array::from(rows.to_vec())); + RecordBatch::try_new(schema(), vec![arr]).unwrap() + } + + fn stream_with(deleted: Vec) -> DeltaDvFilterStream { + // Construct directly without an inner stream — apply() is the unit under test + // and inner is never polled in these tests. + let (_dummy_tx, dummy_rx) = futures::channel::mpsc::unbounded::>(); + let inner: SendableRecordBatchStream = Box::pin(EmptyStream { + schema: schema(), + inner: dummy_rx, + }); + let metrics_set = ExecutionPlanMetricsSet::new(); + let baseline = BaselineMetrics::new(&metrics_set, 0); + let dropped = MetricBuilder::new(&metrics_set).counter("dv_rows_dropped", 0); + DeltaDvFilterStream { + inner, + deleted, + current_row_offset: 0, + next_delete_idx: 0, + schema: schema(), + baseline_metrics: baseline, + rows_dropped_metric: dropped, + } + } + + struct EmptyStream { + schema: SchemaRef, + inner: futures::channel::mpsc::UnboundedReceiver>, + } + impl Stream for EmptyStream { + type Item = DFResult; + fn poll_next( + mut self: Pin<&mut Self>, + cx: &mut Context<'_>, + ) -> Poll> { + Pin::new(&mut self.inner).poll_next(cx) + } + } + impl RecordBatchStream for EmptyStream { + fn schema(&self) -> SchemaRef { + StdArc::clone(&self.schema) + } + } + + #[test] + fn apply_empty_batch_passes_through() { + let mut s = stream_with(vec![1, 3]); + let out = s.apply(batch(&[])).unwrap(); + assert_eq!(out.num_rows(), 0); + assert_eq!(s.current_row_offset, 0); + assert_eq!(s.next_delete_idx, 0); + } + + #[test] + fn apply_no_deletes_is_passthrough() { + let mut s = stream_with(vec![]); + let b = batch(&[10, 20, 30, 40]); + let out = s.apply(b).unwrap(); + assert_eq!(out.num_rows(), 4); + assert_eq!(s.current_row_offset, 4); + assert_eq!(s.next_delete_idx, 0); + } + + #[test] + fn apply_deletes_in_batch() { + // Delete rows at indexes 1 and 3 from a 5-row batch -> keep rows 0, 2, 4. + let mut s = stream_with(vec![1, 3]); + let b = batch(&[10, 20, 30, 40, 50]); + let out = s.apply(b).unwrap(); + let arr = out + .column(0) + .as_any() + .downcast_ref::() + .unwrap(); + let kept: Vec = arr.iter().map(Option::unwrap).collect(); + assert_eq!(kept, vec![10, 30, 50]); + assert_eq!(s.current_row_offset, 5); + assert_eq!(s.next_delete_idx, 2); + } + + #[test] + fn apply_delete_at_batch_boundaries() { + // Delete row 0 (batch_start) and row 4 (batch_end-1) from a 5-row batch. + let mut s = stream_with(vec![0, 4]); + let b = batch(&[10, 20, 30, 40, 50]); + let out = s.apply(b).unwrap(); + let arr = out + .column(0) + .as_any() + .downcast_ref::() + .unwrap(); + let kept: Vec = arr.iter().map(Option::unwrap).collect(); + assert_eq!(kept, vec![20, 30, 40]); + } + + #[test] + fn apply_multi_batch_with_deletes_spanning_boundary() { + let mut s = stream_with(vec![1, 5, 7]); + // First batch: rows 0..4. Deletes index 1 -> keep 10, 30, 40. + let out1 = s.apply(batch(&[10, 20, 30, 40])).unwrap(); + let kept1: Vec = out1 + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(kept1, vec![10, 30, 40]); + assert_eq!(s.current_row_offset, 4); + assert_eq!(s.next_delete_idx, 1); + + // Second batch: rows 4..8. Deletes index 5 and 7 -> keep 50, 70. + let out2 = s.apply(batch(&[50, 60, 70, 80])).unwrap(); + let kept2: Vec = out2 + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(kept2, vec![50, 70]); + assert_eq!(s.current_row_offset, 8); + assert_eq!(s.next_delete_idx, 3); + } + + #[test] + fn apply_deletes_beyond_batch_pass_through() { + // All deletes are at indexes 100+ but batch only spans 0..4 -> passthrough. + let mut s = stream_with(vec![100, 200]); + let b = batch(&[10, 20, 30, 40]); + let out = s.apply(b).unwrap(); + assert_eq!(out.num_rows(), 4); + assert_eq!(s.current_row_offset, 4); + assert_eq!(s.next_delete_idx, 0); + } + + #[test] + fn apply_all_rows_deleted() { + let mut s = stream_with(vec![0, 1, 2]); + let b = batch(&[10, 20, 30]); + let out = s.apply(b).unwrap(); + assert_eq!(out.num_rows(), 0); + assert_eq!(s.current_row_offset, 3); + assert_eq!(s.next_delete_idx, 3); + } + + #[test] + fn apply_delete_index_predating_batch_errors() { + // Pre-set state: we've already consumed up to row 5, but a stale entry + // in `deleted` claims index 3 should be dropped now. That's a contract + // violation and we error out rather than silently producing wrong rows. + let mut s = stream_with(vec![3]); + s.current_row_offset = 5; + // next_delete_idx still 0 -> apply will see 3 < 5 = batch_start. + let err = s.apply(batch(&[100, 200])).unwrap_err(); + let msg = format!("{err}"); + assert!( + msg.contains("predates batch start"), + "unexpected error: {msg}" + ); + } + + #[test] + fn new_validates_partition_count() { + use datafusion::physical_plan::empty::EmptyExec; + let inner = StdArc::new(EmptyExec::new(schema())) as Arc; + // EmptyExec has 1 partition; passing 2 DV entries must be rejected. + let err = + DeltaDvFilterExec::new(inner, vec![vec![1u64], vec![2u64]]).unwrap_err(); + assert!(format!("{err}").contains("got 2 DV entries for 1 partitions")); + } +} diff --git a/contrib/delta/native/src/engine.rs b/contrib/delta/native/src/engine.rs new file mode 100644 index 0000000000..aaae802663 --- /dev/null +++ b/contrib/delta/native/src/engine.rs @@ -0,0 +1,383 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Construction of a delta-kernel-rs `DefaultEngine` backed by `object_store`. +//! +//! Ported from tantivy4java's `delta_reader/engine.rs` (Apache-2.0) with +//! minor changes: uses Comet's error type instead of `anyhow`, and uses the +//! renamed `object_store_kernel` (object_store 0.12) dependency that kernel +//! requires. Comet's main `object_store = "0.13"` tree is untouched. + +use std::collections::HashMap; +use std::sync::{Arc, Mutex, OnceLock}; +use url::Url; + +use delta_kernel::engine::default::executor::tokio::TokioBackgroundExecutor; +use delta_kernel::engine::default::DefaultEngine; +use object_store_kernel::aws::AmazonS3Builder; +use object_store_kernel::azure::MicrosoftAzureBuilder; +use object_store_kernel::local::LocalFileSystem; +use object_store_kernel::ObjectStore; + +use super::error::{DeltaError, DeltaResult}; + +/// Concrete engine type returned by [`get_or_create_engine`]. +pub type DeltaEngine = DefaultEngine; + +/// Storage credentials used to construct kernel's engine. +/// +/// Mirrors tantivy4java's `DeltaStorageConfig`. Field-per-knob rather than a +/// generic map so we can validate at the boundary; the Scala side will +/// populate this from a Spark options map. +#[derive(Debug, Clone, Default, Hash, PartialEq, Eq)] +pub struct DeltaStorageConfig { + pub aws_access_key: Option, + pub aws_secret_key: Option, + pub aws_session_token: Option, + pub aws_region: Option, + pub aws_endpoint: Option, + pub aws_force_path_style: bool, + + pub azure_account_name: Option, + pub azure_access_key: Option, + pub azure_bearer_token: Option, +} + +/// Build an `ObjectStore` for the given URL and credentials. +/// +/// Supports `s3://` / `s3a://`, `az://` / `azure://` / `abfs://` / `abfss://`, +/// and `file://`. Any other scheme is rejected with +/// [`DeltaError::UnsupportedScheme`]. +pub fn create_object_store( + url: &Url, + config: &DeltaStorageConfig, +) -> DeltaResult> { + let scheme = url.scheme(); + + let store: Arc = match scheme { + "s3" | "s3a" => { + let bucket = url.host_str().ok_or_else(|| DeltaError::MissingBucket { + url: url.to_string(), + })?; + let mut builder = AmazonS3Builder::new().with_bucket_name(bucket); + + if let Some(ref key) = config.aws_access_key { + builder = builder.with_access_key_id(key); + } + if let Some(ref secret) = config.aws_secret_key { + builder = builder.with_secret_access_key(secret); + } + if let Some(ref token) = config.aws_session_token { + builder = builder.with_token(token); + } + if let Some(ref region) = config.aws_region { + builder = builder.with_region(region); + } + if let Some(ref endpoint) = config.aws_endpoint { + builder = builder.with_endpoint(endpoint); + } + if config.aws_force_path_style { + builder = builder.with_virtual_hosted_style_request(false); + } + // Allow HTTP endpoints (MinIO, LocalStack, custom S3-compat) + if config + .aws_endpoint + .as_ref() + .is_some_and(|e| e.starts_with("http://")) + { + builder = builder.with_allow_http(true); + } + + Arc::new(builder.build()?) + } + "az" | "azure" | "abfs" | "abfss" => { + let container = url.host_str().ok_or_else(|| DeltaError::MissingBucket { + url: url.to_string(), + })?; + let mut builder = MicrosoftAzureBuilder::new().with_container_name(container); + + if let Some(ref account) = config.azure_account_name { + builder = builder.with_account(account); + } + if let Some(ref key) = config.azure_access_key { + builder = builder.with_access_key(key); + } + if let Some(ref token) = config.azure_bearer_token { + builder = builder.with_bearer_token_authorization(token); + } + + Arc::new(builder.build()?) + } + "file" | "" => Arc::new(LocalFileSystem::new()), + other => { + return Err(DeltaError::UnsupportedScheme { + scheme: other.to_string(), + url: url.to_string(), + }); + } + }; + + Ok(store) +} + +/// Process-wide cache of constructed engines, keyed by (scheme, authority, config). +/// +/// Each `DefaultEngine` owns a `TokioBackgroundExecutor` which spawns one std::thread +/// running a current_thread tokio runtime; the runtime's blocking pool (used by +/// kernel for parquet/object_store IO) holds spawned threads for `thread_keep_alive` +/// (~10s) after each spawn_blocking call. Constructing a fresh engine per JNI +/// `planDeltaScan` call therefore accumulates OS threads during regression runs that +/// hit kernel hundreds of times per minute, eventually tripping the per-process +/// thread cap (e.g. `pthread_create EAGAIN` aborts on macOS where `ulimit -u` +/// defaults to ~1300). Sharing one engine per (scheme, authority, config) bounds the +/// thread count by table-storage diversity instead of by request count. +/// +/// `Arc` is handed out so callers don't hold the mutex while using the +/// engine. We never evict — entries are cheap (one Arc per distinct storage target), +/// and dropping the cache at JVM teardown is acceptable. +type EngineKey = (String, String, DeltaStorageConfig); +fn engine_cache() -> &'static Mutex>> { + static CACHE: OnceLock>>> = OnceLock::new(); + CACHE.get_or_init(|| Mutex::new(HashMap::new())) +} + +fn engine_key(url: &Url, config: &DeltaStorageConfig) -> EngineKey { + let scheme = url.scheme().to_string(); + // host+port form the storage target (e.g. S3 bucket, ABFS account); for file:// + // the authority is empty which collapses every local table to a single entry. + let authority = match (url.host_str(), url.port()) { + (Some(h), Some(p)) => format!("{h}:{p}"), + (Some(h), None) => h.to_string(), + _ => String::new(), + }; + (scheme, authority, config.clone()) +} + +// Suppress dead_code: the standalone constructor stays useful for tests that want +// to exercise a fresh engine without polluting the cache. +#[allow(dead_code)] +pub fn create_engine(table_url: &Url, config: &DeltaStorageConfig) -> DeltaResult { + let store = create_object_store(table_url, config)?; + Ok(DefaultEngine::new(store)) +} + +/// Return a shared `DeltaEngine` for the given URL+config, building one on first use. +pub fn get_or_create_engine( + table_url: &Url, + config: &DeltaStorageConfig, +) -> DeltaResult> { + let key = engine_key(table_url, config); + // Mutex is held only across the (cheap) HashMap lookup and, on miss, the engine + // construction. Multi-threaded JNI callers serialize here on first miss per key + // but proceed lock-free on subsequent hits via the returned Arc clone. + let mut cache = engine_cache().lock().unwrap_or_else(|e| e.into_inner()); + if let Some(existing) = cache.get(&key) { + return Ok(Arc::clone(existing)); + } + let store = create_object_store(table_url, config)?; + let engine = Arc::new(DefaultEngine::new(store)); + cache.insert(key, Arc::clone(&engine)); + Ok(engine) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn url(s: &str) -> Url { + Url::parse(s).unwrap() + } + + fn empty_config() -> DeltaStorageConfig { + DeltaStorageConfig::default() + } + + #[test] + fn create_object_store_local_file() { + let store = create_object_store(&url("file:///tmp/x"), &empty_config()).unwrap(); + // Just verify Arc construction succeeded; LocalFileSystem doesn't expose + // anything we can usefully assert on without doing IO. + assert!(format!("{store:?}").contains("LocalFileSystem")); + } + + #[test] + fn create_object_store_empty_scheme_is_local() { + // The "file" | "" arm maps the empty-scheme case (URL like `relative/path` + // wouldn't actually parse, but the arm exists for code paths that hand us + // a Url with an empty scheme). + let mut u = url("file:///x"); + u.set_scheme("").ok(); // best-effort; if it fails, the file:// arm still hits + let store = create_object_store(&u, &empty_config()).unwrap(); + assert!(format!("{store:?}").contains("LocalFileSystem")); + } + + #[test] + fn create_object_store_s3_requires_bucket() { + // `s3://` with empty host is rejected as MissingBucket. + // url::Url::parse("s3:///x") gives host=None. + let bad = url("s3:///just-a-path"); + let err = create_object_store(&bad, &empty_config()).unwrap_err(); + match err { + DeltaError::MissingBucket { .. } => {} + other => panic!("expected MissingBucket, got {other:?}"), + } + } + + #[test] + fn create_object_store_s3_builds_with_full_creds() { + let cfg = DeltaStorageConfig { + aws_access_key: Some("AKIA…".into()), + aws_secret_key: Some("secret".into()), + aws_session_token: Some("token".into()), + aws_region: Some("us-west-2".into()), + aws_endpoint: Some("https://s3.example.com".into()), + aws_force_path_style: true, + ..Default::default() + }; + let store = create_object_store(&url("s3://my-bucket/path"), &cfg).unwrap(); + assert!(format!("{store:?}").contains("AmazonS3") || format!("{store:?}").contains("S3")); + } + + #[test] + fn create_object_store_s3_http_endpoint_allows_http() { + let cfg = DeltaStorageConfig { + aws_access_key: Some("k".into()), + aws_secret_key: Some("s".into()), + aws_endpoint: Some("http://localhost:9000".into()), + aws_force_path_style: true, + ..Default::default() + }; + // MinIO-style: endpoint starts with http:// → builder enables allow_http. + // We can't introspect the builder's flag, but ensuring construction + // succeeds covers the branch. + create_object_store(&url("s3://minio-bucket"), &cfg).unwrap(); + } + + #[test] + fn create_object_store_azure_requires_container() { + let bad = url("abfss:///just-a-path"); + let err = create_object_store(&bad, &empty_config()).unwrap_err(); + assert!(matches!(err, DeltaError::MissingBucket { .. })); + } + + #[test] + fn create_object_store_azure_builds_with_creds() { + let cfg = DeltaStorageConfig { + azure_account_name: Some("myacct".into()), + azure_access_key: Some("key".into()), + azure_bearer_token: Some("bearer".into()), + ..Default::default() + }; + // Either "az://", "azure://", "abfs://" or "abfss://" should work. + for scheme in ["az", "azure", "abfs", "abfss"] { + let u = url(&format!("{scheme}://my-container/path")); + create_object_store(&u, &cfg).unwrap(); + } + } + + #[test] + fn create_object_store_unsupported_scheme() { + let err = create_object_store(&url("gs://bucket/p"), &empty_config()).unwrap_err(); + match err { + DeltaError::UnsupportedScheme { scheme, .. } => assert_eq!(scheme, "gs"), + other => panic!("expected UnsupportedScheme, got {other:?}"), + } + } + + #[test] + fn engine_key_collapses_local_paths() { + let cfg = empty_config(); + let a = engine_key(&url("file:///tmp/a"), &cfg); + let b = engine_key(&url("file:///tmp/b/c/d"), &cfg); + assert_eq!(a, b, "all local file:// URLs share one engine entry"); + } + + #[test] + fn engine_key_distinguishes_s3_buckets() { + let cfg = empty_config(); + let a = engine_key(&url("s3://bucket-a/path"), &cfg); + let b = engine_key(&url("s3://bucket-b/path"), &cfg); + assert_ne!(a, b); + } + + #[test] + fn engine_key_includes_port() { + let cfg = empty_config(); + let a = engine_key(&url("s3://host:9000/p"), &cfg); + let b = engine_key(&url("s3://host:9001/p"), &cfg); + let c = engine_key(&url("s3://host/p"), &cfg); + assert_ne!(a, b); + assert_ne!(a, c); + assert_ne!(b, c); + } + + #[test] + fn engine_key_distinguishes_credentials() { + let cfg_a = DeltaStorageConfig { + aws_access_key: Some("AKIA1".into()), + ..Default::default() + }; + let cfg_b = DeltaStorageConfig { + aws_access_key: Some("AKIA2".into()), + ..Default::default() + }; + let a = engine_key(&url("s3://bucket/p"), &cfg_a); + let b = engine_key(&url("s3://bucket/p"), &cfg_b); + assert_ne!( + a, b, + "different credentials must NOT share a cached engine" + ); + } + + #[test] + fn engine_key_path_does_not_affect_key() { + let cfg = empty_config(); + let a = engine_key(&url("s3://bucket/path/a"), &cfg); + let b = engine_key(&url("s3://bucket/path/b/c"), &cfg); + assert_eq!(a, b, "paths within the same bucket share one engine"); + } + + #[test] + fn get_or_create_engine_returns_same_arc_on_hit() { + let cfg = empty_config(); + let u = url("file:///tmp/cache-test"); + let e1 = get_or_create_engine(&u, &cfg).unwrap(); + let e2 = get_or_create_engine(&u, &cfg).unwrap(); + assert!( + Arc::ptr_eq(&e1, &e2), + "second call must return the cached Arc, not a fresh engine" + ); + } + + #[test] + fn get_or_create_engine_distinct_keys_yield_distinct_engines() { + let cfg = empty_config(); + let e_file = get_or_create_engine(&url("file:///tmp/distinct-a"), &cfg).unwrap(); + // s3:// would actually try to set up an AWS client; use a different file path + // which collapses to the same key per `engine_key_collapses_local_paths`. So we + // exercise a distinct-key case via a different cred config. + let cfg_b = DeltaStorageConfig { + aws_access_key: Some("dummy".into()), + ..Default::default() + }; + let e_creds = get_or_create_engine(&url("file:///tmp/distinct-a"), &cfg_b).unwrap(); + assert!( + !Arc::ptr_eq(&e_file, &e_creds), + "differing config keys must yield distinct engines" + ); + } +} diff --git a/contrib/delta/native/src/error.rs b/contrib/delta/native/src/error.rs new file mode 100644 index 0000000000..fd61e493ad --- /dev/null +++ b/contrib/delta/native/src/error.rs @@ -0,0 +1,62 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Error types for the delta module. +//! +//! Kept local rather than folded into `CometError` because `delta_kernel` +//! lives in an isolated dep subtree — we don't want kernel's error type +//! leaking into `errors.rs` where it could pull kernel's arrow-57 into the +//! main error path. + +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum DeltaError { + #[error("invalid delta table URL '{url}': {source}")] + InvalidUrl { + url: String, + #[source] + source: url::ParseError, + }, + + #[error("cannot resolve local path '{path}': {source}")] + PathResolution { + path: String, + #[source] + source: std::io::Error, + }, + + #[error("cannot convert path to URL: {path}")] + PathToUrl { path: String }, + + #[error("unsupported URL scheme '{scheme}' for delta table: {url}")] + UnsupportedScheme { scheme: String, url: String }, + + #[error("missing bucket/container in URL: {url}")] + MissingBucket { url: String }, + + #[error("object store construction failed: {0}")] + ObjectStore(#[from] object_store_kernel::Error), + + #[error("delta kernel error: {0}")] + Kernel(#[from] delta_kernel::Error), + + #[error("{0}")] + Internal(String), +} + +pub type DeltaResult = std::result::Result; diff --git a/contrib/delta/native/src/jni.rs b/contrib/delta/native/src/jni.rs new file mode 100644 index 0000000000..81d2ba5c83 --- /dev/null +++ b/contrib/delta/native/src/jni.rs @@ -0,0 +1,402 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Driver-side JNI entry point for Delta log replay. +//! +//! Exposes `Java_org_apache_comet_contrib_delta_Native_planDeltaScan`. The Scala driver +//! calls this once per query to ask kernel for the active file list at a +//! given snapshot version, then distributes the returned tasks across +//! Spark executors via Comet's usual split-mode serialization. + +use jni::{ + objects::{JByteArray, JClass, JMap, JObject, JString}, + sys::{jbyteArray, jlong}, + Env, EnvUnowned, +}; +use prost::Message; + +use crate::scan::plan_delta_scan_with_predicate; +use crate::DeltaStorageConfig; +use datafusion_comet_jni_bridge::errors::{try_unwrap_or_throw, CometError, CometResult}; +// Proto types now live in this contrib's own proto module (was core's +// datafusion_comet_proto::spark_operator). +use crate::proto::{DeltaPartitionValue, DeltaScanTask, DeltaScanTaskList}; + +/// `Java_org_apache_comet_contrib_delta_Native_planDeltaScan`. +/// +/// # Arguments (JNI wire order) +/// 1. `table_url` — absolute URL or bare path of the Delta table root +/// 2. `snapshot_version` — `-1` for latest, otherwise the exact version +/// 3. `storage_options` — a `java.util.Map` of cloud +/// credentials. **Phase 1 currently only consumes a small subset** (the +/// AWS / Azure keys listed in `DeltaStorageConfig`); unknown keys are +/// silently ignored. Full options-map plumbing lands with Phase 2. +/// +/// # Returns +/// A Java `byte[]` containing a prost-encoded [`DeltaScanTaskList`] +/// message, or `null` on error (with a `CometNativeException` thrown on +/// the JVM side via `try_unwrap_or_throw`). +/// +/// # Safety +/// Inherently unsafe because it dereferences raw JNI pointers. +#[no_mangle] +pub unsafe extern "system" fn Java_org_apache_comet_contrib_delta_Native_planDeltaScan( + e: EnvUnowned, + _class: JClass, + table_url: JString, + snapshot_version: jlong, + storage_options: JObject, + predicate_bytes: JByteArray, + column_names: jni::objects::JObjectArray, +) -> jbyteArray { + try_unwrap_or_throw(&e, |env| { + let url_str: String = table_url.try_to_string(env)?; + let version = if snapshot_version < 0 { + None + } else { + Some(snapshot_version as u64) + }; + let config = if storage_options.is_null() { + DeltaStorageConfig::default() + } else { + let jmap: JMap<'_> = env.cast_local::(storage_options)?; + // TODO(contrib-delta): the rich Hadoop credential-provider chain (PR1 on + // delta-kernel-phase-1, commit 461fa4f4) called into + // `core::parquet::objectstore::s3::resolve_static_credentials` to walk + // SimpleAWSCredentialsProvider / TemporaryAWSCredentialsProvider / + // AssumedRoleCredentialProvider / IAMInstanceCredentialsProvider. That + // helper lives in core and is not exposed through `comet-contrib-spi`. + // For PR2 we'll either (a) move the helper into contrib-spi or a shared + // leaf crate, or (b) re-implement a Delta-local credential resolver. The + // local-fs regression doesn't hit this path so we defer for the validation + // build; cloud-storage Delta tables will need this re-enabled before ship. + extract_storage_config(env, &jmap)? + }; + + // Phase 2: read column names for BoundReference resolution. + // storageOptions map carries Hadoop-style keys (fs.s3a.access.key, + // fs.s3a.secret.key, fs.s3a.endpoint, fs.s3a.path.style.access, + // fs.s3a.endpoint.region, fs.s3a.session.token) extracted by + // NativeConfig.extractObjectStoreOptions on the Scala side. + // extract_storage_config below maps these to kernel's DeltaStorageConfig. + let col_names = read_string_array(env, &column_names)?; + + // Phase 2: deserialize the Catalyst predicate (if provided) for + // kernel's stats-based file pruning. Empty bytes = no predicate. + let _predicate_proto: Option> = if predicate_bytes.is_null() { + None + } else { + let bytes = env.convert_byte_array(predicate_bytes)?; + if bytes.is_empty() { + None + } else { + Some(bytes) + } + }; + + // Phase 2: translate Catalyst predicate proto to kernel Predicate for + // stats-based file pruning during log replay. Pass column names for + // BoundReference index-to-name resolution. + let kernel_predicate = _predicate_proto.and_then(|bytes| { + use prost::Message; + match datafusion_comet_proto::spark_expression::Expr::decode(bytes.as_slice()) { + Ok(expr) => Some( + crate::predicate::catalyst_to_kernel_predicate_with_names( + &expr, &col_names, + ), + ), + Err(e) => { + log::warn!( + "Failed to decode predicate for Delta file pruning: {e}; \ + scanning all files" + ); + None + } + } + }); + + let plan = plan_delta_scan_with_predicate(&url_str, &config, version, kernel_predicate) + .map_err(|e| CometError::Internal(format!("delta_kernel log replay failed: {e}")))?; + + // Under column mapping, kernel returns partition_values keyed by the + // PHYSICAL column name (e.g. `col-`), but `partition_schema` + // (and therefore `build_delta_partitioned_files`'s lookup) uses the + // LOGICAL name. Build the inverse lookup so we can translate keys + // back to logical names on the wire. + let physical_to_logical: std::collections::HashMap = plan + .column_mappings + .iter() + .map(|(logical, physical)| (physical.clone(), logical.clone())) + .collect(); + + let tasks: Vec = plan + .entries + .into_iter() + .map(|entry| DeltaScanTask { + file_path: resolve_file_path(&url_str, &entry.path), + file_size: entry.size as u64, + record_count: entry.num_records, + // Partition values are produced by kernel as an + // unordered `HashMap` per file. Translate + // physical -> logical when a column mapping is present so + // `build_delta_partitioned_files` can match by logical name. + partition_values: entry + .partition_values + .into_iter() + .map(|(name, value)| { + let logical_name = physical_to_logical + .get(&name) + .cloned() + .unwrap_or(name); + DeltaPartitionValue { + name: logical_name, + value: Some(value), + } + }) + .collect(), + // Phase 3: the DV is already materialized into a sorted + // `Vec` of deleted row indexes by `plan_delta_scan` + // (which calls `DvInfo::get_row_indexes` on the driver). + deleted_row_indexes: entry.deleted_row_indexes, + // Row tracking: extracted from each scan-files RecordBatch's + // `fileConstantValues.baseRowId` / `defaultRowCommitVersion` columns + // in scan.rs (see `extract_row_tracking_for_selected`). Kernel's + // `visit_scan_files` callback doesn't surface these; we read them + // directly. `None` when the table doesn't have row tracking enabled. + base_row_id: entry.base_row_id, + default_row_commit_version: entry.default_row_commit_version, + // Splitting is done on the Scala side just before serialization, + // not here on the kernel-driver path. Leave unset. + byte_range_start: None, + byte_range_end: None, + }) + .collect(); + + let column_mappings: Vec = plan + .column_mappings + .into_iter() + .map( + |(logical, physical)| crate::proto::DeltaColumnMapping { + logical_name: logical, + physical_name: physical, + }, + ) + .collect(); + + let msg = DeltaScanTaskList { + snapshot_version: plan.version, + table_root: url_str, + tasks, + unsupported_features: plan.unsupported_features, + column_mappings, + }; + + let bytes = msg.encode_to_vec(); + let result = env.byte_array_from_slice(&bytes)?; + Ok(result.into_raw()) + }) +} + +/// Join `entry.path` (Delta add-action path, usually relative to the +/// table root) with `table_root` to yield an absolute URL the native-side +/// `build_delta_partitioned_files` can feed straight into +/// `object_store::path::Path::from_url_path`. +fn resolve_file_path(table_root: &str, relative: &str) -> String { + // Fully-qualified paths (kernel surfaces these for some tables, e.g. after + // MERGE, REPLACE, or SHALLOW CLONE) pass through untouched. Accept both + // `file:///abs` (authority form) and `file:/abs` (Hadoop `Path.toUri` form, + // which SHALLOW CLONE uses when it stores absolute paths in AddFile.path). + if has_uri_scheme(relative) { + return relative.to_string(); + } + + if table_root.ends_with('/') { + format!("{table_root}{relative}") + } else { + format!("{table_root}/{relative}") + } +} + +/// True if `s` starts with a URI scheme — `^[A-Za-z][A-Za-z0-9+.-]*:` per RFC 3986. +/// We check the scheme only (not whether a `//` authority follows) because Hadoop's +/// `Path.toUri.toString` emits `file:/abs` (single slash) for local absolute paths +/// and Delta stores that form verbatim in AddFile.path for SHALLOW CLONE tables. +fn has_uri_scheme(s: &str) -> bool { + let bytes = s.as_bytes(); + if bytes.is_empty() || !bytes[0].is_ascii_alphabetic() { + return false; + } + for (i, &b) in bytes.iter().enumerate().skip(1) { + if b == b':' { + return i >= 1; + } + if !(b.is_ascii_alphanumeric() || b == b'+' || b == b'-' || b == b'.') { + return false; + } + } + false +} + +/// Walk a `java.util.Map` of storage options into a +/// [`DeltaStorageConfig`]. Checks both kernel-style keys (`aws_access_key_id`) +/// and Hadoop-style keys (`fs.s3a.access.key`) since Comet's +/// `NativeConfig.extractObjectStoreOptions` passes the latter. +fn extract_storage_config(env: &mut Env, jmap: &JMap<'_>) -> CometResult { + // Helper: try kernel key first, fall back to Hadoop key. + let get = |env: &mut Env, k1: &str, k2: &str| -> CometResult> { + let v = map_get_string(env, jmap, k1)?; + if v.is_some() { + return Ok(v); + } + map_get_string(env, jmap, k2) + }; + + Ok(DeltaStorageConfig { + aws_access_key: get(env, "aws_access_key_id", "fs.s3a.access.key")?, + aws_secret_key: get(env, "aws_secret_access_key", "fs.s3a.secret.key")?, + aws_session_token: get(env, "aws_session_token", "fs.s3a.session.token")?, + aws_region: get(env, "aws_region", "fs.s3a.endpoint.region")?.or(map_get_string( + env, + jmap, + "fs.s3a.region", + )?), + aws_endpoint: get(env, "aws_endpoint", "fs.s3a.endpoint")?, + aws_force_path_style: get(env, "aws_force_path_style", "fs.s3a.path.style.access")? + .map(|s| s == "true") + .unwrap_or(false), + azure_account_name: map_get_string(env, jmap, "azure_account_name")?, + azure_access_key: map_get_string(env, jmap, "azure_access_key")?, + azure_bearer_token: map_get_string(env, jmap, "azure_bearer_token")?, + }) +} + +/// Read a Java `String[]` into a `Vec`. Returns empty vec for null arrays. +fn read_string_array(env: &mut Env, arr: &jni::objects::JObjectArray) -> CometResult> { + if arr.is_null() { + return Ok(Vec::new()); + } + let len = arr.len(env)?; + let mut result = Vec::with_capacity(len); + for i in 0..len { + let obj = arr.get_element(env, i)?; + // SAFETY: get_element returns a valid local JObject reference that we + // immediately convert to JString. The array is String[], so the cast + // is valid. The env lifetime outlives this scope. + let jstr = unsafe { JString::from_raw(env, obj.into_raw()) }; + result.push(jstr.try_to_string(env)?); + } + Ok(result) +} + +/// Iterate a `java.util.Map` into a Rust `HashMap`. Used when we need to +/// pass the full Hadoop config map to a downstream consumer (e.g., +/// `s3::resolve_static_credentials`) that walks its own provider chain. +/// +/// Uses `env.cast_local::(...)` to safely downcast each key/value entry rather +/// than the `unsafe { JString::from_raw(..., into_raw()) }` shortcut used elsewhere in +/// this file -- the runtime cast performs the same JNI-side type check the JLS implies +/// for `Map` but without the unchecked transmute. +fn jmap_to_hashmap( + env: &mut Env, + jmap: &JMap<'_>, +) -> CometResult> { + let mut out = std::collections::HashMap::new(); + jmap.iter(env).and_then(|mut iter| { + while let Some(entry) = iter.next(env)? { + let k = entry.key(env)?; + let v = entry.value(env)?; + let kstr: JString = env.cast_local::(k)?; + let key = kstr.try_to_string(env)?; + if !v.is_null() { + let vstr: JString = env.cast_local::(v)?; + let value = vstr.try_to_string(env)?; + out.insert(key, value); + } + } + Ok(()) + })?; + Ok(out) +} + +/// `map.get(key)` for a `java.util.Map` surfaced as a +/// `JMap`. Returns `None` if the key is absent or the value is `null`. +fn map_get_string(env: &mut Env, jmap: &JMap<'_>, key: &str) -> CometResult> { + let key_obj = env.new_string(key)?; + let key_jobj: JObject = key_obj.into(); + match jmap.get(env, &key_jobj)? { + None => Ok(None), + Some(value) => { + // SAFETY: Map::get always returns a String. The + // JObject reference is valid because JMap::get returned it from the + // current env frame. We consume the local ref via into_raw(). + let jstr = unsafe { JString::from_raw(env, value.into_raw()) }; + Ok(Some(jstr.try_to_string(env)?)) + } + } +} + +// Re-export the test helpers so the integration_tests module can verify +// `resolve_file_path` without exposing it in the public API surface. +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn resolve_file_path_joins_with_slash() { + assert_eq!( + resolve_file_path("file:///tmp/t/", "part-0.parquet"), + "file:///tmp/t/part-0.parquet" + ); + assert_eq!( + resolve_file_path("file:///tmp/t", "part-0.parquet"), + "file:///tmp/t/part-0.parquet" + ); + } + + #[test] + fn resolve_file_path_passes_through_absolute() { + assert_eq!( + resolve_file_path("file:///tmp/t/", "s3://bucket/data/part-0.parquet"), + "s3://bucket/data/part-0.parquet" + ); + } + + #[test] + fn resolve_file_path_passes_through_single_slash_file_uri() { + // SHALLOW CLONE stores paths as Hadoop `Path.toUri.toString` which uses + // single-slash form `file:/abs/...`. Must not be concat'd onto the clone root. + assert_eq!( + resolve_file_path( + "file:/tmp/clonetable/", + "file:/tmp/parquet_table/part-0.parquet" + ), + "file:/tmp/parquet_table/part-0.parquet" + ); + } + + #[test] + fn has_uri_scheme_matches_schemes() { + assert!(has_uri_scheme("file:/abs")); + assert!(has_uri_scheme("file:///abs")); + assert!(has_uri_scheme("s3://bucket/k")); + assert!(has_uri_scheme("hdfs://nn/path")); + assert!(!has_uri_scheme("part-0.parquet")); + assert!(!has_uri_scheme("/abs/path")); + assert!(!has_uri_scheme("1bad:/scheme")); // must start with letter + assert!(!has_uri_scheme("")); + } +} diff --git a/contrib/delta/native/src/lib.rs b/contrib/delta/native/src/lib.rs new file mode 100644 index 0000000000..6829efd0b8 --- /dev/null +++ b/contrib/delta/native/src/lib.rs @@ -0,0 +1,59 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Delta Lake integration for Apache DataFusion Comet. +//! +//! Enabled in core via `--features contrib-delta`. Default builds carry zero +//! Delta surface; this crate is not linked unless the feature is on. +//! +//! Surfaces: +//! - JNI: `Java_org_apache_comet_contrib_delta_Native_planDeltaScan` (driver-side +//! log replay via delta-kernel-rs; returns a `DeltaScanTaskList` proto) +//! - [`DeltaDvFilterExec`]: deletion-vector filter exec wrapper, constructed by +//! core's planner dispatcher when any task in the scan carries a DV +//! - [`plan_delta_scan`]: helpers core's planner dispatcher invokes to assemble +//! a Delta scan's `DataSourceExec` (kernel-rs is JVM-side, so the per-scan +//! planning the JVM doesn't pre-resolve happens here) +//! +//! No `#[ctor]` registration, no contrib-private operator-planner registry; this +//! crate exposes plain Rust functions that core calls directly under +//! `#[cfg(feature = "contrib-delta")]`. + +pub mod dv_filter; +pub mod engine; +pub mod error; +pub mod jni; +pub mod planner; +pub mod predicate; +pub mod scan; +pub mod synthetic_columns; + +/// Re-export of the Delta proto messages, named so module paths inside this crate +/// can keep their original `use crate::proto::Delta...` form. The messages +/// themselves live in core's proto crate (so the dispatcher arm in core has direct +/// access to the typed variants). +pub mod proto { + pub use datafusion_comet_proto::spark_operator::{ + DeltaColumnMapping, DeltaPartitionValue, DeltaScan, DeltaScanCommon, DeltaScanTask, + DeltaScanTaskList, + }; +} + +pub use dv_filter::DeltaDvFilterExec; +pub use engine::{create_engine, DeltaStorageConfig}; +pub use error::{DeltaError, DeltaResult}; +pub use scan::{list_delta_files, plan_delta_scan, DeltaFileEntry, DeltaScanPlan}; diff --git a/contrib/delta/native/src/planner.rs b/contrib/delta/native/src/planner.rs new file mode 100644 index 0000000000..93b786dc45 --- /dev/null +++ b/contrib/delta/native/src/planner.rs @@ -0,0 +1,619 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Delta-specific helpers core's `OpStruct::DeltaScan` dispatcher arm composes onto +//! the standard parquet datasource path: +//! +//! - [`build_delta_partitioned_files`] -- convert a `DeltaScanTask` list into a +//! `Vec` (Delta's add.path is already absolute on the driver; +//! partition values arrive as strings, parsed here) +//! - [`parse_delta_partition_scalar`] -- string -> `ScalarValue` with Delta's TZ +//! semantics and the DATE -> TIMESTAMP_NTZ widening fallback +//! - [`ColumnMappingFilterRewriter`] -- rewrites pushed-down data filters from +//! logical to physical column names when column mapping is active +//! +//! All take pure DataFusion / arrow types so this crate stays free of any +//! datafusion-comet dependency (no cycle: core can call us, we can't call core). + +use std::collections::HashMap; +use std::sync::Arc; + +use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef}; +use datafusion::common::tree_node::{Transformed, TreeNodeRewriter}; +use datafusion::common::ScalarValue; +use datafusion::datasource::listing::PartitionedFile; +use datafusion::physical_expr::PhysicalExpr; +use datafusion::physical_plan::expressions::Column; +use object_store::path::Path; +use url::Url; + +use crate::proto::DeltaScanTask; + +/// Pre-parsed session timezone, computed once per scan and reused across every partition +/// value parse. Avoids the per-row `chrono_tz::Tz::from_str` lookup +/// `parse_delta_partition_scalar` would otherwise do for every TIMESTAMP partition value. +pub enum SessionTimezone { + Tz(chrono_tz::Tz), + Offset(chrono::FixedOffset), + /// `session_tz` didn't parse as either a named TZ or a fixed offset. We defer the + /// "invalid session TZ" error to the per-row parse path so callers that don't have any + /// TIMESTAMP partitions never see it. + Invalid, +} + +impl SessionTimezone { + pub fn parse(session_tz: &str) -> Self { + if let Ok(tz) = session_tz.parse::() { + return Self::Tz(tz); + } + if let Some(off) = parse_fixed_offset(session_tz) { + return Self::Offset(off); + } + Self::Invalid + } +} + +fn parse_fixed_offset(s: &str) -> Option { + let trimmed = s.trim(); + let body = trimmed + .strip_prefix("GMT") + .or_else(|| trimmed.strip_prefix("UTC")) + .unwrap_or(trimmed); + if body.is_empty() || body.eq_ignore_ascii_case("Z") { + return Some(chrono::FixedOffset::east_opt(0).unwrap()); + } + let (sign, rest) = match body.chars().next()? { + '+' => (1, &body[1..]), + '-' => (-1, &body[1..]), + _ => return None, + }; + let secs = if rest.contains(':') { + let mut parts = rest.splitn(2, ':'); + let h: i32 = parts.next()?.parse().ok()?; + let m: i32 = parts.next()?.parse().ok()?; + h * 3600 + m * 60 + } else if rest.len() == 4 { + let h: i32 = rest[..2].parse().ok()?; + let m: i32 = rest[2..].parse().ok()?; + h * 3600 + m * 60 + } else { + let h: i32 = rest.parse().ok()?; + h * 3600 + }; + chrono::FixedOffset::east_opt(sign * secs) +} + +/// Convert `DeltaScanTask`s into DataFusion `PartitionedFile`s. Delta's add.path is +/// already an absolute URL once kernel has resolved it on the driver. +pub fn build_delta_partitioned_files( + tasks: &[DeltaScanTask], + partition_schema: &Schema, + session_tz: &str, +) -> Result, String> { + let parsed_tz = SessionTimezone::parse(session_tz); + let mut files = Vec::with_capacity(tasks.len()); + // Reused scratch map for per-task partition-value lookup. Without it, the inner + // `partition_schema.fields()` loop walks `task.partition_values` with `.iter().find()` + // for every field -- O(width × values) per task. With it, build the map once per task + // and do O(1) gets. `clear()` keeps the allocation across tasks. + let mut partition_values_by_name: std::collections::HashMap<&str, &str> = + std::collections::HashMap::new(); + for task in tasks { + let url = Url::parse(task.file_path.as_ref()) + .map_err(|e| format!("Invalid Delta file URL: {e}"))?; + let path = Path::from_url_path(url.path()) + .map_err(|e| format!("from_url_path: {e}"))?; + + let mut partitioned_file = match (task.byte_range_start, task.byte_range_end) { + (Some(start), Some(end)) => PartitionedFile::new_with_range( + String::new(), + task.file_size, + start as i64, + end as i64, + ), + _ => PartitionedFile::new(String::new(), task.file_size), + }; + partitioned_file.object_meta.location = path; + + let mut partition_values: Vec = + Vec::with_capacity(partition_schema.fields().len()); + partition_values_by_name.clear(); + for pv in &task.partition_values { + if let Some(v) = pv.value.as_deref() { + partition_values_by_name.insert(pv.name.as_str(), v); + } + } + for field in partition_schema.fields() { + let scalar = match partition_values_by_name.get(field.name().as_str()).copied() { + Some(s) => parse_delta_partition_scalar(s, field.data_type(), &parsed_tz, session_tz) + .map_err(|e| { + format!( + "Failed to parse Delta partition value for column '{}': {e}", + field.name() + ) + })?, + None => ScalarValue::try_from(field.data_type()).map_err(|e| { + format!( + "Failed to build null partition value for column '{}': {e}", + field.name() + ) + })?, + }; + partition_values.push(scalar); + } + partitioned_file.partition_values = partition_values; + files.push(partitioned_file); + } + Ok(files) +} + +/// Parse a Delta partition value string into a `ScalarValue`. Honours session TZ for +/// TIMESTAMP columns. Delta writes TIMESTAMP partition values in the JVM default TZ +/// (`yyyy-MM-dd HH:mm:ss[.S]`); DataFusion's default parser interprets them as UTC +/// which would be off by the session offset. +/// +/// Includes the DATE -> TIMESTAMP_NTZ widening fallback: Delta's TypeWidening leaves +/// the original "YYYY-MM-DD" partition strings in place when the column changes from +/// DATE to TIMESTAMP_NTZ, so we accept the date-only form by promoting to midnight +/// (matches Spark's `cast(DATE as TIMESTAMP)` semantics). +pub fn parse_delta_partition_scalar( + s: &str, + dt: &DataType, + parsed_tz: &SessionTimezone, + session_tz: &str, +) -> Result { + match dt { + DataType::Timestamp(unit, tz_opt) => { + use chrono::{DateTime, NaiveDateTime, TimeZone}; + if tz_opt.is_none() { + let naive = NaiveDateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S%.f") + .or_else(|_| NaiveDateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S")) + .or_else(|_| { + chrono::NaiveDate::parse_from_str(s, "%Y-%m-%d") + .map(|d| d.and_hms_opt(0, 0, 0).unwrap()) + }) + .map_err(|e| format!("cannot parse TIMESTAMP_NTZ '{s}': {e}"))?; + let micros = chrono::Utc.from_utc_datetime(&naive).timestamp_micros(); + return Ok(match unit { + datafusion::arrow::datatypes::TimeUnit::Microsecond => { + ScalarValue::TimestampMicrosecond(Some(micros), None) + } + datafusion::arrow::datatypes::TimeUnit::Millisecond => { + ScalarValue::TimestampMillisecond(Some(micros / 1_000), None) + } + datafusion::arrow::datatypes::TimeUnit::Nanosecond => { + ScalarValue::TimestampNanosecond(Some(micros.saturating_mul(1_000)), None) + } + datafusion::arrow::datatypes::TimeUnit::Second => { + ScalarValue::TimestampSecond(Some(micros / 1_000_000), None) + } + }); + } + let micros = if let Ok(dt_with_tz) = DateTime::parse_from_rfc3339(s) { + dt_with_tz.timestamp_micros() + } else if let Ok(dt_with_tz) = + DateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S%.f %z") + .or_else(|_| DateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S %z")) + { + dt_with_tz.timestamp_micros() + } else { + let naive = NaiveDateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S%.f") + .or_else(|_| NaiveDateTime::parse_from_str(s, "%Y-%m-%d %H:%M:%S")) + .or_else(|_| { + chrono::NaiveDate::parse_from_str(s, "%Y-%m-%d") + .map(|d| d.and_hms_opt(0, 0, 0).unwrap()) + }) + .map_err(|e| format!("cannot parse timestamp '{s}': {e}"))?; + use chrono::LocalResult; + match parsed_tz { + SessionTimezone::Tz(tz) => match tz.from_local_datetime(&naive) { + LocalResult::Single(dt) => dt.timestamp_micros(), + LocalResult::Ambiguous(earlier, _later) => earlier.timestamp_micros(), + LocalResult::None => { + chrono::Utc.from_utc_datetime(&naive).timestamp_micros() + } + }, + SessionTimezone::Offset(off) => match off.from_local_datetime(&naive) { + LocalResult::Single(dt) => dt.timestamp_micros(), + _ => chrono::Utc.from_utc_datetime(&naive).timestamp_micros(), + }, + SessionTimezone::Invalid => { + return Err(format!("invalid session TZ '{session_tz}'")); + } + } + }; + match unit { + datafusion::arrow::datatypes::TimeUnit::Microsecond => Ok( + ScalarValue::TimestampMicrosecond(Some(micros), tz_opt.clone()), + ), + datafusion::arrow::datatypes::TimeUnit::Millisecond => Ok( + ScalarValue::TimestampMillisecond(Some(micros / 1000), tz_opt.clone()), + ), + datafusion::arrow::datatypes::TimeUnit::Nanosecond => Ok( + ScalarValue::TimestampNanosecond(Some(micros * 1000), tz_opt.clone()), + ), + datafusion::arrow::datatypes::TimeUnit::Second => Ok( + ScalarValue::TimestampSecond(Some(micros / 1_000_000), tz_opt.clone()), + ), + } + } + _ => ScalarValue::try_from_string(s.to_string(), dt).map_err(|e| format!("{e}")), + } +} + +/// Rewrites Column references in a PhysicalExpr from logical names/indices (in +/// required_schema) to physical names/indices (in data_schema). Used when Delta column +/// mapping is active so pushed-down data filters match the DataSourceExec's physical +/// names. +pub struct ColumnMappingFilterRewriter<'a> { + pub logical_to_physical: &'a HashMap, + pub data_schema: &'a SchemaRef, +} + +impl TreeNodeRewriter for ColumnMappingFilterRewriter<'_> { + type Node = Arc; + + fn f_down( + &mut self, + node: Self::Node, + ) -> datafusion::common::Result> { + if let Some(column) = node.as_any().downcast_ref::() { + if let Some(physical_name) = self.logical_to_physical.get(column.name()) { + if let Some(idx) = self + .data_schema + .fields() + .iter() + .position(|f| f.name() == physical_name) + { + return Ok(Transformed::yes(Arc::new(Column::new(physical_name, idx)))); + } + log::warn!( + "Column mapping: physical name '{}' for logical '{}' not found in \ + data_schema; filter may fail at execution time", + physical_name, + column.name() + ); + } + Ok(Transformed::no(node)) + } else { + Ok(Transformed::no(node)) + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + use datafusion::arrow::datatypes::{Field, TimeUnit}; + use datafusion::common::tree_node::TreeNode; + + // ---- parse_fixed_offset ---- + + #[test] + fn fixed_offset_utc_z() { + for s in ["UTC", "GMT", "Z", "GMTZ", "utc", "gmt"] { + let off = parse_fixed_offset(s); + // Lowercase variants we don't currently uppercase-normalize; skip those. + if s.chars().any(|c| c.is_lowercase()) { + continue; + } + assert_eq!(off.unwrap().local_minus_utc(), 0, "{s}"); + } + } + + #[test] + fn fixed_offset_signed_hh_mm() { + assert_eq!(parse_fixed_offset("+05:30").unwrap().local_minus_utc(), 5 * 3600 + 30 * 60); + assert_eq!(parse_fixed_offset("-08:00").unwrap().local_minus_utc(), -8 * 3600); + } + + #[test] + fn fixed_offset_hhmm_no_colon() { + assert_eq!(parse_fixed_offset("+0530").unwrap().local_minus_utc(), 5 * 3600 + 30 * 60); + assert_eq!(parse_fixed_offset("-0800").unwrap().local_minus_utc(), -8 * 3600); + } + + #[test] + fn fixed_offset_hour_only() { + assert_eq!(parse_fixed_offset("+5").unwrap().local_minus_utc(), 5 * 3600); + assert_eq!(parse_fixed_offset("-3").unwrap().local_minus_utc(), -3 * 3600); + } + + #[test] + fn fixed_offset_gmt_prefix() { + assert_eq!( + parse_fixed_offset("GMT+05:30").unwrap().local_minus_utc(), + 5 * 3600 + 30 * 60 + ); + assert_eq!(parse_fixed_offset("UTC-3").unwrap().local_minus_utc(), -3 * 3600); + } + + #[test] + fn fixed_offset_invalid_returns_none() { + assert!(parse_fixed_offset("garbage").is_none()); + assert!(parse_fixed_offset("+xx:30").is_none()); + assert!(parse_fixed_offset("America/New_York").is_none()); // named TZ, not offset + } + + // ---- SessionTimezone ---- + + #[test] + fn session_tz_parses_named() { + match SessionTimezone::parse("America/New_York") { + SessionTimezone::Tz(_) => {} + _ => panic!("expected named TZ"), + } + } + + #[test] + fn session_tz_parses_offset() { + match SessionTimezone::parse("+05:30") { + SessionTimezone::Offset(off) => { + assert_eq!(off.local_minus_utc(), 5 * 3600 + 30 * 60); + } + _ => panic!("expected fixed offset"), + } + } + + #[test] + fn session_tz_invalid() { + assert!(matches!(SessionTimezone::parse("nonsense"), SessionTimezone::Invalid)); + } + + // ---- parse_delta_partition_scalar: every primitive type ---- + + fn tz_utc() -> SessionTimezone { + SessionTimezone::parse("UTC") + } + + #[test] + fn partition_scalar_int32() { + let s = parse_delta_partition_scalar("42", &DataType::Int32, &tz_utc(), "UTC").unwrap(); + assert_eq!(s, ScalarValue::Int32(Some(42))); + } + + #[test] + fn partition_scalar_int64() { + let s = parse_delta_partition_scalar("9999999999", &DataType::Int64, &tz_utc(), "UTC") + .unwrap(); + assert_eq!(s, ScalarValue::Int64(Some(9999999999))); + } + + #[test] + fn partition_scalar_int16() { + let s = parse_delta_partition_scalar("123", &DataType::Int16, &tz_utc(), "UTC").unwrap(); + assert_eq!(s, ScalarValue::Int16(Some(123))); + } + + #[test] + fn partition_scalar_utf8() { + let s = parse_delta_partition_scalar("hello", &DataType::Utf8, &tz_utc(), "UTC").unwrap(); + assert_eq!(s, ScalarValue::Utf8(Some("hello".into()))); + } + + #[test] + fn partition_scalar_boolean() { + let s = parse_delta_partition_scalar("true", &DataType::Boolean, &tz_utc(), "UTC").unwrap(); + assert_eq!(s, ScalarValue::Boolean(Some(true))); + } + + #[test] + fn partition_scalar_date() { + // Date32 = days since epoch. 2024-01-15 -> 19737 + let s = parse_delta_partition_scalar("2024-01-15", &DataType::Date32, &tz_utc(), "UTC") + .unwrap(); + assert_eq!(s, ScalarValue::Date32(Some(19737))); + } + + #[test] + fn partition_scalar_timestamp_ntz_micros() { + let s = parse_delta_partition_scalar( + "2024-01-15 12:30:45", + &DataType::Timestamp(TimeUnit::Microsecond, None), + &tz_utc(), + "UTC", + ) + .unwrap(); + match s { + ScalarValue::TimestampMicrosecond(Some(v), None) => { + // 2024-01-15 12:30:45 UTC = epoch micros 1705321845_000_000 + assert_eq!(v, 1705321845_000_000); + } + other => panic!("unexpected: {other:?}"), + } + } + + #[test] + fn partition_scalar_timestamp_ntz_widens_from_date() { + // DATE -> TIMESTAMP_NTZ widening: "2024-01-15" promotes to midnight. + let s = parse_delta_partition_scalar( + "2024-01-15", + &DataType::Timestamp(TimeUnit::Microsecond, None), + &tz_utc(), + "UTC", + ) + .unwrap(); + match s { + ScalarValue::TimestampMicrosecond(Some(_), None) => {} // success + other => panic!("unexpected: {other:?}"), + } + } + + #[test] + fn partition_scalar_timestamp_with_session_tz() { + // 2024-01-15 12:00:00 in America/New_York = 17:00:00 UTC = 1705338000 epoch sec + let parsed = SessionTimezone::parse("America/New_York"); + let s = parse_delta_partition_scalar( + "2024-01-15 12:00:00", + &DataType::Timestamp(TimeUnit::Microsecond, Some("UTC".into())), + &parsed, + "America/New_York", + ) + .unwrap(); + match s { + ScalarValue::TimestampMicrosecond(Some(v), Some(_)) => { + assert_eq!(v, 1705338000_000_000); + } + other => panic!("unexpected: {other:?}"), + } + } + + // ---- build_delta_partitioned_files ---- + + fn task(file_path: &str, partition_values: Vec<(&str, Option<&str>)>) -> DeltaScanTask { + use crate::proto::DeltaPartitionValue; + DeltaScanTask { + file_path: file_path.into(), + file_size: 1000, + partition_values: partition_values + .into_iter() + .map(|(n, v)| DeltaPartitionValue { + name: n.into(), + value: v.map(|s| s.into()), + }) + .collect(), + ..Default::default() + } + } + + #[test] + fn build_files_empty_input() { + let pschema = Schema::new(vec![Field::new("p", DataType::Int32, true)]); + let files = build_delta_partitioned_files(&[], &pschema, "UTC").unwrap(); + assert!(files.is_empty()); + } + + #[test] + fn build_files_no_partition_columns() { + let pschema = Schema::new(Vec::::new()); + let tasks = vec![task("file:///tmp/a.parquet", vec![])]; + let files = build_delta_partitioned_files(&tasks, &pschema, "UTC").unwrap(); + assert_eq!(files.len(), 1); + assert!(files[0].partition_values.is_empty()); + } + + #[test] + fn build_files_single_partition_int() { + let pschema = Schema::new(vec![Field::new("p", DataType::Int32, true)]); + let tasks = vec![task("file:///tmp/a.parquet", vec![("p", Some("42"))])]; + let files = build_delta_partitioned_files(&tasks, &pschema, "UTC").unwrap(); + assert_eq!(files.len(), 1); + assert_eq!(files[0].partition_values, vec![ScalarValue::Int32(Some(42))]); + } + + #[test] + fn build_files_missing_partition_value_yields_null() { + let pschema = Schema::new(vec![Field::new("p", DataType::Int32, true)]); + let tasks = vec![task("file:///tmp/a.parquet", vec![])]; // no value for p + let files = build_delta_partitioned_files(&tasks, &pschema, "UTC").unwrap(); + assert_eq!(files[0].partition_values, vec![ScalarValue::Int32(None)]); + } + + #[test] + fn build_files_invalid_url_errors() { + let pschema = Schema::new(Vec::::new()); + let tasks = vec![task("not a url", vec![])]; + let err = build_delta_partitioned_files(&tasks, &pschema, "UTC").unwrap_err(); + assert!(err.contains("Invalid Delta file URL")); + } + + // ---- ColumnMappingFilterRewriter ---- + + #[test] + fn cm_rewriter_renames_known_logical_column() { + let logical_to_physical: HashMap = + [("user_id".to_string(), "col-1a2b3c".to_string())] + .iter() + .cloned() + .collect(); + let data_schema: SchemaRef = Arc::new(Schema::new(vec![ + Field::new("col-1a2b3c", DataType::Int64, false), + ])); + let mut rewriter = ColumnMappingFilterRewriter { + logical_to_physical: &logical_to_physical, + data_schema: &data_schema, + }; + let expr: Arc = Arc::new(Column::new("user_id", 0)); + let out = expr.rewrite(&mut rewriter).unwrap().data; + let col = out.as_any().downcast_ref::().unwrap(); + assert_eq!(col.name(), "col-1a2b3c"); + assert_eq!(col.index(), 0); + } + + #[test] + fn cm_rewriter_leaves_unmapped_column_alone() { + let logical_to_physical = HashMap::new(); + let data_schema: SchemaRef = Arc::new(Schema::new(vec![ + Field::new("other", DataType::Int64, false), + ])); + let mut rewriter = ColumnMappingFilterRewriter { + logical_to_physical: &logical_to_physical, + data_schema: &data_schema, + }; + let expr: Arc = Arc::new(Column::new("other", 0)); + let out = expr.rewrite(&mut rewriter).unwrap().data; + let col = out.as_any().downcast_ref::().unwrap(); + assert_eq!(col.name(), "other"); + } + + #[test] + fn cm_rewriter_resolves_correct_index() { + let logical_to_physical: HashMap = + [("logical_b".to_string(), "phys_b".to_string())] + .iter() + .cloned() + .collect(); + let data_schema: SchemaRef = Arc::new(Schema::new(vec![ + Field::new("phys_a", DataType::Int64, false), + Field::new("phys_b", DataType::Int64, false), // index 1 + Field::new("phys_c", DataType::Int64, false), + ])); + let mut rewriter = ColumnMappingFilterRewriter { + logical_to_physical: &logical_to_physical, + data_schema: &data_schema, + }; + // Even if input index is 0 (from required_schema position), rewriter resolves to + // physical schema's index for phys_b which is 1. + let expr: Arc = Arc::new(Column::new("logical_b", 0)); + let out = expr.rewrite(&mut rewriter).unwrap().data; + let col = out.as_any().downcast_ref::().unwrap(); + assert_eq!(col.name(), "phys_b"); + assert_eq!(col.index(), 1, "must resolve to physical schema index"); + } + + #[test] + fn cm_rewriter_logs_warning_for_missing_physical() { + // Mapping says logical -> physical, but physical isn't in data_schema. + let logical_to_physical: HashMap = + [("logical".to_string(), "phys_missing".to_string())] + .iter() + .cloned() + .collect(); + let data_schema: SchemaRef = Arc::new(Schema::new(vec![ + Field::new("something_else", DataType::Int64, false), + ])); + let mut rewriter = ColumnMappingFilterRewriter { + logical_to_physical: &logical_to_physical, + data_schema: &data_schema, + }; + let expr: Arc = Arc::new(Column::new("logical", 0)); + // Should not panic; returns the original Column unchanged. + let out = expr.rewrite(&mut rewriter).unwrap().data; + let col = out.as_any().downcast_ref::().unwrap(); + assert_eq!(col.name(), "logical"); // unchanged + } +} diff --git a/contrib/delta/native/src/predicate.rs b/contrib/delta/native/src/predicate.rs new file mode 100644 index 0000000000..7ee5575cc3 --- /dev/null +++ b/contrib/delta/native/src/predicate.rs @@ -0,0 +1,605 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Translates Catalyst-proto `Expr` to delta-kernel `Predicate` for +//! stats-based file pruning. +//! +//! Supported operators: =, !=, <, <=, >, >=, AND, OR, NOT, IS NULL, +//! IS NOT NULL, IN (including NOT IN). Cast wrappers are unwrapped +//! (kernel stats don't need type coercion). Anything else becomes +//! `Predicate::unknown()`, which disables data skipping for that +//! subtree but is never incorrect. + +use datafusion_comet_proto::spark_expression::{self, expr::ExprStruct, literal, Expr}; +use delta_kernel::expressions::{ArrayData, BinaryPredicateOp, Expression, Predicate, Scalar}; +use delta_kernel::schema::{ArrayType, DataType}; + +/// Translate with column name resolution for BoundReferences. +pub fn catalyst_to_kernel_predicate_with_names(expr: &Expr, column_names: &[String]) -> Predicate { + translate_predicate(expr, column_names) +} + +/// Try to translate a Catalyst-proto `Expr` into a kernel `Predicate` +/// (without column name resolution — BoundReferences become Unknown). +pub fn catalyst_to_kernel_predicate(expr: &Expr) -> Predicate { + translate_predicate(expr, &[]) +} + +fn translate_predicate(expr: &Expr, names: &[String]) -> Predicate { + let to_expr = |e: &Expr| catalyst_to_kernel_expression_with_names(e, names); + match expr.expr_struct.as_ref() { + Some(ExprStruct::IsNull(unary)) => match unary.child.as_deref() { + Some(child) => Predicate::is_null(to_expr(child)), + None => Predicate::unknown("missing_child"), + }, + Some(ExprStruct::IsNotNull(unary)) => match unary.child.as_deref() { + Some(child) => Predicate::is_not_null(to_expr(child)), + None => Predicate::unknown("missing_child"), + }, + Some(ExprStruct::Eq(binary)) => binary_pred_n( + Predicate::eq, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::Neq(binary)) => binary_pred_n( + Predicate::ne, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::Lt(binary)) => binary_pred_n( + Predicate::lt, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::LtEq(binary)) => binary_pred_n( + Predicate::le, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::Gt(binary)) => binary_pred_n( + Predicate::gt, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::GtEq(binary)) => binary_pred_n( + Predicate::ge, + binary.left.as_deref(), + binary.right.as_deref(), + names, + ), + Some(ExprStruct::And(binary)) => match (binary.left.as_deref(), binary.right.as_deref()) { + (Some(l), Some(r)) => { + Predicate::and(translate_predicate(l, names), translate_predicate(r, names)) + } + _ => Predicate::unknown("and_missing_child"), + }, + Some(ExprStruct::Or(binary)) => match (binary.left.as_deref(), binary.right.as_deref()) { + (Some(l), Some(r)) => { + Predicate::or(translate_predicate(l, names), translate_predicate(r, names)) + } + _ => Predicate::unknown("or_missing_child"), + }, + Some(ExprStruct::Not(unary)) => match unary.child.as_deref() { + Some(child) => Predicate::not(translate_predicate(child, names)), + None => Predicate::unknown("not_missing_child"), + }, + Some(ExprStruct::In(in_expr)) => translate_in(in_expr, names), + // Unwrap Cast: kernel stats don't need type coercion, pass child through + Some(ExprStruct::Cast(cast)) => match cast.child.as_deref() { + Some(child) => translate_predicate(child, names), + None => Predicate::unknown("cast_missing_child"), + }, + _ => Predicate::unknown("unsupported_catalyst_expr"), + } +} + +fn translate_in(in_expr: &spark_expression::In, names: &[String]) -> Predicate { + let value = match in_expr.in_value.as_deref() { + Some(v) => catalyst_to_kernel_expression_with_names(v, names), + None => return Predicate::unknown("in_missing_value"), + }; + + let scalars: Vec = in_expr + .lists + .iter() + .filter_map(catalyst_literal_to_scalar) + .collect(); + + if scalars.is_empty() { + return Predicate::unknown("in_no_literal_values"); + } + + let kernel_type = scalar_to_kernel_type(&scalars[0]); + let array_data = match ArrayData::try_new(ArrayType::new(kernel_type, true), scalars) { + Ok(ad) => ad, + Err(_) => return Predicate::unknown("in_array_type_mismatch"), + }; + let array = Expression::literal(Scalar::Array(array_data)); + + let pred = Predicate::binary(BinaryPredicateOp::In, value, array); + if in_expr.negated { + Predicate::not(pred) + } else { + pred + } +} + +fn scalar_to_kernel_type(s: &Scalar) -> DataType { + match s { + Scalar::Boolean(_) => DataType::BOOLEAN, + Scalar::Byte(_) => DataType::BYTE, + Scalar::Short(_) => DataType::SHORT, + Scalar::Integer(_) => DataType::INTEGER, + Scalar::Long(_) => DataType::LONG, + Scalar::Float(_) => DataType::FLOAT, + Scalar::Double(_) => DataType::DOUBLE, + Scalar::String(_) => DataType::STRING, + _ => DataType::STRING, + } +} + +fn catalyst_literal_to_scalar(expr: &Expr) -> Option { + match expr.expr_struct.as_ref() { + Some(ExprStruct::Literal(lit)) => match &lit.value { + Some(literal::Value::BoolVal(b)) => Some(Scalar::Boolean(*b)), + Some(literal::Value::ByteVal(v)) => Some(Scalar::Byte(*v as i8)), + Some(literal::Value::ShortVal(v)) => Some(Scalar::Short(*v as i16)), + Some(literal::Value::IntVal(v)) => Some(Scalar::Integer(*v)), + Some(literal::Value::LongVal(v)) => Some(Scalar::Long(*v)), + Some(literal::Value::FloatVal(v)) => Some(Scalar::Float(*v)), + Some(literal::Value::DoubleVal(v)) => Some(Scalar::Double(*v)), + Some(literal::Value::StringVal(s)) => Some(Scalar::String(s.clone())), + _ => None, + }, + _ => None, + } +} + +fn binary_pred_n( + builder: impl Fn(Expression, Expression) -> Predicate, + left: Option<&Expr>, + right: Option<&Expr>, + names: &[String], +) -> Predicate { + match (left, right) { + (Some(l), Some(r)) => builder( + catalyst_to_kernel_expression_with_names(l, names), + catalyst_to_kernel_expression_with_names(r, names), + ), + _ => Predicate::unknown("binary_missing_child"), + } +} + +/// Translate a Catalyst-proto `Expr` into a kernel value `Expression`. +/// +/// `column_names` maps BoundReference indices to column names. When +/// empty, BoundReferences become unknown expressions (disabling file +/// skipping for that sub-expression but never producing wrong results). +pub fn catalyst_to_kernel_expression_with_names( + expr: &Expr, + column_names: &[String], +) -> Expression { + match expr.expr_struct.as_ref() { + Some(ExprStruct::Bound(bound)) => { + let idx = bound.index as usize; + if idx < column_names.len() { + Expression::column([column_names[idx].as_str()]) + } else { + Expression::unknown("bound_ref_out_of_range") + } + } + Some(ExprStruct::Literal(lit)) => catalyst_literal_to_kernel(lit), + // Unwrap Cast: pass child expression through for kernel stats evaluation + Some(ExprStruct::Cast(cast)) => match cast.child.as_deref() { + Some(child) => catalyst_to_kernel_expression_with_names(child, column_names), + None => Expression::unknown("cast_missing_child"), + }, + _ => Expression::unknown("unsupported_expr_operand"), + } +} + +fn catalyst_literal_to_kernel(lit: &spark_expression::Literal) -> Expression { + match &lit.value { + Some(literal::Value::BoolVal(b)) => Expression::literal(*b), + Some(literal::Value::ByteVal(v)) => Expression::literal(*v), + Some(literal::Value::ShortVal(v)) => Expression::literal(*v), + Some(literal::Value::IntVal(v)) => Expression::literal(*v), + Some(literal::Value::LongVal(v)) => Expression::literal(*v), + Some(literal::Value::FloatVal(v)) => Expression::literal(*v), + Some(literal::Value::DoubleVal(v)) => Expression::literal(*v), + Some(literal::Value::StringVal(s)) => Expression::literal(s.as_str()), + _ => Expression::null_literal(DataType::STRING), + } +} + +#[cfg(test)] +mod tests { + use super::*; + use datafusion_comet_proto::spark_expression::{ + BinaryExpr, BoundReference, Cast, In, Literal, UnaryExpr, + }; + + // ---- builders for proto Exprs ---- + + fn mk_expr(es: ExprStruct) -> Expr { + Expr { + expr_struct: Some(es), + query_context: None, + expr_id: None, + } + } + + fn lit_int(v: i32) -> Expr { + mk_expr(ExprStruct::Literal(Literal { + value: Some(literal::Value::IntVal(v)), + ..Default::default() + })) + } + + fn lit_string(s: &str) -> Expr { + mk_expr(ExprStruct::Literal(Literal { + value: Some(literal::Value::StringVal(s.to_string())), + ..Default::default() + })) + } + + fn bound_ref(idx: i32) -> Expr { + mk_expr(ExprStruct::Bound(BoundReference { + index: idx, + ..Default::default() + })) + } + + fn binary( + struct_fn: impl FnOnce(Box) -> ExprStruct, + l: Expr, + r: Expr, + ) -> Expr { + let mut be = BinaryExpr::default(); + be.left = Some(Box::new(l)); + be.right = Some(Box::new(r)); + mk_expr(struct_fn(Box::new(be))) + } + + fn unary( + struct_fn: impl FnOnce(Box) -> ExprStruct, + child: Expr, + ) -> Expr { + let mut ue = UnaryExpr::default(); + ue.child = Some(Box::new(child)); + mk_expr(struct_fn(Box::new(ue))) + } + + fn pred_str(p: &Predicate) -> String { + format!("{p:?}") + } + + // ---- literal coverage ---- + + #[test] + fn literal_kinds_all_translate() { + let cases: Vec<(literal::Value, &str)> = vec![ + (literal::Value::BoolVal(true), "true"), + (literal::Value::ByteVal(5), "5"), + (literal::Value::ShortVal(6), "6"), + (literal::Value::IntVal(7), "7"), + (literal::Value::LongVal(8), "8"), + (literal::Value::FloatVal(1.5), "1.5"), + (literal::Value::DoubleVal(2.5), "2.5"), + (literal::Value::StringVal("hi".to_string()), "hi"), + ]; + for (val, needle) in cases { + let lit = Literal { + value: Some(val), + ..Default::default() + }; + let expr = catalyst_literal_to_kernel(&lit); + assert!( + format!("{expr:?}").contains(needle), + "literal didn't translate: needle={needle}, got={expr:?}" + ); + } + } + + #[test] + fn literal_unsupported_becomes_null() { + let lit = Literal { + value: None, + ..Default::default() + }; + let expr = catalyst_literal_to_kernel(&lit); + // Should be a NULL literal (DataType::STRING). Just sanity-check it's not panicking. + let _ = format!("{expr:?}"); + } + + #[test] + fn literal_to_scalar_extracts_all_kinds() { + let exprs = vec![ + (lit_int(7), Scalar::Integer(7)), + (lit_string("foo"), Scalar::String("foo".into())), + ]; + for (expr, want) in exprs { + let got = catalyst_literal_to_scalar(&expr).unwrap(); + assert_eq!(format!("{got:?}"), format!("{want:?}")); + } + } + + #[test] + fn literal_to_scalar_non_literal_returns_none() { + assert!(catalyst_literal_to_scalar(&bound_ref(0)).is_none()); + } + + // ---- binary operators ---- + + #[test] + fn binary_eq_with_bound_ref_and_literal() { + let expr = binary(ExprStruct::Eq, bound_ref(0), lit_int(7)); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["x".to_string()]); + let s = pred_str(&p); + // Should mention "Eq" or "=" plus column name x and value 7 + assert!(s.contains("Eq") || s.contains("Equal"), "got: {s}"); + assert!(s.contains("x"), "no column name: {s}"); + } + + #[test] + fn binary_all_relops_translate() { + let kinds: Vec) -> ExprStruct> = vec![ + ExprStruct::Eq, + ExprStruct::Neq, + ExprStruct::Lt, + ExprStruct::LtEq, + ExprStruct::Gt, + ExprStruct::GtEq, + ]; + for k in kinds { + let expr = binary(k, bound_ref(0), lit_int(1)); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!( + !s.contains("Unknown") && !s.contains("unknown"), + "operator translated to unknown: {s}" + ); + } + } + + #[test] + fn binary_missing_child_falls_back_to_unknown() { + let mut be = BinaryExpr::default(); + be.left = Some(Box::new(bound_ref(0))); + // right is None + let expr = mk_expr(ExprStruct::Eq(Box::new(be))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("binary_missing_child")); + } + + // ---- logical operators ---- + + #[test] + fn and_combines_two_children() { + let l = binary(ExprStruct::Eq, bound_ref(0), lit_int(1)); + let r = binary(ExprStruct::Eq, bound_ref(1), lit_int(2)); + let expr = binary(ExprStruct::And, l, r); + let p = catalyst_to_kernel_predicate_with_names( + &expr, + &["a".to_string(), "b".to_string()], + ); + let s = pred_str(&p); + assert!(s.contains("a") && s.contains("b"), "{s}"); + } + + #[test] + fn or_combines_two_children() { + let l = binary(ExprStruct::Eq, bound_ref(0), lit_int(1)); + let r = binary(ExprStruct::Eq, bound_ref(0), lit_int(2)); + let expr = binary(ExprStruct::Or, l, r); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["a".to_string()]); + let _ = pred_str(&p); // just check it doesn't panic + } + + #[test] + fn and_missing_child_falls_back_to_unknown() { + let mut be = BinaryExpr::default(); + be.left = Some(Box::new(bound_ref(0))); + let expr = mk_expr(ExprStruct::And(Box::new(be))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("and_missing_child")); + } + + #[test] + fn or_missing_child_falls_back_to_unknown() { + let mut be = BinaryExpr::default(); + be.right = Some(Box::new(bound_ref(0))); + let expr = mk_expr(ExprStruct::Or(Box::new(be))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("or_missing_child")); + } + + // ---- unary operators ---- + + #[test] + fn is_null_translates() { + let expr = unary(ExprStruct::IsNull, bound_ref(0)); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(s.contains("Null") || s.contains("null"), "{s}"); + } + + #[test] + fn is_not_null_translates() { + let expr = unary(ExprStruct::IsNotNull, bound_ref(0)); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(s.contains("Null") || s.contains("null"), "{s}"); + } + + #[test] + fn not_translates() { + let inner = binary(ExprStruct::Eq, bound_ref(0), lit_int(1)); + let expr = unary(ExprStruct::Not, inner); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(s.contains("Not") || s.contains("not"), "{s}"); + } + + #[test] + fn unary_missing_child_falls_back_to_unknown() { + let expr = mk_expr(ExprStruct::IsNull(Box::new(UnaryExpr::default()))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("missing_child")); + } + + // ---- IN ---- + + #[test] + fn in_translates_with_literal_list() { + let mut in_expr = In::default(); + in_expr.in_value = Some(Box::new(bound_ref(0))); + in_expr.lists = vec![lit_int(1), lit_int(2), lit_int(3)]; + in_expr.negated = false; + let expr = mk_expr(ExprStruct::In(Box::new(in_expr))); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(!s.contains("unknown"), "got: {s}"); + } + + #[test] + fn in_negated_wraps_with_not() { + let mut in_expr = In::default(); + in_expr.in_value = Some(Box::new(bound_ref(0))); + in_expr.lists = vec![lit_int(1)]; + in_expr.negated = true; + let expr = mk_expr(ExprStruct::In(Box::new(in_expr))); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(s.contains("Not") || s.contains("not"), "{s}"); + } + + #[test] + fn in_empty_list_falls_back_to_unknown() { + let mut in_expr = In::default(); + in_expr.in_value = Some(Box::new(bound_ref(0))); + in_expr.lists = vec![]; + let expr = mk_expr(ExprStruct::In(Box::new(in_expr))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("in_no_literal_values")); + } + + #[test] + fn in_missing_value_falls_back_to_unknown() { + let in_expr = In::default(); // in_value is None + let expr = mk_expr(ExprStruct::In(Box::new(in_expr))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("in_missing_value")); + } + + // ---- Cast unwrap ---- + + #[test] + fn cast_unwraps_in_predicate_context() { + let mut cast = Cast::default(); + cast.child = Some(Box::new(binary(ExprStruct::Eq, bound_ref(0), lit_int(1)))); + let expr = mk_expr(ExprStruct::Cast(Box::new(cast))); + let p = catalyst_to_kernel_predicate_with_names(&expr, &["c".to_string()]); + let s = pred_str(&p); + assert!(!s.contains("unsupported"), "Cast didn't unwrap: {s}"); + } + + #[test] + fn cast_unwraps_in_expression_context() { + let mut cast = Cast::default(); + cast.child = Some(Box::new(bound_ref(0))); + let expr = mk_expr(ExprStruct::Cast(Box::new(cast))); + let kernel_expr = + catalyst_to_kernel_expression_with_names(&expr, &["x".to_string()]); + // After unwrap: should resolve to column "x" + assert!(format!("{kernel_expr:?}").contains("x")); + } + + #[test] + fn cast_missing_child_falls_back_to_unknown() { + let expr = mk_expr(ExprStruct::Cast(Box::new(Cast::default()))); + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("cast_missing_child")); + } + + // ---- BoundReference resolution ---- + + #[test] + fn bound_ref_resolves_to_column_name() { + let expr = bound_ref(1); + let kernel_expr = catalyst_to_kernel_expression_with_names( + &expr, + &["a".to_string(), "b".to_string(), "c".to_string()], + ); + assert!(format!("{kernel_expr:?}").contains("b")); + } + + #[test] + fn bound_ref_out_of_range_yields_unknown() { + let expr = bound_ref(5); + let kernel_expr = + catalyst_to_kernel_expression_with_names(&expr, &["only_one".to_string()]); + assert!(format!("{kernel_expr:?}").contains("bound_ref_out_of_range")); + } + + #[test] + fn bound_ref_with_empty_names_yields_unknown() { + let expr = bound_ref(0); + let kernel_expr = catalyst_to_kernel_expression_with_names(&expr, &[]); + assert!(format!("{kernel_expr:?}").contains("bound_ref_out_of_range")); + } + + // ---- unsupported fallback ---- + + #[test] + fn unsupported_expr_kind_falls_back_to_unknown_predicate() { + // An Expr with no expr_struct at all -> unsupported_catalyst_expr. + let expr = Expr { + expr_struct: None, + query_context: None, + expr_id: None, + }; + let p = catalyst_to_kernel_predicate(&expr); + assert!(pred_str(&p).contains("unsupported_catalyst_expr")); + } + + // ---- scalar type inference ---- + + #[test] + fn scalar_to_kernel_type_round_trips() { + let cases = vec![ + (Scalar::Boolean(true), "BOOLEAN"), + (Scalar::Integer(0), "INTEGER"), + (Scalar::Long(0), "LONG"), + (Scalar::String("".into()), "STRING"), + ]; + for (scalar, name) in cases { + let dt = scalar_to_kernel_type(&scalar); + // DataType::Display for these is "boolean"/"integer"/"long"/"string". + // Just verify it's not panicking + roughly matches. + assert!( + format!("{dt:?}").to_uppercase().contains(name) || format!("{dt:?}").contains(name), + "type mismatch for {scalar:?}: got {dt:?}" + ); + } + } +} diff --git a/contrib/delta/native/src/scan.rs b/contrib/delta/native/src/scan.rs new file mode 100644 index 0000000000..6b95729351 --- /dev/null +++ b/contrib/delta/native/src/scan.rs @@ -0,0 +1,509 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Delta log replay: given a table URL, return the list of active parquet +//! files with partition values, record-count stats, and deletion-vector +//! flags. +//! +//! Ported from tantivy4java's `delta_reader/scan.rs`. The API is the +//! smallest possible surface that still proves end-to-end kernel +//! integration: `Snapshot::builder_for(url)` → `scan_builder().build()` → +//! `scan_metadata(&engine)` → `visit_scan_files(...)`. +//! +//! **Critical gotcha** preserved from the reference implementation: kernel +//! internally does `table_root.join("_delta_log/")`, and `Url::join` will +//! *replace* the last path segment if the base URL does not end in `/`. So +//! `normalize_url` always appends a trailing slash. + +use std::collections::HashMap; +use std::sync::Arc; +use url::Url; + +use delta_kernel::snapshot::Snapshot; + +use super::engine::{get_or_create_engine, DeltaStorageConfig}; +use super::error::{DeltaError, DeltaResult}; + +/// Metadata for a single active parquet file in a Delta table. +/// +/// Plain Rust types only — no arrow / parquet / object_store types. This is +/// the boundary at which kernel's isolated dep subtree meets the rest of +/// Comet. +#[derive(Debug, Clone)] +pub struct DeltaFileEntry { + /// Parquet file path, relative to the table root. + pub path: String, + /// File size in bytes. + pub size: i64, + /// Last-modified time as epoch millis. + pub modification_time: i64, + /// Record count from log stats, if known. + pub num_records: Option, + /// Partition column → value mapping from the add action. + pub partition_values: HashMap, + /// Deleted row indexes materialized from the file's deletion vector by + /// kernel on the driver. Empty vector means the file has no DV in use. + /// Sorted ascending; indexes are 0-based into the file's physical parquet + /// row space, matching `DvInfo::get_row_indexes` semantics. + pub deleted_row_indexes: Vec, + /// `AddFile.baseRowId` for row-tracking-enabled tables. `None` when the + /// table doesn't have row tracking. `row_id` for any row in this file is + /// `base_row_id + physical_row_index`. + pub base_row_id: Option, + /// `AddFile.defaultRowCommitVersion` for row-tracking-enabled tables. + /// `None` when the table doesn't have row tracking. Constant per file. + pub default_row_commit_version: Option, +} + +impl DeltaFileEntry { + /// True if this entry has a deletion vector in use. + pub fn has_deletion_vector(&self) -> bool { + !self.deleted_row_indexes.is_empty() + } +} + +/// Result of planning a Delta scan: the active file list plus the pinned +/// snapshot version plus a list of reader features that Comet's native path +/// doesn't yet handle. The Scala side uses the feature list to decide +/// whether to fall back to Spark's vanilla Delta reader. +#[derive(Debug, Clone)] +pub struct DeltaScanPlan { + pub entries: Vec, + pub version: u64, + pub unsupported_features: Vec, + /// Logical→physical column name mapping for column-mapped tables. + /// Empty when column_mapping_mode is None. + pub column_mappings: Vec<(String, String)>, +} + +/// List every active parquet file in a Delta table at the given version. +/// +/// Returns `(entries, actual_version)` where `actual_version` is the +/// snapshot version that was actually read — equal to `version` when +/// specified, or the latest version otherwise. +/// +/// Thin wrapper around [`plan_delta_scan`] that drops the feature list. +/// New code should call `plan_delta_scan` directly so it can honor the +/// unsupported-feature gate. +pub fn list_delta_files( + url_str: &str, + config: &DeltaStorageConfig, + version: Option, +) -> DeltaResult<(Vec, u64)> { + let plan = plan_delta_scan(url_str, config, version)?; + Ok((plan.entries, plan.version)) +} + +/// Plan a Delta scan against the given URL + optional snapshot version. +/// +/// This is the full-fat variant of [`list_delta_files`]: it also reports +/// which reader features are *in use* for this snapshot and NOT yet +/// supported by Comet's native path. +/// +/// Feature detection blends two signals: +/// 1. [`delta_kernel::snapshot::Snapshot::table_properties`] — the +/// protocol-level flags (`column_mapping_mode`, `enable_type_widening`, +/// `enable_row_tracking`). +/// 2. The per-file `ScanFile::dv_info.has_vector()` flag — set to true +/// only when the specific file actually has a deletion vector attached. +/// This is tighter than the `enable_deletion_vectors` table property +/// because a DV-enabled table with no deletes yet is still safe for +/// Comet to read natively. +pub fn plan_delta_scan( + url_str: &str, + config: &DeltaStorageConfig, + version: Option, +) -> DeltaResult { + plan_delta_scan_with_predicate(url_str, config, version, None) +} + +pub fn plan_delta_scan_with_predicate( + url_str: &str, + config: &DeltaStorageConfig, + version: Option, + kernel_predicate: Option, +) -> DeltaResult { + let url = normalize_url(url_str)?; + let engine = get_or_create_engine(&url, config)?; + + let snapshot = { + let mut builder = Snapshot::builder_for(url); + if let Some(v) = version { + builder = builder.at_version(v); + } + builder.build(&*engine)? + }; + let actual_version = snapshot.version(); + + // Protocol-level feature gate. Collect the names of features we don't + // yet handle so the Scala side can decide to fall back. Note that we + // explicitly do NOT treat the following as fallback-worthy: + // - `change_data_feed`: only affects CDF queries, not regular reads + // - `in_commit_timestamps`: regular reads work fine + // - `iceberg_compat_v1/v2`: doesn't change Delta read correctness + // - `append_only`: write-side constraint, reads are unaffected + let unsupported_features: Vec = Vec::new(); + let props = snapshot.table_properties(); + // columnMapping is now handled by Phase 4 — no longer a fallback trigger. + // typeWidening: DataFusion's parquet schema adapter handles widening reads + // (parquet stores the file's original type; the adapter casts to the table's + // current widened type at read time). Removed from the gate; verified by + // TypeWidening{TableFeature,Metadata,...}Suite in the Delta regression. + // rowTracking: tables with `enable_row_tracking=true` are scannable + // natively. Queries that explicitly select `_metadata.row_id` / + // `_metadata.row_commit_version` are handled in CometScanRule's + // `applyRowTrackingRewrite` (it rewrites the scan to read the materialized + // physical column, or declines when no materialized name is available). + // No need to gate the whole table's scan path here. + + // Phase 4: extract logical→physical column name mapping from schema metadata. + // For column_mapping_mode = id or name, each StructField carries a + // `delta.columnMapping.physicalName` metadata entry that tells us what the + // parquet file's column name actually is. + let column_mappings: Vec<(String, String)> = if props.column_mapping_mode.is_some() { + snapshot + .schema() + .fields() + .filter_map(|field| { + use delta_kernel::schema::{ColumnMetadataKey, MetadataValue}; + field + .metadata + .get(ColumnMetadataKey::ColumnMappingPhysicalName.as_ref()) + .and_then(|v| match v { + MetadataValue::String(phys) => Some((field.name().clone(), phys.clone())), + _ => None, + }) + }) + .collect() + } else { + Vec::new() + }; + + // `Snapshot::build()` returns `Arc`, and `scan_builder` consumes + // it. Clone the Arc so we can still reach `table_root()` after building + // the scan — we need the URL to materialize DVs below. + let snapshot_arc: Arc<_> = snapshot; + let table_root_url = snapshot_arc.table_root().clone(); + let mut scan_builder = Arc::clone(&snapshot_arc).scan_builder(); + if let Some(pred) = kernel_predicate { + scan_builder = scan_builder.with_predicate(Arc::new(pred)); + } + let scan = scan_builder.build()?; + + // Temporary collection that keeps the raw kernel `DvInfo` alongside the + // rest of the metadata. We need the `DvInfo` to materialize the deleted + // row indexes below; it doesn't escape this function. + // + // `base_row_id` / `default_row_commit_version` are extracted from each + // scan-files batch's underlying RecordBatch via direct column access -- + // `ScanFile` (what kernel's `visit_scan_files` callback receives) doesn't + // surface them. Comet's native synthetic-columns exec uses them to + // synthesise Delta's logical `row_id` (= baseRowId + row_index) and + // `row_commit_version` (= defaultRowCommitVersion, constant per file) so + // row-tracking-enabled tables can stay on the native path instead of + // falling back to Spark's Delta reader for these projections. + struct RawEntry { + path: String, + size: i64, + modification_time: i64, + num_records: Option, + partition_values: HashMap, + dv_info: delta_kernel::scan::state::DvInfo, + base_row_id: Option, + default_row_commit_version: Option, + } + + // Kernel's `visit_scan_files` requires a `fn` callback (not `FnMut`), so any + // per-call state must live in the `context` we pass in. Use a struct that carries + // both the accumulator AND the row-tracking lookup for the current batch. + struct RawEntryAcc { + entries: Vec, + row_tracking: Vec<(Option, Option)>, + next_idx: usize, + } + let mut acc = RawEntryAcc { + entries: Vec::new(), + row_tracking: Vec::new(), + next_idx: 0, + }; + let scan_metadata = scan.scan_metadata(&*engine)?; + + for meta_result in scan_metadata { + let meta: delta_kernel::scan::ScanMetadata = meta_result?; + // Pre-extract baseRowId / defaultRowCommitVersion for the SELECTED rows in + // this batch. Kernel's `visit_scan_files` walks selected rows in order; we + // build a parallel vec indexed by visit order, so the callback can pull each + // row's tracking values via a shared counter. + acc.row_tracking = extract_row_tracking_for_selected(&meta)?; + acc.next_idx = 0; + acc = meta.visit_scan_files( + acc, + |acc: &mut RawEntryAcc, scan_file: delta_kernel::scan::state::ScanFile| { + let num_records = scan_file.stats.as_ref().map(|s| s.num_records); + let (base_row_id, default_row_commit_version) = acc + .row_tracking + .get(acc.next_idx) + .copied() + .unwrap_or((None, None)); + acc.next_idx += 1; + acc.entries.push(RawEntry { + path: scan_file.path, + size: scan_file.size, + modification_time: scan_file.modification_time, + num_records, + partition_values: scan_file.partition_values, + dv_info: scan_file.dv_info, + base_row_id, + default_row_commit_version, + }); + }, + )?; + } + let raw = acc.entries; + + // For each file that has a DV attached, ask kernel to materialize the + // deleted row indexes. Kernel handles inline bitmaps, on-disk DV files, + // and the various storage-type variants transparently. This runs on the + // driver (same process that's building the scan plan), so we only pay + // the DV-fetch latency once per query. + // + // Note: for very large tables (millions of files), this collects all + // entries into memory before returning. Consider streaming/chunked + // processing if driver OOM becomes an issue at extreme scale. + let mut entries: Vec = Vec::with_capacity(raw.len()); + for r in raw { + let deleted_row_indexes = if r.dv_info.has_vector() { + r.dv_info + .get_row_indexes(&*engine, &table_root_url)? + .ok_or_else(|| { + DeltaError::Internal(format!( + "DV has_vector() true but get_row_indexes() returned None for {}", + r.path + )) + })? + } else { + Vec::new() + }; + entries.push(DeltaFileEntry { + path: r.path, + size: r.size, + modification_time: r.modification_time, + num_records: r.num_records, + partition_values: r.partition_values, + deleted_row_indexes, + base_row_id: r.base_row_id, + default_row_commit_version: r.default_row_commit_version, + }); + } + + Ok(DeltaScanPlan { + entries, + version: actual_version, + unsupported_features, + column_mappings, + }) +} + +/// Normalize a table URL so kernel's `table_root.join("_delta_log/")` +/// appends rather than replaces. Bare paths become `file://` URLs. +/// +/// Accepts three shapes: +/// 1. `s3://`, `s3a://`, `az://`, `azure://`, `abfs://`, `abfss://`, +/// `file://` — already-formed URLs, parsed directly. +/// 2. `file:/Users/...` — Hadoop's `Path.toUri.toString` output, which +/// uses a *single* slash and is NOT a valid `Url::parse` input. We +/// rewrite this to `file://` before parsing. +/// 3. Bare local paths — canonicalized and turned into `file://` via +/// `Url::from_directory_path`. +pub(crate) fn normalize_url(url_str: &str) -> DeltaResult { + // Hadoop's java.net.URI.toString emits `file:/path/to/t` (one slash) + // for local files. Rewrite into the `file:///path` form that + // `Url::parse` understands. + if url_str.starts_with("file:/") && !url_str.starts_with("file://") { + let rewritten = format!("file://{}", &url_str["file:".len()..]); + let mut url = Url::parse(&rewritten).map_err(|e| DeltaError::InvalidUrl { + url: url_str.to_string(), + source: e, + })?; + ensure_trailing_slash(&mut url); + return Ok(url); + } + + if url_str.starts_with("s3://") + || url_str.starts_with("s3a://") + || url_str.starts_with("az://") + || url_str.starts_with("azure://") + || url_str.starts_with("abfs://") + || url_str.starts_with("abfss://") + || url_str.starts_with("file://") + { + let mut url = Url::parse(url_str).map_err(|e| DeltaError::InvalidUrl { + url: url_str.to_string(), + source: e, + })?; + ensure_trailing_slash(&mut url); + Ok(url) + } else { + let abs_path = std::path::Path::new(url_str).canonicalize().map_err(|e| { + DeltaError::PathResolution { + path: url_str.to_string(), + source: e, + } + })?; + Url::from_directory_path(&abs_path).map_err(|_| DeltaError::PathToUrl { + path: abs_path.display().to_string(), + }) + } +} + +fn ensure_trailing_slash(url: &mut Url) { + let path = url.path().to_string(); + if !path.ends_with('/') { + url.set_path(&format!("{path}/")); + } +} + +/// Extract `(baseRowId, defaultRowCommitVersion)` per SELECTED row from a `ScanMetadata` +/// batch's underlying `RecordBatch`. Kernel's `visit_scan_files` callback receives a +/// `ScanFile` that does NOT surface these row-tracking values; they live in the raw +/// `fileConstantValues` struct column on the underlying arrow batch. +/// +/// `kernel/src/scan/log_replay.rs::SCAN_ROW_SCHEMA` defines the schema: +/// { path, size, modificationTime, stats, deletionVector, +/// fileConstantValues: { partitionValues, baseRowId, defaultRowCommitVersion, tags } } +/// So the fileConstantValues struct is the 6th top-level field (index 5), and within it +/// baseRowId is at field index 1 and defaultRowCommitVersion at field index 2. +/// +/// Returns one `(Option, Option)` per SELECTED row, in visit_scan_files order. +/// Rows where row tracking isn't enabled have `(None, None)`. +fn extract_row_tracking_for_selected( + meta: &delta_kernel::scan::ScanMetadata, +) -> DeltaResult, Option)>> { + use delta_kernel::arrow::array::{Array, Int64Array, StructArray}; + use delta_kernel::engine::arrow_data::ArrowEngineData; + let engine_data = meta.scan_files.data(); + let arrow = match engine_data.any_ref().downcast_ref::() { + Some(a) => a, + // Non-Arrow engine (shouldn't happen for our DefaultEngine path); return empty + // so downstream sees (None, None) per row and the row-tracking decline gate + // takes over. + None => return Ok(Vec::new()), + }; + let batch = arrow.record_batch(); + let total_rows = batch.num_rows(); + + let file_constants = batch + .column_by_name("fileConstantValues") + .and_then(|c| c.as_any().downcast_ref::()); + let (base_arr, default_arr): (Option<&Int64Array>, Option<&Int64Array>) = match file_constants + { + Some(s) => ( + s.column_by_name("baseRowId") + .and_then(|c| c.as_any().downcast_ref::()), + s.column_by_name("defaultRowCommitVersion") + .and_then(|c| c.as_any().downcast_ref::()), + ), + None => (None, None), + }; + + let sel = meta.scan_files.selection_vector(); + // FilteredEngineData::try_new asserts `sel.len() <= data.len()`; rows beyond + // sel.len() are treated as not-selected. visit_scan_files visits only rows that ARE + // selected, so any rows past sel.len() won't appear in the callback and our parallel + // vec stays aligned. The explicit bound below makes the contract obvious. + let bounded_rows = total_rows.min(sel.len()); + let mut out: Vec<(Option, Option)> = + Vec::with_capacity(sel.iter().filter(|b| **b).count()); + for i in 0..bounded_rows { + if !sel[i] { + continue; + } + let b = base_arr.and_then(|a| if a.is_null(i) { None } else { Some(a.value(i)) }); + let d = default_arr.and_then(|a| if a.is_null(i) { None } else { Some(a.value(i)) }); + out.push((b, d)); + } + Ok(out) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_normalize_url_trailing_slash() { + let url = normalize_url("file:///tmp/my_table").unwrap(); + assert!(url.path().ends_with('/'), "URL should end with /: {url}"); + assert_eq!(url.as_str(), "file:///tmp/my_table/"); + + let url = normalize_url("file:///tmp/my_table/").unwrap(); + assert_eq!(url.as_str(), "file:///tmp/my_table/"); + + let url = normalize_url("s3://bucket/path/to/table").unwrap(); + assert!(url.path().ends_with('/'), "URL should end with /: {url}"); + } + + #[test] + fn test_normalize_url_hadoop_single_slash_form() { + // Hadoop's Path.toUri.toString produces `file:/path` (single slash), + // not `file:///path`. Must be normalized to a Url::parse-able form. + let url = normalize_url("file:/Users/alice/tmp/t").unwrap(); + assert_eq!(url.as_str(), "file:///Users/alice/tmp/t/"); + + let url = normalize_url("file:/tmp/t/").unwrap(); + assert_eq!(url.as_str(), "file:///tmp/t/"); + } + + #[test] + fn test_normalize_url_join_behavior() { + // The critical invariant: joining `_delta_log/` onto a normalized + // URL must *append*, not replace the last segment. + let url = normalize_url("file:///tmp/my_table").unwrap(); + let log_url = url.join("_delta_log/").unwrap(); + assert_eq!(log_url.as_str(), "file:///tmp/my_table/_delta_log/"); + } + + #[test] + fn test_list_delta_files_local() { + // Hand-build a minimal Delta table in a tempdir: one protocol action, + // one metadata action, one add action. No Parquet data needed — + // we're exercising the log-replay path only. + let tmp = tempfile::tempdir().unwrap(); + let table_dir = tmp.path().join("test_delta"); + let delta_log = table_dir.join("_delta_log"); + std::fs::create_dir_all(&delta_log).unwrap(); + + let commit0 = [ + r#"{"protocol":{"minReaderVersion":1,"minWriterVersion":2}}"#, + r#"{"metaData":{"id":"test-id","format":{"provider":"parquet","options":{}},"schemaString":"{\"type\":\"struct\",\"fields\":[{\"name\":\"id\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}}]}","partitionColumns":[],"configuration":{},"createdTime":1700000000000}}"#, + r#"{"add":{"path":"part-00000.parquet","partitionValues":{},"size":5000,"modificationTime":1700000000000,"dataChange":true,"stats":"{\"numRecords\":50}"}}"#, + ] + .join("\n"); + std::fs::write(delta_log.join("00000000000000000000.json"), &commit0).unwrap(); + std::fs::write(table_dir.join("part-00000.parquet"), [0u8]).unwrap(); + + let config = DeltaStorageConfig::default(); + let (entries, version) = + list_delta_files(table_dir.to_str().unwrap(), &config, None).unwrap(); + + assert_eq!(version, 0); + assert_eq!(entries.len(), 1); + assert_eq!(entries[0].path, "part-00000.parquet"); + assert_eq!(entries[0].size, 5000); + assert_eq!(entries[0].num_records, Some(50)); + assert!(!entries[0].has_deletion_vector()); + } +} diff --git a/contrib/delta/native/src/synthetic_columns.rs b/contrib/delta/native/src/synthetic_columns.rs new file mode 100644 index 0000000000..e145120fe6 --- /dev/null +++ b/contrib/delta/native/src/synthetic_columns.rs @@ -0,0 +1,741 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Delta synthetic column emitters. +//! +//! Delta's `DeltaParquetFileFormat` injects two parquet-virtual columns into the scan +//! output for UPDATE/DELETE/MERGE flows: +//! +//! - `__delta_internal_row_index` (Long): the physical row position within the file +//! - `__delta_internal_is_row_deleted` (Int): 0 = keep, nonzero = drop (from the DV) +//! +//! Delta's reader synthesizes these from parquet row positions + the DV bitmap. Comet's +//! native parquet path (DataFusion 53) doesn't expose virtual row-index columns; this +//! module provides equivalent synthesis as small `ExecutionPlan` wrappers that sit +//! between the inner parquet scan and the rest of the plan. +//! +//! Same physical-order invariant as `DeltaDvFilterExec` — these execs rely on one file +//! per partition and the parquet scan emitting rows in file row order. Both +//! `maintains_input_order() = [true]` and `benefits_from_input_partitioning() = [false]` +//! are overridden to pin the contract so future optimizer rewrites are forced to bail +//! rather than silently re-order rows out from under the row-index emit. + +use std::any::Any; +use std::fmt; +use std::pin::Pin; +use std::sync::Arc; +use std::task::{Context, Poll}; + +use arrow::array::{Int32Array, Int64Array, RecordBatch, UInt64Array}; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use datafusion::common::{DataFusionError, Result as DFResult}; +use datafusion::execution::{RecordBatchStream, SendableRecordBatchStream, TaskContext}; +use datafusion::physical_expr::EquivalenceProperties; +use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType}; +use datafusion::physical_plan::metrics::{ + BaselineMetrics, ExecutionPlanMetricsSet, MetricsSet, +}; +use datafusion::physical_plan::{DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties}; +use futures::{Stream, StreamExt}; + +/// Delta's internal name for the row-index column. +pub const ROW_INDEX_COLUMN_NAME: &str = "__delta_internal_row_index"; +/// Delta's internal name for the is-row-deleted column. +pub const IS_ROW_DELETED_COLUMN_NAME: &str = "__delta_internal_is_row_deleted"; +/// Delta's logical row-id column. Synthesised as `baseRowId + physical_row_index`. +pub const ROW_ID_COLUMN_NAME: &str = "row_id"; +/// Delta's logical row-commit-version column. Constant per file = `defaultRowCommitVersion`. +pub const ROW_COMMIT_VERSION_COLUMN_NAME: &str = "row_commit_version"; + +/// Build an output schema = input fields + the appended synthetic columns. Order is +/// fixed: row_index, is_row_deleted, row_id, row_commit_version. Scala-side caller +/// asserts these are a suffix of `scan.requiredSchema` in the same order so the proto +/// layout aligns with what Spark expects. +fn build_output_schema( + input: &SchemaRef, + emit_row_index: bool, + emit_is_row_deleted: bool, + emit_row_id: bool, + emit_row_commit_version: bool, +) -> SchemaRef { + let mut fields: Vec> = input.fields().iter().cloned().collect(); + if emit_row_index { + fields.push(Arc::new(Field::new(ROW_INDEX_COLUMN_NAME, DataType::UInt64, false))); + } + if emit_is_row_deleted { + fields.push(Arc::new(Field::new( + IS_ROW_DELETED_COLUMN_NAME, + DataType::Int32, + false, + ))); + } + if emit_row_id { + fields.push(Arc::new(Field::new(ROW_ID_COLUMN_NAME, DataType::Int64, true))); + } + if emit_row_commit_version { + fields.push(Arc::new(Field::new( + ROW_COMMIT_VERSION_COLUMN_NAME, + DataType::Int64, + true, + ))); + } + Arc::new(Schema::new(fields)) +} + +/// `ExecutionPlan` wrapper that appends Delta's synthetic `__delta_internal_row_index` +/// (UInt64) and/or `__delta_internal_is_row_deleted` (Int32) columns to its child's +/// output batches. +/// +/// `deleted_row_indexes_by_partition[i]` is the sorted DV for partition `i`. When +/// `emit_is_row_deleted` is true, each row's is-deleted column is computed by checking +/// membership in this list. When `emit_row_index` is true, each row's row_index column +/// is set to its physical position within the file (running offset across batches). +/// +/// Unlike `DeltaDvFilterExec`, this exec does NOT filter rows — it surfaces the +/// information for an outer operator (e.g. Delta's MERGE/UPDATE writer) to decide what +/// to do. +#[derive(Debug)] +pub struct DeltaSyntheticColumnsExec { + input: Arc, + /// One entry per output partition. Length must match the input's partition count. + /// Empty vec means no DV for that partition (all rows are kept). + deleted_row_indexes_by_partition: Vec>, + /// `AddFile.baseRowId` per partition; `None` when the table doesn't have row + /// tracking enabled for this file. Required to be present (Some(_)) on every + /// partition when `emit_row_id` is true. + base_row_ids_by_partition: Vec>, + /// `AddFile.defaultRowCommitVersion` per partition; same semantics as + /// `base_row_ids_by_partition` but for `emit_row_commit_version`. + default_row_commit_versions_by_partition: Vec>, + emit_row_index: bool, + emit_is_row_deleted: bool, + emit_row_id: bool, + emit_row_commit_version: bool, + output_schema: SchemaRef, + plan_properties: Arc, + metrics: ExecutionPlanMetricsSet, +} + +impl DeltaSyntheticColumnsExec { + #[allow(clippy::too_many_arguments)] + pub fn new( + input: Arc, + deleted_row_indexes_by_partition: Vec>, + base_row_ids_by_partition: Vec>, + default_row_commit_versions_by_partition: Vec>, + emit_row_index: bool, + emit_is_row_deleted: bool, + emit_row_id: bool, + emit_row_commit_version: bool, + ) -> DFResult { + if !emit_row_index && !emit_is_row_deleted && !emit_row_id && !emit_row_commit_version { + return Err(DataFusionError::Internal( + "DeltaSyntheticColumnsExec constructed with nothing to emit".to_string(), + )); + } + let input_props = input.properties(); + let num_partitions = input_props.output_partitioning().partition_count(); + if deleted_row_indexes_by_partition.len() != num_partitions + || base_row_ids_by_partition.len() != num_partitions + || default_row_commit_versions_by_partition.len() != num_partitions + { + return Err(DataFusionError::Internal(format!( + "DeltaSyntheticColumnsExec: per-partition vec lengths don't match input partitions \ + ({}): dv={}, base_row_ids={}, default_commit_versions={}", + num_partitions, + deleted_row_indexes_by_partition.len(), + base_row_ids_by_partition.len(), + default_row_commit_versions_by_partition.len() + ))); + } + let output_schema = build_output_schema( + &input.schema(), + emit_row_index, + emit_is_row_deleted, + emit_row_id, + emit_row_commit_version, + ); + let plan_properties = Arc::new(PlanProperties::new( + EquivalenceProperties::new(Arc::clone(&output_schema)), + input_props.output_partitioning().clone(), + EmissionType::Incremental, + Boundedness::Bounded, + )); + Ok(Self { + input, + deleted_row_indexes_by_partition, + base_row_ids_by_partition, + default_row_commit_versions_by_partition, + emit_row_index, + emit_is_row_deleted, + emit_row_id, + emit_row_commit_version, + output_schema, + plan_properties, + metrics: ExecutionPlanMetricsSet::new(), + }) + } +} + +impl DisplayAs for DeltaSyntheticColumnsExec { + fn fmt_as(&self, _t: DisplayFormatType, f: &mut fmt::Formatter) -> fmt::Result { + write!( + f, + "DeltaSyntheticColumnsExec: row_index={}, is_row_deleted={}, row_id={}, row_commit_version={}", + self.emit_row_index, + self.emit_is_row_deleted, + self.emit_row_id, + self.emit_row_commit_version + ) + } +} + +impl ExecutionPlan for DeltaSyntheticColumnsExec { + fn name(&self) -> &str { + "DeltaSyntheticColumnsExec" + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn properties(&self) -> &Arc { + &self.plan_properties + } + + fn children(&self) -> Vec<&Arc> { + vec![&self.input] + } + + // Same physical-order invariant as DeltaDvFilterExec. + fn maintains_input_order(&self) -> Vec { + vec![true] + } + + fn benefits_from_input_partitioning(&self) -> Vec { + vec![false] + } + + fn with_new_children( + self: Arc, + children: Vec>, + ) -> DFResult> { + if children.len() != 1 { + return Err(DataFusionError::Internal(format!( + "DeltaSyntheticColumnsExec takes exactly one child, got {}", + children.len() + ))); + } + Ok(Arc::new(Self::new( + Arc::clone(&children[0]), + self.deleted_row_indexes_by_partition.clone(), + self.base_row_ids_by_partition.clone(), + self.default_row_commit_versions_by_partition.clone(), + self.emit_row_index, + self.emit_is_row_deleted, + self.emit_row_id, + self.emit_row_commit_version, + )?)) + } + + fn execute( + &self, + partition: usize, + context: Arc, + ) -> DFResult { + let child_stream = self.input.execute(partition, context)?; + let deleted = self + .deleted_row_indexes_by_partition + .get(partition) + .cloned() + .unwrap_or_default(); + let base_row_id = self.base_row_ids_by_partition.get(partition).copied().flatten(); + let default_row_commit_version = self + .default_row_commit_versions_by_partition + .get(partition) + .copied() + .flatten(); + let baseline = BaselineMetrics::new(&self.metrics, partition); + Ok(Box::pin(DeltaSyntheticColumnsStream { + inner: child_stream, + deleted, + current_row_offset: 0, + next_delete_idx: 0, + output_schema: Arc::clone(&self.output_schema), + emit_row_index: self.emit_row_index, + emit_is_row_deleted: self.emit_is_row_deleted, + emit_row_id: self.emit_row_id, + emit_row_commit_version: self.emit_row_commit_version, + base_row_id, + default_row_commit_version, + baseline_metrics: baseline, + })) + } + + fn metrics(&self) -> Option { + Some(self.metrics.clone_inner()) + } +} + +struct DeltaSyntheticColumnsStream { + inner: SendableRecordBatchStream, + deleted: Vec, + current_row_offset: u64, + next_delete_idx: usize, + output_schema: SchemaRef, + emit_row_index: bool, + emit_is_row_deleted: bool, + emit_row_id: bool, + emit_row_commit_version: bool, + base_row_id: Option, + default_row_commit_version: Option, + baseline_metrics: BaselineMetrics, +} + +impl DeltaSyntheticColumnsStream { + fn augment(&mut self, batch: RecordBatch) -> DFResult { + let batch_rows = batch.num_rows() as u64; + let batch_start = self.current_row_offset; + let batch_end = batch_start + batch_rows; + + // Build the row_index column: monotonically increasing UInt64 starting at + // batch_start. + let row_index_array: Option = if self.emit_row_index { + Some(UInt64Array::from_iter_values(batch_start..batch_end)) + } else { + None + }; + + // Build the is_row_deleted column: walk the deleted indexes alongside the batch + // row range, advancing `next_delete_idx` as we go. Both arrays share the same + // O(rows + deletes) sweep; allocation is one Int32Array of length batch_rows. + let is_deleted_array: Option = if self.emit_is_row_deleted { + let mut values = vec![0i32; batch_rows as usize]; + // Skip deleted entries that fall before this batch. + while self.next_delete_idx < self.deleted.len() + && self.deleted[self.next_delete_idx] < batch_start + { + self.next_delete_idx += 1; + } + // Mark every deleted index within [batch_start, batch_end). Advance + // `self.next_delete_idx` past them so the next batch's skip-before-start + // loop is O(1) instead of re-walking the entire prior batch. + let mut idx = self.next_delete_idx; + while idx < self.deleted.len() && self.deleted[idx] < batch_end { + let local = (self.deleted[idx] - batch_start) as usize; + values[local] = 1; + idx += 1; + } + self.next_delete_idx = idx; + Some(Int32Array::from(values)) + } else { + None + }; + + // row_id: baseRowId + physical row index. Nullable because tables without row + // tracking won't have baseRowId; in that case we emit a null-valued column so the + // schema still matches. + let row_id_array: Option = if self.emit_row_id { + match self.base_row_id { + Some(base) => { + let values: Vec = (batch_start..batch_end) + .map(|idx| base.saturating_add(idx as i64)) + .collect(); + Some(Int64Array::from(values)) + } + None => Some(Int64Array::from(vec![None as Option; batch_rows as usize])), + } + } else { + None + }; + + // row_commit_version: defaultRowCommitVersion (constant per file). Same nullable + // semantics as row_id. + let row_commit_version_array: Option = if self.emit_row_commit_version { + match self.default_row_commit_version { + Some(v) => Some(Int64Array::from(vec![v; batch_rows as usize])), + None => Some(Int64Array::from(vec![None as Option; batch_rows as usize])), + } + } else { + None + }; + + self.current_row_offset = batch_end; + + // Append synthetic columns to the batch. Order matches build_output_schema: + // row_index, is_row_deleted, row_id, row_commit_version. + let mut columns: Vec> = batch.columns().to_vec(); + if let Some(arr) = row_index_array { + columns.push(Arc::new(arr)); + } + if let Some(arr) = is_deleted_array { + columns.push(Arc::new(arr)); + } + if let Some(arr) = row_id_array { + columns.push(Arc::new(arr)); + } + if let Some(arr) = row_commit_version_array { + columns.push(Arc::new(arr)); + } + RecordBatch::try_new(Arc::clone(&self.output_schema), columns).map_err(|e| { + DataFusionError::Internal(format!( + "DeltaSyntheticColumnsExec: failed to append synthetic columns: {e}" + )) + }) + } +} + +impl Stream for DeltaSyntheticColumnsStream { + type Item = DFResult; + + fn poll_next(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll> { + let poll = self.inner.poll_next_unpin(cx); + let result = match poll { + Poll::Ready(Some(Ok(batch))) => Poll::Ready(Some(self.augment(batch))), + other => other, + }; + self.baseline_metrics.record_poll(result) + } +} + +impl RecordBatchStream for DeltaSyntheticColumnsStream { + fn schema(&self) -> SchemaRef { + Arc::clone(&self.output_schema) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use arrow::array::{Array, ArrayRef, Int64Array}; + + fn input_schema() -> SchemaRef { + Arc::new(Schema::new(vec![Field::new("v", DataType::Int64, false)])) + } + + fn batch(rows: &[i64]) -> RecordBatch { + let arr: ArrayRef = Arc::new(Int64Array::from(rows.to_vec())); + RecordBatch::try_new(input_schema(), vec![arr]).unwrap() + } + + /// Helper: build a `DeltaSyntheticColumnsStream` directly, without an exec, so we + /// can drive `augment()` in isolation. Mirrors the real construction path. + fn make_stream( + emit_row_index: bool, + emit_is_row_deleted: bool, + emit_row_id: bool, + emit_row_commit_version: bool, + deleted: Vec, + base_row_id: Option, + default_row_commit_version: Option, + ) -> DeltaSyntheticColumnsStream { + let schema = build_output_schema( + &input_schema(), + emit_row_index, + emit_is_row_deleted, + emit_row_id, + emit_row_commit_version, + ); + let metrics = ExecutionPlanMetricsSet::new(); + let baseline = BaselineMetrics::new(&metrics, 0); + let (_tx, rx) = futures::channel::mpsc::unbounded::>(); + let inner: SendableRecordBatchStream = Box::pin(EmptyStream { + schema: input_schema(), + inner: rx, + }); + DeltaSyntheticColumnsStream { + inner, + deleted, + current_row_offset: 0, + next_delete_idx: 0, + output_schema: schema, + emit_row_index, + emit_is_row_deleted, + emit_row_id, + emit_row_commit_version, + base_row_id, + default_row_commit_version, + baseline_metrics: baseline, + } + } + + struct EmptyStream { + schema: SchemaRef, + inner: futures::channel::mpsc::UnboundedReceiver>, + } + impl Stream for EmptyStream { + type Item = DFResult; + fn poll_next( + mut self: Pin<&mut Self>, + cx: &mut Context<'_>, + ) -> Poll> { + Pin::new(&mut self.inner).poll_next(cx) + } + } + impl RecordBatchStream for EmptyStream { + fn schema(&self) -> SchemaRef { + Arc::clone(&self.schema) + } + } + + // ---- build_output_schema combinations ---- + + #[test] + fn schema_only_row_index() { + let s = build_output_schema(&input_schema(), true, false, false, false); + let names: Vec<&str> = s.fields().iter().map(|f| f.name().as_str()).collect(); + assert_eq!(names, vec!["v", ROW_INDEX_COLUMN_NAME]); + } + + #[test] + fn schema_all_four_in_order() { + let s = build_output_schema(&input_schema(), true, true, true, true); + let names: Vec<&str> = s.fields().iter().map(|f| f.name().as_str()).collect(); + assert_eq!( + names, + vec![ + "v", + ROW_INDEX_COLUMN_NAME, + IS_ROW_DELETED_COLUMN_NAME, + ROW_ID_COLUMN_NAME, + ROW_COMMIT_VERSION_COLUMN_NAME, + ] + ); + // Nullability: row_id and row_commit_version are nullable; the other two are not. + let nullables: Vec = s.fields().iter().map(|f| f.is_nullable()).collect(); + assert_eq!(nullables, vec![false, false, false, true, true]); + } + + #[test] + fn schema_emit_subset_preserves_order() { + // Skip row_index, keep is_row_deleted and row_commit_version -> appended in that order. + let s = build_output_schema(&input_schema(), false, true, false, true); + let names: Vec<&str> = s.fields().iter().map(|f| f.name().as_str()).collect(); + assert_eq!( + names, + vec!["v", IS_ROW_DELETED_COLUMN_NAME, ROW_COMMIT_VERSION_COLUMN_NAME] + ); + } + + // ---- augment correctness ---- + + #[test] + fn augment_row_index_single_batch() { + let mut s = make_stream(true, false, false, false, vec![], None, None); + let out = s.augment(batch(&[10, 20, 30])).unwrap(); + let idx = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + let vals: Vec = idx.iter().map(Option::unwrap).collect(); + assert_eq!(vals, vec![0, 1, 2]); + } + + #[test] + fn augment_row_index_multi_batch_monotonic() { + let mut s = make_stream(true, false, false, false, vec![], None, None); + let out1 = s.augment(batch(&[1, 2, 3])).unwrap(); + let out2 = s.augment(batch(&[4, 5])).unwrap(); + let idx1: Vec = out1 + .column(1) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + let idx2: Vec = out2 + .column(1) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(idx1, vec![0, 1, 2]); + assert_eq!(idx2, vec![3, 4]); + } + + #[test] + fn augment_is_row_deleted_marks_correct_indexes() { + let mut s = make_stream(false, true, false, false, vec![1, 3], None, None); + let out = s.augment(batch(&[10, 20, 30, 40, 50])).unwrap(); + let flags = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + let vals: Vec = flags.iter().map(Option::unwrap).collect(); + assert_eq!(vals, vec![0, 1, 0, 1, 0]); + } + + #[test] + fn augment_is_row_deleted_writes_back_next_delete_idx() { + // After one batch consuming deletes 0,1,2, the second batch should start with + // next_delete_idx already past them — verifying the writeback fix. + let mut s = make_stream(false, true, false, false, vec![0, 1, 2, 7], None, None); + let _ = s.augment(batch(&[10, 20, 30])).unwrap(); // 0,1,2 deleted, next_delete_idx -> 3 + assert_eq!(s.next_delete_idx, 3); + let out2 = s.augment(batch(&[40, 50, 60, 70, 80])).unwrap(); // covers 3..8; 7 deleted + let flags: Vec = out2 + .column(1) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(flags, vec![0, 0, 0, 0, 1]); + assert_eq!(s.next_delete_idx, 4); + } + + #[test] + fn augment_row_id_with_base() { + let mut s = make_stream(false, false, true, false, vec![], Some(1000), None); + let out = s.augment(batch(&[10, 20, 30])).unwrap(); + let ids = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + let vals: Vec = ids.iter().map(Option::unwrap).collect(); + assert_eq!(vals, vec![1000, 1001, 1002]); + + // Second batch: row_id continues from where current_row_offset left off. + let out2 = s.augment(batch(&[40])).unwrap(); + let v2: Vec = out2 + .column(1) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(v2, vec![1003]); + } + + #[test] + fn augment_row_id_without_base_emits_nulls() { + let mut s = make_stream(false, false, true, false, vec![], None, None); + let out = s.augment(batch(&[10, 20])).unwrap(); + let ids = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(ids.null_count(), 2); + } + + #[test] + fn augment_row_commit_version_constant() { + let mut s = make_stream(false, false, false, true, vec![], None, Some(7)); + let out = s.augment(batch(&[10, 20, 30])).unwrap(); + let v = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + let vals: Vec = v.iter().map(Option::unwrap).collect(); + assert_eq!(vals, vec![7, 7, 7]); + } + + #[test] + fn augment_row_commit_version_without_default_emits_nulls() { + let mut s = make_stream(false, false, false, true, vec![], None, None); + let out = s.augment(batch(&[1, 2])).unwrap(); + let v = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(v.null_count(), 2); + } + + #[test] + fn augment_all_four_columns_combined() { + let mut s = make_stream(true, true, true, true, vec![1], Some(500), Some(42)); + let out = s.augment(batch(&[10, 20, 30])).unwrap(); + assert_eq!(out.schema().fields().len(), 5); + + // col 0: data + // col 1: row_index 0,1,2 + let ri: Vec = out + .column(1) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(ri, vec![0, 1, 2]); + // col 2: is_row_deleted 0,1,0 + let dl: Vec = out + .column(2) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(dl, vec![0, 1, 0]); + // col 3: row_id 500,501,502 + let id: Vec = out + .column(3) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(id, vec![500, 501, 502]); + // col 4: row_commit_version 42,42,42 + let cv: Vec = out + .column(4) + .as_any() + .downcast_ref::() + .unwrap() + .iter() + .map(Option::unwrap) + .collect(); + assert_eq!(cv, vec![42, 42, 42]); + } + + #[test] + fn augment_empty_batch_preserves_schema() { + let mut s = make_stream(true, true, true, true, vec![], Some(0), Some(0)); + let out = s.augment(batch(&[])).unwrap(); + assert_eq!(out.schema().fields().len(), 5); + assert_eq!(out.num_rows(), 0); + } + + #[test] + fn new_validates_partition_count_mismatch() { + use datafusion::physical_plan::empty::EmptyExec; + let inner = Arc::new(EmptyExec::new(input_schema())) as Arc; + // EmptyExec has 1 partition; pass 2 entries. + let err = DeltaSyntheticColumnsExec::new( + inner, + vec![vec![], vec![]], + vec![None, None], + vec![None, None], + true, false, false, false, + ) + .unwrap_err(); + assert!(format!("{err}").contains("partition count mismatch") || format!("{err}").contains("partitions")); + } +} diff --git a/contrib/delta/native/tests/end_to_end.rs b/contrib/delta/native/tests/end_to_end.rs new file mode 100644 index 0000000000..f5ec0fb86c --- /dev/null +++ b/contrib/delta/native/tests/end_to_end.rs @@ -0,0 +1,201 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! End-to-end integration test for the contrib-delta crate. +//! +//! Crate-root integration test (compiled as a separate binary that links against +//! the contrib's *public* API only). The shape mirrors the Phase-1 +//! `native/core/src/delta/integration_tests.rs` but goes through the current +//! contrib's `plan_delta_scan` -> `build_delta_partitioned_files` -> +//! DataFusion `ParquetSource` -> RecordBatch assertion path. +//! +//! What this proves end-to-end: +//! +//! - delta-kernel-rs reads a real `_delta_log` and returns the right `add` +//! - the file path round-trips through URL normalization correctly +//! - the parquet file actually exists at the resolved path +//! - kernel + DataFusion ParquetSource produce the same rows you wrote +//! +//! What it does NOT cover (those are unit tests): +//! +//! - DV materialization (kernel test only) +//! - synthetic column emission (synthetic_columns.rs tests) +//! - column mapping rewrite (planner.rs tests) +//! - predicate translation (predicate.rs tests) + +use std::path::Path; +use std::sync::Arc; + +use arrow::array::{ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use comet_contrib_delta::{list_delta_files, plan_delta_scan, DeltaStorageConfig}; +use parquet::arrow::ArrowWriter; +use parquet::file::properties::WriterProperties; + +/// Build a `id: i64, name: Utf8` schema that matches the JSON schema we'll write +/// into the `_delta_log` `metaData` action. +fn sample_schema() -> SchemaRef { + Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, true), + Field::new("name", DataType::Utf8, true), + ])) +} + +fn sample_batch(schema: &SchemaRef) -> RecordBatch { + let ids: ArrayRef = Arc::new(Int64Array::from(vec![1_i64, 2, 3])); + let names: ArrayRef = Arc::new(StringArray::from(vec!["a", "b", "c"])); + RecordBatch::try_new(Arc::clone(schema), vec![ids, names]).unwrap() +} + +/// Write `batch` to `path` as a Parquet file. Returns the file size in bytes. +fn write_parquet_file(path: &Path, batch: &RecordBatch) -> u64 { + let file = std::fs::File::create(path).unwrap(); + let props = WriterProperties::builder().build(); + let mut writer = ArrowWriter::try_new(file, batch.schema(), Some(props)).unwrap(); + writer.write(batch).unwrap(); + writer.close().unwrap(); + std::fs::metadata(path).unwrap().len() +} + +/// Build a minimal Delta `_delta_log/00000000000000000000.json` pointing at a +/// single parquet file. Schema must match what `sample_schema()` produces. +fn commit_delta_table(table_dir: &Path, data_file: &str, size: u64, rows: usize) { + let delta_log = table_dir.join("_delta_log"); + std::fs::create_dir_all(&delta_log).unwrap(); + + // Schema in Delta's JSON form: identical to what `sample_schema()` produces in arrow. + // Backslashes pre-escaped for the embedded JSON string. + let schema_json = "{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[\ + {\\\"name\\\":\\\"id\\\",\\\"type\\\":\\\"long\\\",\\\"nullable\\\":true,\\\"metadata\\\":{}},\ + {\\\"name\\\":\\\"name\\\",\\\"type\\\":\\\"string\\\",\\\"nullable\\\":true,\\\"metadata\\\":{}}\ + ]}"; + + let commit0 = format!( + "{}\n{}\n{}", + r#"{"protocol":{"minReaderVersion":1,"minWriterVersion":2}}"#, + format!( + "{{\"metaData\":{{\"id\":\"e2e-test\",\"format\":{{\"provider\":\"parquet\",\"options\":{{}}}},\"schemaString\":\"{}\",\"partitionColumns\":[],\"configuration\":{{}},\"createdTime\":1700000000000}}}}", + schema_json + ), + format!( + "{{\"add\":{{\"path\":\"{data_file}\",\"partitionValues\":{{}},\"size\":{size},\"modificationTime\":1700000000000,\"dataChange\":true,\"stats\":\"{{\\\"numRecords\\\":{rows}}}\"}}}}" + ), + ); + + std::fs::write(delta_log.join("00000000000000000000.json"), commit0).unwrap(); +} + +// ----------------------------------------------------------------------------- +// Test 1: list_delta_files end-to-end against a real parquet file +// ----------------------------------------------------------------------------- + +#[test] +fn list_delta_files_finds_real_parquet() { + let tmp = tempfile::tempdir().unwrap(); + let table_dir = tmp.path().join("delta-e2e"); + std::fs::create_dir_all(&table_dir).unwrap(); + + let schema = sample_schema(); + let batch = sample_batch(&schema); + let parquet_path = table_dir.join("part-00000.parquet"); + let size = write_parquet_file(&parquet_path, &batch); + commit_delta_table(&table_dir, "part-00000.parquet", size, batch.num_rows()); + + let cfg = DeltaStorageConfig::default(); + let (entries, version) = + list_delta_files(table_dir.to_str().unwrap(), &cfg, None).unwrap(); + + assert_eq!(version, 0, "snapshot version"); + assert_eq!(entries.len(), 1, "one add file"); + let e = &entries[0]; + assert_eq!(e.path, "part-00000.parquet", "relative path preserved"); + assert_eq!(e.size as u64, size, "size matches actual parquet file"); + assert_eq!(e.num_records, Some(3)); + assert!(!e.has_deletion_vector()); + assert!(e.partition_values.is_empty()); +} + +// ----------------------------------------------------------------------------- +// Test 2: plan_delta_scan returns a scan plan with the right entries +// ----------------------------------------------------------------------------- + +#[test] +fn plan_delta_scan_returns_one_entry_for_single_file_table() { + let tmp = tempfile::tempdir().unwrap(); + let table_dir = tmp.path().join("delta-plan"); + std::fs::create_dir_all(&table_dir).unwrap(); + + let schema = sample_schema(); + let batch = sample_batch(&schema); + let parquet_path = table_dir.join("part-00000.parquet"); + let size = write_parquet_file(&parquet_path, &batch); + commit_delta_table(&table_dir, "part-00000.parquet", size, batch.num_rows()); + + let cfg = DeltaStorageConfig::default(); + let plan = plan_delta_scan(table_dir.to_str().unwrap(), &cfg, None).unwrap(); + assert_eq!(plan.entries.len(), 1); + assert_eq!(plan.version, 0); + // unsupported_features should be empty for a basic single-file table. + assert!(plan.unsupported_features.is_empty()); +} + +// ----------------------------------------------------------------------------- +// Test 3: snapshot pinning - version=Some(0) returns version 0 +// ----------------------------------------------------------------------------- + +#[test] +fn list_delta_files_pinned_version_returns_that_version() { + let tmp = tempfile::tempdir().unwrap(); + let table_dir = tmp.path().join("delta-pinned"); + std::fs::create_dir_all(&table_dir).unwrap(); + + let schema = sample_schema(); + let batch = sample_batch(&schema); + let parquet_path = table_dir.join("part-00000.parquet"); + let size = write_parquet_file(&parquet_path, &batch); + commit_delta_table(&table_dir, "part-00000.parquet", size, batch.num_rows()); + + let cfg = DeltaStorageConfig::default(); + let (_entries, version) = + list_delta_files(table_dir.to_str().unwrap(), &cfg, Some(0)).unwrap(); + assert_eq!(version, 0); +} + +// ----------------------------------------------------------------------------- +// Test 4: empty table (commit 0 has only protocol + metadata, no adds) +// ----------------------------------------------------------------------------- + +#[test] +fn list_delta_files_empty_table_returns_no_entries() { + let tmp = tempfile::tempdir().unwrap(); + let table_dir = tmp.path().join("delta-empty"); + let delta_log = table_dir.join("_delta_log"); + std::fs::create_dir_all(&delta_log).unwrap(); + + // No `add` action — just protocol + metaData. + let commit0 = [ + r#"{"protocol":{"minReaderVersion":1,"minWriterVersion":2}}"#, + "{\"metaData\":{\"id\":\"empty\",\"format\":{\"provider\":\"parquet\",\"options\":{}},\"schemaString\":\"{\\\"type\\\":\\\"struct\\\",\\\"fields\\\":[{\\\"name\\\":\\\"id\\\",\\\"type\\\":\\\"long\\\",\\\"nullable\\\":true,\\\"metadata\\\":{}}]}\",\"partitionColumns\":[],\"configuration\":{},\"createdTime\":1700000000000}}", + ].join("\n"); + std::fs::write(delta_log.join("00000000000000000000.json"), &commit0).unwrap(); + + let cfg = DeltaStorageConfig::default(); + let (entries, version) = + list_delta_files(table_dir.to_str().unwrap(), &cfg, None).unwrap(); + assert_eq!(version, 0); + assert!(entries.is_empty(), "expected no entries for empty table"); +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/CometDeltaNativeScan.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/CometDeltaNativeScan.scala new file mode 100644 index 0000000000..b8677dcc8f --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/CometDeltaNativeScan.scala @@ -0,0 +1,1283 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import java.util.Locale + +import scala.collection.mutable.ListBuffer +import scala.jdk.CollectionConverters._ + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{And, BoundReference, Expression, InterpretedPredicate} +import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.comet.{CometDeltaNativeScanExec, CometNativeExec, CometScanExec} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + +import org.apache.comet.{CometConf, ConfigEntry} +// Contrib-private Java proto types generated by protoc-jar-maven-plugin from +// contrib/delta/native/src/proto/delta_operator.proto. The proto declares +// `option java_package = "org.apache.comet.contrib.delta.proto"` so the generated +// outer class lands under a Comet-prefixed Java package. +// Typed Delta proto messages now live in core's operator.proto (alongside IcebergScan) +// instead of a contrib-private proto package. +import org.apache.comet.serde.OperatorOuterClass.{DeltaScan, DeltaScanCommon, DeltaScanTaskList} +import org.apache.comet.objectstore.NativeConfig +import org.apache.comet.serde.{CometOperatorSerde, Compatible, ExprOuterClass, OperatorOuterClass, SupportLevel} +import org.apache.comet.serde.ExprOuterClass.Expr +import org.apache.comet.serde.OperatorOuterClass.Operator +import org.apache.comet.serde.QueryPlanSerde.exprToProto +import org.apache.comet.serde.operator.schema2Proto + +/** + * Validation and serde logic for the native Delta Lake scan. + * + * `convert()` calls `Native.planDeltaScan` to enumerate files via `delta-kernel-rs`, builds the + * `DeltaScanCommon` proto with schemas/filters/options, applies static partition pruning, and + * stashes the task list in a ThreadLocal. `createExec()` retrieves it and builds a + * `CometDeltaNativeScanExec` with split-mode serialization: common data serialized once at + * planning time, per-partition task lists materialized lazily at execution time. DPP filters are + * applied at execution time in the exec's `serializedPartitionData`. + */ +/** + * Delta-scan serde + exec factory. Extends Comet's core `CometOperatorSerde` trait so + * the existing convertToComet path in `CometExecRule` invokes it just like the + * built-in handlers (CometNativeScan, CometIcebergNativeScan, ...). What is NOT here + * is any *extension/discovery* SPI -- core's `CometExecRule` resolves this object via + * `DeltaIntegration.scanHandler` (one reflective class lookup, no ServiceLoader, no + * registry). The wire format is the typed `OpStruct::DeltaScan` variant. + */ +object CometDeltaNativeScan extends CometOperatorSerde[CometScanExec] with Logging { + + /** + * `kind` string for the `ContribOp` envelope this serde produces. The native side's + * `comet-contrib-delta` rlib registers `DeltaScanPlanner` under this same kind via + * `register_contrib_planner(DELTA_SCAN_KIND, ...)` in `contrib/delta/native/src/lib.rs`. Keep + * the two in sync. + */ + val DeltaScanKind: String = "delta-scan" + + /** + * `scanImpl` tag the contrib uses on `CometScanExec` markers produced by + * `DeltaScanRuleExtension.transformV1`. Contrib-local constant (not in core's CometConf), + * declared as `nativeParquetScanImpls` in `DeltaOperatorSerdeExtension` so + * `CometScanExec.supportedDataFilters` applies the right exclusions, and matched in + * `DeltaOperatorSerdeExtension.matchOperator` to route through this serde. + */ + val ScanImpl: String = "native_delta_compat" + + /** Private lazy handle to the native library - one instance per JVM. */ + private lazy val nativeLib = new org.apache.comet.contrib.delta.Native() + + // Phase 5: stash the raw task-list bytes between convert() and createExec() + // so the exec can do per-partition splitting at execution time. Single-threaded + // during planning so a simple ThreadLocal is safe. + private val lastTaskListBytes = new ThreadLocal[Array[Byte]]() + + // #75 design A: when the surrounding plan references `input_file_name()` / + // `input_file_block_*`, CometScanRule tags the relation's options with + // `CometScanRule.NeedsInputFileNameOption`. We read it here to (a) skip + // byte-range splitting in splitTasks and (b) emit `oneTaskPerPartition = true` + // on the CometDeltaNativeScanExec so packTasks keeps each task in its own + // partition. With 1 task per partition, `CometExecRDD.compute` (via `InputFileBlockHolder.set`) + // sets InputFileBlockHolder to the correct path and Spark's JVM-side + // input_file_name() evaluation (no native serde exists) returns the right + // value. + /** + * Translate Delta's `delta.columnMapping.id` metadata key to Spark+parquet's standard + * `parquet.field.id` key on every StructField at every level of nesting. Required for + * column-mapping `id` mode: Delta writes parquet files with `PARQUET:field_id` metadata + * (i.e. the same field IDs it stores in its own metadata), but Spark's + * `ParquetUtils.hasFieldId` -- and therefore Comet's serialisers -- only look at + * `parquet.field.id`. Without this translation, `use_field_id=true` would still find + * no IDs on the Spark schema and silently degrade to name-based matching. + * + * Top-level field metadata gets the new entry merged in via `MetadataBuilder`; nested + * StructTypes recurse; ArrayType and MapType walk into their element/key/value types. + * Fields without `delta.columnMapping.id` are passed through unchanged (e.g. partition + * columns, synthetic row-index columns, struct-leaf fields the metadata strip elided). + */ + private[delta] def translateDeltaFieldIdToParquet(field: StructField): StructField = { + val newDataType = translateDataTypeFieldIds(field.dataType) + val newMetadata = + if (field.metadata.contains(DeltaReflection.FieldIdMetadataKey) && + !field.metadata.contains(DeltaReflection.ParquetFieldIdMetadataKey)) { + val fieldId = field.metadata.getLong(DeltaReflection.FieldIdMetadataKey) + new org.apache.spark.sql.types.MetadataBuilder() + .withMetadata(field.metadata) + .putLong(DeltaReflection.ParquetFieldIdMetadataKey, fieldId) + .build() + } else field.metadata + StructField(field.name, newDataType, field.nullable, newMetadata) + } + + private def translateDataTypeFieldIds( + dt: org.apache.spark.sql.types.DataType): org.apache.spark.sql.types.DataType = + dt match { + case s: StructType => StructType(s.fields.map(translateDeltaFieldIdToParquet)) + case a: org.apache.spark.sql.types.ArrayType => + org.apache.spark.sql.types.ArrayType( + translateDataTypeFieldIds(a.elementType), + a.containsNull) + case m: org.apache.spark.sql.types.MapType => + org.apache.spark.sql.types.MapType( + translateDataTypeFieldIds(m.keyType), + translateDataTypeFieldIds(m.valueType), + m.valueContainsNull) + case other => other + } + + /** Visible to `DeltaOperatorSerdeExtension.matchOperator` for routing decisions. */ + private[delta] def scanNeedsInputFileName(scan: CometScanExec): Boolean = + scan.relation.options + .get(DeltaConf.NeedsInputFileNameOption) + .contains("true") + + /** + * Reflectively resolve Hadoop's AWSCredentialProviderList for an s3/s3a URI and merge + * the resulting (access, secret, optional token) triple into `baseOptions` under the + * standard `fs.s3a.access.key` / `fs.s3a.secret.key` / `fs.s3a.session.token` keys -- + * the same keys `NativeConfig.extractObjectStoreOptions` would have picked up if the + * user had set them explicitly in `core-site.xml`. + * + * Reflection is intentional: `hadoop-aws` is an optional dep; on a default Comet + * deployment without S3 support on the classpath, `Class.forName` fails and we return + * the base options unchanged. Non-s3/s3a URIs return base options unchanged too -- + * Azure / GCS / OSS resolve their own credential chains in kernel-rs's object_store + * (or via the static keys already in `baseOptions`). + * + * Skip when the user has already set explicit static keys (don't overwrite an explicit + * config with a resolved IAM-instance token). + * + * If reflection succeeds but credential resolution fails (e.g. IMDS unreachable, no + * provider configured), log a warning and return `baseOptions` -- the engine will + * still try anonymous access or surface a clearer error than a silent crash on first + * S3 read. + */ + // Cached reflective binding for the S3A credential chain. Resolved once per JVM. + // The whole augment path is invoked on every Delta scan -- without caching, each scan + // pays a Class.forName + getMethod round-trip just to find the bridge available. + // + // `None` means we tried once and failed (hadoop-aws not on classpath, signature drift, + // etc.) -- subsequent calls short-circuit. + private case class S3ACredentialBinding( + createProviderList: java.lang.reflect.Method, + getCredentials: java.lang.reflect.Method, + getAccessKey: java.lang.reflect.Method, + getSecretKey: java.lang.reflect.Method, + sessionCredsCls: Option[Class[_]], + getSessionToken: Option[java.lang.reflect.Method]) + + @volatile private var s3aCredentialBindingCache: Option[Option[S3ACredentialBinding]] = None + + private def s3aCredentialBinding: Option[S3ACredentialBinding] = + s3aCredentialBindingCache.getOrElse { + val binding = try { + // scalastyle:off classforname + val utilsCls = Class.forName("org.apache.hadoop.fs.s3a.S3AUtils") + // scalastyle:on classforname + val createMethod = utilsCls.getMethod( + "createAWSCredentialProviderList", + classOf[java.net.URI], + classOf[org.apache.hadoop.conf.Configuration]) + // Resolve the provider-list + credentials methods off the runtime classes + // returned by createAWSCredentialProviderList. Method.invoke walks subclasses, so + // a one-time lookup on the declared return / argument types is enough. + val providerListCls = createMethod.getReturnType + val getCredentialsMethod = providerListCls.getMethod("getCredentials") + val credentialsCls = getCredentialsMethod.getReturnType + val getAccessKeyMethod = credentialsCls.getMethod("getAWSAccessKeyId") + val getSecretKeyMethod = credentialsCls.getMethod("getAWSSecretKey") + val (sessionCredsCls, getSessionTokenMethod) = try { + // scalastyle:off classforname + val cls = Class.forName("com.amazonaws.auth.AWSSessionCredentials") + // scalastyle:on classforname + (Some(cls), Some(cls.getMethod("getSessionToken"))) + } catch { case _: ClassNotFoundException => (None, None) } + Some( + S3ACredentialBinding( + createMethod, + getCredentialsMethod, + getAccessKeyMethod, + getSecretKeyMethod, + sessionCredsCls, + getSessionTokenMethod)) + } catch { + // hadoop-aws not on classpath, or signature drift -- mark as unavailable for the + // rest of the JVM's lifetime. + case _: ClassNotFoundException => None + case _: NoSuchMethodException => None + case scala.util.control.NonFatal(e) => + logWarning( + s"S3A credential-chain reflection lookup failed; falling back to static-only " + + s"keys in Delta log replay: ${e.getMessage}", + e) + None + } + s3aCredentialBindingCache = Some(binding) + binding + } + + private[delta] def augmentWithResolvedAwsCredentials( + baseOptions: Map[String, String], + tableRootUri: java.net.URI, + hadoopConf: org.apache.hadoop.conf.Configuration): Map[String, String] = { + val scheme = Option(tableRootUri.getScheme).map(_.toLowerCase).getOrElse("") + if (scheme != "s3" && scheme != "s3a") return baseOptions + if (baseOptions.contains("fs.s3a.access.key") && + baseOptions.contains("fs.s3a.secret.key")) { + return baseOptions + } + s3aCredentialBinding match { + case None => baseOptions // hadoop-aws not available; nothing to resolve + case Some(binding) => + try { + val providerList = binding.createProviderList.invoke(null, tableRootUri, hadoopConf) + val credentials = binding.getCredentials.invoke(providerList) + val accessKey = binding.getAccessKey.invoke(credentials) + val secretKey = binding.getSecretKey.invoke(credentials) + val sessionToken: Option[String] = (binding.sessionCredsCls, binding.getSessionToken) match { + case (Some(cls), Some(m)) if cls.isInstance(credentials) => + Option(m.invoke(credentials)).map(_.toString) + case _ => None + } + val resolved = scala.collection.mutable.Map[String, String]() ++= baseOptions + Option(accessKey).map(_.toString).filter(_.nonEmpty).foreach { ak => + resolved("fs.s3a.access.key") = ak + } + Option(secretKey).map(_.toString).filter(_.nonEmpty).foreach { sk => + resolved("fs.s3a.secret.key") = sk + } + sessionToken.filter(_.nonEmpty).foreach { st => + resolved("fs.s3a.session.token") = st + } + resolved.toMap + } catch { + case scala.util.control.NonFatal(e) => + logWarning( + s"Delta log-replay credential resolution failed for $tableRootUri: " + + s"${e.getMessage}; falling back to static-only keys in storage options", + e) + baseOptions + } + } + } + + override def enabledConfig: Option[ConfigEntry[Boolean]] = Some( + DeltaConf.COMET_DELTA_NATIVE_ENABLED) + + override def getSupportLevel(operator: CometScanExec): SupportLevel = Compatible() + + override def convert( + scan: CometScanExec, + builder: Operator.Builder, + childOp: OperatorOuterClass.Operator*): Option[OperatorOuterClass.Operator] = { + + // Resolve the table root via the HadoopFsRelation API - standard Spark, no spark-delta + // compile-time dep required. + val relation = scan.relation + val tableRoot = DeltaReflection.extractTableRoot(relation).getOrElse { + logWarning( + s"CometDeltaNativeScan: unable to extract table root from relation " + + s"${relation.location}; falling back to Spark's Delta reader.") + return None + } + + // Detect Delta synthetic columns the surrounding plan requested. We strip them + // from the proto schemas sent to native so the parquet reader doesn't look for + // columns that don't exist on disk, and set the proto emit flags so the dispatcher + // wraps the parquet scan in `DeltaSyntheticColumnsExec` to append them back. + // - `__delta_internal_row_index` / `__delta_internal_is_row_deleted` are + // UPDATE/DELETE/MERGE internals (#144). + // - `row_id` / `row_commit_version` are row-tracking columns when the table has + // `delta.enableRowTracking=true` but no materialised columns -- synthesised + // from baseRowId + physical row index per task. + val emitRowIndex = scan.requiredSchema.fieldNames.exists( + _.equalsIgnoreCase(DeltaReflection.RowIndexColumnName)) + val emitIsRowDeleted = scan.requiredSchema.fieldNames.exists( + _.equalsIgnoreCase(DeltaReflection.IsRowDeletedColumnName)) + val emitRowId = scan.requiredSchema.fieldNames.exists(_.equalsIgnoreCase(DeltaReflection.RowIdColumnName)) + val emitRowCommitVersion = scan.requiredSchema.fieldNames.exists( + _.equalsIgnoreCase(DeltaReflection.RowCommitVersionColumnName)) + + val ignoreMissingFiles = + SQLConf.get.ignoreMissingFiles || + relation.options.get("ignoremissingfiles").contains("true") + + // Cloud storage options, keyed identically to NativeScan. Kernel's DefaultEngine picks + // up aws_* / azure_* keys; anything else is ignored on the native side (for now). + // + // We key off the table root URI rather than `inputFiles.head` because data file names + // can contain characters that aren't URI-safe when Spark's test harness injects + // prefixes like `test%file%prefix-` (breaks `java.net.URI.create`). The table root + // string comes straight from `HadoopFsRelation.location.rootPaths.head.toUri` inside + // `DeltaReflection.extractTableRoot`, so it's already properly encoded. Storage options + // are bucket-level anyway - any file under the same root resolves to the same config. + val hadoopConf = + relation.sparkSession.sessionState.newHadoopConfWithOptions(relation.options) + val tableRootUri = java.net.URI.create(tableRoot) + val baseOptions: Map[String, String] = + NativeConfig.extractObjectStoreOptions(hadoopConf, tableRootUri) + // For s3/s3a tables, resolve Hadoop's credential provider chain here so log replay + // authenticates under SimpleAWSCredentialsProvider / TemporaryAWSCredentialsProvider / + // AssumedRoleCredentialProvider / IAMInstanceCredentialsProvider just like the data + // path does. The contrib's native engine (delta-kernel-rs's DefaultEngine backed by + // object_store_kernel) doesn't run core's `build_credential_provider`, so we feed it + // resolved static keys instead. SNAPSHOT resolution: log replay completes in seconds, + // well within any reasonable credential TTL. + val storageOptions: java.util.Map[String, String] = + CometDeltaNativeScan + .augmentWithResolvedAwsCredentials(baseOptions, tableRootUri, hadoopConf) + .asJava + + // Honor Delta's time-travel options (versionAsOf / timestampAsOf) via the Delta- + // resolved snapshot version sitting on the FileIndex. Delta's analysis phase pins + // the exact snapshot before we ever see the plan, so by the time `CometScanExec` is + // built, `relation.location` is a `PreparedDeltaFileIndex` whose toString looks like + // `Delta[version=0, file:/...]`. We parse the version out via + // `DeltaReflection.extractSnapshotVersion` and pass it through to kernel. + // + // When no version can be extracted (non-Delta file index, parser miss, etc.) we pass + // -1 which asks kernel for the current latest snapshot. + val snapshotVersion: Long = + DeltaReflection.extractSnapshotVersion(relation).getOrElse(-1L) + + // Phase 2: serialize the data filters so kernel can apply stats-based file + // pruning during log replay. The same filters will also be pushed down into + // ParquetSource for row-group-level pruning - the two layers are additive. + // + // We combine all supported data filters into a single AND conjunction so + // kernel receives one predicate tree. BoundReferences carry the column INDEX + // into scan.output; the native side resolves indices to column names using + // the columnNames array we pass alongside. + val predicateBytes: Array[Byte] = { + val protoFilters = new ListBuffer[Expr]() + scan.supportedDataFilters.foreach { filter => + exprToProto(filter, scan.output) match { + case Some(proto) => protoFilters += proto + case _ => + } + } + if (protoFilters.isEmpty) { + Array.emptyByteArray + } else if (protoFilters.size == 1) { + protoFilters.head.toByteArray + } else { + // Combine filters into a balanced AND tree (depth O(log N) instead of + // O(N)). A linear left-deep fold overflows protobuf's default 100-level + // recursion limit for plans with many ANDed conditions (Delta data + // skipping predicates routinely build deep stats expressions: e.g. + // DataSkippingDeltaTests "remove redundant stats column references"). + // Both the JVM serde (CometNativeColumnarToRowExec re-parses the plan + // for explain output) and the Rust prost decoder are subject to that + // limit, so balancing the tree fixes both sides. + def balancedAnd(slice: IndexedSeq[Expr]): Expr = { + if (slice.size == 1) { + slice.head + } else { + val mid = slice.size / 2 + val left = balancedAnd(slice.slice(0, mid)) + val right = balancedAnd(slice.slice(mid, slice.size)) + val and = ExprOuterClass.BinaryExpr + .newBuilder() + .setLeft(left) + .setRight(right) + .build() + Expr.newBuilder().setAnd(and).build() + } + } + balancedAnd(protoFilters.toIndexedSeq).toByteArray + } + } + + // Column name list for resolving BoundReference indices to kernel column + // names. Must match the order of scan.output because exprToProto binds + // attribute references by position in that schema. + val columnNames: Array[String] = scan.output.map(_.name).toArray + + // --- 1. Get the active file list. --- + // + // Two code paths: + // (a) Pre-materialized FileIndex (`TahoeBatchFileIndex`, `CdcAddFileIndex`): + // Delta's streaming micro-batch reads AND MERGE / UPDATE / DELETE + // post-join rewrites both carry an exact `addFiles: Seq[AddFile]` on + // the FileIndex. Kernel log replay against the snapshot would return a + // DIFFERENT file set (the whole snapshot, or a version's deltas), which + // is a correctness hazard -- empty streaming batches, MERGE rewrites + // that see the whole table instead of only touched files. Build the + // DeltaScanTaskList proto directly from those AddFiles, skipping kernel. + // (b) Regular scan against a snapshot: call kernel for log replay as before. + val taskListBytes = + if (DeltaReflection.isBatchFileIndex(relation.location)) { + DeltaReflection.extractBatchAddFiles(relation.location) match { + case Some(addFiles) => + // Under column mapping, Delta stores partition values in AddFile keyed by the + // PHYSICAL column name. `relation.partitionSchema.fields[*].metadata` has had + // Delta's columnMapping metadata stripped by HadoopFsRelation, so look in the + // authoritative Snapshot schema (via reflection) and restrict to fields that + // appear in the relation's partition schema. + val partitionNames = relation.partitionSchema.fields.map(_.name).toSet + val snapshotFields = DeltaReflection + .extractSnapshotSchema(relation) + .map(_.fields) + .getOrElse(Array.empty[StructField]) + val physToLogical = snapshotFields.flatMap { f => + if (partitionNames.contains(f.name) && + f.metadata.contains(DeltaReflection.PhysicalNameMetadataKey)) { + Some(f.metadata.getString(DeltaReflection.PhysicalNameMetadataKey) -> f.name) + } else { + None + } + }.toMap + // DV materialization for the pre-materialised-index path (streaming + MERGE). + // For AddFiles that carry a DeletionVectorDescriptor, read the DV via Delta's + // `HadoopFileSystemDVStore` on the driver and feed the resulting row-index list + // through the proto's existing `deleted_row_indexes` field. The native side then + // wraps the file group in `DeltaDvFilterExec` (planner.rs ~1460) which already + // honours per-file deleted row indexes. If any DV fails to materialise we have + // to fall back -- silently dropping a DV is a correctness violation (would + // return rows that should have been hidden). + val hadoopConf = relation.sparkSession.sessionState + .newHadoopConfWithOptions(relation.options) + val deletedRowIndexesByPath: Map[String, Array[Long]] = { + val builder = scala.collection.mutable.Map.empty[String, Array[Long]] + val it = addFiles.iterator + var failed = false + while (it.hasNext && !failed) { + val af = it.next() + if (af.hasDeletionVector) { + DeltaReflection.materializeDeletedRowIndexes( + af.dvDescriptor, + tableRoot, + hadoopConf) match { + case Some(arr) => builder.put(af.path, arr) + case None => failed = true + } + } + } + if (failed) { + import org.apache.comet.CometSparkSessionExtensions.withInfo + withInfo( + scan, + "Native Delta scan: pre-materialised FileIndex with deletion vectors " + + "but failed to materialise one or more DVs (DV file missing, unsupported " + + "Delta version, or read error); falling back to Spark+Delta.") + return None + } + builder.toMap + } + buildTaskListFromAddFiles( + tableRoot, + snapshotVersion, + addFiles, + nativeOp = null, + columnNames, + physicalToLogicalPartitionNames = physToLogical, + deletedRowIndexesByPath = deletedRowIndexesByPath).toByteArray + case None => + // Reflection failed; fall back conservatively. + import org.apache.comet.CometSparkSessionExtensions.withInfo + withInfo( + scan, + s"Native Delta scan could not extract AddFiles from " + + s"${relation.location.getClass.getName}; falling back.") + return None + } + } else { + // Non-batch indexes (TahoeLogFileIndex, ...). DV-bearing + // PreparedDeltaFileIndex is now classified as a batch index above + // (see `isBatchFileIndex`), so its DV-fallback case is already + // handled by the `case Some(_)` arm at the top of this match. For + // remaining non-batch indexes the Delta-PreprocessTableWithDVs + // wrapper detection upstream in `CometScanRule.scanBelowFallsBackForDvs` + // is responsible for keeping DV-aware internal reads on vanilla. + try { + nativeLib.planDeltaScan( + tableRoot, + snapshotVersion, + storageOptions, + predicateBytes, + columnNames) + } catch { + case scala.util.control.NonFatal(e) => + logWarning( + s"CometDeltaNativeScan: delta-kernel-rs log replay failed for $tableRoot", + e) + return None + } + } + val taskList0 = DeltaScanTaskList.parseFrom(taskListBytes) + // The kernel path populates `column_mappings` from kernel's schema metadata. + // The pre-materialised-index path (`buildTaskListFromAddFiles`) doesn't have + // that information yet, so re-derive the mapping from the relation's data + // + partition schema -- each StructField carries + // `delta.columnMapping.physicalName` in its metadata when the table uses + // column mapping. Without this the native scan can't translate logical + // column references to physical parquet column names and returns nulls. + // Fetch the Snapshot-level schema via reflection once here; used both to populate column + // mappings from the data-schema side (metadata on relation.dataSchema is stripped) and + // later to physicalise nested field names before serialisation. + val snapshotSchemaEarly: Option[StructType] = DeltaReflection.extractSnapshotSchema(relation) + // Only honour physicalName metadata when the table actually has column mapping + // mode enabled. Some Delta test helpers (e.g. `DeltaSourceSuiteBase.withMetadata`) + // call `DeltaColumnMapping.assignColumnIdAndPhysicalName` unconditionally, which + // attaches `delta.columnMapping.physicalName` to every StructField even when the + // table's `delta.columnMapping.mode` is unset / `none`. In that case the writer + // still uses LOGICAL names in the parquet file, so physicalising our scan would + // look up non-existent physical column names and return empty rows. + val tableColumnMappingMode = DeltaReflection + .extractMetadataConfiguration(relation) + .flatMap(_.get("delta.columnMapping.mode")) + .filter(m => m != null && !m.equalsIgnoreCase("none")) + val taskList = + if (!taskList0.getColumnMappingsList.isEmpty || tableColumnMappingMode.isEmpty) { + taskList0 + } else { + // `relation.dataSchema.fields[*].metadata` is stripped of Delta's column-mapping + // metadata by HadoopFsRelation, so the lookup here nearly always returns empty. + // Use the Snapshot schema we extracted (which preserves physical names at every + // level) for the data-column mappings, and `relation.partitionSchema` only for + // partition columns (whose metadata isn't stripped). + val dataFieldsSource: Array[StructField] = + snapshotSchemaEarly.map(_.fields).getOrElse(relation.dataSchema.fields) + val allFields = dataFieldsSource ++ relation.partitionSchema.fields + val logicalToPhysical = allFields.flatMap { f => + if (f.metadata.contains(DeltaReflection.PhysicalNameMetadataKey)) { + Some(f.name -> f.metadata.getString(DeltaReflection.PhysicalNameMetadataKey)) + } else { + None + } + } + if (logicalToPhysical.isEmpty) { + taskList0 + } else { + val b = DeltaScanTaskList.newBuilder(taskList0) + logicalToPhysical.foreach { case (logical, physical) => + b.addColumnMappings( + OperatorOuterClass.DeltaColumnMapping + .newBuilder() + .setLogicalName(logical) + .setPhysicalName(physical) + .build()) + } + b.build() + } + } + + // Phase 6 reader-feature gate. Kernel reports any Delta reader features that + // are currently in use in this snapshot and that Comet's native path does NOT + // correctly handle. Falling back is mandatory for correctness: reading through + // the native path would silently produce wrong results (e.g. returning rows + // that a deletion vector should have hidden). The gate becomes obsolete feature + // by feature as later phases ship: + // deletionVectors -> Phase 3 + // columnMapping -> Phase 4 + // typeWidening -> future phase + // rowTracking -> future phase + val unsupportedFeatures = taskList.getUnsupportedFeaturesList.asScala.toSeq + if (unsupportedFeatures.nonEmpty && + DeltaConf.COMET_DELTA_FALLBACK_ON_UNSUPPORTED_FEATURE.get(scan.conf)) { + logInfo( + s"CometDeltaNativeScan: falling back for table $tableRoot " + + s"due to unsupported reader features: ${unsupportedFeatures.mkString(", ")}") + import org.apache.comet.CometSparkSessionExtensions.withInfo + withInfo( + scan, + s"Native Delta scan does not yet support these features in use on this " + + s"snapshot: ${unsupportedFeatures.mkString(", ")}. Falling back to Spark's " + + s"Delta reader. Set ${DeltaConf.COMET_DELTA_FALLBACK_ON_UNSUPPORTED_FEATURE.key}=false " + + s"to bypass this check (NOT recommended - may produce incorrect results).") + return None + } + + // Apply Spark's partition filters to the task list so that queries like + // `WHERE partition_col = X` don't drag in files from other partitions. Kernel + // itself is given the whole snapshot (no predicate yet - that lands in Phase 2), + // so we do the pruning in Scala by evaluating each task's partition-value map + // against Spark's `partitionFilters`. This is a single driver-side loop; filtered + // tasks never go over the wire to executors. + val filteredTasks0 = + prunePartitions(taskList.getTasksList.asScala.toSeq, scan, relation.partitionSchema) + + // Split files larger than `maxSplitBytes` into byte-range chunks so a single + // big parquet file can be read across multiple Spark partitions, matching + // Spark's `FilePartition.splitFiles` semantics. This is what makes + // FILES_MAX_PARTITION_BYTES, files.openCostInBytes, and + // files.minPartitionNum take effect on Delta tables: without it every file + // is exactly one partition and the *.size assertions in + // DeletionVectorsSuite's PredicatePushdown tests fail (they configure + // FILES_MAX_PARTITION_BYTES=2MB on a multi-row-group fixture and assert + // exactly 2 splits). + val filteredTasks = + splitTasks(scan, filteredTasks0) + + // --- 2. Build the common block --- + val commonBuilder = DeltaScanCommon.newBuilder() + commonBuilder.setSource(scan.simpleStringWithNodeId()) + commonBuilder.setTableRoot(taskList.getTableRoot) + commonBuilder.setSnapshotVersion(taskList.getSnapshotVersion) + commonBuilder.setSessionTimezone(scan.conf.sessionLocalTimeZone) + commonBuilder.setCaseSensitive(scan.conf.getConf[Boolean](SQLConf.CASE_SENSITIVE)) + commonBuilder.setIgnoreMissingFiles(ignoreMissingFiles) + commonBuilder.setDataFileConcurrencyLimit( + DeltaConf.COMET_DELTA_DATA_FILE_CONCURRENCY_LIMIT.get()) + + // Schemas. Delta is different from vanilla Parquet: `relation.dataSchema` on a Delta + // table INCLUDES partition columns, but the physical parquet files on disk do NOT. + // So we compute the actual file schema by subtracting the partition columns from + // `relation.dataSchema`. Mirrors what delta-kernel itself reports as the scan schema. + val partitionNames = + relation.partitionSchema.fields.map(_.name.toLowerCase(Locale.ROOT)).toSet + val fileDataSchemaFields = + relation.dataSchema.fields.filterNot(f => + partitionNames.contains(f.name.toLowerCase(Locale.ROOT))) + + // When column mapping (id or name) is active, Delta writes parquet files using physical + // names at EVERY level of nesting -- struct inner fields, array elements, map keys/values. + // `schema2Proto` otherwise serialises the Spark StructField tree with logical names, so the + // native parquet reader would look for e.g. `b1` and its inner `c` but the file has + // `col-` and `col-`, yielding a null-struct read. Substitute physical names + // recursively before serialising so the proto schema matches the on-disk names at every + // level. The `column_mappings` proto carries only top-level logical->physical so that + // filter column references (expressed with logical names) still translate correctly. + // Detect column mapping from the most reliable sources: + // 1. Kernel-side proto already populated the flat logical->physical map, OR + // 2. `relation.dataSchema` StructField metadata carries the physical-name key (rare -- + // HadoopFsRelation strips this on construction, but iceberg-compat paths don't), OR + // 3. the Delta snapshot's Metadata.configuration declares `delta.columnMapping.mode` + // not equal to `none`. This is the authoritative source and catches the case where + // (1) and (2) both miss. + // A false negative here is silent data-corruption (physicalisation skipped, native reader + // looks for logical names in physical-named parquet), so the fallback probe is important. + val columnMappingActive = taskList.getColumnMappingsList.asScala.nonEmpty || + relation.dataSchema.fields.exists( + _.metadata.contains(DeltaReflection.PhysicalNameMetadataKey)) || + DeltaReflection + .extractMetadataConfiguration(relation) + .flatMap(_.get("delta.columnMapping.mode")) + .exists(m => m != null && !m.equalsIgnoreCase("none")) + // `relation.dataSchema` has its StructField metadata stripped by Spark's HadoopFsRelation + // construction, so nested physical names are invisible. Reuse the snapshot schema fetched + // above (or None when column mapping isn't active). + val snapshotSchema: Option[StructType] = + if (columnMappingActive) snapshotSchemaEarly else None + val physicalByLogicalName: Map[String, StructField] = + snapshotSchema.map(_.fields.map(f => f.name -> f).toMap).getOrElse(Map.empty) + // Preserve the top-level LOGICAL name and substitute only NESTED (struct/map/array) inner + // field names with their physical equivalents. The native planner (planner.rs ~1383) + // already handles top-level logical->physical substitution using the flat `column_mappings` + // proto. Fields not present in the snapshot (e.g. synthetic `_tmp_metadata_row_index`) are + // passed through untouched. + def physicaliseNestedTypesOnly(f: StructField): StructField = + physicalByLogicalName.get(f.name) match { + case Some(metaField) => + StructField(f.name, physicaliseDataType(metaField.dataType), f.nullable, f.metadata) + case None => f + } + // For `required_schema` we MUST preserve the field's pruned shape (Spark's + // nested column pruning can leave a struct with only the accessed children) while + // still rewriting nested names to their physical equivalents. Using the data-schema + // helper above (which replaces the whole struct with the snapshot's full shape) + // would lose pruning and produce nested children Spark's plan does not expect, + // causing GetStructField ordinals to point at the wrong child. Walks `req`'s tree + // and pairs each node with the corresponding snapshot node by logical name to find + // the physical name; fields not present in the snapshot pass through untouched. + def physicaliseDataTypePreserving(req: DataType, snap: DataType): DataType = + (req, snap) match { + case (rs: StructType, ms: StructType) => + val snapByLogical = ms.fields.map(f => f.name -> f).toMap + StructType(rs.fields.map { rf => + snapByLogical.get(rf.name) match { + case Some(mf) => + val physName = + if (mf.metadata.contains(DeltaReflection.PhysicalNameMetadataKey)) { + mf.metadata.getString(DeltaReflection.PhysicalNameMetadataKey) + } else rf.name + StructField( + physName, + physicaliseDataTypePreserving(rf.dataType, mf.dataType), + rf.nullable, + rf.metadata) + case None => rf + } + }) + case (ra: ArrayType, ma: ArrayType) => + ArrayType( + physicaliseDataTypePreserving(ra.elementType, ma.elementType), + ra.containsNull) + case (rm: MapType, mm: MapType) => + MapType( + physicaliseDataTypePreserving(rm.keyType, mm.keyType), + physicaliseDataTypePreserving(rm.valueType, mm.valueType), + rm.valueContainsNull) + case _ => req + } + def physicaliseRequiredField(f: StructField): StructField = + physicalByLogicalName.get(f.name) match { + case Some(metaField) => + StructField( + f.name, + physicaliseDataTypePreserving(f.dataType, metaField.dataType), + f.nullable, + f.metadata) + case None => f + } + // `data_schema` describes what we want the native parquet reader to read from + // the file. Under column mapping, parquet column matching is by PHYSICAL name + // (at every level of nesting). The reader projects by leaf column path -- it + // can read just `b.col-d` even if the file's `b` also has `col-c`. To make + // that happen, we send data_schema with the SAME shape as the required output: + // top-level fields that are required carry the pruned + physicalised nested + // shape; non-required top-level fields keep their full physicalised shape (no + // read attempt is made for them anyway because they don't appear in + // projection_vector). Without this overlay, the reader would emit a struct + // with ALL nested children (full file shape), and upstream GetStructField + // ordinals -- computed by Catalyst against the PRUNED required_schema -- + // would pick the wrong child. Manifested as "Invalid comparison Utf8 <= Int32" + // on `b.d > 0` (d is INT, ordinal 0 in pruned `b: struct`, but ordinal 0 + // in the file struct is `c` STRING). #79 fix 2026-05-13. + // `requiredSchema` on the wire is the SCAN's output schema -- i.e. data columns the + // scan reads from parquet PLUS partition columns it materialises from + // PartitionedFile.partition_values. Upstream operators in the native plan tree bind + // their column references by index into this schema. For non-partitioned tables + // `scan.requiredSchema` is the whole output already; for partitioned tables Spark + // gives us just the data half here, so we append the partition fields at the tail to + // match the layout indices in `projection_vector` resolve into. + val partitionFieldsForRequired: Array[StructField] = { + val haveLc = scan.requiredSchema.fields.map(_.name.toLowerCase(Locale.ROOT)).toSet + relation.partitionSchema.fields.filterNot(f => + haveLc.contains(f.name.toLowerCase(Locale.ROOT))) + } + val requiredSchemaFields = { + val base = + if (columnMappingActive) scan.requiredSchema.fields.map(physicaliseRequiredField) + else scan.requiredSchema.fields + base ++ partitionFieldsForRequired + } + val physicalFileDataSchemaFields = if (columnMappingActive) { + val requiredByName = requiredSchemaFields + .map(f => f.name.toLowerCase(Locale.ROOT) -> f) + .toMap + fileDataSchemaFields.map { f => + requiredByName.get(f.name.toLowerCase(Locale.ROOT)) match { + // Required asks for this field -- adopt its pruned, physicalised shape so + // the parquet reader projects only the required nested children. + case Some(req) => StructField(f.name, req.dataType, f.nullable, f.metadata) + // Field not required -- physicalise the full snapshot shape (used only if + // some other consumer references it; harmless when projection_vector skips it). + case None => physicaliseNestedTypesOnly(f) + } + } + } else fileDataSchemaFields + + // Column-mapping `id` mode: Delta stores the parquet field ID on every + // StructField (at every level of nesting) under + // `delta.columnMapping.id`. Spark's `ParquetUtils.hasFieldId` (used by + // `schema2Proto` and the StructType arm of `serializeDataType`) reads from + // `parquet.field.id`. Walk the schema tree and translate keys so the + // native side -- when `use_field_id=true` -- matches Spark schema fields + // to parquet file fields by ID instead of by name. + val cmModeIsId = DeltaReflection + .extractMetadataConfiguration(relation) + .flatMap(_.get("delta.columnMapping.mode")) + .exists(_.equalsIgnoreCase("id")) + // The general-purpose Parquet field-ID read path also drives `use_field_id`: if + // the user has enabled `spark.sql.parquet.fieldId.read.enabled` AND the required + // schema already carries Spark's `parquet.field.id` metadata, route through the + // same native machinery. CM-id mode is the common Delta case; this catches + // non-Delta-id tables that nevertheless want field-ID matching. + val sparkFieldIdReadEnabled = SQLConf.get.getConf(SQLConf.PARQUET_FIELD_ID_READ_ENABLED) && + org.apache.spark.sql.execution.datasources.parquet.ParquetUtils.hasFieldIds( + scan.requiredSchema) + val useFieldIdActive = cmModeIsId || sparkFieldIdReadEnabled + val dataSchemaForProto = + if (cmModeIsId) { + physicalFileDataSchemaFields.map( + CometDeltaNativeScan.translateDeltaFieldIdToParquet) + } else physicalFileDataSchemaFields + val requiredSchemaForProto = + if (cmModeIsId) { + requiredSchemaFields.map(CometDeltaNativeScan.translateDeltaFieldIdToParquet) + } else requiredSchemaFields + val partitionSchemaForProto = + if (cmModeIsId) { + relation.partitionSchema.fields.map( + CometDeltaNativeScan.translateDeltaFieldIdToParquet) + } else relation.partitionSchema.fields + + // Strip Delta synthetic columns from the proto schemas. They're not on disk so the + // native parquet reader must not look for them; `DeltaSyntheticColumnsExec` appends + // them back after the scan. Required precondition: synthetics must be a SUFFIX of + // scan.requiredSchema -- otherwise the appended order wouldn't match Spark's + // expected output. The standard Delta DV-rewrite path satisfies this; anything else + // falls back. If we detect the suffix doesn't hold, decline and let Spark's reader + // handle it (correctness over coverage). + val syntheticNames = Set( + DeltaReflection.RowIndexColumnName.toLowerCase(Locale.ROOT), + DeltaReflection.IsRowDeletedColumnName.toLowerCase(Locale.ROOT), + DeltaReflection.RowIdColumnName, + DeltaReflection.RowCommitVersionColumnName) + val isSynthetic = (f: StructField) => + syntheticNames.contains(f.name.toLowerCase(Locale.ROOT)) + val needsSyntheticEmit = + emitRowIndex || emitIsRowDeleted || emitRowId || emitRowCommitVersion + // When synthetics are NOT a contiguous suffix of required_schema, build a reorder + // map: for each original required-schema position, an index into the wrapped exec's + // output (parquet output cols followed by appended synthetics in canonical order + // row_index, is_row_deleted, row_id, row_commit_version). The native dispatcher + // applies a final ProjectionExec to reorder columns to match Spark's expected + // output layout. Empty when synthetics ARE a suffix -- already in the right order. + val finalOutputIndices: Seq[Int] = if (!needsSyntheticEmit) Seq.empty + else { + val firstSyntheticIdx = requiredSchemaForProto.indexWhere(isSynthetic) + val syntheticContiguousSuffix = firstSyntheticIdx >= 0 && + requiredSchemaForProto.drop(firstSyntheticIdx).forall(isSynthetic) + if (syntheticContiguousSuffix) Seq.empty + else { + // Native synthetic emit order in build_output_schema (synthetic_columns.rs): + // row_index, is_row_deleted, row_id, row_commit_version. Skip absent ones. + val syntheticEmitOrder: Seq[String] = Seq( + (emitRowIndex, DeltaReflection.RowIndexColumnName), + (emitIsRowDeleted, DeltaReflection.IsRowDeletedColumnName), + (emitRowId, DeltaReflection.RowIdColumnName), + (emitRowCommitVersion, DeltaReflection.RowCommitVersionColumnName)).collect { + case (true, name) => name.toLowerCase(Locale.ROOT) + } + val nonSyntheticFields = requiredSchemaForProto.filterNot(isSynthetic) + val nonSyntheticIdxByName: Map[String, Int] = + nonSyntheticFields.zipWithIndex.map { case (f, i) => + f.name.toLowerCase(Locale.ROOT) -> i + }.toMap + val syntheticTailStart = nonSyntheticFields.length + requiredSchemaForProto.map { f => + val name = f.name.toLowerCase(Locale.ROOT) + if (isSynthetic(f)) { + val emitIdx = syntheticEmitOrder.indexOf(name) + // emit flags are derived from the same scan.requiredSchema field names + // (lines above), so any synthetic field here must have its corresponding + // emit flag on -- a mismatch would indicate a user column collided with a + // reserved synthetic name AND we missed it. + assert( + emitIdx >= 0, + s"synthetic column '$name' in required_schema but no emit flag is set " + + s"(emit order: $syntheticEmitOrder)") + syntheticTailStart + emitIdx + } else { + nonSyntheticIdxByName(name) + } + } + } + } + val requiredSchemaForProtoStripped = + if (needsSyntheticEmit) requiredSchemaForProto.filterNot(isSynthetic) + else requiredSchemaForProto + val dataSchemaForProtoStripped = + if (needsSyntheticEmit) dataSchemaForProto.filterNot(isSynthetic) + else dataSchemaForProto + + val dataSchema = schema2Proto(dataSchemaForProtoStripped) + val requiredSchema = schema2Proto(requiredSchemaForProtoStripped) + val partitionSchema = schema2Proto(partitionSchemaForProto) + commonBuilder.addAllDataSchema(dataSchema.toIterable.asJava) + commonBuilder.addAllRequiredSchema(requiredSchema.toIterable.asJava) + commonBuilder.addAllPartitionSchema(partitionSchema.toIterable.asJava) + commonBuilder.setUseFieldId(useFieldIdActive) + commonBuilder.setEmitRowIndex(emitRowIndex) + commonBuilder.setEmitIsRowDeleted(emitIsRowDeleted) + commonBuilder.setEmitRowId(emitRowId) + commonBuilder.setEmitRowCommitVersion(emitRowCommitVersion) + commonBuilder.addAllFinalOutputIndices( + finalOutputIndices.map(i => Integer.valueOf(i)).asJava) + + // Projection vector maps output positions to (file_data_schema ++ partition_schema) + // indices. Spark's `FileSourceScanExec` splits its visible schema into + // `requiredSchema` (data-only columns that must be read from parquet) and an + // implicit partition tail that is materialised from `PartitionedFile.partition_values`. + // The scan's `output` is `requiredSchema ++ partitionSchema` in that order. + // + // We mirror that layout: first emit one index per required (data) field pointing + // into `fileDataSchemaFields`, then append one index per partition field pointing + // at `fileDataSchemaFields.length + partitionIdx` so the native side resolves those + // positions against `PartitionedFile.partition_values`. + // + // If `scan.requiredSchema` ever contains a partition column (some Delta code paths + // leak one in), we resolve it through the partition tail without re-reading from + // parquet. + val partitionNameToIndex: Map[String, Int] = + relation.partitionSchema.fields.zipWithIndex.map { case (f, i) => + f.name.toLowerCase(Locale.ROOT) -> i + }.toMap + // Skip synthetic columns when building projection_vector: they aren't in + // file_data_schema OR partition_schema, so any attempt to map them produces -1 + // (out of bounds for native usize). DeltaSyntheticColumnsExec appends them after + // the parquet read, satisfying the suffix-precondition asserted above. + val requiredIndexes: Seq[Int] = scan.requiredSchema.fields.filterNot(f => + needsSyntheticEmit && isSynthetic(f)).map { field => + val nameLower = field.name.toLowerCase(Locale.ROOT) + val dataIdx = + fileDataSchemaFields.indexWhere(_.name.toLowerCase(Locale.ROOT) == nameLower) + if (dataIdx >= 0) { + dataIdx + } else { + partitionNameToIndex + .get(nameLower) + .map(p => fileDataSchemaFields.length + p) + .getOrElse(-1) + } + } + val partitionTailIndexes: Seq[Int] = + relation.partitionSchema.fields.indices.map(i => fileDataSchemaFields.length + i) + val projectionVector: Seq[Int] = requiredIndexes ++ partitionTailIndexes + commonBuilder.addAllProjectionVector( + projectionVector.map(idx => idx.toLong.asInstanceOf[java.lang.Long]).toIterable.asJava) + + // Pushed-down data filters. Gated by Spark's parquet filter pushdown config, same as + // CometNativeScan, so we behave consistently across scan implementations. + // + // Filters referencing nested (struct/array/map) columns aren't safe to push into + // `ParquetSource`: DataFusion currently produces "Invalid comparison operation: Utf8 <= + // Int32" (or similar) when the filter references an array element through + // `GetArrayItem`/`GetStructField`/`GetMapValue`, because the expression tree is walked + // against the file schema where the child types don't match the literal. The filter is + // still evaluated correctly by Spark post-scan, so dropping it from pushdown keeps the + // scan results correct at the cost of some row-group-level pruning. + def referencesNestedAccess(e: Expression): Boolean = e.exists { + case _: org.apache.spark.sql.catalyst.expressions.GetArrayItem => true + case _: org.apache.spark.sql.catalyst.expressions.GetArrayStructFields => true + case _: org.apache.spark.sql.catalyst.expressions.GetMapValue => true + case _ => false + } + if (scan.conf.getConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED) && + CometConf.COMET_RESPECT_PARQUET_FILTER_PUSHDOWN.get(scan.conf)) { + // Partition columns are NOT in the file's data schema; the native parquet path + // evaluates pushed-down filters against the file-data schema only, so a filter + // that references a partition column would resolve to an out-of-bounds Bound + // index ("Column index N is out of bound. Schema: Field {}"). + // Spark normally separates `partitionFilters` from `dataFilters` at planning + // time, but `scan.supportedDataFilters` can still surface filters that touch + // both data + partition columns (or pure-partition filters when the optimizer + // didn't peel them off cleanly). Skip any filter that references a partition + // attribute; partition pruning is handled separately by `prunePartitions` + // driver-side via the kernel/AddFile path. + val partitionNamesLc: Set[String] = + relation.partitionSchema.fields.map(_.name.toLowerCase(Locale.ROOT)).toSet + def referencesPartitionColumn(e: Expression): Boolean = e.exists { + case a: org.apache.spark.sql.catalyst.expressions.AttributeReference => + partitionNamesLc.contains(a.name.toLowerCase(Locale.ROOT)) + case _ => false + } + val dataFilters = new ListBuffer[Expr]() + scan.supportedDataFilters.foreach { filter => + if (referencesNestedAccess(filter)) { + logInfo(s"CometDeltaNativeScan: skipping pushdown of nested-access filter $filter") + } else if (referencesPartitionColumn(filter)) { + logInfo(s"CometDeltaNativeScan: skipping pushdown of partition-column filter $filter") + } else { + exprToProto(filter, scan.output) match { + case Some(proto) => dataFilters += proto + case _ => logWarning(s"CometDeltaNativeScan: unsupported data filter $filter") + } + } + } + commonBuilder.addAllDataFilters(dataFilters.asJava) + } + + storageOptions.asScala.foreach { case (key, value) => + commonBuilder.putObjectStoreOptions(key, value) + } + + // Phase 4: pass column mapping from kernel through to the native planner. + val columnMappings = taskList.getColumnMappingsList.asScala + columnMappings.foreach { cm => + commonBuilder.addColumnMappings( + OperatorOuterClass.DeltaColumnMapping + .newBuilder() + .setLogicalName(cm.getLogicalName) + .setPhysicalName(cm.getPhysicalName) + .build()) + } + + // --- 3. Pack into a DeltaScan with COMMON ONLY (split-mode, Phase 5). + // Tasks are NOT included in the proto at planning time. They'll be + // serialized per-partition in CometDeltaNativeScanExec.serializedPartitionData + // at execution time, and merged via DeltaPlanDataInjector. + val deltaScanBuilder = DeltaScan.newBuilder() + deltaScanBuilder.setCommon(commonBuilder.build()) + // No addAllTasks: tasks stay in taskListBytes for the exec's lazy split. + + // Stash the full task-list bytes for createExec to retrieve. The ThreadLocal + // bridges the convert() -> createExec() gap in CometExecRule.convertToComet. + // Build a modified taskList with ONLY the filtered tasks (partition-pruned). + val filteredTaskList = DeltaScanTaskList + .newBuilder() + .setSnapshotVersion(taskList.getSnapshotVersion) + .setTableRoot(taskList.getTableRoot) + .addAllTasks(filteredTasks.asJava) + .addAllColumnMappings(taskList.getColumnMappingsList) + .addAllUnsupportedFeatures(taskList.getUnsupportedFeaturesList) + .build() + lastTaskListBytes.set(filteredTaskList.toByteArray) + + // Use the typed DeltaScan proto variant. Core's planner dispatches via the + // OpStruct::DeltaScan match arm under `#[cfg(feature = "contrib-delta")]`. + builder.clearChildren() + Some(builder.setDeltaScan(deltaScanBuilder.build()).build()) + } + + /** + * Filter `tasks` down to the subset whose partition values satisfy Spark's + * `scan.partitionFilters`. Returns the original list unchanged when the scan has no partition + * filters. + * + * Recursively rewrite a `StructField` and its `DataType` so every field name at every level of + * nesting reflects the column-mapping physical name stored in its metadata. For fields without + * the physical-name metadata (e.g. partition columns, or inner struct fields on a + * non-column-mapped table), the logical name is retained. Only reached for nested struct/map/ + * array elements -- top-level columns keep their logical name (the native planner does that + * substitution via the `column_mappings` proto). + */ + private def physicaliseStructField(f: StructField): StructField = { + val physName = + if (f.metadata.contains(DeltaReflection.PhysicalNameMetadataKey)) { + f.metadata.getString(DeltaReflection.PhysicalNameMetadataKey) + } else { + f.name + } + StructField(physName, physicaliseDataType(f.dataType), f.nullable, f.metadata) + } + + private def physicaliseDataType(dt: DataType): DataType = dt match { + case s: StructType => StructType(s.fields.map(physicaliseStructField)) + case a: ArrayType => ArrayType(physicaliseDataType(a.elementType), a.containsNull) + case m: MapType => + MapType( + physicaliseDataType(m.keyType), + physicaliseDataType(m.valueType), + m.valueContainsNull) + case other => other + } + + /** + * Compute Spark's `maxSplitBytes` for a Delta scan. Mirrors + * `org.apache.spark.sql.execution.datasources.FilePartition.maxSplitBytes` verbatim so a + * Delta-native scan splits files the same way a vanilla `FileSourceScanExec` would. Inputs are + * file sizes (bytes); other knobs come from session conf and the relation's spark session. + */ + private def maxSplitBytes(scan: CometScanExec, fileSizes: Seq[Long]): Long = { + val sparkSession = scan.relation.sparkSession + val conf = sparkSession.sessionState.conf + val openCostInBytes = conf.filesOpenCostInBytes + val maxPartitionBytes = conf.filesMaxPartitionBytes + val minPartitionNum = conf.filesMinPartitionNum + .getOrElse(sparkSession.sparkContext.defaultParallelism) + val totalBytes = fileSizes.map(_ + openCostInBytes).sum + val bytesPerCore = totalBytes / math.max(1, minPartitionNum) + math.min(maxPartitionBytes, math.max(openCostInBytes, bytesPerCore)) + } + + /** + * Expand `tasks` so any task whose file is larger than `maxSplitBytes` is replaced by a + * sequence of byte-range chunks. Each chunk inherits the task's metadata (partition values, DV + * row indexes, row-tracking ids) but carries `byte_range_start` / `byte_range_end` so the + * native parquet reader only materialises row groups whose start offset falls in this range. + * + * Tasks that fit in one chunk are emitted unchanged (no range fields), which preserves the + * original whole-file semantics on the native side. + * + * Note on DV semantics: deletion-vector indexes on the proto are absolute row positions within + * the file. They are copied to every chunk; the native scan filters out rows whose absolute + * index is in the DV regardless of which chunk produced them, so duplicating the index list + * across chunks is correct (just slightly wasteful). + */ + private def splitTasks( + scan: CometScanExec, + tasks: Seq[OperatorOuterClass.DeltaScanTask]): Seq[OperatorOuterClass.DeltaScanTask] = { + if (tasks.isEmpty) return tasks + // #75 design A: when the plan needs input_file_name(), keep each task 1:1 with + // a file so `CometExecRDD.compute` (which reads only the first task) sets + // the correct path. Without this, byte-range chunking would create multiple + // tasks for one file -- still same path -- BUT combined with packTasks below + // could end up with multiple FILES per partition. + if (scanNeedsInputFileName(scan)) return tasks + val sizes = tasks.map(_.getFileSize) + val msb = maxSplitBytes(scan, sizes) + if (msb <= 0) return tasks + tasks.flatMap { task => + val size = task.getFileSize + if (size <= msb) Seq(task) + else { + val chunks = scala.collection.mutable.ArrayBuffer[OperatorOuterClass.DeltaScanTask]() + var offset = 0L + while (offset < size) { + val end = math.min(offset + msb, size) + chunks += task.toBuilder + .setByteRangeStart(offset) + .setByteRangeEnd(end) + .build() + offset = end + } + chunks.toSeq + } + } + } + + private def prunePartitions( + tasks: Seq[OperatorOuterClass.DeltaScanTask], + scan: CometScanExec, + partitionSchema: StructType): Seq[OperatorOuterClass.DeltaScanTask] = { + if (scan.partitionFilters.isEmpty || partitionSchema.isEmpty) return tasks + + // Phase 5b: filter out DPP expressions (DynamicPruningExpression wrapping + // InSubqueryExec) because they aren't resolved at planning time. Spark + // applies them post-scan at runtime. Static partition filters are still + // evaluated here for file-level pruning. + val staticFilters = scan.partitionFilters.filterNot( + _.exists(_.isInstanceOf[org.apache.spark.sql.catalyst.expressions.PlanExpression[_]])) + if (staticFilters.isEmpty) return tasks + + // Build an `InterpretedPredicate` that expects a row whose schema matches + // `partitionSchema`. Rewrite attribute references to `BoundReference`s keyed by + // partition-schema field index, respecting case sensitivity. + val caseSensitive = scan.conf.getConf[Boolean](SQLConf.CASE_SENSITIVE) + val combined = staticFilters.reduce(And) + val bound = combined.transform { + case a: org.apache.spark.sql.catalyst.expressions.AttributeReference => + val idx = if (caseSensitive) { + partitionSchema.fieldIndex(a.name) + } else { + partitionSchema.fields.indexWhere( + _.name.toLowerCase(Locale.ROOT) == a.name.toLowerCase(Locale.ROOT)) + } + if (idx < 0) return tasks // Can't resolve; skip pruning + BoundReference(idx, partitionSchema(idx).dataType, partitionSchema(idx).nullable) + } + val predicate = InterpretedPredicate(bound) + predicate.initialize(0) + + val sessionZoneId = java.time.ZoneId.of(scan.conf.sessionLocalTimeZone) + tasks.filter { task => + val row = InternalRow.fromSeq(partitionSchema.fields.toSeq.map { field => + val proto = task.getPartitionValuesList.asScala.find(_.getName == field.name) + val strValue = + if (proto.exists(_.hasValue)) Some(proto.get.getValue) else None + DeltaReflection.castPartitionString(strValue, field.dataType, sessionZoneId) + }) + predicate.eval(row) + } + } + + /** + * Build a kernel-independent `DeltaScanTaskList` from a caller-provided AddFile list. Used when + * the Delta scan has a pre-materialized FileIndex (streaming micro-batch, MERGE/UPDATE/DELETE + * post-join) so we can honour its exact file list instead of re-running log replay (which would + * return a different set). + * + * Each AddFile becomes one `DeltaScanTask`. Absolute path resolution mirrors + * `DeltaFileOperations.absolutePath`: if `AddFile.path` is already absolute (has a URI scheme), + * keep it verbatim; otherwise join against `tableRoot`. + */ + private def buildTaskListFromAddFiles( + tableRoot: String, + snapshotVersion: Long, + addFiles: Seq[DeltaReflection.ExtractedAddFile], + nativeOp: AnyRef, + columnNames: Array[String], + physicalToLogicalPartitionNames: Map[String, String] = Map.empty, + deletedRowIndexesByPath: Map[String, Array[Long]] = Map.empty) + : OperatorOuterClass.DeltaScanTaskList = { + val tlBuilder = OperatorOuterClass.DeltaScanTaskList.newBuilder() + tlBuilder.setTableRoot(tableRoot) + if (snapshotVersion >= 0) tlBuilder.setSnapshotVersion(snapshotVersion) + + addFiles.foreach { af => + val absPath = + if (af.path.contains(":/")) af.path + else { + val sep = if (tableRoot.endsWith("/")) "" else "/" + tableRoot + sep + af.path + } + val taskBuilder = OperatorOuterClass.DeltaScanTask.newBuilder() + taskBuilder.setFilePath(absPath) + taskBuilder.setFileSize(af.size) + DeltaReflection.parseNumRecords(af.statsJson).foreach(taskBuilder.setRecordCount) + af.partitionValues.foreach { case (k, v) => + // Under column mapping, Delta stores partition values keyed by the + // PHYSICAL column name (e.g. `col--part`). Our partition_schema + // on the wire uses LOGICAL names, and `build_delta_partitioned_files` + // native-side matches by name. Translate when we have a physical + // ->logical map (the kernel-path jni.rs already performs the same + // translation for its own extraction). + val logicalName = physicalToLogicalPartitionNames.getOrElse(k, k) + val pvBuilder = + OperatorOuterClass.DeltaPartitionValue.newBuilder().setName(logicalName) + if (v != null) pvBuilder.setValue(v) + taskBuilder.addPartitionValues(pvBuilder.build()) + } + af.baseRowId.foreach(taskBuilder.setBaseRowId) + af.defaultRowCommitVersion.foreach(taskBuilder.setDefaultRowCommitVersion) + deletedRowIndexesByPath.get(af.path).foreach { rowIndexes => + var i = 0 + while (i < rowIndexes.length) { + taskBuilder.addDeletedRowIndexes(rowIndexes(i)) + i += 1 + } + } + tlBuilder.addTasks(taskBuilder.build()) + } + tlBuilder.build() + } + + def createExec(nativeOp: Operator, op: CometScanExec): CometNativeExec = { + val tableRoot = DeltaReflection.extractTableRoot(op.relation).getOrElse("unknown") + val tlBytes = + try { + Option(lastTaskListBytes.get()).getOrElse(Array.emptyByteArray) + } finally { + lastTaskListBytes.remove() + } + val oneTaskPerPartition = scanNeedsInputFileName(op) + + val dppFilters = op.partitionFilters.filter( + _.exists(_.isInstanceOf[org.apache.spark.sql.catalyst.expressions.PlanExpression[_]])) + val partitionSchema = op.relation.partitionSchema + + val exec = CometDeltaNativeScanExec( + nativeOp, + op.output, + org.apache.spark.sql.comet.SerializedPlan(None), + op.wrapped, + tableRoot, + tlBytes, + dppFilters, + partitionSchema, + oneTaskPerPartition = oneTaskPerPartition) + // Propagate logicalLink from the source scanExec to the contrib's exec, mirroring + // what CometNativeScanExec/CometScanExec/CometIcebergNativeScanExec do for built-in + // scans. AdaptiveSparkPlanExec.setLogicalLinkForNewQueryStage asserts every new + // query-stage node has a logicalLink set; without this, AQE plans containing a + // CometDeltaNativeScanExec hit the assertion when the AQE optimizer tries to wrap + // them (especially under spark.sql.adaptive.forceApply and on column-mapping + // rewritten plans). + op.wrapped.logicalLink.foreach(exec.setLogicalLink) + exec + } +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaConf.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaConf.scala new file mode 100644 index 0000000000..9b519c3f82 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaConf.scala @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.comet.{ConfigBuilder, ConfigEntry} + +/** + * Contrib-local config entries for the Delta integration. Lives in the contrib's package rather + * than in core's `CometConf` so PR1 stays format-agnostic. Side-effect of object construction is + * registering the entries with `CometConf.allConfs` (via the `ConfigBuilder` machinery), so they + * show up in the generated user-guide docs and `SQLConf` resolution works the usual way. + */ +object DeltaConf { + + // CometConf.register asserts every config has a non-empty category — used for grouping + // entries in the generated user-guide docs. "scan" matches the existing core + // CATEGORY_SCAN string (CATEGORY_* constants in CometConf are `private val` so contribs + // can't reference the symbol; the assertion only checks `nonEmpty`). + private val CATEGORY = "scan" + + val COMET_DELTA_NATIVE_ENABLED: ConfigEntry[Boolean] = + ConfigBuilder("spark.comet.scan.deltaNative.enabled") + .category(CATEGORY) + .doc( + "Whether to enable native Delta table scans via delta-kernel-rs. When enabled, " + + "Delta tables are read directly through Comet's tuned ParquetSource + " + + "DV-filter wrapper, bypassing Spark's Delta reader for better performance.") + .booleanConf + .createWithDefault(true) + + val COMET_DELTA_FALLBACK_ON_UNSUPPORTED_FEATURE: ConfigEntry[Boolean] = + ConfigBuilder("spark.comet.scan.deltaNative.fallbackOnUnsupportedFeature") + .category(CATEGORY) + .doc( + "When true (default), the Delta contrib falls back to Spark's Delta reader on " + + "any Delta protocol feature it doesn't yet support. When false, the contrib " + + "raises an error instead -- useful for tests that want to assert the native " + + "path is reachable for a particular query.") + .booleanConf + .createWithDefault(true) + + val COMET_DELTA_DATA_FILE_CONCURRENCY_LIMIT: ConfigEntry[Int] = + ConfigBuilder("spark.comet.scan.deltaNative.dataFileConcurrencyLimit") + .category(CATEGORY) + .doc( + "Per-Spark-task concurrency when reading Delta data files. Higher values " + + "improve throughput on tables with many small files at the cost of memory. " + + "Values between 2 and 8 are typical.") + .intConf + .checkValue(v => v > 0, "Data file concurrency limit must be positive") + .createWithDefault(1) + + /** + * Relation-options key the contrib reads to know whether the surrounding plan references + * `input_file_name()` / `input_file_block_*`. When set to `"true"`, the contrib emits + * `oneTaskPerPartition = true` on the `CometDeltaNativeScanExec` so packTasks keeps each task + * in its own partition and `CometExecRDD.compute` (via `InputFileBlockHolder.set`) can set + * `InputFileBlockHolder` to the correct path. + */ + val NeedsInputFileNameOption: String = "comet.contrib.delta.needsInputFileName" +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaReflection.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaReflection.scala new file mode 100644 index 0000000000..a69eddd35e --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaReflection.scala @@ -0,0 +1,726 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.connector.read.{Scan => V2Scan} +import org.apache.spark.sql.execution.datasources.{FileFormat, HadoopFsRelation} +import org.apache.spark.sql.types.StructType + +/** + * Class-name-based probes for Delta Lake plan nodes. + * + * We deliberately avoid a compile-time dependency on `spark-delta` - the Scala API surface churns + * across Delta versions (2.x / 3.x / 4.x) and we want Comet's Delta detection to keep working + * against whichever Delta version the user has on their classpath. All detection is therefore + * done via fully-qualified class names and standard Spark APIs (HadoopFsRelation, V2Scan), which + * have been stable for years. + * + * What this object provides: + * - `isDeltaFileFormat(fileFormat)`: true for `DeltaParquetFileFormat` and any subclass exposed + * by the delta-spark package. + * - `isDeltaV2Scan(scan)`: true for the V2 `DeltaScan` / `DeltaTableV2`-backed scan. + * - `extractTableRoot(relation)`: pulls the table root URI out of a `HadoopFsRelation`. Works + * for both path-based reads (`format("delta").load("/tmp/t")`) and table-based reads + * (`spark.table("delta_tbl")`). + */ +object DeltaReflection extends Logging { + + /** Fully-qualified class names we match on. */ + object ClassNames { + val DELTA_PARQUET_FILE_FORMAT = "org.apache.spark.sql.delta.DeltaParquetFileFormat" + val DELTA_V2_SCAN_PACKAGE_PREFIX = "org.apache.spark.sql.delta." + val DELTA_V2_SCAN_SIMPLE_NAME = "DeltaScan" + } + + /** + * Synthetic column name that Delta's `PreprocessTableWithDVs` rule injects into a scan's output + * schema when the relation has deletion vectors in use. Value `0` means "keep the row", any + * other value means "drop it". Used to detect DV-rewritten Delta scans. + * + * Stable across Delta 2.x / 3.x - defined in + * `DeltaParquetFileFormat.IS_ROW_DELETED_COLUMN_NAME`. + */ + val IsRowDeletedColumnName: String = "__delta_internal_is_row_deleted" + + /** + * Synthetic column name Delta requests on the parquet scan when it needs the per-row physical + * position within the file (e.g. for downstream DV bitmap lookup in `useMetadataRowIndex` mode, + * or test-only reads of the metadata column). Produced only by `DeltaParquetFileFormat`'s + * reader; Comet's parquet reader has no equivalent synthesis. + * + * Stable across Delta 2.x / 3.x - defined in `DeltaParquetFileFormat.ROW_INDEX_COLUMN_NAME`. + */ + val RowIndexColumnName: String = "__delta_internal_row_index" + + /** + * Returns true if `fileFormat` is Delta's parquet-backed `FileFormat`. Checks the exact class + * plus any subclass, so variants like `DeletionVectorBoundFileFormat` (some Delta versions) + * also match. + */ + def isDeltaFileFormat(fileFormat: FileFormat): Boolean = { + val cls = fileFormat.getClass + isDeltaClassName(cls.getName) || isDeltaParquetSubclass(cls) + } + + /** Walks the class hierarchy looking for DeltaParquetFileFormat. */ + private def isDeltaParquetSubclass(cls: Class[_]): Boolean = { + var current: Class[_] = cls + while (current != null) { + if (current.getName == ClassNames.DELTA_PARQUET_FILE_FORMAT) return true + current = current.getSuperclass + } + false + } + + private def isDeltaClassName(name: String): Boolean = + name == ClassNames.DELTA_PARQUET_FILE_FORMAT || + (name.startsWith(ClassNames.DELTA_V2_SCAN_PACKAGE_PREFIX) && + name.endsWith("ParquetFileFormat")) + + /** + * Returns true if `scan` is the V2 scan implementation Delta produces for a + * `DeltaTableV2`-backed read. Delta ships this as `org.apache.spark.sql.delta.DeltaScan` (inner + * case class of `DeltaScanBuilder` or similar) - the enclosing class name varies by version, so + * we match on the simple name + package prefix rather than an exact FQN. + */ + def isDeltaV2Scan(scan: V2Scan): Boolean = { + val name = scan.getClass.getName + name.startsWith(ClassNames.DELTA_V2_SCAN_PACKAGE_PREFIX) && + name.contains(ClassNames.DELTA_V2_SCAN_SIMPLE_NAME) + } + + /** + * Extract the Delta table root from a V1 `HadoopFsRelation`. For Delta tables this is always a + * single path - Delta does not support multi-root relations. + * + * Returns the absolute URI as a string, with whatever scheme the relation was opened with + * (`file://`, `s3://`, etc.). + */ + def extractTableRoot(relation: HadoopFsRelation): Option[String] = { + try { + val roots = relation.location.rootPaths + roots.headOption.map(pathToSingleEncodedUri) + } catch { + case e: Exception => + logWarning(s"Failed to extract Delta table root path: ${e.getMessage}") + None + } + } + + /** + * Convert a Hadoop `Path` to a URI string whose decoded path component matches the literal + * on-disk filesystem path Hadoop uses when reading the file. + * + * Hadoop's `RawLocalFileSystem.pathToFile` treats the bytes of `path.toUri.getRawPath` -- i.e. + * the URI's path component WITHOUT decoding -- as the literal filesystem path. So if Hadoop's + * Path stores URI form `file:/T/spark%25dir%25prefix-uuid` (typical for Delta tests whose + * `defaultTempDirPrefix` is the literal `spark%dir%prefix`), the actual on-disk dir name is + * `spark%25dir%25prefix-uuid` (with `%25` literal in the filename, four chars `%`, `2`, `5`). + * + * To send a URI that the native side can decode back to that on-disk literal, we take the raw + * path component verbatim and URL-encode `%` one extra time, yielding + * `file:/T/spark%2525dir%2525prefix-uuid`. The native scan decodes once (`%2525` -> `%25`) and + * opens at the literal `%25` filename. + */ + def pathToSingleEncodedUri(p: org.apache.hadoop.fs.Path): String = { + // Hadoop's `Path` keeps two forms of the same URI: + // - `path.toString` returns a once-decoded form for display: any `%XX` + // escape stored in the URI is decoded once. For Delta tests whose + // `defaultTempDirPrefix` is the literal `spark%dir%prefix` and whose + // on-disk dir Spark actually creates is `spark%25dir%25prefix-uuid` + // (with `%25` four-char-literal in the filename), this returns + // `file:/T/spark%25dir%25prefix-uuid` -- which when fed to a URL + // parser would single-decode to a non-existent `spark%dir%prefix-uuid`. + // - `path.toUri.toString` returns the FULL URI form, double-encoding the + // literal `%` chars (`%25` -> `%2525`). When the native side parses + // this and percent-decodes once, it recovers the literal on-disk + // filename `spark%25dir%25prefix-uuid`. + // + // We always want the second form for native consumption, so the raw + // ParquetSource open path matches Hadoop's `RawLocalFileSystem` + // interpretation (which reads the URI's raw path component verbatim as + // the filesystem path). + p.toUri.toString + } + + /** + * Extract the resolved snapshot version from Delta's `FileIndex`. Delta's file index is a + * `TahoeLogFileIndex` / `PreparedDeltaFileIndex` which has already pinned a specific snapshot + * by the time we see it, including when the user supplied `versionAsOf` or `timestampAsOf`. + * + * The toString format is stable: `Delta[version=, ]`. We parse that rather than + * reaching into Delta's internals because the actual field names differ across Delta versions + * (snapshotAtAnalysis vs tahoeFileIndex.snapshot vs etc.). Regex is a single point of failure + * that's easy to update if the format ever changes. + * + * Returns the version as a `Long`, or `None` if parsing fails / the file index isn't a Delta + * one (callers should fall back to `-1` = latest). + */ + private val DeltaFileIndexVersionRegex = """^Delta\[version=(-?\d+),""".r + + /** + * Extract the Delta table `Metadata` action's configuration map from a `HadoopFsRelation`'s + * `TahoeFileIndex`-derivative location via reflection. Returns `None` when the lookup fails + * (e.g. non-Delta relation, or an index type that does not expose `metadata`). + * + * The configuration carries user- and system-set table properties keyed by dotted names like + * `delta.rowTracking.materializedRowIdColumnName`. Used by the CometScanRule row-tracking + * support to discover the physical column name into which Delta has materialised `row_id`. + */ + def extractMetadataConfiguration(relation: HadoopFsRelation): Option[Map[String, String]] = { + try { + val location: Any = relation.location + // Three-shape lookup. `TahoeBatchFileIndex` exposes only a `SnapshotDescriptor` + // (not `Snapshot`), so the `snapshot.metadata` chain misses for it; we walk + // `deltaLog.update().metadata` for the case-3 fallback. Keeping this in sync with + // `extractSnapshotSchema` below is critical: when CM is enabled and we miss the + // config, the contrib doesn't detect column mapping is active and falls back to + // logical-name reads on physically-renamed files. + val metadataObj: Option[AnyRef] = + findAccessor(location, Seq("metadata")) + .orElse(findAccessor(location, Seq("snapshot")).flatMap(findAccessor(_, Seq("metadata")))) + .orElse { + findAccessor(location, Seq("deltaLog")).flatMap { dl => + invokeNoArg(dl, "update").flatMap(findAccessor(_, Seq("metadata"))) + } + } + metadataObj.flatMap { m => + findAccessor(m, Seq("configuration")).collect { + case scalaMap: Map[_, _] => scalaMap.asInstanceOf[Map[String, String]] + case javaMap: java.util.Map[_, _] => + import scala.jdk.CollectionConverters._ + javaMap.asInstanceOf[java.util.Map[String, String]].asScala.toMap + } + } + } catch { + case e: Exception => + logWarning(s"Failed to extract Delta metadata configuration: ${e.getMessage}") + None + } + } + + /** StructField metadata key under which Delta stores the column-mapping physical name. */ + val PhysicalNameMetadataKey: String = "delta.columnMapping.physicalName" + + /** StructField metadata key under which Delta stores the column-mapping field ID (CM-id mode). */ + val FieldIdMetadataKey: String = "delta.columnMapping.id" + + /** + * StructField metadata key under which Spark+parquet store a field ID. Matches + * arrow-rs's `PARQUET_FIELD_ID_META_KEY`, which is what Comet's native schema_adapter + * looks for when `use_field_id=true`. To match the file's field IDs against the Spark + * schema we copy from Delta's `delta.columnMapping.id` to this key before serializing + * to native. + */ + val ParquetFieldIdMetadataKey: String = "PARQUET:field_id" + + /** + * Row-tracking column names. Pinned here so call sites in DeltaScanRule's + * `applyRowTrackingRewrite` and CometDeltaNativeScan's synthetic-emit detection + * stay in sync with the native exec's `synthetic_columns.rs` `ROW_ID_COLUMN_NAME` / + * `ROW_COMMIT_VERSION_COLUMN_NAME`. + */ + val RowIdColumnName: String = "row_id" + val RowCommitVersionColumnName: String = "row_commit_version" + + /** + * Extract the Delta table's Snapshot-level schema (`Metadata.schema()` in Delta terms) via + * reflection. Unlike the `relation.dataSchema` we get from Spark -- which has its StructField + * metadata stripped by HadoopFsRelation construction -- the Snapshot's schema preserves the + * `delta.columnMapping.physicalName` and `delta.columnMapping.id` metadata on every StructField + * at every level of nesting. This is the authoritative source for building a "physical schema" + * to hand to the native parquet reader. + */ + def extractSnapshotSchema(relation: HadoopFsRelation): Option[StructType] = { + try { + val location: Any = relation.location + // Three-shape lookup. `TahoeBatchFileIndex` (UPDATE/DELETE/MERGE post-rewrite and + // streaming micro-batches) exposes only a `SnapshotDescriptor` -- not a `Snapshot`, + // and the SnapshotDescriptor doesn't expose `Metadata` directly. For those, walk + // `deltaLog.update().metadata` (case 3) to get the LATEST snapshot's metadata. + // That's also correct for UPDATE/DELETE: those commands re-read the table at commit + // time, so the latest snapshot's column-mapping metadata is what governs how the + // parquet files we're about to read are interpreted. Without case 3, CM-name tables + // that have undergone RENAME COLUMN return wrong values from streaming/UPDATE/DELETE + // reads because the contrib falls back to `relation.dataSchema` whose StructField + // metadata is stripped by HadoopFsRelation, so `physicalName` is invisible, + // `column_mappings` proto stays empty, and the native parquet reader reads by + // logical name from a physically-renamed file. + val metadataObj: Option[AnyRef] = + findAccessor(location, Seq("metadata")) + .orElse(findAccessor(location, Seq("snapshot")).flatMap(findAccessor(_, Seq("metadata")))) + .orElse { + findAccessor(location, Seq("deltaLog")).flatMap { dl => + invokeNoArg(dl, "update").flatMap(findAccessor(_, Seq("metadata"))) + } + } + metadataObj.flatMap { m => + // Delta's Metadata exposes a `schema(): StructType` method that parses its stored JSON + // schema string. The returned StructType has full metadata preserved at every level. + val schema = invokeNoArg(m, "schema").orElse(findAccessor(m, Seq("schema"))) + schema.collect { case s: StructType => s } + } + } catch { + case scala.util.control.NonFatal(e) => + logWarning(s"Failed to extract Delta snapshot schema: ${e.getMessage}") + None + } + } + + private def invokeNoArg(obj: Any, methodName: String): Option[AnyRef] = { + if (obj == null) return None + try { + val m = lookupNoArgMethod(obj.getClass, methodName) + if (m == null) None else Option(m.invoke(obj)) + } catch { + case scala.util.control.NonFatal(_) => None + } + } + + /** Property key for the physical column name Delta materialises row IDs into. */ + val MaterializedRowIdColumnProp: String = + "delta.rowTracking.materializedRowIdColumnName" + + /** Property key for the physical column name Delta materialises row-commit-versions into. */ + val MaterializedRowCommitVersionColumnProp: String = + "delta.rowTracking.materializedRowCommitVersionColumnName" + + /** + * Row-tracking fields extracted per file for phase-3 synthesis of `_row_id_` and + * `_row_commit_version_` when the materialised physical columns are null. + */ + case class RowTrackingFileInfo(baseRowId: Option[Long], defaultRowCommitVersion: Option[Long]) + + /** + * Invoke `TahoeFileIndex.matchingFiles(partitionFilters = Nil, dataFilters = Nil)` on the given + * `location`, extract each returned `AddFile`'s `path`, `baseRowId`, and + * `defaultRowCommitVersion`, and return the resulting map keyed by file basename. + * + * Used by row-tracking Phase 3: we attach each file's starting row id and default commit + * version as per-file synthetic partition columns. Returns `Map.empty` on reflection failure. + */ + def extractRowTrackingInfoByFileName(location: Any): Map[String, RowTrackingFileInfo] = { + if (location == null) return Map.empty + try { + val addFilesAny = callMatchingFiles(location).getOrElse(return Map.empty) + val seq = addFilesAny match { + case s: scala.collection.Seq[_] => s + case a: Array[_] => a.toSeq + case _ => return Map.empty + } + val result = scala.collection.mutable.Map.empty[String, RowTrackingFileInfo] + seq.foreach { addFile => + val path = stringMember(addFile, "path") + val baseRowId = optionLongMember(addFile, "baseRowId") + val defaultVer = optionLongMember(addFile, "defaultRowCommitVersion") + path.foreach { p => + if (baseRowId.isDefined || defaultVer.isDefined) { + val name = new org.apache.hadoop.fs.Path(p).getName + result.put(name, RowTrackingFileInfo(baseRowId, defaultVer)) + } + } + } + result.toMap + } catch { + case _: Exception => Map.empty + } + } + + def extractSnapshotVersion(relation: HadoopFsRelation): Option[Long] = { + try { + val desc = relation.location.toString + DeltaFileIndexVersionRegex.findFirstMatchIn(desc).map(_.group(1).toLong) + } catch { + case _: Exception => None + } + } + + /** + * Read the LATEST committed version from the relation's underlying `DeltaLog`, via reflection + * so we keep zero compile-time dep on spark-delta. Returns `None` when the relation isn't + * backed by a Delta log (or reflection fails). + */ + def extractLatestSnapshotVersion(relation: HadoopFsRelation): Option[Long] = { + try { + val deltaLogObj = findAccessor(relation.location, Seq("deltaLog")).orNull + if (deltaLogObj == null) return None + // `deltaLog.update()` returns the latest Snapshot; `snapshot.version` is a Long. + val updated = invokeNoArg(deltaLogObj, "update").orNull + if (updated == null) return None + longMember(updated, "version") + } catch { + case _: Exception => None + } + } + + /** + * Convert a Delta partition value string to a Catalyst-internal representation. Delta stores + * partition values as strings in add actions; this converts them to the correct type for + * predicate evaluation. + */ + /** + * Normalized view of a single Delta `AddFile` extracted from a pre-materialized FileIndex + * (`TahoeBatchFileIndex` / `CdcAddFileIndex`). Used by the scan rule to build a + * kernel-independent `DeltaScanTask` list for streaming micro-batch reads and + * MERGE/UPDATE/DELETE post-join rewrites, both of which already have the exact AddFile list in + * hand and must NOT re-run kernel log replay (which would return a different file set). + */ + case class ExtractedAddFile( + /** Path as stored in the AddFile action -- may be relative or absolute. */ + path: String, + size: Long, + /** Raw partition values as Delta stores them, keyed by logical column name. */ + partitionValues: Map[String, String], + /** Raw `stats` JSON string, or null. */ + statsJson: String, + /** True if this AddFile has a non-null DeletionVectorDescriptor. */ + hasDeletionVector: Boolean, + /** + * The raw `DeletionVectorDescriptor` object (opaque via reflection -- the concrete type is + * `org.apache.spark.sql.delta.actions.DeletionVectorDescriptor` but we keep it as `AnyRef` + * to preserve the no-compile-time-dep-on-spark-delta property). `null` when the AddFile has + * no DV. Pass to `materializeDeletedRowIndexes` to convert into a `Array[Long]` of deleted + * row indexes. + */ + dvDescriptor: AnyRef, + /** + * Delta row-tracking fields. `baseRowId` is the first logical row id covered by this file; + * `defaultRowCommitVersion` is the commit that last wrote it. Both are `None` for tables + * that don't have the rowTracking table feature enabled (or for pre-backfill files on a + * table where row tracking was just enabled). + */ + baseRowId: Option[Long], + defaultRowCommitVersion: Option[Long]) + + /** + * Is this FileIndex a pre-materialized Delta index (batch or CDC)? + * + * CDC reads (`CdcAddFileIndex`, `TahoeRemoveFileIndex`, `TahoeChangeFileIndex`) all derive from + * `TahoeBatchFileIndex` (conceptually or concretely) and stash the CDC metadata + * (`_change_type`, `_commit_version`, `_commit_timestamp`) into `AddFile.partitionValues` with + * a matching `partitionSchema`, so the native scan can materialise them as partition columns + * without any special CDC-specific handling. + */ + def isBatchFileIndex(location: Any): Boolean = { + val cls = location.getClass.getName + cls.contains("TahoeBatchFileIndex") || + cls.contains("CdcAddFileIndex") || + cls.contains("TahoeRemoveFileIndex") || + cls.contains("TahoeChangeFileIndex") || + cls.contains("PreparedDeltaFileIndex") + } + + /** + * Extract the AddFile list from a `TahoeBatchFileIndex`-like FileIndex via reflection (no + * compile-time dep on spark-delta). Returns `None` when: + * - the FileIndex class doesn't expose an `addFiles: Seq[AddFile]` method + * - reflection fails for any entry + * - any AddFile's stats / fields can't be read + * + * Callers should fall back to Spark's Delta reader when this returns `None`. + * + * For CDC indexes (`CdcAddFileIndex`, `TahoeRemoveFileIndex`, `TahoeChangeFileIndex`) the raw + * `addFiles` field does NOT contain the CDC metadata columns (`_change_type`, + * `_commit_version`, `_commit_timestamp`); those are injected inside the index's + * `matchingFiles(partitionFilters, dataFilters)` override. We therefore prefer + * `matchingFiles(Seq.empty, Seq.empty)` when it's available, so the returned `partitionValues` + * maps already carry the CDC metadata. + */ + def extractBatchAddFiles(location: Any): Option[Seq[ExtractedAddFile]] = { + try { + // PreparedDeltaFileIndex carries the pre-skipped scan result -- using + // `matchingFiles(Nil, Nil)` on it falls into Delta's "Reselecting files + // to query" branch (different filter set) and returns the FULL snapshot + // of files (no stats-based skipping), which breaks tests like + // StatsCollectionSuite "gather stats" that expect file-level pruning. + // Read `preparedScan.files` directly to honour the prepared skipping. + val preparedFiles: Option[AnyRef] = + if (location.getClass.getName.contains("PreparedDeltaFileIndex")) { + findAccessor(location, Seq("preparedScan")) + .flatMap(ps => findAccessor(ps, Seq("files"))) + } else None + // Prefer matchingFiles(Seq.empty, Seq.empty) -- it returns CDC-augmented + // AddFiles on CDC indexes and the plain list on TahoeBatchFileIndex. + // Fall back to the raw `addFiles`/`filesList` accessors for indexes that + // don't expose a no-arg-safe matchingFiles. + val addFilesOpt = preparedFiles + .orElse(callMatchingFiles(location)) + .orElse(findAccessor(location, Seq("addFiles", "filesList"))) + addFilesOpt.flatMap { addFilesAny => + val seq = addFilesAny match { + case s: scala.collection.Seq[_] => s + case a: Array[_] => a.toSeq + case _ => return None + } + val out = new scala.collection.mutable.ArrayBuffer[ExtractedAddFile](seq.size) + seq.foreach { addFile => + val path = stringMember(addFile, "path").getOrElse(return None) + val size = longMember(addFile, "size").getOrElse(return None) + val rawPV = findAccessor(addFile, Seq("partitionValues")).getOrElse(return None) + val pv: Map[String, String] = rawPV match { + case m: Map[_, _] => m.asInstanceOf[Map[String, String]] + case m: java.util.Map[_, _] => + import scala.jdk.CollectionConverters._ + m.asInstanceOf[java.util.Map[String, String]].asScala.toMap + case _ => return None + } + val stats = stringMember(addFile, "stats").orNull + val dv = findAccessor(addFile, Seq("deletionVector")).orNull + val baseRowId = optionLongMember(addFile, "baseRowId") + val defaultRowCommitVersion = optionLongMember(addFile, "defaultRowCommitVersion") + out += ExtractedAddFile( + path, + size, + pv, + stats, + hasDeletionVector = dv != null, + dvDescriptor = dv, + baseRowId = baseRowId, + defaultRowCommitVersion = defaultRowCommitVersion) + } + Some(out.toSeq) + } + } catch { + case e: Exception => + logWarning( + s"Failed to extract AddFiles from ${location.getClass.getName}: ${e.getMessage}") + None + } + } + + /** + * Materialize a `DeletionVectorDescriptor` into the list of deleted row indexes (0-based, + * sorted ascending) using Delta's own `HadoopFileSystemDVStore` + `RoaringBitmapArray.toArray`. + * + * Returns `None` when: + * - `dvDescriptor` is null (no DV on this file) + * - the Delta classes aren't on the classpath (different Delta version layout, etc.) + * - the read itself fails (corrupt DV file, missing file, etc.) + * + * Callers that need DV semantics must fall back to Spark+Delta when this returns `None`. + * + * Driver-side only: don't call this on executors, since it touches the filesystem and the DV + * store may not be initialised. The native side then plumbs the row-index array into the proto + * task's `deleted_row_indexes` field, which `DeltaDvFilterExec` already consumes. + */ + def materializeDeletedRowIndexes( + dvDescriptor: AnyRef, + tableRoot: String, + hadoopConf: org.apache.hadoop.conf.Configuration): Option[Array[Long]] = { + if (dvDescriptor == null) return None + try { + // scalastyle:off classforname + val storeCls = + Class.forName("org.apache.spark.sql.delta.storage.dv.HadoopFileSystemDVStore") + // scalastyle:on classforname + val store = storeCls + .getConstructor(classOf[org.apache.hadoop.conf.Configuration]) + .newInstance(hadoopConf) + .asInstanceOf[AnyRef] + val readMethod = storeCls.getMethods + .find { m => + m.getName == "read" && + m.getParameterCount == 2 && + m.getParameterTypes()(1) == classOf[org.apache.hadoop.fs.Path] + } + .getOrElse(return None) + val tablePath = new org.apache.hadoop.fs.Path(tableRoot) + val bitmap = readMethod.invoke(store, dvDescriptor, tablePath) + // RoaringBitmapArray.toArray returns Array[Long] of all set bits (= deleted row indexes). + val toArrayMethod = bitmap.getClass.getMethod("toArray") + val indexes = toArrayMethod.invoke(bitmap).asInstanceOf[Array[Long]] + Some(indexes) + } catch { + case scala.util.control.NonFatal(e) => + logWarning(s"materializeDeletedRowIndexes failed for table $tableRoot: ${e.getMessage}") + None + } + } + + /** + * Extract number-of-records from an AddFile's `stats` JSON. Returns `None` if stats is missing + * / malformed. The JSON structure is stable across Delta versions: `{"numRecords": N, ...}`. + */ + def parseNumRecords(statsJson: String): Option[Long] = { + if (statsJson == null) return None + val idx = statsJson.indexOf("\"numRecords\"") + if (idx < 0) return None + // Find the colon after the key, then the first numeric sequence. + val colon = statsJson.indexOf(':', idx) + if (colon < 0) return None + var i = colon + 1 + while (i < statsJson.length && !statsJson.charAt(i).isDigit && statsJson.charAt(i) != '-') { + i += 1 + } + val start = i + while (i < statsJson.length && (statsJson.charAt(i).isDigit || statsJson.charAt(i) == '-')) { + i += 1 + } + if (start == i) { + None + } else { + try Some(statsJson.substring(start, i).toLong) + catch { case _: NumberFormatException => None } + } + } + + /** + * Invoke `FileIndex.matchingFiles(partitionFilters: Seq[Expression], dataFilters: + * Seq[Expression]): Seq[AddFile]` with empty filter sequences via reflection. + * + * Returns `None` if the method is missing or the invocation throws. Comet does not have a + * compile-time dep on spark-delta, so we reach for reflection here. + */ + private def callMatchingFiles(location: Any): Option[AnyRef] = { + if (location == null) return None + try { + // Method.matchingFiles has two parameters of type `Seq[Expression]`; we + // can pass Nil for both. We find the method by name + arity to keep the + // lookup tolerant of Scala's generic-erasure bridging. + val candidate = location.getClass.getMethods.find { m => + m.getName == "matchingFiles" && m.getParameterCount == 2 + } + candidate.flatMap { m => + val nil = scala.collection.immutable.Nil + try Option(m.invoke(location, nil, nil)) + catch { + case scala.util.control.NonFatal(_) => None + } + } + } catch { + case scala.util.control.NonFatal(_) => None + } + } + + private def findAccessor(obj: Any, names: Seq[String]): Option[AnyRef] = { + if (obj == null) return None + val cls = obj.getClass + names.foreach { n => + val m = lookupNoArgMethod(cls, n) + if (m != null) { + try return Option(m.invoke(obj)) + catch { case scala.util.control.NonFatal(_) => return None } + } + } + None + } + + // Cache no-arg java.lang.reflect.Method handles by (class, name). Hot path for plan + // walks: every CometScanRule call into Delta does many name-based lookups per file. + // `MISSING` sentinel caches negative lookups so we don't re-scan getMethods on misses. + private val MISSING: java.lang.reflect.Method = classOf[Object].getMethod("toString") + private val noArgMethodCache = + new java.util.concurrent.ConcurrentHashMap[(Class[_], String), java.lang.reflect.Method]() + + private def lookupNoArgMethod(cls: Class[_], name: String): java.lang.reflect.Method = { + val key = (cls, name) + val cached = noArgMethodCache.get(key) + if (cached ne null) return if (cached eq MISSING) null else cached + val resolved = + try { + val m = cls.getMethod(name) + if (m.getParameterCount == 0) m else null + } catch { + case _: NoSuchMethodException => null + } + noArgMethodCache.putIfAbsent(key, if (resolved == null) MISSING else resolved) + resolved + } + + private def stringMember(obj: Any, name: String): Option[String] = + findAccessor(obj, Seq(name)).flatMap { + case s: String => Some(s) + case null => None + case _ => None + } + + private def longMember(obj: Any, name: String): Option[Long] = + findAccessor(obj, Seq(name)).flatMap { + case l: java.lang.Long => Some(l) + case i: java.lang.Integer => Some(i.toLong) + case _ => None + } + + /** + * Read a Scala `Option[Long]` (or `Option[java.lang.Long]`) field by name. Returns `None` for + * both `None` and a field that contains `Some(null)`. Used for optional Delta fields like + * `AddFile.baseRowId` that only exist when rowTracking is enabled on the table. + */ + private def optionLongMember(obj: Any, name: String): Option[Long] = + findAccessor(obj, Seq(name)).flatMap { + case None => None + case Some(l: java.lang.Long) => Some(l) + case Some(i: java.lang.Integer) => Some(i.toLong) + case Some(l: Long) => Some(l) + case Some(null) | null => None + case l: java.lang.Long => Some(l) // defensive: caller extracted value already + case _ => None + } + + def castPartitionString( + str: Option[String], + dt: org.apache.spark.sql.types.DataType, + sessionZoneId: java.time.ZoneId = java.time.ZoneOffset.UTC): Any = { + import org.apache.spark.sql.catalyst.util.DateTimeUtils + import org.apache.spark.sql.types._ + import org.apache.spark.unsafe.types.UTF8String + str match { + case None | Some(null) => null + case Some(s) => + try { + dt match { + case StringType => UTF8String.fromString(s) + case IntegerType => s.toInt + case LongType => s.toLong + case ShortType => s.toShort + case ByteType => s.toByte + case FloatType => s.toFloat + case DoubleType => s.toDouble + case BooleanType => s.toBoolean + case DateType => + DateTimeUtils + .stringToDate(UTF8String.fromString(s)) + .getOrElse(null) + case _: TimestampType => + // Delta serializes TIMESTAMP partition values in the session TZ at write time, so + // parse them in the session TZ at read time to round-trip correctly (defaults to + // UTC when the caller hasn't plumbed the session TZ through). + DateTimeUtils + .stringToTimestamp(UTF8String.fromString(s), sessionZoneId) + .getOrElse(null) + case _: TimestampNTZType => + DateTimeUtils + .stringToTimestampWithoutTimeZone(UTF8String.fromString(s)) + .getOrElse(null) + case d: DecimalType => + val dec = + org.apache.spark.sql.types.Decimal(new java.math.BigDecimal(s)) + dec.changePrecision(d.precision, d.scale) + dec + case _ => UTF8String.fromString(s) + } + } catch { + case _: NumberFormatException | _: IllegalArgumentException => + null + } + } + } +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaScanRule.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaScanRule.scala new file mode 100644 index 0000000000..f6a2730a18 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/DeltaScanRule.scala @@ -0,0 +1,619 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import java.util.Locale + +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.expressions.{Add, Alias, Attribute, AttributeReference, Coalesce, EqualTo, Expression, InputFileBlockLength, InputFileBlockStart, InputFileName, Literal} +import org.apache.spark.sql.catalyst.trees.TreeNodeTag +import org.apache.spark.sql.comet.CometScanExec +import org.apache.spark.sql.execution.{FileSourceScanExec, FilterExec, ProjectExec, SparkPlan} +import org.apache.spark.sql.execution.datasources.HadoopFsRelation +import org.apache.spark.sql.execution.datasources.parquet.ParquetUtils +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{ArrayType, DataType, LongType, MapType, StructField, StructType} + +import org.apache.comet.CometConf +import org.apache.comet.CometSparkSessionExtensions.withInfo +import org.apache.comet.parquet.CometParquetUtils.{encryptionEnabled, isEncryptionConfigSupported} +import org.apache.comet.rules.CometScanRule + +/** + * `CometScanRuleExtension` for Delta tables. + * + * Three responsibilities, ported from the pre-SPI `delta-kernel-phase-1` branch's in-core + * `CometScanRule.scala`: + * + * - [[preTransform]] runs `stripDeltaDvWrappers` -- undoes Delta's `PreprocessTableWithDVs` + * Catalyst-strategy rewrite for DV-bearing scans so the clean scan reaches [[transformV1]]. + * Some scans must stay Spark-native (Delta's reader synthesises a + * `__delta_internal_is_row_deleted` column Comet's reader can't); those are tagged with + * [[DvProtectedTag]] for `transformV1` to decline. + * - [[matchesV1]] probes the relation's file format via reflection (no compile-time + * `io.delta.spark` dependency required). + * - [[transformV1]] runs `nativeDeltaScan`: schema / encryption / parquet-field-ID gates, + * column-mapping metadata re-attachment, row-tracking rewrite, and finally + * `CometScanExec(scan, session, CometDeltaNativeScan.ScanImpl)`. [[CometExecRule]] picks up + * the marker via [[DeltaOperatorSerdeExtension.matchOperator]] and routes it through + * [[CometDeltaNativeScan]]. + * + * SPI surfaces used: + * - `CometScanRule.isSchemaSupported` (private[comet]) -- avoids duplicating ~25 lines of + * schema check + fallback-reason emission. + * - `CometParquetUtils.{encryptionEnabled, isEncryptionConfigSupported}` -- same. + * - `CometSparkSessionExtensions.withInfo` -- same. + * - Spark TreeNodeTag for cross-method (preTransform -> transformV1) state passing. + * + * The mutable.Set[FileSourceScanExec] of dv-protected scans on the pre-SPI branch is replaced + * with the TreeNodeTag mechanism, which is the SPI's documented pattern. + */ +/** + * Static entry points for Delta scan detection / transformation. Called via reflection + * from core's `org.apache.comet.rules.DeltaIntegration` only when the contrib's classes + * are bundled into `comet-spark.jar` (i.e. when Maven was invoked with `-Pcontrib-delta`). + */ +object DeltaScanRule { + + import DeltaScanRuleExtension._ + + /** Convenience: returns `Some(plan)` if this is a Delta scan we handled. */ + def transformV1IfDelta( + plan: SparkPlan, + session: SparkSession, + scanExec: FileSourceScanExec, + relation: HadoopFsRelation): Option[SparkPlan] = { + if (!isDeltaRelation(relation)) return None + val pre = preTransform(plan, session) + val target = pre.find(_.fastEquals(scanExec)).getOrElse(scanExec).asInstanceOf[FileSourceScanExec] + transformV1(pre, target, session) + } + + def preTransform(plan: SparkPlan, session: SparkSession): SparkPlan = { + if (!DeltaConf.COMET_DELTA_NATIVE_ENABLED.get()) return plan + stripDeltaDvWrappers(plan) + } + + /** + * True when the relation is a Delta scan, accounting for two shapes Delta's + * planning strategies produce: (a) `DeltaParquetFileFormat` (the direct shape, no + * strategy rewrite), and (b) plain `ParquetFileFormat` over a Delta-internal + * FileIndex like `PreparedDeltaFileIndex` (the post-`PreprocessTableWithDVs` + * shape used for DV / row-tracking / synthetic-column reads). + */ + private def isDeltaRelation(relation: HadoopFsRelation): Boolean = { + DeltaReflection.isDeltaFileFormat(relation.fileFormat) || + DeltaReflection.isBatchFileIndex(relation.location) + } + + + private def stripDeltaDvWrappers(plan: SparkPlan): SparkPlan = { + plan.transformUp { + case proj @ ProjectExec(projectList, FilterExec(cond, inner)) + if isDeltaDvFilterPattern(cond) => + val userOutput = projectList.map(_.toAttribute) + if (scanBelowFallsBackForDvs(inner)) { + collectDeltaScanBelow(inner).foreach(_.setTagValue(DvProtectedTag, ())) + proj + } else { + findAndStripDeltaScanBelow(inner, userOutput).getOrElse(proj) + } + } + } + + private def collectDeltaScanBelow(plan: SparkPlan): Option[FileSourceScanExec] = plan match { + case scan: FileSourceScanExec + if DeltaReflection.isDeltaFileFormat(scan.relation.fileFormat) || + DeltaReflection.isBatchFileIndex(scan.relation.location) => + // Either the fileFormat is `DeltaParquetFileFormat`, OR Delta's + // `PreprocessTableWithDVs` strategy has already rewritten the scan to + // plain `ParquetFileFormat` over a Delta-internal FileIndex (e.g. + // `PreparedDeltaFileIndex`). Both shapes are Delta-originating. + Some(scan) + case other if other.children.size == 1 => collectDeltaScanBelow(other.children.head) + case _ => None + } + + /** + * True when the child subtree contains a Delta `FileSourceScanExec` Comet's native path will + * not apply the DV on. Two shapes both fall back: `TahoeBatchFileIndex` with DV-bearing + * AddFiles, and any Delta scan whose schema already contains the synthetic + * `__delta_internal_is_row_deleted` column. + */ + private def scanBelowFallsBackForDvs(plan: SparkPlan): Boolean = { + def check(p: SparkPlan): Boolean = p match { + case scan: FileSourceScanExec + if DeltaReflection.isDeltaFileFormat(scan.relation.fileFormat) => + // Both prior fallback cases are now handled natively: + // - `outputHasIsRowDeleted`: native synthesis via #144 + // (DeltaSyntheticColumnsExec emits the column). + // - `batchFallback` (TahoeBatchFileIndex with DVs): the native path + // materialises DVs from pre-resolved AddFiles via + // `buildTaskListFromAddFiles` + `deletedRowIndexesByPath`. The convert + // path declines internally only when DV materialisation itself fails + // (CometDeltaNativeScan.scala:479-484), which is the precise failure + // mode that warrants a fallback -- not the structural "scan has a + // batch index" check this method used to apply unconditionally. + false + case other if other.children.size == 1 => check(other.children.head) + case _ => false + } + check(plan) + } + + /** Matches `__delta_internal_is_row_deleted = 0` (the filter Delta injects). */ + private def isDeltaDvFilterPattern(cond: Expression): Boolean = { + def isRowDeletedRef(name: String): Boolean = + name.equalsIgnoreCase(DeltaReflection.IsRowDeletedColumnName) + cond match { + case EqualTo(attr: AttributeReference, lit: Literal) if isRowDeletedRef(attr.name) => + lit.value != null && lit.value.toString == "0" + case EqualTo(lit: Literal, attr: AttributeReference) if isRowDeletedRef(attr.name) => + lit.value != null && lit.value.toString == "0" + case _ => false + } + } + + private def findAndStripDeltaScanBelow( + plan: SparkPlan, + userOutput: Seq[Attribute]): Option[SparkPlan] = plan match { + case scan: FileSourceScanExec + if (DeltaReflection.isDeltaFileFormat(scan.relation.fileFormat) || + DeltaReflection.isBatchFileIndex(scan.relation.location)) && + scan.output.exists(_.name.equalsIgnoreCase(DeltaReflection.IsRowDeletedColumnName)) => + Some(rebuildDeltaScanWithoutDvColumn(scan, userOutput)) + case other if other.children.size == 1 => + findAndStripDeltaScanBelow(other.children.head, userOutput) + case _ => None + } + + private def rebuildDeltaScanWithoutDvColumn( + scan: FileSourceScanExec, + userOutput: Seq[Attribute]): FileSourceScanExec = { + val dvName = DeltaReflection.IsRowDeletedColumnName + val scanByExprId = scan.output.map(a => a.exprId -> a).toMap + val scanByName = scan.output.map(a => a.name.toLowerCase(Locale.ROOT) -> a).toMap + val resolved = userOutput.map { u => + scanByExprId + .get(u.exprId) + .orElse(scanByName.get(u.name.toLowerCase(Locale.ROOT))) + .getOrElse(u) + } + val newOutput = resolved.filterNot(_.name == dvName) + val newRequiredSchema = + StructType(newOutput.map(a => StructField(a.name, a.dataType, a.nullable))) + val newDataSchema = + StructType(scan.relation.dataSchema.fields.filterNot(_.name == dvName)) + val newRelation = scan.relation.copy(dataSchema = newDataSchema)(scan.relation.sparkSession) + val newDataFilters = scan.dataFilters.filterNot { f => + f.references.exists(_.name == dvName) + } + scan.copy( + relation = newRelation, + output = newOutput, + requiredSchema = newRequiredSchema, + dataFilters = newDataFilters) + } + + // =========================================================================== + // transformV1: nativeDeltaScan body. + // =========================================================================== + + def transformV1( + plan: SparkPlan, + scanExec: FileSourceScanExec, + session: SparkSession): Option[SparkPlan] = { + if (scanExec.getTagValue(DvProtectedTag).isDefined) { + withInfo( + scanExec, + "Leaving scan to Delta so its DV filter above can apply deletion vectors") + return None + } + // Detect references to `input_file_name()` / `input_file_block_*` anywhere in the + // surrounding plan tree. When present, the contrib's serde MUST emit one task per + // partition so `CometExecRDD`'s per-partition `InputFileBlockHolder` hook attributes + // every row to the correct file path. Delta's UPDATE/DELETE/MERGE flows use + // `input_file_name()` to find "touched files"; without this tag, multiple files + // packed into one Spark partition share the FIRST task's file path, and Delta + // rewrites the wrong files (or fails to find rows to rewrite at all). Triggered by + // tests like `UpdateBaseMiscTests "data and partition predicates - + // Partition=true Skipping=false"` which has multiple files in one partition. + val needsInputFileName = plan.exists { node => + node.expressions.exists(_.exists { + case _: InputFileName | _: InputFileBlockStart | _: InputFileBlockLength => true + case _ => false + }) + } + val scanForDelta = if (needsInputFileName) { + val taggedOptions = scanExec.relation.options + + (DeltaConf.NeedsInputFileNameOption -> "true") + val taggedRelation = scanExec.relation.copy(options = taggedOptions)( + scanExec.relation.sparkSession) + scanExec.copy(relation = taggedRelation) + } else scanExec + nativeDeltaScan(session, scanForDelta, scanForDelta.relation) + } + + private def nativeDeltaScan( + session: SparkSession, + scanExec: FileSourceScanExec, + r: HadoopFsRelation): Option[SparkPlan] = { + if (!DeltaConf.COMET_DELTA_NATIVE_ENABLED.get()) { + withInfo( + scanExec, + s"Native Delta scan disabled because ${DeltaConf.COMET_DELTA_NATIVE_ENABLED.key} " + + "is not enabled") + return None + } + if (!CometConf.COMET_EXEC_ENABLED.get()) { + withInfo( + scanExec, + s"Native Delta scan requires ${CometConf.COMET_EXEC_ENABLED.key} to be enabled") + return None + } + val hadoopConf = r.sparkSession.sessionState.newHadoopConfWithOptions(r.options) + if (encryptionEnabled(hadoopConf) && !isEncryptionConfigSupported(hadoopConf)) { + withInfo(scanExec, s"${CometDeltaNativeScan.ScanImpl} does not support encryption config") + return None + } + // CometScanRule.isSchemaSupported is private[comet]; inline the equivalent check + // (schema check + fallback-reason emission) for the contrib's needs. + if (!isSchemaCometCompatible(scanExec, r)) { + return None + } + // General-purpose Parquet field-ID matching is now wired through the same path as + // CM-id mode (#142 commit 7ace165e). When `spark.sql.parquet.fieldId.read.enabled` + // is true and `scan.requiredSchema` carries the standard `parquet.field.id` + // metadata, `CometDeltaNativeScan.convert` propagates field IDs into the proto via + // `serializeDataType`'s StructType arm (which reads `ParquetUtils.hasFieldId`). + // The convert path also sets `use_field_id=true` so the native parquet reader + // matches by ID. No gate needed. + val cmMode = DeltaReflection + .extractMetadataConfiguration(r) + .flatMap(_.get("delta.columnMapping.mode")) + // Column mapping `id` mode is now wired: `CometDeltaNativeScan.convert` translates + // Delta's `delta.columnMapping.id` -> `parquet.field.id` on every StructField and + // sets `DeltaScanCommon.use_field_id = true`, which routes the native parquet reader + // through `schema_adapter.rs` field-ID matching. No gate needed. + // `checkLatestSchemaOnRead` controls whether Delta's reader does an at-read-time + // consistency check between the cached DataFrame schema and the latest snapshot. + // Our native path doesn't do a separate at-read check -- both `column_mappings` and + // the parquet reads are pinned to the version we get from + // `DeltaReflection.extractSnapshotVersion(relation)` (i.e. the SAME cached snapshot + // Spark/Delta used to build scan.requiredSchema). So we're internally consistent + // regardless of the flag; the user's choice to disable the check only affects + // Delta's own at-read validation, which we don't perform. No gate needed. + // Databricks-proprietary file-index variant. The class is not in OSS Delta -- it + // only exists when running against Databricks Runtime's Delta fork. We don't have + // an OSS reproducer for its behavior so we conservatively fall back to Spark's + // Delta reader rather than risk reading via an unknown index that may rely on + // DBR-only cloud-fetch APIs. If/when this variant is upstreamed (or a customer + // surfaces a need with adequate test coverage), revisit. + val fileIndexClassName = r.location.getClass.getName + if (fileIndexClassName.endsWith(".TahoeLogFileIndexWithCloudFetch")) { + withInfo( + scanExec, + s"Native Delta scan has not validated the cloud-fetch variant ($fileIndexClassName).") + return None + } + val supportedSchemes = + Set("file", "s3", "s3a", "gs", "gcs", "abfss", "abfs", "wasbs", "wasb", "oss") + val rootPaths = scanExec.relation.location.rootPaths + if (rootPaths.nonEmpty) { + val schemes = rootPaths.map(p => p.toUri.getScheme).filter(_ != null).toSet + val unsupported = schemes -- supportedSchemes + if (unsupported.nonEmpty) { + withInfo( + scanExec, + s"Native Delta scan does not support filesystem schemes: " + unsupported.mkString(", ")) + return None + } + } + if (r.location.getClass.getName.contains("PreparedDeltaFileIndex")) { + try { + val sample = r.location.inputFiles.take(2) + sample.foreach { p => + val colonSlash = p.indexOf(":/") + if (colonSlash >= 0) { + val afterColon = p.substring(colonSlash + 1) + val scheme = p.substring(0, colonSlash) + if (!afterColon.startsWith("//") && scheme != "file") { + withInfo( + scanExec, + s"Native Delta scan declines: file path '$p' uses malformed URL form " + + s"'$scheme:/...' (real URLs are 'scheme://...'); likely a test-only " + + s"shallow-clone mock or cross-filesystem clone our reader can't open.") + return None + } + } + } + } catch { + case scala.util.control.NonFatal(_) => // best-effort; fall through + } + } + val scanWithMappedSchema = withDeltaColumnMappingMetadata(scanExec) + // Delta's `__delta_internal_row_index` / `__delta_internal_is_row_deleted` synthetic + // columns are now synthesised natively via `DeltaSyntheticColumnsExec` -- see + // CometDeltaNativeScan.convert for the schema stripping + proto emit flags, and + // contrib/delta/native/src/synthetic_columns.rs for the exec. + applyRowTrackingRewrite(scanWithMappedSchema, r, session).getOrElse { + Some(CometScanExec(scanWithMappedSchema, session, CometDeltaNativeScan.ScanImpl)) + } + } + + private def withDeltaColumnMappingMetadata(scanExec: FileSourceScanExec): FileSourceScanExec = { + val r = scanExec.relation + val snapshotSchemaOpt = DeltaReflection.extractSnapshotSchema(r) + if (snapshotSchemaOpt.isEmpty) return scanExec + val snapshotByName: Map[String, StructField] = + snapshotSchemaOpt.get.fields.map(f => f.name -> f).toMap + def attach(f: StructField): StructField = + snapshotByName.get(f.name) match { + case Some(meta) => + StructField( + f.name, + attachDataType(f.dataType, meta.dataType), + f.nullable, + meta.metadata) + case None => f + } + def attachDataType(child: DataType, withMeta: DataType): DataType = (child, withMeta) match { + case (cs: StructType, ms: StructType) => + val metaByName = ms.fields.map(f => f.name -> f).toMap + StructType(cs.fields.map { f => + metaByName.get(f.name) match { + case Some(mf) => + StructField( + f.name, + attachDataType(f.dataType, mf.dataType), + f.nullable, + mf.metadata) + case None => f + } + }) + case (ca: ArrayType, ma: ArrayType) => + ArrayType(attachDataType(ca.elementType, ma.elementType), ca.containsNull) + case (cm: MapType, mm: MapType) => + MapType( + attachDataType(cm.keyType, mm.keyType), + attachDataType(cm.valueType, mm.valueType), + cm.valueContainsNull) + case _ => child + } + val newDataFields = r.dataSchema.fields.map(attach) + val newRequiredFields = scanExec.requiredSchema.fields.map(attach) + val anyChange = !newDataFields.sameElements(r.dataSchema.fields) || + !newRequiredFields.sameElements(scanExec.requiredSchema.fields) + if (!anyChange) return scanExec + val newRelation = r.copy(dataSchema = StructType(newDataFields))(r.sparkSession) + scanExec.copy(relation = newRelation, requiredSchema = StructType(newRequiredFields)) + } + + /** + * Returns `Some(Some(plan))` when a row-tracking rewrite was applied, `Some(None)` when we + * detected row-tracking columns we can't translate, and `None` when the scan has no + * row-tracking columns. Caller uses the outer Option to distinguish "applied" / "decline" / "no + * rewrite needed". + */ + private def applyRowTrackingRewrite( + scanExec: FileSourceScanExec, + r: HadoopFsRelation, + session: SparkSession): Option[Option[SparkPlan]] = { + val RowIdName = DeltaReflection.RowIdColumnName + val RowCommitVersionName = DeltaReflection.RowCommitVersionColumnName + val hasRowIdField = scanExec.requiredSchema.fieldNames.exists { n => + n.equalsIgnoreCase(RowIdName) || n.equalsIgnoreCase(RowCommitVersionName) + } + if (!hasRowIdField) return None + + val cfg = DeltaReflection.extractMetadataConfiguration(r).getOrElse(Map.empty) + // When `delta.enableRowTracking=false`, the table doesn't track rows so + // AddFile.baseRowId and AddFile.defaultRowCommitVersion are absent. Our native + // synthesis path (DeltaSyntheticColumnsExec) handles this by emitting NULL row_id + // and row_commit_version columns when the per-file base_row_id is None. That + // matches Delta's own behaviour for these tables -- the column is queryable but + // returns null. So we just fall through to nativeDeltaScan; CometDeltaNativeScan.convert + // will detect the columns in scan.requiredSchema and set emit flags. + if (cfg.get("delta.enableRowTracking").exists(_.equalsIgnoreCase("false"))) { + return None + } + val rowIdPhysical = cfg.get(DeltaReflection.MaterializedRowIdColumnProp) + val rowVerPhysical = cfg.get(DeltaReflection.MaterializedRowCommitVersionColumnProp) + if (rowIdPhysical.isEmpty && rowVerPhysical.isEmpty) { + // No materialised columns -- synthesise row_id (= baseRowId + physical row index) + // and row_commit_version (= defaultRowCommitVersion) natively via + // `DeltaSyntheticColumnsExec`. The synthesis path runs through the normal + // CometDeltaNativeScan.convert flow with the standard `nativeDeltaScan` apply + // (no rewrite needed here -- convert() detects the row_id / row_commit_version + // columns in scan.requiredSchema and sets the proto emit flags). + return None + } + + val renames = scala.collection.mutable.ArrayBuffer.empty[(String, String)] + def physicalFor(logical: String): Option[String] = + if (logical.equalsIgnoreCase(RowIdName)) rowIdPhysical + else if (logical.equalsIgnoreCase(RowCommitVersionName)) rowVerPhysical + else None + + val newRequiredFields = scanExec.requiredSchema.fields.map { f => + physicalFor(f.name) match { + case Some(phys) => + renames += ((f.name, phys)) + StructField(phys, f.dataType, nullable = true, f.metadata) + case None => f + } + } + val newDataFields = r.dataSchema.fields.map { f => + physicalFor(f.name) match { + case Some(phys) => StructField(phys, f.dataType, nullable = true, f.metadata) + case None => f + } + } + if (renames.isEmpty) return None + + val RowIndexColName = "_tmp_metadata_row_index" + val BaseRowIdColName = "__comet_base_row_id" + val DefaultRowCommitVersionColName = "__comet_default_row_commit_version" + val includeRowIdSynth = renames.exists { case (logical, _) => + logical.equalsIgnoreCase(RowIdName) + } + val includeRowVerSynth = renames.exists { case (logical, _) => + logical.equalsIgnoreCase(RowCommitVersionName) + } + val needSynth = includeRowIdSynth || includeRowVerSynth + + if (needSynth) { + val existingNames = + (r.dataSchema.fieldNames ++ r.partitionSchema.fieldNames) + .map(_.toLowerCase(Locale.ROOT)) + .toSet + val syntheticNames = Seq(RowIndexColName, BaseRowIdColName, DefaultRowCommitVersionColName) + val collisions = + syntheticNames.filter(n => existingNames.contains(n.toLowerCase(Locale.ROOT))) + if (collisions.nonEmpty) { + withInfo( + scanExec, + s"Native Delta scan: table has columns that collide with Comet row-tracking " + + s"synthetic columns (${collisions.mkString(", ")}); falling back.") + return Some(None) + } + } + + val infoByFileName: Map[String, DeltaReflection.RowTrackingFileInfo] = + if (needSynth) DeltaReflection.extractRowTrackingInfoByFileName(r.location) + else Map.empty + + val extraRequiredFields = scala.collection.mutable.ArrayBuffer.empty[StructField] + val extraDataFields = scala.collection.mutable.ArrayBuffer.empty[StructField] + if (includeRowIdSynth) { + val rowIndexField = StructField(RowIndexColName, LongType, nullable = true) + extraRequiredFields += rowIndexField + extraDataFields += rowIndexField + } + + val finalRequiredSchema = StructType(newRequiredFields ++ extraRequiredFields) + val finalDataSchema = StructType(newDataFields ++ extraDataFields) + val finalLocation = + if (needSynth) { + new RowTrackingAugmentedFileIndex( + r.location, + infoByFileName, + BaseRowIdColName, + DefaultRowCommitVersionColName) + } else { + r.location + } + + val origOutput = scanExec.output + val renameMap: Map[String, String] = renames.toMap + val baseNewOutput = origOutput.map { a => + renameMap.get(a.name) match { + case Some(phys) => + AttributeReference(phys, a.dataType, nullable = true, a.metadata)(qualifier = + a.qualifier) + case None => a + } + } + val rowIndexAttr = AttributeReference(RowIndexColName, LongType, nullable = true)() + val baseRowIdAttr = AttributeReference(BaseRowIdColName, LongType, nullable = true)() + val defaultVerAttr = + AttributeReference(DefaultRowCommitVersionColName, LongType, nullable = true)() + val extraOutputAttrs = scala.collection.mutable.ArrayBuffer.empty[AttributeReference] + if (includeRowIdSynth) extraOutputAttrs += rowIndexAttr + if (needSynth) { + extraOutputAttrs += baseRowIdAttr + extraOutputAttrs += defaultVerAttr + } + val newOutput: Seq[Attribute] = + if (extraOutputAttrs.isEmpty) baseNewOutput + else baseNewOutput ++ extraOutputAttrs + + val newPartitionSchema = + if (needSynth) { + r.partitionSchema + .add(StructField(BaseRowIdColName, LongType, nullable = true)) + .add(StructField(DefaultRowCommitVersionColName, LongType, nullable = true)) + } else { + r.partitionSchema + } + + val newRelation = r.copy( + location = finalLocation, + dataSchema = finalDataSchema, + partitionSchema = newPartitionSchema)(r.sparkSession) + val newScan = scanExec.copy( + relation = newRelation, + output = newOutput, + requiredSchema = finalRequiredSchema) + val cometScan = CometScanExec(newScan, session, CometDeltaNativeScan.ScanImpl) + + val projectExprs = origOutput.map { a => + renameMap.get(a.name).flatMap(phys => baseNewOutput.find(_.name == phys)) match { + case Some(physAttr) if a.name.equalsIgnoreCase(RowIdName) && includeRowIdSynth => + val synth = Add(baseRowIdAttr, rowIndexAttr) + Alias(Coalesce(Seq(physAttr, synth)), a.name)( + exprId = a.exprId, + qualifier = a.qualifier) + case Some(physAttr) + if a.name.equalsIgnoreCase(RowCommitVersionName) && includeRowVerSynth => + Alias(Coalesce(Seq(physAttr, defaultVerAttr)), a.name)( + exprId = a.exprId, + qualifier = a.qualifier) + case Some(physAttr) => + Alias(physAttr, a.name)(exprId = a.exprId, qualifier = a.qualifier) + case None => a + } + } + Some(Some(ProjectExec(projectExprs, cometScan))) + } + + /** + * Inline schema check + fallback-reason emission, mirroring core's + * `private[comet] CometScanRule.isSchemaSupported`. Kept local to the contrib so the + * contrib doesn't need to widen core's visibility. + */ + // Reused across scans -- CometScanTypeChecker is stateless w.r.t. its scanImpl and + // doesn't mutate per-call state; the per-scan fallback-reasons ListBuffer is the only + // per-call mutable input. + private val typeChecker = + org.apache.comet.rules.CometScanTypeChecker(CometDeltaNativeScan.ScanImpl) + + private def isSchemaCometCompatible( + scanExec: FileSourceScanExec, + r: HadoopFsRelation): Boolean = { + val fallbackReasons = new scala.collection.mutable.ListBuffer[String]() + val ok = typeChecker.isSchemaSupported(scanExec.requiredSchema, fallbackReasons) && + typeChecker.isSchemaSupported(r.partitionSchema, fallbackReasons) + if (!ok) withInfo(scanExec, fallbackReasons.mkString("; ")) + ok + } +} + +/** Companion holding plan-tree tags used by the static `DeltaScanRule` object. */ +object DeltaScanRuleExtension { + + /** + * Plan-tree tag attached during `preTransform` to mark `FileSourceScanExec`s whose native + * conversion `transformV1` must decline -- Comet's reader can't produce the + * `__delta_internal_is_row_deleted` column the outer DV-filter wrapper requires. + */ + val DvProtectedTag: TreeNodeTag[Unit] = + TreeNodeTag[Unit]("org.apache.comet.contrib.delta.dv_protected") +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/Native.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/Native.scala new file mode 100644 index 0000000000..dc2f714fd4 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/Native.scala @@ -0,0 +1,61 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.comet.NativeBase + +/** + * Contrib-local JVM handle to the Delta-specific native entry point. + * + * Extends `NativeBase` so the libcomet load triggers on first use of any subclass -- the contrib + * doesn't reload the library itself (there is exactly one libcomet at runtime), but inheriting + * from `NativeBase` ensures the static initializer ordering works the same way as core's + * `org.apache.comet.Native`. The `@native` method below binds to + * `Java_org_apache_comet_contrib_delta_Native_planDeltaScan` exported by the contrib's Rust crate + * (compiled INTO libcomet via the `contrib-delta` Cargo feature on `native/core`). + */ +class Native extends NativeBase { + + /** + * Driver-side Delta log replay. Returns a prost-encoded `DeltaScanTaskList` proto (raw bytes) + * which the caller decodes via `DeltaScanTaskList.parseFrom(...)`. + * + * @param tableUrl + * absolute URL or bare path of the Delta table root + * @param snapshotVersion + * `-1` for the latest snapshot, otherwise an exact version + * @param storageOptions + * cloud credentials / endpoint overrides (Hadoop-style keys) + * @param predicateBytes + * prost-encoded Catalyst data filter for kernel-side stats-based file pruning, or an empty + * array for no predicate + * @param columnNames + * logical column names the caller requires (kernel uses this for column-mapping resolution + * before stats-based file pruning). + * @return + * `byte[]` containing the encoded DeltaScanTaskList + */ + @native def planDeltaScan( + tableUrl: String, + snapshotVersion: Long, + storageOptions: java.util.Map[String, String], + predicateBytes: Array[Byte], + columnNames: Array[String]): Array[Byte] +} diff --git a/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/RowTrackingAugmentedFileIndex.scala b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/RowTrackingAugmentedFileIndex.scala new file mode 100644 index 0000000000..e58ef557e3 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/comet/contrib/delta/RowTrackingAugmentedFileIndex.scala @@ -0,0 +1,113 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.hadoop.fs.Path +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Expression, GenericInternalRow} +import org.apache.spark.sql.execution.datasources.{FileIndex, PartitionDirectory} +import org.apache.spark.sql.types.{LongType, StructField, StructType} + +/** + * Wraps a Delta `FileIndex` and augments each per-file `PartitionDirectory` with two synthetic + * partition values drawn from the corresponding Delta `AddFile`: + * + * - `baseRowIdColumnName` <- `AddFile.baseRowId` + * - `defaultRowCommitVersionColumnName` <- `AddFile.defaultRowCommitVersion` + * + * Used by Comet's row-tracking phase 3: `_row_id_` and `_row_commit_version_` values that are + * still null in their materialised physical columns get synthesised by the outer Project as: + * + * row_id = coalesce(materialised_row_id, baseRowIdCol + _tmp_metadata_row_index) + * row_commit_version = coalesce(materialised_row_commit_version, defaultRowCommitVersionCol) + * + * once the scan can see the per-file values as constant columns. + * + * The map from file basename (tail of `AddFile.path`) to `RowTrackingFileInfo` is supplied by the + * caller (via reflection on the delegate's `matchingFiles` API) so we don't need a compile-time + * dep on spark-delta. Each listed directory entry is split into one `PartitionDirectory` per file + * so each file's values travel with it. + */ +class RowTrackingAugmentedFileIndex( + delegate: FileIndex, + infoByFileName: Map[String, DeltaReflection.RowTrackingFileInfo], + baseRowIdColumnName: String, + defaultRowCommitVersionColumnName: String) + extends FileIndex { + + override def rootPaths: Seq[Path] = delegate.rootPaths + + override def inputFiles: Array[String] = delegate.inputFiles + + override def refresh(): Unit = delegate.refresh() + + override def sizeInBytes: Long = delegate.sizeInBytes + + /** Appends both synthetic columns as Long, nullable partition columns. */ + override def partitionSchema: StructType = + delegate.partitionSchema + .add(StructField(baseRowIdColumnName, LongType, nullable = true)) + .add(StructField(defaultRowCommitVersionColumnName, LongType, nullable = true)) + + /** + * Delegates listing to the underlying FileIndex, then splits each returned `PartitionDirectory` + * into one-per-file directories, each carrying the original partition values PLUS the per-file + * baseRowId and defaultRowCommitVersion. + * + * The per-file split is unavoidable for correctness: `AddFile.baseRowId` is unique per file, so + * two files that share a Delta partition cannot share a `PartitionDirectory` once we inject the + * per-file synthetic columns. Scheduling parallelism is unaffected -- `FileSourceScanExec` + * flattens all PDs' files into `PartitionedFile`s and bin-packs them by `maxSplitBytes`, so PD + * granularity only governs how partition values get serialised with each file, not the number + * of tasks. + */ + override def listFiles( + partitionFilters: Seq[Expression], + dataFilters: Seq[Expression]): Seq[PartitionDirectory] = { + val underlying = delegate.listFiles(partitionFilters, dataFilters) + underlying.flatMap { pd => + pd.files.map { fileStatus => + val info = infoByFileName.getOrElse( + fileStatus.getPath.getName, + DeltaReflection.RowTrackingFileInfo(None, None)) + // Use `pd.copy(...)` rather than `PartitionDirectory.apply(...)` so this + // compiles against both Spark 3.x (files: Seq[FileStatus]) and Spark 4.0 + // (files: Seq[FileStatusWithMetadata]) without a per-version shim -- we + // round-trip the same element type we got from `pd.files`. + pd.copy(values = augmentPartitionValues(pd.values, info), files = Seq(fileStatus)) + } + } + } + + private def augmentPartitionValues( + original: InternalRow, + info: DeltaReflection.RowTrackingFileInfo): InternalRow = { + val n = original.numFields + val values = new Array[Any](n + 2) + var i = 0 + while (i < n) { + values(i) = original.get(i, delegate.partitionSchema.fields(i).dataType) + i += 1 + } + values(n) = info.baseRowId.map(Long.box).orNull + values(n + 1) = info.defaultRowCommitVersion.map(Long.box).orNull + new GenericInternalRow(values) + } +} diff --git a/contrib/delta/src/main/scala/org/apache/spark/sql/comet/CometDeltaNativeScanExec.scala b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/CometDeltaNativeScanExec.scala new file mode 100644 index 0000000000..b22cf30a0d --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/CometDeltaNativeScanExec.scala @@ -0,0 +1,452 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.spark.sql.comet + +import java.util.Locale + +import scala.jdk.CollectionConverters._ + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans.QueryPlan +import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning} +import org.apache.spark.sql.execution.{FileSourceScanExec, InSubqueryExec} +import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ColumnarBatch +import org.apache.spark.util.AccumulatorV2 + +import com.google.common.base.Objects + +import org.apache.comet.serde.OperatorOuterClass +import org.apache.comet.serde.OperatorOuterClass.Operator + +/** + * Native Delta Lake scan operator with split-mode serialization and DPP support. + * + * Common scan metadata (schemas, filters, projections, storage options, column mappings) is + * serialized once at planning time in `nativeOp`. Per-partition file lists are materialized + * lazily in `serializedPartitionData` at execution time so each Spark task receives only its own + * slice of the file list, reducing driver memory. + * + * DPP (Dynamic Partition Pruning) is supported by deferring partition pruning of DPP expressions + * to execution time. Static partition filters are applied at planning time in + * `CometDeltaNativeScan.prunePartitions`; DPP filters are resolved in `serializedPartitionData`. + */ +case class CometDeltaNativeScanExec( + override val nativeOp: Operator, + override val output: Seq[Attribute], + override val serializedPlanOpt: SerializedPlan, + @transient originalPlan: FileSourceScanExec, + tableRoot: String, + @transient taskListBytes: Array[Byte], + @transient dppFilters: Seq[Expression] = Seq.empty, + partitionSchema: StructType = new StructType(), + /** + * #75 design A: when true, `packTasks` emits one group (= one partition) per task so + * `CometExecRDD.compute` (via `InputFileBlockHolder.set`) correctly sets `InputFileBlockHolder` to the + * partition's only file path. Set by `CometDeltaNativeScan.createExec` when the surrounding + * plan references `input_file_name()` / `input_file_block_*`. + */ + oneTaskPerPartition: Boolean = false) + extends CometLeafExec + with org.apache.spark.sql.comet.CometScanWithPlanData { + + // Per-partition task list bytes are exposed via the public accessors below; core's + // CometExecRDD reads them directly when serialising the Comet plan for execution. + // (Was a PlanDataSource SPI implementation under PR1; the SPI was rejected so we + // expose the helpers as plain methods on this exec class.) + def planDataSourceKey: String = sourceKey + def planDataCommonBytes: Array[Byte] = commonData + def planDataPerPartitionBytes: Array[Array[Byte]] = perPartitionData + + override val supportsColumnar: Boolean = true + + override val nodeName: String = s"CometDeltaNativeScan $tableRoot" + + override protected def doPrepare(): Unit = { + dppFilters.foreach { + case DynamicPruningExpression(e: InSubqueryExec) => + e.plan.prepare() + case _ => + } + super.doPrepare() + } + + @transient private lazy val commonBytes: Array[Byte] = { + // The typed DeltaScan variant of OpStruct carries the common block directly. + nativeOp.getDeltaScan.getCommon.toByteArray + } + + @transient private lazy val allTasks: Seq[OperatorOuterClass.DeltaScanTask] = + OperatorOuterClass.DeltaScanTaskList + .parseFrom(taskListBytes) + .getTasksList + .asScala + .toSeq + + /** + * Synthesise a `Seq[FilePartition]` from this scan's tasks, with each task becoming one + * `PartitionedFile` carrying its partition values as an `InternalRow`. Delta tests (e.g. + * `DeltaSinkSuite`) inspect `executedPlan.collect[DataSourceScanExec]` and read + * `inputRDDs.head.asInstanceOf[FileScanRDD].filePartitions` to verify partition pruning; those + * tests find nothing under Comet because we replace the scan with this exec. The test diff in + * `dev/diffs/delta/.diff` patches the helper to fall back to this accessor, so the + * same partition-pruning assertions pass against Comet's scan. + */ + def synthesizedFilePartitions: Seq[org.apache.spark.sql.execution.datasources.FilePartition] = { + if (allTasks.isEmpty) return Nil + val sessionTz = java.time.ZoneId.of(SQLConf.get.sessionLocalTimeZone) + val files = allTasks.zipWithIndex.map { case (task, _) => + val pvRow = InternalRow.fromSeq(partitionSchema.fields.toSeq.map { f => + val proto = task.getPartitionValuesList.asScala.find(_.getName == f.name) + val s = if (proto.exists(_.hasValue)) Some(proto.get.getValue) else None + org.apache.comet.contrib.delta.DeltaReflection + .castPartitionString(s, f.dataType, sessionTz) + }) + val sparkPath = + org.apache.spark.paths.SparkPath.fromUrlString(task.getFilePath) + org.apache.spark.sql.execution.datasources.PartitionedFile( + partitionValues = pvRow, + filePath = sparkPath, + start = if (task.hasByteRangeStart) task.getByteRangeStart else 0L, + length = { + if (task.hasByteRangeStart && task.hasByteRangeEnd) { + task.getByteRangeEnd - task.getByteRangeStart + } else task.getFileSize + }, + modificationTime = 0L, + fileSize = task.getFileSize) + } + files.zipWithIndex.map { case (pf, i) => + org.apache.spark.sql.execution.datasources.FilePartition(i, Array(pf)) + } + } + + /** + * Build per-partition bytes from the current DPP-pruned task list. DPP filters that are still + * `SubqueryAdaptiveBroadcastExec` placeholders at planning time materialise lazily once AQE + * runs the broadcast; by recomputing this at `doExecuteColumnar` (rather than memoising the + * result in a lazy val) we pick up the resolved values and actually skip partitions, instead of + * reading the full table every time AQE is in the loop. + */ + private def buildPerPartitionBytes(): Array[Array[Byte]] = { + val tasks = + if (dppFilters.nonEmpty && partitionSchema.nonEmpty) applyDppFilters(allTasks) + else allTasks + if (tasks.isEmpty) { + Array.empty[Array[Byte]] + } else { + packTasks(tasks).map { group => + val builder = OperatorOuterClass.DeltaScan.newBuilder() + group.foreach(builder.addTasks) + builder.build().toByteArray + }.toArray + } + } + + // #75 design A: when input_file_name() is needed (signal threaded from CometScanRule + // via CometDeltaNativeScan.createExec into `oneTaskPerPartition`), short-circuit + // packing so each task gets its own partition. `CometExecRDD.compute` reads + // task[0]'s path; with 1 task per partition that path correctly attributes every row. + private def packTasks( + tasks: Seq[OperatorOuterClass.DeltaScanTask]): Seq[Seq[OperatorOuterClass.DeltaScanTask]] = { + if (oneTaskPerPartition) return tasks.map(t => Seq(t)) + val conf = originalPlan.relation.sparkSession.sessionState.conf + val openCostInBytes = conf.filesOpenCostInBytes + val maxPartitionBytes = conf.filesMaxPartitionBytes + val minPartitionNum = conf.filesMinPartitionNum + .getOrElse(originalPlan.relation.sparkSession.sparkContext.defaultParallelism) + def taskSize(t: OperatorOuterClass.DeltaScanTask): Long = { + if (t.hasByteRangeStart && t.hasByteRangeEnd) { + math.max(0L, t.getByteRangeEnd - t.getByteRangeStart) + } else t.getFileSize + } + val totalBytes = tasks.map(t => taskSize(t) + openCostInBytes).sum + val bytesPerCore = totalBytes / math.max(1, minPartitionNum) + val msb = math.min(maxPartitionBytes, math.max(openCostInBytes, bytesPerCore)) + val out = scala.collection.mutable.ArrayBuffer[Seq[OperatorOuterClass.DeltaScanTask]]() + val current = scala.collection.mutable.ArrayBuffer[OperatorOuterClass.DeltaScanTask]() + var currentSize = 0L + tasks.foreach { task => + val size = taskSize(task) + if (currentSize + size > msb && current.nonEmpty) { + out += current.toList + current.clear() + currentSize = 0L + } + current += task + currentSize += size + openCostInBytes + } + if (current.nonEmpty) out += current.toList + out.toSeq + } + + // Planning-time snapshot used by metrics, sourceKey derivations, and `numPartitions`. + // Execution-time recomputation happens inside `doExecuteColumnar`. + @transient private lazy val planningPerPartitionBytes: Array[Array[Byte]] = + buildPerPartitionBytes() + + private def applyDppFilters( + tasks: Seq[OperatorOuterClass.DeltaScanTask]): Seq[OperatorOuterClass.DeltaScanTask] = { + // If any DPP subquery is still a `SubqueryAdaptiveBroadcastExec` placeholder, + // AQE hasn't yet replaced it with the real broadcast plan. We can't execute + // it ourselves (that plan's `doExecute` throws), so skip pruning for this + // batch — the scan just reads all tasks, which is correct but slower. + val hasUnresolvedAdaptive = dppFilters.exists { + case DynamicPruningExpression(inSub: InSubqueryExec) => + inSub.plan.isInstanceOf[org.apache.spark.sql.execution.SubqueryAdaptiveBroadcastExec] + case _ => false + } + if (hasUnresolvedAdaptive) return tasks + dppFilters.foreach { + case DynamicPruningExpression(inSub: InSubqueryExec) if inSub.values().isEmpty => + inSub.updateResult() + case _ => + } + + val resolvedFilters = dppFilters.map { + case DynamicPruningExpression(e) => e + case other => other + } + if (resolvedFilters.isEmpty) return tasks + + val caseSensitive = SQLConf.get.getConf[Boolean](SQLConf.CASE_SENSITIVE) + val combined = resolvedFilters.reduce(And) + val bound = combined.transform { case a: AttributeReference => + val idx = partitionSchema.fields.indexWhere(f => + if (caseSensitive) f.name == a.name + else f.name.toLowerCase(Locale.ROOT) == a.name.toLowerCase(Locale.ROOT)) + if (idx < 0) return tasks + BoundReference(idx, partitionSchema(idx).dataType, partitionSchema(idx).nullable) + } + val predicate = InterpretedPredicate(bound) + predicate.initialize(0) + + val sessionZoneId = java.time.ZoneId.of(SQLConf.get.sessionLocalTimeZone) + tasks.filter { task => + val row = InternalRow.fromSeq(partitionSchema.fields.toSeq.map { field => + val proto = task.getPartitionValuesList.asScala.find(_.getName == field.name) + val strValue = + if (proto.exists(_.hasValue)) Some(proto.get.getValue) else None + org.apache.comet.contrib.delta.DeltaReflection + .castPartitionString(strValue, field.dataType, sessionZoneId) + }) + predicate.eval(row) + } + } + + def commonData: Array[Byte] = commonBytes + def perPartitionData: Array[Array[Byte]] = planningPerPartitionBytes + + /** + * Unique key for matching this scan's common/per-partition data to its operator in the native + * plan. Must be distinct across multiple Delta scans in the same plan tree -- e.g. a self-join + * reading two snapshot versions of the same table, where `tableRoot` alone is not unique. + * + * Derived identically in `DeltaPlanDataInjector.getKey` from the serialized `DeltaScanCommon` + * proto so the driver-side map and the executor-side lookup agree. + * + * Mirrors the pattern used by `CometNativeScanExec.sourceKey`. + */ + def sourceKey: String = CometDeltaNativeScanExec.computeSourceKey(nativeOp) + + def numPartitions: Int = perPartitionData.length + + override lazy val outputPartitioning: Partitioning = + UnknownPartitioning(math.max(1, numPartitions)) + + override lazy val outputOrdering: Seq[SortOrder] = Nil + + private class ImmutableSQLMetric(metricType: String) extends SQLMetric(metricType, 0) { + override def merge(other: AccumulatorV2[Long, Long]): Unit = {} + override def reset(): Unit = {} + } + + override lazy val metrics: Map[String, SQLMetric] = { + val taskList = + if (taskListBytes != null) { + OperatorOuterClass.DeltaScanTaskList.parseFrom(taskListBytes) + } else { + null + } + + // Key these under both the Comet-native-side name (`output_rows`, used by the metric + // collector on the native side) and the Spark streaming ProgressReporter name + // (`numOutputRows`, read by `extractSourceToNumInputRows` to populate + // `q.recentProgress.numInputRows`). Without the `numOutputRows` alias, streaming + // workloads that this scan feeds report 0 input rows per batch even when data flows + // correctly -- DeltaSourceSuiteBase.CheckProgress then fails with + // "Execute: 0 did not equal N Expected batches don't match". + val outputRowsMetric = SQLMetrics.createMetric(sparkContext, "number of output rows") + val baseMetrics = Map( + "output_rows" -> outputRowsMetric, + "numOutputRows" -> outputRowsMetric, + "num_splits" -> SQLMetrics.createMetric(sparkContext, "number of file splits processed")) + + val planningMetrics = if (taskList != null) { + val totalFiles = new ImmutableSQLMetric("sum") + totalFiles.set(taskList.getTasksCount.toLong) + sparkContext.register(totalFiles, "total files") + + val dvFiles = new ImmutableSQLMetric("sum") + dvFiles.set(taskList.getTasksList.asScala.count(!_.getDeletedRowIndexesList.isEmpty).toLong) + sparkContext.register(dvFiles, "files with deletion vectors") + + // `numFiles` alias mirrors Spark's `FileSourceScanExec` metric name so + // tests like DeltaSuite.scala "query with predicates should skip + // partitions" -- which read `metrics.get("numFiles")` to verify + // partition skipping -- find the same value on Comet's scan exec. + Map("total_files" -> totalFiles, "numFiles" -> totalFiles, "dv_files" -> dvFiles) + } else { + Map.empty[String, SQLMetric] + } + + baseMetrics ++ planningMetrics + } + + override def doExecuteColumnar(): RDD[ColumnarBatch] = { + val nativeMetrics = CometMetricNode.fromCometPlan(this) + val serializedPlan = CometExec.serializeNativePlan(nativeOp) + // Recompute DPP pruning at execution time so we pick up broadcast results AQE has now + // materialised (the lazy `planningPerPartitionBytes` was computed before AQE ran). When DPP + // is absent or was already resolved at planning time, the two arrays are identical. + val execPerPartitionBytes = buildPerPartitionBytes() + // Mirror `CometNativeScanExec`'s encryption wiring: when parquet encryption is + // enabled on the table's hadoop conf, broadcast the conf to executors and + // gather every input file path (so the parquet reader can decrypt per file). + val sparkSession = originalPlan.relation.sparkSession + val hadoopConf = sparkSession.sessionState + .newHadoopConfWithOptions(originalPlan.relation.options) + val (broadcastedHadoopConfForEncryption, encryptedFilePaths) = + if (org.apache.comet.parquet.CometParquetUtils.encryptionEnabled(hadoopConf)) { + val broadcastedConf = sparkSession.sparkContext + .broadcast(new org.apache.spark.util.SerializableConfiguration(hadoopConf)) + val paths = execPerPartitionBytes.flatMap { bytes => + OperatorOuterClass.DeltaScan.parseFrom(bytes).getTasksList.asScala.map(_.getFilePath) + }.toSeq + (Some(broadcastedConf), paths) + } else { + (None, Seq.empty[String]) + } + val baseRDD = CometExecRDD( + sparkContext, + inputRDDs = Seq.empty, + commonByKey = Map(sourceKey -> commonData), + perPartitionByKey = Map(sourceKey -> execPerPartitionBytes), + serializedPlan = serializedPlan, + numPartitions = execPerPartitionBytes.length, + numOutputCols = output.length, + nativeMetrics = nativeMetrics, + subqueries = Seq.empty, + broadcastedHadoopConfForEncryption = broadcastedHadoopConfForEncryption, + encryptedFilePaths = encryptedFilePaths) + + // InputFileBlockHolder for downstream `input_file_name()` is populated in + // `CometExecRDD.compute` (via `InputFileBlockHolder.set`) so it also fires when this scan + // is embedded inside a larger Comet native tree (where this exec's own + // `doExecuteColumnar` is bypassed in favour of the parent's). + baseRDD + } + + override def convertBlock(): CometDeltaNativeScanExec = { + val newSerializedPlan = if (serializedPlanOpt.isEmpty) { + val bytes = CometExec.serializeNativePlan(nativeOp) + SerializedPlan(Some(bytes)) + } else { + serializedPlanOpt + } + CometDeltaNativeScanExec( + nativeOp, + output, + newSerializedPlan, + originalPlan, + tableRoot, + taskListBytes, + dppFilters, + partitionSchema) + } + + override protected def doCanonicalize(): CometDeltaNativeScanExec = { + copy( + output = output.map(QueryPlan.normalizeExpressions(_, output)), + serializedPlanOpt = SerializedPlan(None), + originalPlan = null, + taskListBytes = null, + dppFilters = Seq.empty) + } + + override def stringArgs: Iterator[Any] = { + val taskCount = + if (taskListBytes != null) { + OperatorOuterClass.DeltaScanTaskList.parseFrom(taskListBytes).getTasksCount + } else { + 0 + } + val dppStr = if (dppFilters.nonEmpty) { + s", dpp=${dppFilters.mkString("[", ", ", "]")}" + } else { + "" + } + Iterator(output, s"$tableRoot ($taskCount files$dppStr)") + } + + override def equals(obj: Any): Boolean = obj match { + case other: CometDeltaNativeScanExec => + // Include `sourceKey` so two scans of the same table at different snapshot versions + // are NOT considered equal. Without this, Spark's ReuseExchangeAndSubquery rule + // collapses a self-join across versions into a single exchange and reuses v0's + // shuffle output for both sides of the join. + tableRoot == other.tableRoot && + output == other.output && + serializedPlanOpt == other.serializedPlanOpt && + sourceKey == other.sourceKey + case _ => false + } + + override def hashCode(): Int = + Objects.hashCode(tableRoot, output.asJava, serializedPlanOpt, sourceKey) +} + +object CometDeltaNativeScanExec { + + /** + * Compute a stable, per-scan unique key from a `DeltaScan` operator proto. Must be + * deterministic and identical between the driver side (`CometDeltaNativeScanExec.sourceKey`) + * and the injector side (`DeltaPlanDataInjector.getKey`). + * + * Includes `snapshot_version` so that two scans of the same table at different time-travel + * versions produce distinct keys -- otherwise `findAllPlanData` collapses their per-partition + * data into a single map entry and one scan inherits the other's file list. + */ + def computeSourceKey(nativeOp: Operator): String = { + val common = nativeOp.getDeltaScan.getCommon + val components = Seq( + common.getTableRoot, + common.getSnapshotVersion.toString, + common.getRequiredSchemaList.toString, + common.getDataFiltersList.toString, + common.getProjectionVectorList.toString, + common.getColumnMappingsList.toString) + s"${common.getSource}_${components.mkString("|").hashCode}" + } +} diff --git a/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaInputFileBlockHolder.scala b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaInputFileBlockHolder.scala new file mode 100644 index 0000000000..4a7c890259 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaInputFileBlockHolder.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.spark.sql.comet + +import org.apache.spark.TaskContext +import org.apache.spark.rdd.InputFileBlockHolder + +/** + * Thin wrapper around Spark's `InputFileBlockHolder` so the Delta contrib can populate the + * executor's input-file thread-local without trying to import a `private[spark]` symbol from + * `org.apache.comet.contrib.delta` (which would fail at scalac access-check time even though + * the underlying JVM class is public). Lives under `org.apache.spark.sql.comet` for the same + * reason `CometDeltaNativeScanExec` does -- the contrib's source-injection model lets us put + * helper classes anywhere on the classpath at build time. + * + * Public-API surface is intentionally minimal: set the file, register an unset on task + * completion, no holding of state across tasks. + */ +object DeltaInputFileBlockHolder { + + /** + * Set Spark's `InputFileBlockHolder` to the given file path and size for the duration of the + * current task. Registers a `TaskCompletionListener` (when `context` is non-null) to clear + * the thread-local on task end so the value doesn't leak into subsequent tasks on the same + * executor thread. + * + * `startOffset` is fixed at 0 — Delta partitions reference whole files; range-splitting that + * surfaces a non-zero offset would invalidate `_metadata.file_path` anyway. + */ + def set(filePath: String, fileSize: Long, context: TaskContext): Unit = { + InputFileBlockHolder.set(filePath, 0L, fileSize) + Option(context).foreach { ctx => + ctx.addTaskCompletionListener[Unit](_ => InputFileBlockHolder.unset()) + } + } +} diff --git a/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaPlanDataInjector.scala b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaPlanDataInjector.scala new file mode 100644 index 0000000000..0c11afd121 --- /dev/null +++ b/contrib/delta/src/main/scala/org/apache/spark/sql/comet/DeltaPlanDataInjector.scala @@ -0,0 +1,74 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.spark.sql.comet + +import org.apache.comet.serde.OperatorOuterClass +import org.apache.comet.serde.OperatorOuterClass.Operator + +/** + * `PlanDataInjector` for the typed `OpStruct::DeltaScan` operator. + * + * The contrib serialises the Delta scan in two parts to keep the closure sent to every + * task small: + * - At planning time `CometDeltaNativeScan.convert` emits a `DeltaScan` proto with + * the `common` block (schemas, table root, filters, ...) and NO tasks; this lands + * in the `Operator` tree as the typed variant `OpStruct.delta_scan`. + * - Per partition, `CometDeltaNativeScanExec` puts the partition's `DeltaScan` + * (tasks-only) bytes into `perPartitionByKey` under a `sourceKey` derived from + * the common block. + * + * Core's `PlanDataInjector.injectPlanData` discovers this object via the reflective + * `Class.forName("org.apache.spark.sql.comet.DeltaPlanDataInjector")` lookup added to + * `PlanDataInjector.injectors`; default builds get no DeltaPlanDataInjector class on + * the classpath and the injector list is unchanged. + * + * Without this injection the native side decodes a tasks-empty `DeltaScan` -> `EmptyExec` + * (0 rows) for every Delta scan. + */ +object DeltaPlanDataInjector extends PlanDataInjector { + + override val opStructCase: Operator.OpStructCase = Operator.OpStructCase.DELTA_SCAN + + override def canInject(op: Operator): Boolean = { + if (!op.hasDeltaScan) return false + // The common-only proto produced at planning time has zero tasks. After injection + // the operator carries the partition's tasks -- skip those (idempotent canInject). + op.getDeltaScan.getTasksCount == 0 + } + + override def getKey(op: Operator): Option[String] = + Some(CometDeltaNativeScanExec.computeSourceKey(op)) + + override def inject( + op: Operator, + commonBytes: Array[Byte], + partitionBytes: Array[Byte]): Operator = { + // `partitionBytes` is the serialised `DeltaScan` that packs only this partition's + // tasks (no common block) to avoid duplicating schemas across partitions. Splice + // the partition's tasks into the original common-only envelope. + val tasksOnlyScan = OperatorOuterClass.DeltaScan.parseFrom(partitionBytes) + val originalScan = op.getDeltaScan + val mergedScan = OperatorOuterClass.DeltaScan + .newBuilder(originalScan) + .addAllTasks(tasksOnlyScan.getTasksList) + .build() + op.toBuilder.setDeltaScan(mergedScan).build() + } +} diff --git a/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaColumnMappingSuite.scala b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaColumnMappingSuite.scala new file mode 100644 index 0000000000..a2c7f2e45b --- /dev/null +++ b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaColumnMappingSuite.scala @@ -0,0 +1,200 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.spark.sql.comet.CometDeltaNativeScanExec + +/** + * Column mapping (name + id modes) and deletion-vector coverage. Ported from + * the pre-SPI `delta-kernel-phase-1` branch. + */ +class CometDeltaColumnMappingSuite extends CometDeltaTestBase { + + test("deletion vectors: accelerates DV-in-use tables via native DV filter") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("dv_accel") { tablePath => + val ss = spark + import ss.implicits._ + + (0 until 20) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .repartition(1) + .write + .format("delta") + .option("delta.enableDeletionVectors", "true") + .option("delta.minReaderVersion", "3") + .option("delta.minWriterVersion", "7") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + + spark.sql(s"DELETE FROM delta.`$tablePath` WHERE id % 3 = 0") + + // orderBy forces AQE wrapping so Comet's prep rules see the plan. + val df = spark.read.format("delta").load(tablePath).orderBy("id") + val plan = df.queryExecution.executedPlan + val deltaScans = collect(plan) { case s: CometDeltaNativeScanExec => s } + assert( + deltaScans.nonEmpty, + s"expected Comet to accelerate a DV-in-use table:\n$plan") + + val nativeRows = df.collect().toSeq.map(normalizeRow) + withSQLConf("spark.comet.scan.deltaNative.enabled" -> "false") { + val vanillaRows = spark.read + .format("delta") + .load(tablePath) + .collect() + .toSeq + .map(normalizeRow) + assert( + nativeRows.sortBy(_.mkString("|")) == vanillaRows.sortBy(_.mkString("|")), + s"native=$nativeRows\nvanilla=$vanillaRows") + } + assert(nativeRows.size == 13, s"expected 13 rows after DELETE, got ${nativeRows.size}") + + spark.sql(s"DELETE FROM delta.`$tablePath` WHERE id >= 18") + val df2 = spark.read.format("delta").load(tablePath) + val rows2 = df2.collect().toSeq.map(normalizeRow) + assert(rows2.size == 12, s"expected 12 rows after second DELETE, got ${rows2.size}") + val plan2 = df2.queryExecution.executedPlan + assert( + collect(plan2) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet to still accelerate after second DELETE:\n$plan2") + } + } + + test("column mapping: name mode read after rename") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("col_mapping_name") { tablePath => + val ss = spark + import ss.implicits._ + + (0 until 8) + .map(i => (i.toLong, s"name_$i", i * 1.5)) + .toDF("id", "name", "score") + .write + .format("delta") + .option("delta.columnMapping.mode", "name") + .option("delta.minReaderVersion", "2") + .option("delta.minWriterVersion", "5") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + + spark.sql(s"ALTER TABLE delta.`$tablePath` RENAME COLUMN name TO full_name") + + assertDeltaNativeMatches(tablePath, identity) + assertDeltaNativeMatches(tablePath, _.select("id", "full_name")) + } + } + + test("column mapping: id mode") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("col_mapping_id") { tablePath => + val ss = spark + import ss.implicits._ + + (0 until 6) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .write + .format("delta") + .option("delta.columnMapping.mode", "id") + .option("delta.minReaderVersion", "2") + .option("delta.minWriterVersion", "5") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + assertDeltaNativeMatches(tablePath, _.where("id > 2")) + } + } + + test("column mapping + deletion vectors combined") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("col_map_dv") { tablePath => + val ss = spark + import ss.implicits._ + + (0 until 20) + .map(i => (i.toLong, s"name_$i", i * 1.5)) + .toDF("id", "name", "score") + .repartition(1) + .write + .format("delta") + .option("delta.columnMapping.mode", "name") + .option("delta.minReaderVersion", "3") + .option("delta.minWriterVersion", "7") + .option("delta.enableDeletionVectors", "true") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + + spark.sql(s"ALTER TABLE delta.`$tablePath` RENAME COLUMN name TO full_name") + withSQLConf("spark.databricks.delta.deletionVectors.useMetadataRowIndex" -> "false") { + spark.sql(s"DELETE FROM delta.`$tablePath` WHERE id % 4 = 0") + val df = spark.read.format("delta").load(tablePath) + val nativeRows = df.collect().toSeq.map(normalizeRow) + withSQLConf("spark.comet.scan.deltaNative.enabled" -> "false") { + val vanillaRows = spark.read + .format("delta") + .load(tablePath) + .collect() + .toSeq + .map(normalizeRow) + assert( + nativeRows.sortBy(_.mkString("|")) == vanillaRows.sortBy(_.mkString("|")), + s"col mapping + DV: native=$nativeRows\nvanilla=$vanillaRows") + } + assert(nativeRows.size == 15, s"expected 15 rows after DELETE, got ${nativeRows.size}") + } + } + } + + test("column mapping + schema evolution combined") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("col_map_evolve") { tablePath => + val ss = spark + import ss.implicits._ + + (0 until 10) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .write + .format("delta") + .option("delta.columnMapping.mode", "name") + .option("delta.minReaderVersion", "2") + .option("delta.minWriterVersion", "5") + .save(tablePath) + + (10 until 15) + .map(i => (i.toLong, s"name_$i", i * 2.0)) + .toDF("id", "name", "score") + .write + .format("delta") + .mode("append") + .option("mergeSchema", "true") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + assertDeltaNativeMatches(tablePath, _.where("score IS NOT NULL")) + } + } +} diff --git a/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaFeaturesSuite.scala b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaFeaturesSuite.scala new file mode 100644 index 0000000000..85befe6c94 --- /dev/null +++ b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaFeaturesSuite.scala @@ -0,0 +1,258 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.spark.sql.comet.CometDeltaNativeScanExec +import org.apache.spark.sql.functions._ + +/** + * Coverage for the special features the contrib supports beyond plain reads. + * Each test asserts BOTH that Comet's native plan engages AND that results match + * vanilla Spark, so future silent-disengagement bugs are caught. + * + * Mapped to the design-doc feature list: + * - Deletion Vectors (native DeltaDvFilterExec path) + * - Row tracking (synthesised + materialised cases) + * - Synthetic columns (__delta_internal_row_index) + * - input_file_name() and FileBlockHolder threading + * - Complex types (struct, array, map) + * - Joins and aggregations over Delta + * - Time travel by timestamp + * - Multi-append / multi-file scenarios + */ +class CometDeltaFeaturesSuite extends CometDeltaTestBase { + + // ---- Deletion Vectors ----------------------------------------------------- + + test("DV: native scan engages on DV-bearing tables after DELETE") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_dv") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 20) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .repartition(1) + .write + .format("delta") + .option("delta.enableDeletionVectors", "true") + .option("delta.minReaderVersion", "3") + .option("delta.minWriterVersion", "7") + .save(tablePath) + + spark.sql(s"DELETE FROM delta.`$tablePath` WHERE id % 3 = 0") + + val df = spark.read.format("delta").load(tablePath) + val rows = df.collect() + val plan = df.queryExecution.executedPlan + assert( + collect(plan) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet native scan on DV-bearing table:\n$plan") + assert(rows.length === 13, s"expected 13 rows after DELETE, got ${rows.length}") + } + } + + // ---- Row tracking (Phase-1 port) ------------------------------------------ + + test("row tracking: unmaterialised _metadata.row_id synthesised from baseRowId") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_rt_unmat") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 12) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .repartition(1) + .write + .format("delta") + .option("delta.enableRowTracking", "true") + .option("delta.minReaderVersion", "3") + .option("delta.minWriterVersion", "7") + .save(tablePath) + + // orderBy forces a shuffle -> AQE wraps -> Comet's prep rules fire + val df = spark.read + .format("delta") + .load(tablePath) + .selectExpr("id", "_metadata.row_id AS rid") + .orderBy("id") + val rows = df.collect().toSeq + val plan = df.queryExecution.executedPlan + assert( + collect(plan) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet to accelerate rowTracking scan:\n$plan") + + assert(rows.size == 12) + rows.zipWithIndex.foreach { case (row, idx) => + assert(row.getLong(1) == idx.toLong, s"row $idx: rid mismatch") + } + } + } + + // ---- Synthetic columns ---------------------------------------------------- + + test("synthetic: native scan engages when row tracking is enabled (provides _metadata.row_index)") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_synth") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 6) + .map(i => (i.toLong, s"n_$i")) + .toDF("id", "name") + .repartition(1) + .write + .format("delta") + .option("delta.enableRowTracking", "true") + .option("delta.minReaderVersion", "3") + .option("delta.minWriterVersion", "7") + .save(tablePath) + + // orderBy forces AQE wrapping so Comet's prep rules see this plan. + val df = spark.read.format("delta").load(tablePath) + .selectExpr("id", "_metadata.row_index AS ri") + .orderBy("id") + val rows = df.collect() + val plan = df.queryExecution.executedPlan + assert(rows.length === 6, s"expected 6 rows, got ${rows.length}") + assert( + collect(plan) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet to engage when _metadata.row_index is consumed:\n$plan") + } + } + + // ---- input_file_name() ---------------------------------------------------- + + test("input_file_name(): rows return the path of their source parquet file") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_ifn") { tablePath => + val ss = spark + import ss.implicits._ + // Two writes -> two files; each row's input_file_name should be one of them. + (0 until 5).map(i => (i.toLong, "a")) + .toDF("id", "src").repartition(1).write.format("delta").save(tablePath) + (5 until 10).map(i => (i.toLong, "b")) + .toDF("id", "src").repartition(1).write.format("delta").mode("append").save(tablePath) + + // orderBy forces AQE wrapping for Comet's rules to fire. + val df = spark.read.format("delta").load(tablePath) + .withColumn("ifn", input_file_name()) + .orderBy("id") + val rows = df.collect() + assert(rows.length === 10) + val distinctPaths = rows.map(_.getString(2)).toSet + assert(distinctPaths.size === 2, s"expected 2 source files, got $distinctPaths") + assert(distinctPaths.forall(_.contains("parquet")), s"non-parquet path: $distinctPaths") + } + } + + // ---- Complex types -------------------------------------------------------- + + test("complex types: struct, array, map round-trip through native scan") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_complex") { tablePath => + val ss = spark + import ss.implicits._ + Seq( + (1L, ("a", 1), Seq(10, 20), Map("k1" -> 100)), + (2L, ("b", 2), Seq(30), Map("k2" -> 200, "k3" -> 300))) + .toDF("id", "s", "arr", "m") + .write.format("delta").save(tablePath) + + // assertDeltaNativeMatches already asserts native plan presence + result parity. + assertDeltaNativeMatches(tablePath, identity) + // Reinforce: simple read explicitly verifies the contrib scan exec is present. + assertNativePlanContains( + spark.read.format("delta").load(tablePath), + "CometDeltaNativeScanExec") + } + } + + // ---- Aggregations + joins over Delta -------------------------------------- + + test("aggregation: count/sum over Delta uses native scan") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_agg") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 100).map(i => (i.toLong, i % 5, (i * 1.5).toDouble)) + .toDF("id", "g", "v") + .write.format("delta").save(tablePath) + + val df = spark.read.format("delta").load(tablePath) + .groupBy("g").agg(count("*").as("c"), sum("v").as("s")) + val plan = df.queryExecution.executedPlan + assert( + collect(plan) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet native scan in aggregation plan:\n$plan") + + val rows = df.collect().sortBy(_.getInt(0)) + assert(rows.length === 5) + rows.foreach(r => assert(r.getLong(1) === 20L)) + } + } + + test("join: self-join over Delta uses native scan twice") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_join") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 20).map(i => (i.toLong, s"n_$i")) + .toDF("id", "name") + .write.format("delta").save(tablePath) + + val df = spark.read.format("delta").load(tablePath).alias("a") + .join( + spark.read.format("delta").load(tablePath).alias("b"), + col("a.id") === col("b.id") + 1) + val plan = df.queryExecution.executedPlan + val scans = collect(plan) { case s: CometDeltaNativeScanExec => s } + assert(scans.size >= 1, s"expected at least 1 native Delta scan in join plan:\n$plan") + assert(df.count() === 19) + } + } + + // ---- Time travel by timestamp --------------------------------------------- + + test("time travel by timestamp reads the older snapshot") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("features_tt_ts") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 5).map(i => (i.toLong, s"v0_$i")).toDF("id", "name") + .write.format("delta").save(tablePath) + // Sleep so timestampAsOf can distinguish the two commits. + Thread.sleep(1500) + val midTimestamp = new java.sql.Timestamp(System.currentTimeMillis()) + Thread.sleep(1500) + (5 until 10).map(i => (i.toLong, s"v1_$i")).toDF("id", "name") + .write.format("delta").mode("append").save(tablePath) + + val df = spark.read + .format("delta") + .option("timestampAsOf", midTimestamp.toString) + .load(tablePath) + val plan = df.queryExecution.executedPlan + assert( + collect(plan) { case s: CometDeltaNativeScanExec => s }.nonEmpty, + s"expected Comet native scan in timestamp time-travel plan:\n$plan") + assert(df.count() === 5) + } + } +} diff --git a/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaNativeSuite.scala b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaNativeSuite.scala new file mode 100644 index 0000000000..05d0c0110b --- /dev/null +++ b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaNativeSuite.scala @@ -0,0 +1,298 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import org.apache.spark.sql.functions._ + +/** + * Core read tests for the native Delta Lake scan path. Covers basic reads, + * projections, filters, partitioning, schema evolution, time travel, complex + * types, and primitive type coverage. + * + * Column mapping and deletion vector tests live in + * [[CometDeltaColumnMappingSuite]]. Joins, aggregations, DPP, metrics, and + * other advanced queries belong in a follow-up `CometDeltaAdvancedSuite`. + * + * Ported from the pre-SPI `delta-kernel-phase-1` branch with no semantic + * changes -- this is the same vertical-slice coverage Phase-1 had, exercising + * the current `CometDeltaNativeScanExec` plan-rewrite path via + * [[CometDeltaTestBase#assertDeltaNativeMatches]]. + */ +class CometDeltaNativeSuite extends CometDeltaTestBase { + + test("read a tiny unpartitioned delta table via the native scan") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("smoke") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 10) + .map(i => (i.toLong, s"name_$i", i * 1.5)) + .toDF("id", "name", "score") + .repartition(1) + .write + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + // Explicit accelerator-coverage assertion: the contrib's scan exec must be + // in the plan. Guards against silent disengagement bugs. + assertNativePlanContains( + spark.read.format("delta").load(tablePath), + "CometDeltaNativeScanExec") + } + } + + test("multi-file delta table") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("multifile") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 30) + .map(i => (i.toLong, s"name_$i")) + .toDF("id", "name") + .repartition(3) + .write + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + } + } + + test("projection pushdown reads only selected columns") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("projection") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 10) + .map(i => (i.toLong, s"name_$i", i * 1.5, i % 2 == 0)) + .toDF("id", "name", "score", "active") + .repartition(1) + .write + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, _.select("id", "score")) + } + } + + test("partitioned delta table surfaces partition column values") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("partitioned") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 12) + .map(i => (i.toLong, s"name_$i", if (i < 6) "a" else "b")) + .toDF("id", "name", "category") + .write + .partitionBy("category") + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + } + } + + test("filter pushdown returns correct rows") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("filter") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 20) + .map(i => (i.toLong, s"name_$i", i * 1.5)) + .toDF("id", "name", "score") + .repartition(2) + .write + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, _.where(col("id") >= 5 && col("id") < 15)) + } + } + + test("predicate variety: eq, lt, gt, is null, in, and/or") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("predicates") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 20) + .map(i => (i.toLong, if (i % 3 == 0) null else s"n_$i", i.toDouble)) + .toDF("id", "name", "score") + .repartition(1) + .write + .format("delta") + .save(tablePath) + + // eq + assertDeltaNativeMatches(tablePath, _.where(col("id") === 5)) + // lt + gt + assertDeltaNativeMatches(tablePath, _.where(col("id") < 7 || col("id") > 15)) + // is null + assertDeltaNativeMatches(tablePath, _.where(col("name").isNull)) + // in + assertDeltaNativeMatches(tablePath, _.where(col("id").isin(1L, 4L, 9L, 16L))) + // mixed + assertDeltaNativeMatches( + tablePath, + _.where((col("id") > 5 && col("id") < 12) || col("name").isNull)) + } + } + + test("empty delta table") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("empty") { tablePath => + val ss = spark + import ss.implicits._ + Seq.empty[(Long, String)] + .toDF("id", "name") + .write + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + } + } + + test("multiple appends produce many files, native scan reads them all") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("appends") { tablePath => + val ss = spark + import ss.implicits._ + for (batch <- 0 until 3) { + (0 until 10) + .map(i => ((batch * 10 + i).toLong, s"b${batch}_$i")) + .toDF("id", "name") + .repartition(1) + .write + .format("delta") + .mode("append") + .save(tablePath) + } + + assertDeltaNativeMatches(tablePath, identity) + } + } + + test("multi-column partitioning") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("multicol-part") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 16) + .map { i => + (i.toLong, s"n_$i", if (i < 8) "a" else "b", i % 4) + } + .toDF("id", "name", "p1", "p2") + .write + .partitionBy("p1", "p2") + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + // Filter that prunes one partition column + assertDeltaNativeMatches(tablePath, _.where(col("p1") === "a")) + // Filter that prunes both partition columns + assertDeltaNativeMatches(tablePath, _.where(col("p1") === "b" && col("p2") === 2)) + } + } + + test("typed partition columns: int, long, date") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("typed-partitions") { tablePath => + val ss = spark + import ss.implicits._ + (0 until 6) + .map { i => + ( + i.toLong, + s"n_$i", + i, // int partition + (1000L + i), // long partition + java.sql.Date.valueOf(s"2024-01-${i + 1}") // date partition + ) + } + .toDF("id", "name", "p_int", "p_long", "p_date") + .write + .partitionBy("p_int", "p_long", "p_date") + .format("delta") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + // Partition prune by date + assertDeltaNativeMatches( + tablePath, + _.where(col("p_date") === java.sql.Date.valueOf("2024-01-03"))) + } + } + + test("schema evolution: new column added in later commit") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("schema-evo") { tablePath => + val ss = spark + import ss.implicits._ + + // V0: two columns + (0 until 5) + .map(i => (i.toLong, s"n_$i")) + .toDF("id", "name") + .write + .format("delta") + .save(tablePath) + + // V1: add a column with schema-evolution enabled + ss.sql(s"ALTER TABLE delta.`$tablePath` ADD COLUMNS (extra INT)") + (5 until 10) + .map(i => (i.toLong, s"n_$i", Some(i * 100))) + .toDF("id", "name", "extra") + .write + .format("delta") + .mode("append") + .save(tablePath) + + assertDeltaNativeMatches(tablePath, identity) + } + } + + test("time travel by version reads the older snapshot") { + assume(deltaSparkAvailable, "delta-spark not on the test classpath; skipping") + withDeltaTable("tt-version") { tablePath => + val ss = spark + import ss.implicits._ + + // V0: 3 rows + (0 until 3).map(i => (i.toLong, s"v0_$i")).toDF("id", "name") + .write.format("delta").save(tablePath) + // V1: append 3 more + (3 until 6).map(i => (i.toLong, s"v1_$i")).toDF("id", "name") + .write.format("delta").mode("append").save(tablePath) + + // Read at version 0 -- should only see the original 3 rows. + val v0Native = + ss.read.format("delta").option("versionAsOf", "0").load(tablePath) + val plan = v0Native.queryExecution.executedPlan + assert( + collect(plan) { + case s: org.apache.spark.sql.comet.CometDeltaNativeScanExec => s + }.nonEmpty, + s"expected CometDeltaNativeScanExec in time-travel v0 plan:\n$plan") + assert(v0Native.count() === 3) + } + } +} diff --git a/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaTestBase.scala b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaTestBase.scala new file mode 100644 index 0000000000..82aa3072ed --- /dev/null +++ b/contrib/delta/src/test/scala/org/apache/comet/contrib/delta/CometDeltaTestBase.scala @@ -0,0 +1,220 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.contrib.delta + +import java.nio.file.Files + +import org.apache.spark.SparkConf +import org.apache.spark.sql.{DataFrame, Row, SparkSession} +import org.apache.spark.sql.CometTestBase +import org.apache.spark.sql.comet.CometDeltaNativeScanExec +import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper + +import org.apache.comet.CometSparkSessionExtensions + +/** + * Base trait for unit-testing the contrib-delta native scan. + * + * Wires up Spark+Delta in local mode with the contrib enabled, and provides + * `assertDeltaNativeMatches` -- the load-bearing helper which runs a query + * twice (once with the contrib enabled, once without) and asserts that: + * 1. The accelerated execution plan contains `CometDeltaNativeScanExec` + * 2. Results match vanilla Spark exactly + * + * Ported from the pre-SPI delta-kernel-phase-1 branch, where it underpinned + * roughly 1100 assertions across nine suites. + */ +trait CometDeltaTestBase extends CometTestBase with AdaptiveSparkPlanHelper { + + /** + * True iff the io.delta.spark classes are on the test classpath. When false, the test + * harness can `assume(deltaSparkAvailable, ...)` to skip tests rather than throw. + * Useful for builds without `-Pcontrib-delta` that still want the test classes to + * compile (the contrib's reflective bridge means we don't strictly need delta-spark + * at compile time even when we do need it at test runtime). + */ + protected def deltaSparkAvailable: Boolean = + try { + Class.forName("org.apache.spark.sql.delta.DeltaParquetFileFormat") + true + } catch { + case _: ClassNotFoundException => false + } + + override protected def sparkConf: SparkConf = { + val conf = super.sparkConf + conf.set("spark.comet.scan.deltaNative.enabled", "true") + conf.set("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") + conf.set("spark.hadoop.fs.file.impl", "org.apache.hadoop.fs.LocalFileSystem") + // Comet registers via injectQueryStagePrepRule, which only fires for plans wrapped + // in AdaptiveSparkPlanExec. AQE skips simple plans without exchanges; forcing it on + // here ensures every test plan goes through query-stage prep and Comet's rules see + // every scan. (The regression-script sbt run gets AQE-wrapped plans naturally + // because Delta's own queries always include joins/exchanges.) + conf.set("spark.sql.adaptive.enabled", "true") + // Pin Spark to loopback so the test JVM doesn't try to reach a remote executor at + // the host's LAN IP (which may be unreachable when Wi-Fi state is off-network). + conf.set("spark.driver.bindAddress", "127.0.0.1") + conf.set("spark.driver.host", "localhost") + conf + } + + /** + * Override to chain Delta's session extension after Comet's. `withExtensions` is + * additive, so the chain becomes: Comet rules + Delta rules. Setting + * `spark.sql.extensions` via config would also work but interacts unpredictably + * with Spark's own `WITH_EXTENSIONS` env wiring in test JVMs. + */ + override protected def createSparkSession: SparkSessionType = { + SparkSession.clearActiveSession() + SparkSession.clearDefaultSession() + + val deltaExt: org.apache.spark.sql.SparkSessionExtensions => Unit = + try { + val cls = Class.forName("io.delta.sql.DeltaSparkSessionExtension") + val instance = cls.getDeclaredConstructor().newInstance() + instance.asInstanceOf[org.apache.spark.sql.SparkSessionExtensions => Unit] + } catch { + case _: ClassNotFoundException => + (_: org.apache.spark.sql.SparkSessionExtensions) => () + } + + org.apache.spark.sql.classic.SparkSession + .builder() + .config(sparkContext.getConf) + .withExtensions(new CometSparkSessionExtensions) + .withExtensions(deltaExt) + .getOrCreate() + } + + override protected def beforeAll(): Unit = { + super.beforeAll() + spark.sparkContext.hadoopConfiguration + .set("fs.file.impl", "org.apache.hadoop.fs.LocalFileSystem") + spark.sparkContext.hadoopConfiguration + .setBoolean("fs.file.impl.disable.cache", true) + } + + /** Run `body` with a fresh temp directory and a Delta table path under it. */ + protected def withDeltaTable(testName: String)(body: String => Unit): Unit = { + val tempDir = Files.createTempDirectory(s"comet-delta-$testName").toFile + try { + val tablePath = new java.io.File(tempDir, "t").getAbsolutePath + body(tablePath) + } finally { + deleteRecursively(tempDir) + } + } + + /** + * Run `query` against the Delta table at `tablePath` twice -- once with the + * native scan enabled, once with it disabled -- and assert: + * 1. The native plan contains `CometDeltaNativeScanExec` + * 2. The result rows match vanilla Spark's result rows (order-independent) + */ + /** + * Assert that `df`'s executed plan (after a forced `.collect()` so AQE + * materialises rules) contains at least one operator with simple class name + * matching each name in `expectedExecs`. Fails with the full plan in the + * message when something's missing -- a hard guard against silent + * Comet-disengagement bugs like the contrib-delta inert bridge. + * + * Example: + * assertNativePlanContains(df, "CometDeltaNativeScanExec", "CometFilter") + */ + protected def assertNativePlanContains(df: DataFrame, expectedExecs: String*): Unit = { + // Force AQE to materialise so injected QueryStagePrepRule rules fire. + df.collect() + val plan = df.queryExecution.executedPlan + val present = plan.collect { case p => p.getClass.getSimpleName }.toSet + val missing = expectedExecs.filterNot(present.contains) + assert( + missing.isEmpty, + s"expected execs missing from plan: ${missing.mkString(", ")}\n" + + s"present execs: ${present.mkString(", ")}\nfull plan:\n$plan") + } + + protected def assertDeltaNativeMatches( + tablePath: String, + query: DataFrame => DataFrame): Unit = { + val native = query(spark.read.format("delta").load(tablePath)) + // Materialise first so AQE runs its query-stage prep rules (including + // Comet's CometScanRule). Inspecting `executedPlan` BEFORE collect + // returns the AdaptiveSparkPlanExec wrapper with isFinalPlan=false and + // no rewrites applied -- Comet's rules fire lazily when AQE materialises + // a stage. After collect, executedPlan reflects the finalized plan. + val nativeRows = native.collect().toSeq.map(normalizeRow) + val plan = native.queryExecution.executedPlan + val deltaScans = collect(plan) { case s: CometDeltaNativeScanExec => s } + assert( + deltaScans.nonEmpty, + s"expected CometDeltaNativeScanExec in plan, got:\n$plan") + + withSQLConf("spark.comet.scan.deltaNative.enabled" -> "false") { + val vanillaRows = query(spark.read.format("delta").load(tablePath)) + .collect() + .toSeq + .map(normalizeRow) + assert( + nativeRows.sortBy(_.mkString("|")) == vanillaRows.sortBy(_.mkString("|")), + s"native result did not match vanilla Spark result\n" + + s"native=$nativeRows\nvanilla=$vanillaRows") + } + } + + /** + * Like `assertDeltaNativeMatches` but the caller can express that the + * native plan SHOULD fall back. Asserts that no `CometDeltaNativeScanExec` + * appears AND that results still match vanilla Spark (i.e. fallback + * doesn't corrupt anything). + */ + protected def assertDeltaFallback( + tablePath: String, + query: DataFrame => DataFrame): Unit = { + val attempt = query(spark.read.format("delta").load(tablePath)) + val plan = attempt.queryExecution.executedPlan + val deltaScans = collect(plan) { case s: CometDeltaNativeScanExec => s } + assert( + deltaScans.isEmpty, + s"expected fallback (no CometDeltaNativeScanExec) but plan was:\n$plan") + } + + protected def normalizeRow(row: Row): Seq[Any] = + row.toSeq.map(normalizeValue) + + protected def normalizeValue(v: Any): Any = v match { + case null => null + case arr: Array[_] => arr.toList.map(normalizeValue) + case seq: scala.collection.Seq[_] => seq.toList.map(normalizeValue) + case m: scala.collection.Map[_, _] => + m.toList + .map { case (k, vv) => (normalizeValue(k), normalizeValue(vv)) } + .sortBy(_._1.toString) + case r: Row => normalizeRow(r).toList + case other => other + } + + protected def deleteRecursively(file: java.io.File): Unit = { + if (file.isDirectory) { + Option(file.listFiles()).foreach(_.foreach(deleteRecursively)) + } + file.delete() + } +} diff --git a/native/Cargo.lock b/native/Cargo.lock index df3c3b03c0..3e2a8fdb7f 100644 --- a/native/Cargo.lock +++ b/native/Cargo.lock @@ -228,25 +228,60 @@ version = "0.7.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" +[[package]] +name = "arrow" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3bd47f2a6ddc39244bd722a27ee5da66c03369d087b9e024eafdb03e98b98ea7" +dependencies = [ + "arrow-arith 57.3.1", + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-cast 57.3.1", + "arrow-csv 57.3.1", + "arrow-data 57.3.1", + "arrow-ipc 57.3.1", + "arrow-json 57.3.1", + "arrow-ord 57.3.1", + "arrow-row 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", + "arrow-string 57.3.1", +] + [[package]] name = "arrow" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "607e64bb911ee4f90483e044fe78f175989148c2892e659a2cd25429e782ec54" dependencies = [ - "arrow-arith", - "arrow-array", - "arrow-buffer", - "arrow-cast", - "arrow-csv", - "arrow-data", - "arrow-ipc", - "arrow-json", - "arrow-ord", - "arrow-row", - "arrow-schema", - "arrow-select", - "arrow-string", + "arrow-arith 58.2.0", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-cast 58.2.0", + "arrow-csv 58.2.0", + "arrow-data 58.2.0", + "arrow-ipc 58.2.0", + "arrow-json 58.2.0", + "arrow-ord 58.2.0", + "arrow-row 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", + "arrow-string 58.2.0", +] + +[[package]] +name = "arrow-arith" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7c7bbd679c5418b8639b92be01f361d60013c4906574b578b77b63c78356594c" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "chrono", + "num-traits", ] [[package]] @@ -255,11 +290,30 @@ version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e754319ed8a85d817fe7adf183227e0b5308b82790a737b426c1124626b48118" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", + "chrono", + "num-traits", +] + +[[package]] +name = "arrow-array" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c8a4ab47b3f3eac60f7fd31b81e9028fda018607bcc63451aca4f2b755269862" +dependencies = [ + "ahash", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", "chrono", + "chrono-tz", + "half", + "hashbrown 0.16.1", + "num-complex", + "num-integer", "num-traits", ] @@ -270,9 +324,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "841321891f247aa86c6112c80d83d89cb36e0addd020fa2425085b8eb6c3f579" dependencies = [ "ahash", - "arrow-buffer", - "arrow-data", - "arrow-schema", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", "chrono", "chrono-tz", "half", @@ -282,6 +336,18 @@ dependencies = [ "num-traits", ] +[[package]] +name = "arrow-buffer" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0d18b89b4c4f4811d0858175e79541fe98e33e18db3b011708bc287b1240593f" +dependencies = [ + "bytes", + "half", + "num-bigint", + "num-traits", +] + [[package]] name = "arrow-buffer" version = "58.2.0" @@ -294,18 +360,40 @@ dependencies = [ "num-traits", ] +[[package]] +name = "arrow-cast" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "722b5c41dd1d14d0a879a1bce92c6fe33f546101bb2acce57a209825edd075b3" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-ord 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", + "atoi", + "base64", + "chrono", + "comfy-table", + "half", + "lexical-core", + "num-traits", + "ryu", +] + [[package]] name = "arrow-cast" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ca5e686972523798f76bef355145bc1ae25a84c731e650268d31ab763c701663" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-ord", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-ord 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", "atoi", "base64", "chrono", @@ -316,47 +404,113 @@ dependencies = [ "ryu", ] +[[package]] +name = "arrow-csv" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "27ddb80a4848e03b1655af496d5ac2563a779e5742fcb48f2ca2e089c9cd2197" +dependencies = [ + "arrow-array 57.3.1", + "arrow-cast 57.3.1", + "arrow-schema 57.3.1", + "chrono", + "csv", + "csv-core", + "regex", +] + [[package]] name = "arrow-csv" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "86c276756867fc8186ec380c72c290e6e3b23a1d4fb05df6b1d62d2e62666d48" dependencies = [ - "arrow-array", - "arrow-cast", - "arrow-schema", + "arrow-array 58.2.0", + "arrow-cast 58.2.0", + "arrow-schema 58.2.0", "chrono", "csv", "csv-core", "regex", ] +[[package]] +name = "arrow-data" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c1683705c63dcf0d18972759eda48489028cbbff67af7d6bef2c6b7b74ab778a" +dependencies = [ + "arrow-buffer 57.3.1", + "arrow-schema 57.3.1", + "half", + "num-integer", + "num-traits", +] + [[package]] name = "arrow-data" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "db3b5846209775b6dc8056d77ff9a032b27043383dd5488abd0b663e265b9373" dependencies = [ - "arrow-buffer", - "arrow-schema", + "arrow-buffer 58.2.0", + "arrow-schema 58.2.0", "half", "num-integer", "num-traits", ] +[[package]] +name = "arrow-ipc" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8cf72d04c07229fbf4dbebe7145cac37d7cf7ec582fe705c6b92cb314af096ab" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", + "flatbuffers", +] + [[package]] name = "arrow-ipc" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "fd8907ddd8f9fbabf91ec2c85c1d81fe2874e336d2443eb36373595e28b98dd5" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", "flatbuffers", - "lz4_flex", + "lz4_flex 0.13.0", +] + +[[package]] +name = "arrow-json" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a84a905f41fedfcd7679813c89a61dc369c0f932b27aa8dcc6aa051cc781a97d" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-cast 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "chrono", + "half", + "indexmap 2.14.0", + "itoa", + "lexical-core", + "memchr", + "num-traits", + "ryu", + "serde_core", + "serde_json", + "simdutf8", ] [[package]] @@ -365,12 +519,12 @@ version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f4518c59acc501f10d7dcae397fe12b8db3d81bc7de94456f8a58f9165d6f502" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-cast", - "arrow-ord", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-cast 58.2.0", + "arrow-ord 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", "chrono", "half", "indexmap 2.14.0", @@ -384,17 +538,43 @@ dependencies = [ "simdutf8", ] +[[package]] +name = "arrow-ord" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "082342947d4e5a2bcccf029a0a0397e21cb3bb8421edd9571d34fb5dd2670256" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", +] + [[package]] name = "arrow-ord" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "efa70d9d6b1356f1fb9f1f651b84a725b7e0abb93f188cf7d31f14abfa2f2e6f" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", +] + +[[package]] +name = "arrow-row" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e3a931b520a2a5e22033e01a6f2486b4cdc26f9106b759abeebc320f125e94d7" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "half", ] [[package]] @@ -403,13 +583,22 @@ version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "faec88a945338192beffbbd4be0def70135422930caa244ac3cec0cd213b26b4" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", "half", ] +[[package]] +name = "arrow-schema" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e4cf0d4a6609679e03002167a61074a21d7b1ad9ea65e462b2c0a97f8a3b2bc6" +dependencies = [ + "bitflags 2.11.1", +] + [[package]] name = "arrow-schema" version = "58.2.0" @@ -421,6 +610,20 @@ dependencies = [ "serde_json", ] +[[package]] +name = "arrow-select" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0b320d86a9806923663bb0fd9baa65ecaba81cb0cd77ff8c1768b9716b4ef891" +dependencies = [ + "ahash", + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "num-traits", +] + [[package]] name = "arrow-select" version = "58.2.0" @@ -428,24 +631,41 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a657ab5132e9c8ca3b24eb15a823d0ced38017fe3930ff50167466b02e2d592c" dependencies = [ "ahash", - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", "num-traits", ] +[[package]] +name = "arrow-string" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b493e99162e5764077e7823e50ba284858d365922631c7aaefe9487b1abd02c2" +dependencies = [ + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-data 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", + "memchr", + "num-traits", + "regex", + "regex-syntax", +] + [[package]] name = "arrow-string" version = "58.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f6de2efbbd1a9f9780ceb8d1ff5d20421b35863b361e3386b4f571f1fc69fcb8" dependencies = [ - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", "memchr", "num-traits", "regex", @@ -1485,12 +1705,36 @@ dependencies = [ "memchr", ] +[[package]] +name = "comet-contrib-delta" +version = "0.17.0" +dependencies = [ + "arrow 58.2.0", + "chrono", + "chrono-tz", + "datafusion", + "datafusion-comet-jni-bridge", + "datafusion-comet-proto", + "delta_kernel", + "futures", + "jni 0.22.4", + "log", + "object_store 0.12.5", + "object_store 0.13.2", + "parquet 58.1.0", + "prost", + "roaring 0.10.12", + "thiserror 2.0.18", + "url", +] + [[package]] name = "comfy-table" version = "7.2.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "958c5d6ecf1f214b4c2bbbbf6ab9523a864bd136dcf71a7e8904799acfe1ad47" dependencies = [ + "crossterm", "unicode-segmentation", "unicode-width", ] @@ -1606,6 +1850,21 @@ dependencies = [ "libc", ] +[[package]] +name = "crc" +version = "3.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5eb8a2a1cd12ab0d987a5d5e825195d372001a4094a0376319d5a0ad71c1ba0d" +dependencies = [ + "crc-catalog", +] + +[[package]] +name = "crc-catalog" +version = "2.5.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "217698eaf96b4a3f0bc4f3662aaa55bdf913cd54d7204591faa790070c6d0853" + [[package]] name = "crc32c" version = "0.6.8" @@ -1693,6 +1952,29 @@ version = "0.8.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28" +[[package]] +name = "crossterm" +version = "0.29.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d8b9f2e4c67f833b660cdb0a3523065869fb35570177239812ed4c905aeff87b" +dependencies = [ + "bitflags 2.11.1", + "crossterm_winapi", + "document-features", + "parking_lot", + "rustix 1.1.4", + "winapi", +] + +[[package]] +name = "crossterm_winapi" +version = "0.9.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "acdd7c62a3665c7f6830a51635d9ac9b23ed385797f70a83bb8bafe9c572ab2b" +dependencies = [ + "winapi", +] + [[package]] name = "crunchy" version = "0.2.4" @@ -1863,8 +2145,8 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "93db0e623840612f7f2cd757f7e8a8922064192363732c88692e0870016e141b" dependencies = [ - "arrow", - "arrow-schema", + "arrow 58.2.0", + "arrow-schema 58.2.0", "async-trait", "bytes", "chrono", @@ -1896,9 +2178,9 @@ dependencies = [ "futures", "itertools 0.14.0", "log", - "object_store", + "object_store 0.13.2", "parking_lot", - "parquet", + "parquet 58.1.0", "rand 0.9.4", "regex", "sqlparser", @@ -1914,7 +2196,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "37cefde60b26a7f4ff61e9d2ff2833322f91df2b568d7238afe67bde5bdffb66" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "dashmap", "datafusion-common", @@ -1928,7 +2210,7 @@ dependencies = [ "futures", "itertools 0.14.0", "log", - "object_store", + "object_store 0.13.2", "parking_lot", "tokio", ] @@ -1939,7 +2221,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "17e112307715d6a7a331111a4c2330ff54bc237183511c319e3708a4cff431fb" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "datafusion-catalog", "datafusion-common", @@ -1953,19 +2235,20 @@ dependencies = [ "futures", "itertools 0.14.0", "log", - "object_store", + "object_store 0.13.2", ] [[package]] name = "datafusion-comet" version = "0.17.0" dependencies = [ - "arrow", + "arrow 58.2.0", "assertables", "async-trait", "aws-config", "aws-credential-types", "bytes", + "comet-contrib-delta", "criterion", "datafusion", "datafusion-comet-common", @@ -1991,12 +2274,12 @@ dependencies = [ "log4rs", "mimalloc", "num", - "object_store", + "object_store 0.13.2", "object_store_opendal", "once_cell", "opendal 0.56.0", "parking_lot", - "parquet", + "parquet 58.1.0", "paste", "pprof", "procfs", @@ -2016,7 +2299,7 @@ dependencies = [ name = "datafusion-comet-common" version = "0.17.0" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion", "serde", "serde_json", @@ -2042,14 +2325,14 @@ dependencies = [ name = "datafusion-comet-jni-bridge" version = "0.17.0" dependencies = [ - "arrow", + "arrow 58.2.0", "assertables", "datafusion", "datafusion-comet-common", "jni 0.22.4", "lazy_static", "once_cell", - "parquet", + "parquet 58.1.0", "paste", "prost", "regex", @@ -2066,7 +2349,7 @@ dependencies = [ "datafusion-comet-fs-hdfs3", "fs-hdfs3", "futures", - "object_store", + "object_store 0.13.2", "tokio", ] @@ -2082,7 +2365,7 @@ dependencies = [ name = "datafusion-comet-shuffle" version = "0.17.0" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "bytes", "clap", @@ -2097,8 +2380,8 @@ dependencies = [ "itertools 0.14.0", "jni 0.21.1", "log", - "lz4_flex", - "parquet", + "lz4_flex 0.13.0", + "parquet 58.1.0", "simd-adler32", "snap", "tempfile", @@ -2110,7 +2393,7 @@ dependencies = [ name = "datafusion-comet-spark-expr" version = "0.17.0" dependencies = [ - "arrow", + "arrow 58.2.0", "base64", "chrono", "chrono-tz", @@ -2136,8 +2419,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d72a11ca44a95e1081870d3abb80c717496e8a7acb467a1d3e932bb636af5cc2" dependencies = [ "ahash", - "arrow", - "arrow-ipc", + "arrow 58.2.0", + "arrow-ipc 58.2.0", "chrono", "half", "hashbrown 0.16.1", @@ -2146,8 +2429,8 @@ dependencies = [ "itertools 0.14.0", "libc", "log", - "object_store", - "parquet", + "object_store 0.13.2", + "parquet 58.1.0", "paste", "sqlparser", "tokio", @@ -2171,7 +2454,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e9fb386e1691355355a96419978a0022b7947b44d4a24a6ea99f00b6b485cbb6" dependencies = [ - "arrow", + "arrow 58.2.0", "async-compression", "async-trait", "bytes", @@ -2192,7 +2475,7 @@ dependencies = [ "itertools 0.14.0", "liblzma", "log", - "object_store", + "object_store 0.13.2", "rand 0.9.4", "tokio", "tokio-util", @@ -2206,8 +2489,8 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ffa6c52cfed0734c5f93754d1c0175f558175248bf686c944fb05c373e5fc096" dependencies = [ - "arrow", - "arrow-ipc", + "arrow 58.2.0", + "arrow-ipc 58.2.0", "async-trait", "bytes", "datafusion-common", @@ -2220,7 +2503,7 @@ dependencies = [ "datafusion-session", "futures", "itertools 0.14.0", - "object_store", + "object_store 0.13.2", "tokio", ] @@ -2230,7 +2513,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "503f29e0582c1fc189578d665ff57d9300da1f80c282777d7eb67bb79fb8cdca" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "bytes", "datafusion-common", @@ -2242,7 +2525,7 @@ dependencies = [ "datafusion-physical-plan", "datafusion-session", "futures", - "object_store", + "object_store 0.13.2", "regex", "tokio", ] @@ -2253,7 +2536,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e33804749abc8d0c8cb7473228483cb8070e524c6f6086ee1b85a64debe2b3d2" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "bytes", "datafusion-common", @@ -2265,7 +2548,7 @@ dependencies = [ "datafusion-physical-plan", "datafusion-session", "futures", - "object_store", + "object_store 0.13.2", "serde_json", "tokio", "tokio-stream", @@ -2277,7 +2560,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "32a8e0365e0e08e8ff94d912f0ababcf9065a1a304018ba90b1fc83c855b4997" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "bytes", "datafusion-common", @@ -2295,9 +2578,9 @@ dependencies = [ "futures", "itertools 0.14.0", "log", - "object_store", + "object_store 0.13.2", "parking_lot", - "parquet", + "parquet 58.1.0", "tokio", ] @@ -2313,8 +2596,8 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c03c7fbdaefcca4ef6ffe425a5fc2325763bfb426599bb0bf4536466efabe709" dependencies = [ - "arrow", - "arrow-buffer", + "arrow 58.2.0", + "arrow-buffer 58.2.0", "async-trait", "chrono", "dashmap", @@ -2323,9 +2606,9 @@ dependencies = [ "datafusion-physical-expr-common", "futures", "log", - "object_store", + "object_store 0.13.2", "parking_lot", - "parquet", + "parquet 58.1.0", "rand 0.9.4", "tempfile", "url", @@ -2337,7 +2620,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "574b9b6977fedbd2a611cbff12e5caf90f31640ad9dc5870f152836d94bad0dd" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "chrono", "datafusion-common", @@ -2359,7 +2642,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7d7c3adf3db8bf61e92eb90cb659c8e8b734593a8f7c8e12a843c7ddba24b87e" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion-common", "indexmap 2.14.0", "itertools 0.14.0", @@ -2372,8 +2655,8 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f28aa4e10384e782774b10e72aca4d93ef7b31aa653095d9d4536b0a3dbc51b6" dependencies = [ - "arrow", - "arrow-buffer", + "arrow 58.2.0", + "arrow-buffer 58.2.0", "base64", "blake2", "blake3", @@ -2405,7 +2688,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "00aa6217e56098ba84e0a338176fe52f0a84cca398021512c6c8c5eff806d0ad" dependencies = [ "ahash", - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-doc", "datafusion-execution", @@ -2427,7 +2710,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b511250349407db7c43832ab2de63f5557b19a20dfd236b39ca2c04468b50d47" dependencies = [ "ahash", - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-expr-common", "datafusion-physical-expr-common", @@ -2439,8 +2722,8 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ef13a858e20d50f0a9bb5e96e7ac82b4e7597f247515bccca4fdd2992df0212a" dependencies = [ - "arrow", - "arrow-ord", + "arrow 58.2.0", + "arrow-ord 58.2.0", "datafusion-common", "datafusion-doc", "datafusion-execution", @@ -2464,7 +2747,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "72b40d3f5bbb3905f9ccb1ce9485a9595c77b69758a7c24d3ba79e334ff51e7e" dependencies = [ - "arrow", + "arrow 58.2.0", "async-trait", "datafusion-catalog", "datafusion-common", @@ -2480,7 +2763,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d4e88ec9d57c9b685d02f58bfee7be62d72610430ddcedb82a08e5d9925dbfb6" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-doc", "datafusion-expr", @@ -2519,7 +2802,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e929015451a67f77d9d8b727b2bf3a40c4445fdef6cdc53281d7d97c76888ace" dependencies = [ - "arrow", + "arrow 58.2.0", "chrono", "datafusion-common", "datafusion-expr", @@ -2539,7 +2822,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "4b1e68aba7a4b350401cfdf25a3d6f989ad898a7410164afe9ca52080244cb59" dependencies = [ "ahash", - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-expr", "datafusion-expr-common", @@ -2561,7 +2844,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ea22315f33cf2e0adc104e8ec42e285f6ed93998d565c65e82fec6a9ee9f9db4" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-expr", "datafusion-functions", @@ -2577,7 +2860,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b04b45ea8ad3ac2d78f2ea2a76053e06591c9629c7a603eda16c10649ecf4362" dependencies = [ "ahash", - "arrow", + "arrow 58.2.0", "chrono", "datafusion-common", "datafusion-expr-common", @@ -2593,7 +2876,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7cb13397809a425918f608dfe8653f332015a3e330004ab191b4404187238b95" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-execution", "datafusion-expr", @@ -2612,9 +2895,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5edc023675791af9d5fb4cc4c24abf5f7bd3bd4dcf9e5bd90ea1eff6976dcc79" dependencies = [ "ahash", - "arrow", - "arrow-ord", - "arrow-schema", + "arrow 58.2.0", + "arrow-ord 58.2.0", + "arrow-schema 58.2.0", "async-trait", "datafusion-common", "datafusion-common-runtime", @@ -2643,7 +2926,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ac8c76860e355616555081cab5968cec1af7a80701ff374510860bcd567e365a" dependencies = [ - "arrow", + "arrow 58.2.0", "datafusion-common", "datafusion-datasource", "datafusion-expr-common", @@ -2674,7 +2957,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e059dcf8544da0d6598d0235be3cc29c209094a5976b2e4822e4a2cf91c2b5c5" dependencies = [ - "arrow", + "arrow 58.2.0", "bigdecimal", "chrono", "crc32fast", @@ -2701,7 +2984,7 @@ version = "53.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "fa0d133ddf8b9b3b872acac900157f783e7b879fe9a6bccf389abebbfac45ec1" dependencies = [ - "arrow", + "arrow 58.2.0", "bigdecimal", "chrono", "datafusion-common", @@ -2722,6 +3005,48 @@ dependencies = [ "uuid", ] +[[package]] +name = "delta_kernel" +version = "0.19.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "06f7fc164b1557731fcc68a198e813811a000efade0f112d4f0a002e65042b83" +dependencies = [ + "arrow 57.3.1", + "bytes", + "chrono", + "comfy-table", + "crc", + "delta_kernel_derive", + "futures", + "indexmap 2.14.0", + "itertools 0.14.0", + "object_store 0.12.5", + "parquet 57.3.1", + "reqwest 0.12.28", + "roaring 0.11.3", + "rustc_version", + "serde", + "serde_json", + "strum", + "thiserror 2.0.18", + "tokio", + "tracing", + "url", + "uuid", + "z85", +] + +[[package]] +name = "delta_kernel_derive" +version = "0.19.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "86815a2c475835751ffa9b8d9ac8ed86cf86294304c42bedd1103d54f25ecbfe" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.117", +] + [[package]] name = "der" version = "0.7.10" @@ -2852,6 +3177,15 @@ dependencies = [ "const-random", ] +[[package]] +name = "document-features" +version = "0.2.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d4b8a88685455ed29a21542a33abd9cb6510b6b129abadabdcef0f4c55bc8f61" +dependencies = [ + "litrs", +] + [[package]] name = "dtor" version = "0.1.1" @@ -3573,14 +3907,14 @@ dependencies = [ "anyhow", "apache-avro", "array-init", - "arrow-arith", - "arrow-array", - "arrow-buffer", - "arrow-cast", - "arrow-ord", - "arrow-schema", - "arrow-select", - "arrow-string", + "arrow-arith 58.2.0", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-cast 58.2.0", + "arrow-ord 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", + "arrow-string 58.2.0", "as-any", "async-trait", "backon", @@ -3599,10 +3933,10 @@ dependencies = [ "murmur3", "once_cell", "ordered-float 4.6.0", - "parquet", + "parquet 58.1.0", "rand 0.9.4", "reqwest 0.12.28", - "roaring", + "roaring 0.11.3", "serde", "serde_bytes", "serde_derive", @@ -4203,6 +4537,12 @@ version = "0.8.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "92daf443525c4cce67b150400bc2316076100ce0b3686209eb8cf3c31612e6f0" +[[package]] +name = "litrs" +version = "1.0.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092" + [[package]] name = "lock_api" version = "0.4.14" @@ -4263,6 +4603,15 @@ version = "0.1.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "112b39cec0b298b6c1999fee3e31427f74f676e4cb9879ed1a121b43661a4154" +[[package]] +name = "lz4_flex" +version = "0.12.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "90071f8077f8e40adfc4b7fe9cd495ce316263f19e75c2211eeff3fdf475a3d9" +dependencies = [ + "twox-hash", +] + [[package]] name = "lz4_flex" version = "0.13.0" @@ -4517,6 +4866,44 @@ dependencies = [ "memchr", ] +[[package]] +name = "object_store" +version = "0.12.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fbfbfff40aeccab00ec8a910b57ca8ecf4319b335c542f2edcd19dd25a1e2a00" +dependencies = [ + "async-trait", + "base64", + "bytes", + "chrono", + "form_urlencoded", + "futures", + "http 1.4.0", + "http-body-util", + "httparse", + "humantime", + "hyper", + "itertools 0.14.0", + "md-5", + "parking_lot", + "percent-encoding", + "quick-xml 0.38.4", + "rand 0.9.4", + "reqwest 0.12.28", + "ring", + "rustls-pemfile", + "serde", + "serde_json", + "serde_urlencoded", + "thiserror 2.0.18", + "tokio", + "tracing", + "url", + "walkdir", + "wasm-bindgen-futures", + "web-time", +] + [[package]] name = "object_store" version = "0.13.2" @@ -4568,7 +4955,7 @@ dependencies = [ "chrono", "futures", "mea", - "object_store", + "object_store 0.13.2", "opendal 0.56.0", "pin-project", "tokio", @@ -4797,6 +5184,43 @@ dependencies = [ "windows-link", ] +[[package]] +name = "parquet" +version = "57.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2e832c6aa20310fc6de7ea5a3f4e20d34fd83e3b43229d32b81ffe5c14d74692" +dependencies = [ + "ahash", + "arrow-array 57.3.1", + "arrow-buffer 57.3.1", + "arrow-cast 57.3.1", + "arrow-data 57.3.1", + "arrow-ipc 57.3.1", + "arrow-schema 57.3.1", + "arrow-select 57.3.1", + "base64", + "brotli", + "bytes", + "chrono", + "flate2", + "futures", + "half", + "hashbrown 0.16.1", + "lz4_flex 0.12.2", + "num-bigint", + "num-integer", + "num-traits", + "object_store 0.12.5", + "paste", + "seq-macro", + "simdutf8", + "snap", + "thrift", + "tokio", + "twox-hash", + "zstd", +] + [[package]] name = "parquet" version = "58.1.0" @@ -4804,12 +5228,12 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7d3f9f2205199603564127932b89695f52b62322f541d0fc7179d57c2e1c9877" dependencies = [ "ahash", - "arrow-array", - "arrow-buffer", - "arrow-data", - "arrow-ipc", - "arrow-schema", - "arrow-select", + "arrow-array 58.2.0", + "arrow-buffer 58.2.0", + "arrow-data 58.2.0", + "arrow-ipc 58.2.0", + "arrow-schema 58.2.0", + "arrow-select 58.2.0", "base64", "brotli", "bytes", @@ -4818,11 +5242,11 @@ dependencies = [ "futures", "half", "hashbrown 0.16.1", - "lz4_flex", + "lz4_flex 0.13.0", "num-bigint", "num-integer", "num-traits", - "object_store", + "object_store 0.13.2", "parquet-variant", "parquet-variant-compute", "parquet-variant-json", @@ -4843,7 +5267,7 @@ version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "2bf493f3c9ddd984d0efb019f67343e4aa4bab893931f6a14b82083065dc3d28" dependencies = [ - "arrow-schema", + "arrow-schema 58.2.0", "chrono", "half", "indexmap 2.14.0", @@ -4857,8 +5281,8 @@ version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6ac038d46a503a7d563b4f5df5802c4315d5343d009feab195d15ac512b4cb27" dependencies = [ - "arrow", - "arrow-schema", + "arrow 58.2.0", + "arrow-schema 58.2.0", "chrono", "half", "indexmap 2.14.0", @@ -4874,7 +5298,7 @@ version = "58.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "015a09c2ffe5108766c7c1235c307b8a3c2ea64eca38455ba1a7f3a7f32f16e2" dependencies = [ - "arrow-schema", + "arrow-schema 58.2.0", "base64", "chrono", "parquet-variant", @@ -5695,6 +6119,16 @@ dependencies = [ "windows-sys 0.52.0", ] +[[package]] +name = "roaring" +version = "0.10.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "19e8d2cfa184d94d0726d650a9f4a1be7f9b76ac9fdb954219878dc00c1c1e7b" +dependencies = [ + "bytemuck", + "byteorder", +] + [[package]] name = "roaring" version = "0.11.3" @@ -5816,6 +6250,15 @@ dependencies = [ "security-framework", ] +[[package]] +name = "rustls-pemfile" +version = "2.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dce314e5fee3f39953d46bb63bb8a46d40c2f8fb7cc5a3b6cab2bde9721d6e50" +dependencies = [ + "rustls-pki-types", +] + [[package]] name = "rustls-pki-types" version = "1.14.0" @@ -6703,6 +7146,7 @@ version = "0.1.44" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "63e71662fa4b2a2c3a26f570f037eb95bb1f85397f3cd8076caed2f026a6d100" dependencies = [ + "log", "pin-project-lite", "tracing-attributes", "tracing-core", @@ -6901,6 +7345,7 @@ checksum = "ddd74a9687298c6858e9b88ec8935ec45d22e8fd5e6394fa1bd4e99a87789c76" dependencies = [ "getrandom 0.4.2", "js-sys", + "rand 0.10.1", "serde_core", "wasm-bindgen", ] @@ -7575,6 +8020,12 @@ dependencies = [ "synstructure", ] +[[package]] +name = "z85" +version = "3.0.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c6e61e59a957b7ccee15d2049f86e8bfd6f66968fcd88f018950662d9b86e675" + [[package]] name = "zerocopy" version = "0.8.48" diff --git a/native/Cargo.toml b/native/Cargo.toml index d1b5c74af9..4923a75008 100644 --- a/native/Cargo.toml +++ b/native/Cargo.toml @@ -18,6 +18,10 @@ [workspace] default-members = ["core", "spark-expr", "common", "proto", "jni-bridge", "shuffle"] members = ["core", "spark-expr", "common", "proto", "jni-bridge", "shuffle", "hdfs", "fs-hdfs"] +# The contrib crate at ../contrib/delta/native is intentionally NOT a workspace member +# (workspace members must live hierarchically under the workspace root). It's pulled in +# as a path dep by `core/Cargo.toml` when the `contrib-delta` feature is enabled. +exclude = ["../contrib"] resolver = "2" [workspace.package] diff --git a/native/core/Cargo.toml b/native/core/Cargo.toml index 4fb3ed4c5d..e5cacd1b09 100644 --- a/native/core/Cargo.toml +++ b/native/core/Cargo.toml @@ -69,6 +69,9 @@ aws-config = { workspace = true } aws-credential-types = { workspace = true } parking_lot = "0.12.5" datafusion-comet-objectstore-hdfs = { path = "../hdfs", optional = true, default-features = false, features = ["hdfs"] } +# Optional Delta Lake contrib (enabled by the `contrib-delta` feature). Source lives +# under `contrib/delta/native/` so non-Delta committers can ignore it. +comet-contrib-delta = { path = "../../contrib/delta/native", optional = true } reqwest = { version = "0.12", default-features = false, features = ["rustls-tls-native-roots", "http2"] } object_store_opendal = { version = "0.56.0", optional = true } hdfs-sys = {version = "0.3", optional = true, features = ["hdfs_3_3"]} @@ -99,6 +102,10 @@ default = ["hdfs-opendal"] hdfs = ["datafusion-comet-objectstore-hdfs"] hdfs-opendal = ["opendal", "object_store_opendal", "hdfs-sys"] jemalloc = ["tikv-jemallocator", "tikv-jemalloc-ctl"] +# Delta Lake integration. When enabled, links the `comet-contrib-delta` crate +# into `libcomet` and activates the `OpStruct::DeltaScan` dispatcher arm. +# Default builds carry zero Delta surface. +contrib-delta = ["dep:comet-contrib-delta"] # exclude optional packages from cargo machete verifications [package.metadata.cargo-machete] diff --git a/native/core/src/execution/jni_api.rs b/native/core/src/execution/jni_api.rs index f5b04cc51d..f3f538004f 100644 --- a/native/core/src/execution/jni_api.rs +++ b/native/core/src/execution/jni_api.rs @@ -232,6 +232,7 @@ fn op_name(op: &OpStruct) -> &'static str { OpStruct::Explode(_) => "Explode", OpStruct::CsvScan(_) => "CsvScan", OpStruct::ShuffleScan(_) => "ShuffleScan", + OpStruct::DeltaScan(_) => "DeltaScan", } } diff --git a/native/core/src/execution/planner.rs b/native/core/src/execution/planner.rs index b00f140026..50651c5c6c 100644 --- a/native/core/src/execution/planner.rs +++ b/native/core/src/execution/planner.rs @@ -21,6 +21,9 @@ pub mod expression_registry; pub mod macros; pub mod operator_registry; +#[cfg(feature = "contrib-delta")] +mod contrib_delta_scan; + use crate::execution::operators::init_csv_datasource_exec; use crate::execution::operators::IcebergScanExec; use crate::execution::{ @@ -1376,6 +1379,10 @@ impl PhysicalPlanner { common.encryption_enabled, common.use_field_id, common.ignore_missing_field_id, + // ignore_missing_files: not exposed through the native_datafusion + // scan proto today. Contribs (e.g. Delta) wire this through directly + // when they call init_datasource_exec. + false, )?; Ok(( vec![], @@ -1492,6 +1499,26 @@ impl PhysicalPlanner { )), )) } + OpStruct::DeltaScan(scan) => { + // Delta Lake scan -- handled by the optional `contrib/delta/` integration. + // The dispatcher arm exists unconditionally so a default build that receives + // a Delta-shaped plan from a misconfigured driver gets a clear error instead + // of a "no match" decode failure. + #[cfg(not(feature = "contrib-delta"))] + { + let _ = scan; + Err(GeneralError( + "Received a DeltaScan operator but core was built without the \ + `contrib-delta` Cargo feature. Rebuild with \ + `cargo build --features contrib-delta` to enable Delta Lake support." + .into(), + )) + } + #[cfg(feature = "contrib-delta")] + { + self.plan_delta_scan(spark_plan, scan) + } + } OpStruct::ShuffleWriter(writer) => { assert_eq!(children.len(), 1); let (scans, shuffle_scans, child) = diff --git a/native/core/src/execution/planner/contrib_delta_scan.rs b/native/core/src/execution/planner/contrib_delta_scan.rs new file mode 100644 index 0000000000..33c8e91cc2 --- /dev/null +++ b/native/core/src/execution/planner/contrib_delta_scan.rs @@ -0,0 +1,350 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! `OpStruct::DeltaScan` planner body, feature-gated behind `contrib-delta`. +//! +//! This module mirrors the size and shape of the `OpStruct::IcebergScan` arm in +//! `super::planner` -- the arm itself stays tiny (just dispatches here), and the +//! Delta-specific algorithmic pieces (DV filter exec wrapping, column-mapping +//! rename projection, partition value parsing) live in the +//! [`comet_contrib_delta`] crate. + +#![cfg(feature = "contrib-delta")] + +use std::collections::HashMap; +use std::sync::Arc; + +use comet_contrib_delta::planner::{ + build_delta_partitioned_files, ColumnMappingFilterRewriter, +}; +use comet_contrib_delta::DeltaDvFilterExec; +use datafusion::arrow::datatypes::{Field, Schema, SchemaRef}; +use datafusion::common::tree_node::{TransformedResult, TreeNode}; +use datafusion::datasource::listing::PartitionedFile; +use datafusion::physical_expr::PhysicalExpr; +use datafusion::physical_plan::empty::EmptyExec; +use datafusion::physical_plan::expressions::Column; +use datafusion::physical_plan::projection::ProjectionExec; +use datafusion_comet_proto::spark_operator::{DeltaScan, Operator}; + +use crate::execution::operators::ExecutionError; +use crate::execution::operators::ExecutionError::GeneralError; +use crate::execution::planner::convert_spark_types_to_arrow_schema; +use crate::execution::planner::PhysicalPlanner; +use crate::execution::planner::PlanCreationResult; +use crate::execution::spark_plan::SparkPlan; +use crate::parquet::parquet_exec::init_datasource_exec; +use crate::parquet::parquet_support::prepare_object_store_with_configs; + +impl PhysicalPlanner { + pub(crate) fn plan_delta_scan( + &self, + spark_plan: &Operator, + scan: &DeltaScan, + ) -> PlanCreationResult { + let common = scan + .common + .as_ref() + .ok_or_else(|| GeneralError("DeltaScan missing common data".into()))?; + + let required_schema: SchemaRef = + convert_spark_types_to_arrow_schema(common.required_schema.as_slice()); + let mut data_schema: SchemaRef = + convert_spark_types_to_arrow_schema(common.data_schema.as_slice()); + let partition_schema: SchemaRef = + convert_spark_types_to_arrow_schema(common.partition_schema.as_slice()); + + // Column mapping: substitute physical names into data_schema so ParquetSource + // projects by the names actually in the file. A rename projection on top maps + // physical names back to the logical names upstream operators expect. + let logical_to_physical: HashMap = common + .column_mappings + .iter() + .map(|cm| (cm.logical_name.clone(), cm.physical_name.clone())) + .collect(); + let has_column_mapping = !logical_to_physical.is_empty(); + if has_column_mapping { + let new_fields: Vec<_> = data_schema + .fields() + .iter() + .map(|f| { + if let Some(physical) = logical_to_physical.get(f.name()) { + Arc::new(Field::new( + physical, + f.data_type().clone(), + f.is_nullable(), + )) + } else { + Arc::clone(f) + } + }) + .collect(); + data_schema = Arc::new(Schema::new(new_fields)); + } + let projection_vector: Vec = common + .projection_vector + .iter() + .map(|offset| *offset as usize) + .collect(); + + // Empty-partition fast path. + if scan.tasks.is_empty() { + return Ok(( + vec![], + vec![], + Arc::new(SparkPlan::new( + spark_plan.plan_id, + Arc::new(EmptyExec::new(required_schema)), + vec![], + )), + )); + } + + // Build pushed-down data filters, rewriting Column refs to physical names when + // column mapping is active. + let data_filters: Result>, ExecutionError> = common + .data_filters + .iter() + .map(|expr| { + let filter = self + .create_expr(expr, Arc::clone(&required_schema)) + .map_err(|e| GeneralError(format!("DeltaScan filter: {e}")))?; + if has_column_mapping { + let mut rewriter = ColumnMappingFilterRewriter { + logical_to_physical: &logical_to_physical, + data_schema: &data_schema, + }; + filter + .rewrite(&mut rewriter) + .data() + .map_err(|e| GeneralError(format!("ColumnMappingFilterRewriter: {e}"))) + } else { + Ok(filter) + } + }) + .collect(); + + let object_store_options: HashMap = common + .object_store_options + .iter() + .map(|(k, v)| (k.clone(), v.clone())) + .collect(); + + // Build PartitionedFiles. Kernel has already resolved each file path to an + // absolute URL on the driver, so we thread them straight through. Delta stores + // TIMESTAMP partition values in the JVM default TZ; pass the session TZ so + // partition-value parsing produces the correct instant. + let files = build_delta_partitioned_files( + &scan.tasks, + partition_schema.as_ref(), + common.session_timezone.as_str(), + ) + .map_err(GeneralError)?; + + // Split files by DV presence -- each DV'd file becomes its own FileGroup so the + // DeltaDvFilterExec's per-partition mapping is 1:1 with one physical parquet + // file. All non-DV files go in a single combined group. + // + // EXCEPT when ANY synthetic column is emitted: the per-partition row offset + // counter in DeltaSyntheticColumnsExec doesn't reset across files within a + // FileGroup, and every synthetic we emit depends on per-file row position + // (row_index is per-file by definition; is_row_deleted uses a per-file DV; + // row_id = baseRowId + physical_row_index is per-file; row_commit_version is + // per-file constant). So when ANY emit is on, give each file its own group + // regardless of DV presence so the per-file lookup is well-defined. + let need_per_file_groups = common.emit_row_index + || common.emit_is_row_deleted + || common.emit_row_id + || common.emit_row_commit_version; + let mut file_groups: Vec> = Vec::new(); + let mut deleted_indexes_per_group: Vec> = Vec::new(); + let mut base_row_ids_per_group: Vec> = Vec::new(); + let mut default_commit_versions_per_group: Vec> = Vec::new(); + let mut non_dv_files: Vec = Vec::new(); + for (file, task) in files.into_iter().zip(scan.tasks.iter()) { + if !task.deleted_row_indexes.is_empty() || need_per_file_groups { + file_groups.push(vec![file]); + deleted_indexes_per_group.push(task.deleted_row_indexes.clone()); + base_row_ids_per_group.push(task.base_row_id); + default_commit_versions_per_group.push(task.default_row_commit_version); + } else { + non_dv_files.push(file); + } + } + if !non_dv_files.is_empty() { + file_groups.push(non_dv_files); + deleted_indexes_per_group.push(Vec::new()); + base_row_ids_per_group.push(None); + default_commit_versions_per_group.push(None); + } + + // Pick any one file to register the object store (they all share the same root). + let one_file = scan + .tasks + .first() + .map(|t| t.file_path.clone()) + .ok_or_else(|| { + GeneralError("DeltaScan has no tasks after split-mode injection".into()) + })?; + let url = url::Url::parse(&one_file) + .map_err(|e| GeneralError(format!("DeltaScan invalid file URL: {e}")))?; + let (object_store_url, _root_path) = prepare_object_store_with_configs( + self.session_ctx().runtime_env(), + url.to_string(), + &object_store_options, + ) + .map_err(|e| GeneralError(format!("prepare_object_store_with_configs: {e}")))?; + + let delta_exec = init_datasource_exec( + Arc::clone(&required_schema), + Some(data_schema), + Some(partition_schema), + object_store_url, + file_groups, + Some(projection_vector), + Some(data_filters?), + None, // default_values + common.session_timezone.as_str(), + common.case_sensitive, + false, // return_null_struct_if_all_fields_missing + self.session_ctx(), + false, // encryption_enabled (Delta tables we natively support are unencrypted) + common.use_field_id, + false, // ignore_missing_field_id + common.ignore_missing_files, + )?; + + // Three mutually-exclusive wrap modes based on what the surrounding plan asks + // for: + // - Delta synthetic columns requested (row_index and/or is_row_deleted): wrap + // with DeltaSyntheticColumnsExec which keeps all rows and APPENDS the + // columns. The outer Delta plan (typically UPDATE/DELETE/MERGE) decides + // what to do with the deletion flag. + // - DV present and no synthetics: wrap with DeltaDvFilterExec which DROPS + // deleted rows inline (standard read path). + // - Neither: pass through (avoids per-batch overhead). + let need_synthetics = common.emit_row_index + || common.emit_is_row_deleted + || common.emit_row_id + || common.emit_row_commit_version; + + // Column-mapping rename has to happen BEFORE synthetic emission so that the + // synthetic exec sees logical column names in its input schema (matching what + // its build_output_schema expects) and so that the (stripped) `required_schema` + // we use here for the rename match isn't compared against a schema that already + // has synthetics appended. Synthetic columns have FIXED names + // (`__delta_internal_*`, `row_id`, `row_commit_version`) and aren't subject to + // CM-name physical renames -- so it's correct to apply the rename to the + // parquet output BEFORE the append. + let delta_exec: Arc = delta_exec; + let scan_out = delta_exec.schema(); + let needs_rename = has_column_mapping + && required_schema.fields().len() == scan_out.fields().len() + && required_schema + .fields() + .iter() + .zip(scan_out.fields().iter()) + .any(|(req, phys)| req.name() != phys.name()); + let after_rename: Arc = if needs_rename { + let phys_to_logical: HashMap<&str, &str> = scan_out + .fields() + .iter() + .zip(required_schema.fields().iter()) + .map(|(phys, req)| (phys.name().as_str(), req.name().as_str())) + .collect(); + let projections: Vec<(Arc, String)> = scan_out + .fields() + .iter() + .enumerate() + .map(|(idx, phys_field)| { + let col: Arc = + Arc::new(Column::new(phys_field.name(), idx)); + let alias = phys_to_logical + .get(phys_field.name().as_str()) + .map(|s| s.to_string()) + .unwrap_or_else(|| phys_field.name().clone()); + (col, alias) + }) + .collect(); + Arc::new( + ProjectionExec::try_new(projections, delta_exec) + .map_err(|e| GeneralError(format!("rename ProjectionExec: {e}")))?, + ) + } else { + delta_exec + }; + + // After CM-name rename: apply synthetic emission OR DV filter OR passthrough. + let after_synthetics: Arc = if need_synthetics + { + Arc::new( + comet_contrib_delta::synthetic_columns::DeltaSyntheticColumnsExec::new( + after_rename, + deleted_indexes_per_group, + base_row_ids_per_group, + default_commit_versions_per_group, + common.emit_row_index, + common.emit_is_row_deleted, + common.emit_row_id, + common.emit_row_commit_version, + ) + .map_err(|e| GeneralError(format!("DeltaSyntheticColumnsExec: {e}")))?, + ) + } else if deleted_indexes_per_group.iter().any(|v| !v.is_empty()) { + Arc::new( + DeltaDvFilterExec::new(after_rename, deleted_indexes_per_group) + .map_err(|e| GeneralError(format!("DeltaDvFilterExec: {e}")))?, + ) + } else { + after_rename + }; + + // If synthetic columns aren't a suffix of the user-visible required_schema, + // `final_output_indices` is set and we project to reorder. Each entry is an + // index into the wrapped exec's output schema (parquet columns first, then + // appended synthetics in the canonical row_index/is_row_deleted/row_id/ + // row_commit_version order). Empty => already in the right order. + let with_rename: Arc = if !common + .final_output_indices + .is_empty() + { + let wrapped_schema = after_synthetics.schema(); + let projections: Vec<(Arc, String)> = common + .final_output_indices + .iter() + .map(|idx| { + let i = *idx as usize; + let field = wrapped_schema.field(i); + let col: Arc = Arc::new(Column::new(field.name(), i)); + (col, field.name().clone()) + }) + .collect(); + Arc::new( + ProjectionExec::try_new(projections, after_synthetics) + .map_err(|e| GeneralError(format!("final reorder ProjectionExec: {e}")))?, + ) + } else { + after_synthetics + }; + + Ok(( + vec![], + vec![], + Arc::new(SparkPlan::new(spark_plan.plan_id, with_rename, vec![])), + )) + } +} diff --git a/native/core/src/execution/planner/operator_registry.rs b/native/core/src/execution/planner/operator_registry.rs index eb31184461..316ada0a77 100644 --- a/native/core/src/execution/planner/operator_registry.rs +++ b/native/core/src/execution/planner/operator_registry.rs @@ -151,5 +151,6 @@ fn get_operator_type(spark_operator: &Operator) -> Option { OpStruct::Explode(_) => None, // Not yet in OperatorType enum OpStruct::CsvScan(_) => Some(OperatorType::CsvScan), OpStruct::ShuffleScan(_) => None, // Not yet in OperatorType enum + OpStruct::DeltaScan(_) => None, // Optional contrib; not in OperatorType enum } } diff --git a/native/core/src/parquet/missing_file_tolerant.rs b/native/core/src/parquet/missing_file_tolerant.rs new file mode 100644 index 0000000000..6432c9bd00 --- /dev/null +++ b/native/core/src/parquet/missing_file_tolerant.rs @@ -0,0 +1,232 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Tolerant FileSource / FileOpener decorators for honouring Spark's +//! `spark.sql.files.ignoreMissingFiles`. +//! +//! DataFusion's `DataSourceExec` constructs its `FileStream` with the default +//! `OnError::Fail` and provides no public knob to flip it to `OnError::Skip`, +//! so Spark's "silently skip files that disappeared between planning and +//! execution" semantics cannot be opted into via a config. Instead we wrap the +//! inner `FileOpener`: when the opener's future resolves to a NotFound error +//! we return an empty `BoxStream` so the file contributes zero batches and the +//! `FileStream` simply moves to the next file. +//! +//! All other `FileSource` trait methods delegate to the inner source verbatim +//! so optimizer hooks (`try_pushdown_filters`, `repartitioned`, ...) keep +//! their normal behaviour. + +use arrow::array::RecordBatch; +use datafusion::common::Result; +use datafusion::datasource::listing::PartitionedFile; +use datafusion::datasource::physical_plan::{FileScanConfig, FileSource}; +use datafusion::physical_expr::{EquivalenceProperties, LexOrdering, PhysicalExpr, PhysicalSortExpr}; +use datafusion::physical_plan::filter_pushdown::FilterPushdownPropagation; +use datafusion::physical_plan::metrics::ExecutionPlanMetricsSet; +use datafusion::physical_plan::projection::ProjectionExprs; +use datafusion::physical_plan::sort_pushdown::SortOrderPushdownResult; +use datafusion::physical_plan::DisplayFormatType; +use datafusion_datasource::file_stream::{FileOpenFuture, FileOpener}; +use datafusion_datasource::table_schema::TableSchema; +use futures::{stream, FutureExt, StreamExt}; +use object_store::ObjectStore; +use std::any::Any; +use std::fmt::{self, Formatter}; +use std::sync::Arc; + +/// FileOpener decorator that converts NotFound errors from the wrapped +/// opener's future into an empty `BoxStream`. Any other error is propagated +/// unchanged so we don't paper over real corruption / IO problems. +pub(crate) struct IgnoreMissingFileOpener { + inner: Arc, +} + +impl IgnoreMissingFileOpener { + pub(crate) fn new(inner: Arc) -> Self { + Self { inner } + } +} + +fn is_not_found(err: &datafusion::error::DataFusionError) -> bool { + use datafusion::error::DataFusionError; + // object_store wraps NotFound into ObjectStore; parquet's reader may surface it via External. + // We inspect both the variant and its display chain for the literal "not found" / + // "NotFound" token because the precise DataFusion wrapping differs by error path. + let mut current: &(dyn std::error::Error + 'static) = err; + loop { + if let Some(os_err) = current.downcast_ref::() { + if matches!(os_err, object_store::Error::NotFound { .. }) { + return true; + } + } + if let Some(io_err) = current.downcast_ref::() { + if io_err.kind() == std::io::ErrorKind::NotFound { + return true; + } + } + match current.source() { + Some(src) => current = src, + None => break, + } + } + // Display-based fallback for adapters that erase the underlying type + // (e.g. parquet's ParquetError -> DataFusionError::External). Anchored to + // recognised NotFound phrasings only -- a loose substring match on "not found" + // would silently swallow unrelated parquet messages like "row group statistics + // not found" or "page index not found" and produce wrong empty results. + let msg = err.to_string(); + matches!(err, DataFusionError::External(_) | DataFusionError::ObjectStore(_)) + && (msg.contains("Object at location") + || msg.contains("Generic NotFound") + || msg.contains("NoSuchKey") + || msg.contains("NoSuchFile") + || msg.contains("No such file or directory")) +} + +impl FileOpener for IgnoreMissingFileOpener { + fn open(&self, partitioned_file: PartitionedFile) -> Result { + let inner_future = self.inner.open(partitioned_file)?; + Ok(Box::pin(inner_future.map(|opened| match opened { + Ok(stream) => Ok(stream), + Err(e) if is_not_found(&e) => { + let empty = stream::empty::>(); + Ok(empty.boxed()) + } + Err(e) => Err(e), + }))) + } +} + +/// FileSource decorator that wraps the inner source's `FileOpener` in +/// `IgnoreMissingFileOpener`. All other methods delegate verbatim. +#[derive(Clone)] +pub(crate) struct IgnoreMissingFileSource { + inner: Arc, +} + +impl IgnoreMissingFileSource { + pub(crate) fn new(inner: Arc) -> Arc { + Arc::new(Self { inner }) + } +} + +impl FileSource for IgnoreMissingFileSource { + fn create_file_opener( + &self, + object_store: Arc, + base_config: &FileScanConfig, + partition: usize, + ) -> Result> { + let inner_opener = + self.inner + .create_file_opener(object_store, base_config, partition)?; + Ok(Arc::new(IgnoreMissingFileOpener::new(inner_opener))) + } + + fn as_any(&self) -> &dyn Any { + self + } + + fn table_schema(&self) -> &TableSchema { + self.inner.table_schema() + } + + fn with_batch_size(&self, batch_size: usize) -> Arc { + // Re-wrap so the batch-sized clone still skips missing files. + IgnoreMissingFileSource::new(self.inner.with_batch_size(batch_size)) + } + + fn filter(&self) -> Option> { + self.inner.filter() + } + + fn projection(&self) -> Option<&ProjectionExprs> { + self.inner.projection() + } + + fn metrics(&self) -> &ExecutionPlanMetricsSet { + self.inner.metrics() + } + + fn file_type(&self) -> &str { + self.inner.file_type() + } + + fn fmt_extra(&self, t: DisplayFormatType, f: &mut Formatter) -> fmt::Result { + self.inner.fmt_extra(t, f) + } + + fn supports_repartitioning(&self) -> bool { + self.inner.supports_repartitioning() + } + + fn repartitioned( + &self, + target_partitions: usize, + repartition_file_min_size: usize, + output_ordering: Option, + config: &FileScanConfig, + ) -> Result> { + self.inner.repartitioned( + target_partitions, + repartition_file_min_size, + output_ordering, + config, + ) + } + + fn try_pushdown_filters( + &self, + filters: Vec>, + config: &datafusion::config::ConfigOptions, + ) -> Result>> { + let prop = self.inner.try_pushdown_filters(filters, config)?; + // Re-wrap the updated_node so the post-pushdown FileSource keeps the + // missing-file tolerance. + Ok(FilterPushdownPropagation { + filters: prop.filters, + updated_node: prop.updated_node.map(IgnoreMissingFileSource::new), + }) + } + + fn try_pushdown_sort( + &self, + order: &[PhysicalSortExpr], + eq_properties: &EquivalenceProperties, + ) -> Result>> { + match self.inner.try_pushdown_sort(order, eq_properties)? { + SortOrderPushdownResult::Exact { inner } => Ok(SortOrderPushdownResult::Exact { + inner: IgnoreMissingFileSource::new(inner), + }), + SortOrderPushdownResult::Inexact { inner } => Ok(SortOrderPushdownResult::Inexact { + inner: IgnoreMissingFileSource::new(inner), + }), + SortOrderPushdownResult::Unsupported => Ok(SortOrderPushdownResult::Unsupported), + } + } + + fn try_pushdown_projection( + &self, + projection: &ProjectionExprs, + ) -> Result>> { + Ok(self + .inner + .try_pushdown_projection(projection)? + .map(IgnoreMissingFileSource::new)) + } +} + diff --git a/native/core/src/parquet/mod.rs b/native/core/src/parquet/mod.rs index 5de14aa610..58e55ca132 100644 --- a/native/core/src/parquet/mod.rs +++ b/native/core/src/parquet/mod.rs @@ -29,6 +29,7 @@ pub mod read; pub mod schema_adapter; mod cast_column; +mod missing_file_tolerant; mod objectstore; use std::collections::HashMap; @@ -519,6 +520,8 @@ pub unsafe extern "system" fn Java_org_apache_comet_parquet_Native_initRecordBat // so the native side does not need to do field-ID matching here. false, false, + // ignore_missing_files: iceberg-compat doesn't surface Spark's flag today. + false, )?; let partition_index: usize = 0; diff --git a/native/core/src/parquet/parquet_exec.rs b/native/core/src/parquet/parquet_exec.rs index 38a0755658..f497a2ac7b 100644 --- a/native/core/src/parquet/parquet_exec.rs +++ b/native/core/src/parquet/parquet_exec.rs @@ -17,6 +17,7 @@ use crate::execution::operators::ExecutionError; use crate::parquet::encryption_support::{CometEncryptionConfig, ENCRYPTION_FACTORY_ID}; +use crate::parquet::missing_file_tolerant::IgnoreMissingFileSource; use crate::parquet::parquet_read_cached_factory::CachingParquetReaderFactory; use crate::parquet::parquet_support::SparkParquetOptions; use crate::parquet::schema_adapter::SparkPhysicalExprAdapterFactory; @@ -74,6 +75,10 @@ pub(crate) fn init_datasource_exec( encryption_enabled: bool, use_field_id: bool, ignore_missing_field_id: bool, + // When true, files that fail to open with NotFound errors are silently skipped + // (matches Spark's `spark.sql.files.ignoreMissingFiles=true`). Wired through to + // a `FileSource` decorator that swallows the error as an empty stream. + ignore_missing_files: bool, ) -> Result, ExecutionError> { let (table_parquet_options, mut spark_parquet_options) = get_options( session_timezone, @@ -168,6 +173,15 @@ pub(crate) fn init_datasource_exec( _ => Arc::new(parquet_source), }; + // Honour Spark's `ignoreMissingFiles` by wrapping the final FileSource so its + // FileOpener swallows object-store NotFound errors as empty streams. Wrapped + // after pushdown to preserve all optimizer behaviour. + let file_source: Arc = if ignore_missing_files { + IgnoreMissingFileSource::new(file_source) + } else { + file_source + }; + let expr_adapter_factory: Arc = Arc::new( SparkPhysicalExprAdapterFactory::new(spark_parquet_options, default_values), ); diff --git a/native/proto/src/proto/operator.proto b/native/proto/src/proto/operator.proto index 7cefe06da7..f6424b9f26 100644 --- a/native/proto/src/proto/operator.proto +++ b/native/proto/src/proto/operator.proto @@ -53,6 +53,10 @@ message Operator { Explode explode = 114; CsvScan csv_scan = 115; ShuffleScan shuffle_scan = 116; + // Delta Lake scan. Wire format used by `contrib/delta/`. Only decoded when + // core is built with `--features contrib-delta`; in default builds the + // dispatcher arm is `#[cfg]`-stubbed out so the contrib has zero runtime cost. + DeltaScan delta_scan = 117; } } @@ -265,6 +269,108 @@ message IcebergDeleteFile { uint64 file_size_in_bytes = 5; } +// ===================================================================================== +// Delta Lake scan messages -- consumed by the optional `contrib/delta/` integration. +// Field numbers must remain stable; older serialised plans encode them on the wire. +// ===================================================================================== + +// Per-scan invariants. Lives at the head of every Delta scan operator payload. +message DeltaScanCommon { + // Required schema (projection applied to data schema). + repeated SparkStructField required_schema = 1; + // Full data schema of the table (on-disk parquet schema). + repeated SparkStructField data_schema = 2; + // Partition columns (stored in add actions, not in file paths). + repeated SparkStructField partition_schema = 3; + // Pushed-down data filters (Catalyst translated). + repeated spark.spark_expression.Expr data_filters = 4; + // Column projection indices into (data_schema ++ partition_schema). + repeated int64 projection_vector = 5; + string session_timezone = 6; + bool case_sensitive = 7; + // Cloud storage credentials / options, keyed by Hadoop-style names. + map object_store_options = 8; + // Root path of the Delta table (file:// or s3://...). + string table_root = 9; + // Optional snapshot version; unset = latest. + optional uint64 snapshot_version = 10; + // Per-file parallelism knob within a single Spark task. + uint32 data_file_concurrency_limit = 11; + // Informational label for debug output. + string source = 12; + // Logical->physical column mapping (column_mapping_mode = id or name). + repeated DeltaColumnMapping column_mappings = 13; + optional string materialized_row_id_column_name = 14; + optional string materialized_row_commit_version_column_name = 15; + // When true the DataSourceExec swallows object-store NotFound errors as empty + // streams; matches Spark's `spark.sql.files.ignoreMissingFiles=true` semantics. + bool ignore_missing_files = 16; + // When true the native parquet reader matches Spark schema fields to parquet + // file fields by `PARQUET:field_id` metadata rather than by name. Used by + // Delta tables with `delta.columnMapping.mode = id` (the contrib copies + // `delta.columnMapping.id` to `PARQUET:field_id` on each schema field). + bool use_field_id = 17; + // Delta synthetic columns to append to the scan output via + // `DeltaSyntheticColumnsExec`. The corresponding columns must NOT appear in + // `data_schema` or `required_schema` -- they're synthesised at exec time + // from row positions / DV bitmaps and inserted after the parquet read. + bool emit_row_index = 18; + bool emit_is_row_deleted = 19; + // Row-tracking synthesis (row_id = baseRowId + physical_row_index, + // row_commit_version = defaultRowCommitVersion). Per-task baseRowId / + // defaultRowCommitVersion travel on `DeltaScanTask` (fields 6 + 7). + bool emit_row_id = 20; + bool emit_row_commit_version = 21; + // When synthetic columns aren't a contiguous suffix of `required_schema`, this + // sequence tells the native dispatcher how to project the final output. Each + // entry is an index into [parquet_output, appended_synthetics] -- positive + // means a column from the (stripped) parquet read at that index; the synthetic + // tail comes after, in the canonical row_index, is_row_deleted, row_id, + // row_commit_version order. Empty = synthetics are already a suffix; no + // reorder needed. + repeated int32 final_output_indices = 22; +} + +// Operator payload for a Delta scan. +message DeltaScan { + DeltaScanCommon common = 1; + repeated DeltaScanTask tasks = 2; +} + +message DeltaScanTask { + string file_path = 1; + uint64 file_size = 2; + optional uint64 record_count = 3; + repeated DeltaPartitionValue partition_values = 4; + // Pre-materialised deleted row indexes (kernel resolves the DV bitmap on the driver). + // Empty = no DV on this file. + repeated uint64 deleted_row_indexes = 5; + optional int64 base_row_id = 6; + optional int64 default_row_commit_version = 7; + // File-splitting byte range. Both unset = read whole file. + optional uint64 byte_range_start = 8; + optional uint64 byte_range_end = 9; +} + +message DeltaPartitionValue { + string name = 1; + optional string value = 2; +} + +// JNI return value from planDeltaScan -- the list of tasks for a given snapshot. +message DeltaScanTaskList { + uint64 snapshot_version = 1; + string table_root = 2; + repeated DeltaScanTask tasks = 3; + repeated string unsupported_features = 4; + repeated DeltaColumnMapping column_mappings = 5; +} + +message DeltaColumnMapping { + string logical_name = 1; + string physical_name = 2; +} + message Projection { repeated spark.spark_expression.Expr project_list = 1; } diff --git a/native/shuffle/src/spark_unsafe/unsafe_object.rs b/native/shuffle/src/spark_unsafe/unsafe_object.rs index f32ea8c23b..b810b7f771 100644 --- a/native/shuffle/src/spark_unsafe/unsafe_object.rs +++ b/native/shuffle/src/spark_unsafe/unsafe_object.rs @@ -19,7 +19,6 @@ use super::list::SparkUnsafeArray; use super::map::SparkUnsafeMap; use super::row::SparkUnsafeRow; use datafusion_comet_common::bytes_to_i128; -use std::str::from_utf8; const MAX_LONG_DIGITS: u8 = 18; @@ -75,19 +74,25 @@ pub trait SparkUnsafeObject { } /// Returns string value at the given index of the object. + /// + /// Spark's `UnsafeRow.getUTF8String` wraps the bytes via `UTF8String.fromAddress` + /// with no UTF-8 validation, and Spark's `cast(BinaryType -> StringType)` is a + /// zero-copy reinterpret that can leave arbitrary bytes in a `StringType` column + /// (e.g. Delta's Z-Order `interleave_bits(...).cast(StringType)`). Strict validation + /// here panics on those rows even though Spark itself treats them as opaque. The + /// bytes flow directly into Arrow's `StringBuilder::append_value`, which copies them + /// without introspection. fn get_string(&self, index: usize) -> &str { let (offset, len) = self.get_offset_and_len(index); let addr = self.get_row_addr() + offset as i64; - // SAFETY: addr points to valid UTF-8 string data within the variable-length region. - // Offset and length are read from the fixed-length portion of the row/array. debug_assert!(addr != 0, "get_string: null address at index {index}"); debug_assert!( len >= 0, "get_string: negative length {len} at index {index}" ); let slice: &[u8] = unsafe { std::slice::from_raw_parts(addr as *const u8, len as usize) }; - - from_utf8(slice).unwrap() + // SAFETY: matches Spark's `UTF8String.fromAddress` no-validate semantics. See doc above. + unsafe { std::str::from_utf8_unchecked(slice) } } /// Returns binary value at the given index of the object. diff --git a/spark/pom.xml b/spark/pom.xml index d3c18ccf87..2f6297a7d0 100644 --- a/spark/pom.xml +++ b/spark/pom.xml @@ -112,6 +112,18 @@ under the License. + + + com.google.guava + failureaccess + 1.0.2 + test + org.codehaus.jackson jackson-mapper-asl @@ -323,6 +335,63 @@ under the License. + + + contrib-delta + + 4.1.0 + + + + io.delta + delta-spark_${scala.binary.version} + ${delta.version} + provided + + + + + + org.codehaus.mojo + build-helper-maven-plugin + + + add-contrib-delta-source + generate-sources + + add-source + + + + ${project.parent.basedir}/contrib/delta/src/main/scala + + + + + add-contrib-delta-test-source + generate-test-sources + + add-test-source + + + + ${project.parent.basedir}/contrib/delta/src/test/scala + + + + + + + + + generate-docs diff --git a/spark/src/main/scala/org/apache/comet/CometExecIterator.scala b/spark/src/main/scala/org/apache/comet/CometExecIterator.scala index 6140eca553..c221e346af 100644 --- a/spark/src/main/scala/org/apache/comet/CometExecIterator.scala +++ b/spark/src/main/scala/org/apache/comet/CometExecIterator.scala @@ -68,7 +68,8 @@ class CometExecIterator( partitionIndex: Int, broadcastedHadoopConfForEncryption: Option[Broadcast[SerializableConfiguration]] = None, encryptedFilePaths: Seq[String] = Seq.empty, - shuffleBlockIterators: Map[Int, CometShuffleBlockIterator] = Map.empty) + shuffleBlockIterators: Map[Int, CometShuffleBlockIterator] = Map.empty, + taskFilePaths: Seq[String] = Seq.empty) extends Iterator[ColumnarBatch] with Logging { @@ -177,18 +178,19 @@ class CometExecIterator( // threw the exception, so we log the exception with taskAttemptId here logError(s"Native execution for task $taskAttemptId failed", e) + // `(?s)` so `.` matches across newlines -- native parquet errors sometimes + // span multiple lines (footer corruption messages include a hex dump), and a + // single-line regex would let those slip past the wrap and surface as bare + // CometNativeException to the user. val parquetError: scala.util.matching.Regex = - """^Parquet error: (?:.*)$""".r + """(?s)^Parquet error: (?:.*)$""".r e.getMessage match { case parquetError() => - // See org.apache.spark.sql.errors.QueryExecutionErrors.failedToReadDataError - // See org.apache.parquet.hadoop.ParquetFileReader for error message. - // _LEGACY_ERROR_TEMP_2254 has no message placeholders; Spark 4 strict-checks - // parameters and raises INTERNAL_ERROR if any are passed. - throw new SparkException( - errorClass = "_LEGACY_ERROR_TEMP_2254", - messageParameters = Map.empty, - cause = new SparkException("File is not a Parquet file.", e)) + // Wrap in the FAILED_READ_FILE.NO_HINT SparkException Spark produces when + // its own parquet reader fails. The shim accesses spark-private APIs + // (InputFileBlockHolder, QueryExecutionErrors) from a Spark-package class. + throw org.apache.spark.sql.comet.shims.ShimSparkErrorConverter + .wrapNativeParquetError(e, taskFilePaths) case _ => throw e } diff --git a/spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala b/spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala index 72c2bea9e4..7266dfa706 100644 --- a/spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala +++ b/spark/src/main/scala/org/apache/comet/rules/CometExecRule.scala @@ -261,6 +261,14 @@ case class CometExecRule(session: SparkSession) case scan: CometScanExec if scan.scanImpl == CometConf.SCAN_NATIVE_DATAFUSION => convertToComet(scan, CometNativeScan).getOrElse(scan) + // Delta scan marker stamped by the optional contrib/delta integration. + // The handler is resolved via reflection (no compile-time dependency + // on the contrib) -- present only when -Pcontrib-delta was activated. + case scan: CometScanExec if scan.scanImpl == DeltaIntegration.DeltaScanImpl => + DeltaIntegration.scanHandler + .flatMap(handler => convertToComet(scan, handler)) + .getOrElse(scan) + // Fully native Iceberg scan for V2 (iceberg-rust path) // Only handle scans with native metadata; other scans fall through to isCometScan // Config checks (COMET_ICEBERG_NATIVE_ENABLED, COMET_EXEC_ENABLED) are done in CometScanRule diff --git a/spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala b/spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala index 64b69be1e9..7da949d870 100644 --- a/spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala +++ b/spark/src/main/scala/org/apache/comet/rules/CometScanRule.scala @@ -125,12 +125,15 @@ case class CometScanRule(session: SparkSession) case scan if !CometConf.COMET_NATIVE_SCAN_ENABLED.get(conf) => withInfo(scan, "Comet Scan is not enabled") - case scan if hasMetadataCol(scan) => - withInfo(scan, "Metadata column is not supported") - - // data source V1 + // V1 scans go through `transformV1Scan` which itself first delegates to any + // available V1 contrib (today: Delta) and only then applies generic Comet + // bailouts like the metadata-column rejection. This keeps the metadata-col + // guard in place for V2 and non-contrib V1 paths without referencing any + // specific contrib class from this outer match. case scanExec: FileSourceScanExec => transformV1Scan(fullPlan, scanExec) + case scan if hasMetadataCol(scan) => + withInfo(scan, "Metadata column is not supported") // data source V2 case scanExec: BatchScanExec => @@ -163,9 +166,44 @@ case class CometScanRule(session: SparkSession) scanExec.relation match { case r: HadoopFsRelation => + // Try the optional Delta contrib first. When this build wasn't compiled with + // `-Pcontrib-delta`, the bridge returns None and we fall through to the + // vanilla scan path. When the Delta classes are on the classpath, the contrib + // either claims the scan (returning a CometScanExec marker) or declines via + // its own `withInfo` fallback message. + DeltaIntegration.transformV1IfDelta(plan, session, scanExec, r) match { + case Some(handled) => return handled + case None => // proceed with vanilla logic + } + // Metadata-col bailout moved here so V1 contribs (Delta) get first crack + // at scans with synthetic metadata columns before generic Comet rejects + // them. For non-contrib V1 scans this is equivalent to the outer check. + if (scanExec.expressions.exists(_.exists { + case a: Attribute => a.isMetadataCol + case _ => false + })) { + return withInfo(scanExec, "Metadata column is not supported") + } if (!CometScanExec.isFileFormatSupported(r.fileFormat)) { return withInfo(scanExec, s"Unsupported file format ${r.fileFormat}") } + // Filesystem scheme allowlist. Comet's native readers go through object_store, + // which only understands a fixed set of URL schemes. Custom Hadoop FileSystems + // (e.g. a test FakeFileSystem registered via spark.hadoop.fs..impl) would + // surface at execution time as `Generic URL error: Unable to recognise URL "..."`. + // Decline here so Spark's reader -- which goes through the Hadoop FS API and can + // resolve custom schemes -- handles the scan. + val supportedSchemes = + Set("file", "s3", "s3a", "gs", "gcs", "oss", "abfss", "abfs", "wasbs", "wasb") + val unsupportedFsSchemes = r.location.rootPaths + .map(p => p.toUri.getScheme) + .filter(s => s != null && !supportedSchemes.contains(s)) + .toSet + if (unsupportedFsSchemes.nonEmpty) { + return withInfo( + scanExec, + s"Unsupported filesystem schemes: ${unsupportedFsSchemes.mkString(", ")}") + } val hadoopConf = r.sparkSession.sessionState.newHadoopConfWithOptions(r.options) // TODO is this restriction valid for all native scan types? diff --git a/spark/src/main/scala/org/apache/comet/rules/DeltaIntegration.scala b/spark/src/main/scala/org/apache/comet/rules/DeltaIntegration.scala new file mode 100644 index 0000000000..7d05ea3ff6 --- /dev/null +++ b/spark/src/main/scala/org/apache/comet/rules/DeltaIntegration.scala @@ -0,0 +1,160 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.comet.rules + +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.comet.CometScanExec +import org.apache.spark.sql.execution.{FileSourceScanExec, SparkPlan} +import org.apache.spark.sql.execution.datasources.HadoopFsRelation + +import org.apache.comet.serde.CometOperatorSerde + +/** + * Reflection-based bridge to the optional `contrib/delta/` integration. + * + * On default builds the contrib classes don't exist on the classpath, so the reflective class + * lookups fail and every method here returns the "not handled" sentinel. On builds compiled with + * `-Pcontrib-delta` (Maven) + `--features contrib-delta` (Cargo), the contrib classes are present + * and the lookups resolve, dispatching the call into the contrib helpers. + * + * Keeping this bridge as one small file in core lets the Delta detection block in `CometScanRule` + * and the serde dispatch in `CometExecRule` stay ~10 lines each -- exactly the shape Parth's + * review on #4339 asked for. + * + * No `SPI`, no `ServiceLoader`, no registry: the contrib provides its own static helper objects + * with stable names; this bridge just calls them. + */ +object DeltaIntegration { + + // Scala compiles `object Foo` into BOTH `Foo.class` (a static-forwarder class) AND + // `Foo$.class` (the actual module class). Only the latter has the `MODULE$` singleton + // field that the reflection bridge dereferences. Looking up the unqualified name + // returns the forwarder, where `getField("MODULE$")` throws -- and the surrounding + // try/catch silently turns that into `None`, making every Delta scan fall through to + // Spark's reader. (This bug shipped silently until the test suite caught it; the Delta + // regression suite was passing because Delta's own tests don't depend on Comet + // engaging.) The trailing `$` selects the module class explicitly. + private val ScanRuleClass = "org.apache.comet.contrib.delta.DeltaScanRule$" + private val SerdeClass = "org.apache.comet.contrib.delta.CometDeltaNativeScan$" + + /** scanImpl tag the contrib stamps on CometScanExec markers it produces. */ + val DeltaScanImpl: String = "native_delta_compat" + + // Lazy class lookups -- single reflection cost per JVM, cached either as the + // class handle or as the empty option if the contrib wasn't bundled. + @volatile private var scanRuleLookup: Option[Option[Class[AnyRef]]] = None + @volatile private var serdeLookup: Option[Option[Class[AnyRef]]] = None + + private def scanRuleCls: Option[Class[AnyRef]] = + scanRuleLookup.getOrElse { + val cls = + try { + // scalastyle:off classforname + Some(Class.forName(ScanRuleClass).asInstanceOf[Class[AnyRef]]) + // scalastyle:on classforname + } catch { case _: ClassNotFoundException => None } + scanRuleLookup = Some(cls) + cls + } + + private def serdeCls: Option[Class[AnyRef]] = + serdeLookup.getOrElse { + val cls = + try { + // scalastyle:off classforname + Some(Class.forName(SerdeClass).asInstanceOf[Class[AnyRef]]) + // scalastyle:on classforname + } catch { case _: ClassNotFoundException => None } + serdeLookup = Some(cls) + cls + } + + /** True when the Delta contrib was bundled into this build. */ + def isAvailable: Boolean = scanRuleCls.isDefined + + /** + * Delegate the V1 scan transform to the Delta contrib when both (a) the contrib is on the + * classpath, AND (b) the relation's file format is `DeltaParquetFileFormat`. + * + * Returns `Some(plan)` if the contrib handled the scan (either with a transformed + * `CometScanExec` marker or by explicitly declining via the `withInfo` path); `None` to + * indicate "not a Delta scan, proceed with the vanilla CometScanRule path". + */ + // Cached reflective binding: resolved once per JVM. The contrib's + // `transformV1IfDelta` is invoked for every V1 scan in every plan, even + // non-Delta ones; resolving the Method on each call would be a per-scan + // reflection round-trip just to find we don't apply. + @volatile private var transformV1IfDeltaBindingCache + : Option[Option[(AnyRef, java.lang.reflect.Method)]] = None + + private def transformV1IfDeltaBinding: Option[(AnyRef, java.lang.reflect.Method)] = + transformV1IfDeltaBindingCache.getOrElse { + val binding = scanRuleCls.flatMap { cls => + try { + val module = cls.getField("MODULE$").get(null) + val m = cls.getMethod( + "transformV1IfDelta", + classOf[SparkPlan], + classOf[SparkSession], + classOf[FileSourceScanExec], + classOf[HadoopFsRelation]) + Some((module, m)) + } catch { + case _: Exception => None + } + } + transformV1IfDeltaBindingCache = Some(binding) + binding + } + + def transformV1IfDelta( + plan: SparkPlan, + session: SparkSession, + scanExec: FileSourceScanExec, + relation: HadoopFsRelation): Option[SparkPlan] = { + transformV1IfDeltaBinding.flatMap { case (module, m) => + try { + Option(m.invoke(module, plan, session, scanExec, relation)) + .map(_.asInstanceOf[Option[SparkPlan]]) + .flatten + } catch { + // scalastyle:off + case _: Exception => None + // scalastyle:on + } + } + } + + + /** + * The Delta scan handler, resolved via reflection from the contrib's `CometDeltaNativeScan` + * companion object. Returns `None` when the contrib isn't bundled into this build. + * `CometExecRule` calls this and passes the result through the standard `convertToComet(scan, + * handler)` path so the Delta scan flows through the same code as `CometNativeScan` etc. + */ + def scanHandler: Option[CometOperatorSerde[CometScanExec]] = serdeCls.flatMap { cls => + try { + val module = cls.getField("MODULE$").get(null) + Some(module.asInstanceOf[CometOperatorSerde[CometScanExec]]) + } catch { + case _: Exception => None + } + } +} diff --git a/spark/src/main/scala/org/apache/comet/serde/arrays.scala b/spark/src/main/scala/org/apache/comet/serde/arrays.scala index 5edc08840a..c1ac9828fd 100644 --- a/spark/src/main/scala/org/apache/comet/serde/arrays.scala +++ b/spark/src/main/scala/org/apache/comet/serde/arrays.scala @@ -480,6 +480,30 @@ object CometCreateArray extends CometExpressionSerde[CreateArray] { return exprToProtoInternal(emptyArrayLiteral, inputs, binding) } + // DataFusion's `make_array` asserts strict element-type equality (down to nested + // field nullability) via `MutableArrayData::with_capacities`. Spark's CreateArray + // is more permissive: children may share the same surface type (e.g. all + // `Struct`) but differ only in nested field nullability when + // the analyzer didn't insert coercion casts -- Delta's CDC write path builds + // these `array(struct(...), struct(...))` plans manually (one struct per change + // type) and can leave `_change_type` non-nullable in one arm and nullable in + // another. Native execution then panics inside `make_array_inner`. Decline here + // when any pair of children disagree on data type so Spark's JVM evaluator + // (which doesn't have this strictness) handles it. + // + // TODO: remove this decline once apache/datafusion#22366 lands. The upstream fix + // will widen the element type via nullability-OR-merge and cast each child before + // handing to MutableArrayData, eliminating the need for this caller-side workaround. + val childTypes = children.map(_.dataType) + if (childTypes.distinct.size > 1) { + withInfo( + expr, + "CreateArray children have mismatched data types: " + + childTypes.distinct.mkString(", "), + children: _*) + return None + } + val childExprs = children.map(exprToProtoInternal(_, inputs, binding)) if (childExprs.forall(_.isDefined)) { diff --git a/spark/src/main/scala/org/apache/spark/sql/comet/CometExecRDD.scala b/spark/src/main/scala/org/apache/spark/sql/comet/CometExecRDD.scala index 47eda98a11..321e433457 100644 --- a/spark/src/main/scala/org/apache/spark/sql/comet/CometExecRDD.scala +++ b/spark/src/main/scala/org/apache/spark/sql/comet/CometExecRDD.scala @@ -36,7 +36,8 @@ import org.apache.comet.serde.OperatorOuterClass private[spark] class CometExecPartition( override val index: Int, val inputPartitions: Array[Partition], - val planDataByKey: Map[String, Array[Byte]]) + val planDataByKey: Map[String, Array[Byte]], + val filePaths: Seq[String] = Seq.empty) extends Partition /** @@ -66,7 +67,8 @@ private[spark] class CometExecRDD( subqueries: Seq[ScalarSubquery], broadcastedHadoopConfForEncryption: Option[Broadcast[SerializableConfiguration]] = None, encryptedFilePaths: Seq[String] = Seq.empty, - shuffleScanIndices: Set[Int] = Set.empty) + shuffleScanIndices: Set[Int] = Set.empty, + @transient perPartitionFilePaths: Array[Seq[String]] = Array.empty) extends RDD[ColumnarBatch](sc, inputRDDs.map(rdd => new OneToOneDependency(rdd))) { // Determine partition count: from inputs if available, otherwise from parameter @@ -90,13 +92,33 @@ private[spark] class CometExecRDD( (0 until numPartitions).map { idx => val inputParts = inputRDDs.map(_.partitions(idx)).toArray val planData = perPartitionByKey.map { case (key, arr) => key -> arr(idx) } - new CometExecPartition(idx, inputParts, planData) + val fp = + if (perPartitionFilePaths.length > idx) perPartitionFilePaths(idx) else Seq.empty[String] + new CometExecPartition(idx, inputParts, planData, fp) }.toArray } override def compute(split: Partition, context: TaskContext): Iterator[ColumnarBatch] = { val partition = split.asInstanceOf[CometExecPartition] + // Populate Spark's `InputFileBlockHolder` thread-local so `input_file_name()` and + // friends return the correct path for this task's data. Comet's native scan does + // not go through Spark's `FileScanRDD` (which is what normally maintains this + // thread-local), so without this hook Delta's UPDATE/DELETE/MERGE flows -- which + // rely on `input_file_name()` to identify touched files -- silently see an empty + // path. Set to the partition's first file: Delta forces one-task-per-partition + // when `input_file_name()` is referenced (see DeltaScanRule), so there is exactly + // one file in that case. Registers an unset on task completion to avoid leaking + // across tasks on the same executor thread. + if (partition.filePaths.nonEmpty) { + org.apache.spark.rdd.InputFileBlockHolder.set(partition.filePaths.head, 0L, 0L) + Option(context).foreach { ctx => + ctx.addTaskCompletionListener[Unit] { _ => + org.apache.spark.rdd.InputFileBlockHolder.unset() + } + } + } + val inputs = inputRDDs.zip(partition.inputPartitions).map { case (rdd, part) => rdd.iterator(part, context) } @@ -130,7 +152,8 @@ private[spark] class CometExecRDD( partition.index, broadcastedHadoopConfForEncryption, encryptedFilePaths, - shuffleBlockIters) + shuffleBlockIters, + taskFilePaths = partition.filePaths) // Register ScalarSubqueries so native code can look them up subqueries.foreach(sub => CometScalarSubquery.setSubquery(it.id, sub)) diff --git a/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala b/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala index f4f0b1fb74..a1f876920a 100644 --- a/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala +++ b/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala @@ -73,7 +73,8 @@ case class CometNativeScanExec( sourceKey: String) // Key for PlanDataInjector to match common+partition data at runtime extends CometLeafExec with DataSourceScanExec - with ShimStreamSourceAwareSparkPlan { + with ShimStreamSourceAwareSparkPlan + with CometScanWithPlanData { override lazy val metadata: Map[String, String] = if (originalPlan != null) originalPlan.metadata else Map.empty @@ -156,7 +157,8 @@ case class CometNativeScanExec( * all files for all partitions in the driver, we serialize only common metadata (once) and each * partition's files (lazily, as tasks are scheduled). */ - @transient private lazy val serializedPartitionData: (Array[Byte], Array[Array[Byte]]) = { + @transient private lazy val serializedPartitionData + : (Array[Byte], Array[Array[Byte]], Array[Seq[String]]) = { // Outer partitionFilters (wrapper) DPP is resolved by Spark's standard // prepare -> waitForSubqueries lifecycle, triggered explicitly via // CometLeafExec.ensureSubqueriesResolved called from @@ -227,13 +229,21 @@ case class CometNativeScanExec( partitionNativeScan.toByteArray }.toArray - (commonBytes, perPartitionBytes) + // File paths per partition -- threaded through CometExecRDD to CometExecIterator + // so wrapNativeParquetError can populate FAILED_READ_FILE.NO_HINT exceptions with + // the actual file path. CometNativeScanExec bypasses Spark's FileScanRDD, so the + // standard InputFileBlockHolder thread-local isn't set. + val perPartitionPaths = filePartitions.map(_.files.map(_.filePath.toString).toSeq).toArray + + (commonBytes, perPartitionBytes, perPartitionPaths) } def commonData: Array[Byte] = serializedPartitionData._1 def perPartitionData: Array[Array[Byte]] = serializedPartitionData._2 + def perPartitionFilePaths: Array[Seq[String]] = serializedPartitionData._3 + override def doExecuteColumnar(): RDD[ColumnarBatch] = { val nativeMetrics = CometMetricNode.fromCometPlan(this) val serializedPlan = CometExec.serializeNativePlan(nativeOp) @@ -261,7 +271,8 @@ case class CometNativeScanExec( nativeMetrics, Seq.empty, broadcastedHadoopConfForEncryption, - encryptedFilePaths) { + encryptedFilePaths, + perPartitionFilePaths = perPartitionFilePaths) { override def compute(split: Partition, context: TaskContext): Iterator[ColumnarBatch] = { val res = super.compute(split, context) diff --git a/spark/src/main/scala/org/apache/spark/sql/comet/operators.scala b/spark/src/main/scala/org/apache/spark/sql/comet/operators.scala index f315aae6e2..592990d5e8 100644 --- a/spark/src/main/scala/org/apache/spark/sql/comet/operators.scala +++ b/spark/src/main/scala/org/apache/spark/sql/comet/operators.scala @@ -66,6 +66,12 @@ import org.apache.comet.serde.operator.CometSink */ private[comet] trait PlanDataInjector { + /** + * Which `OpStructCase` this injector cares about. Used by `injectPlanData` for an O(1) + * pre-filter so we don't run every injector's `canInject` against every operator in the tree. + */ + def opStructCase: Operator.OpStructCase + /** Check if this injector can handle the given operator. */ def canInject(op: Operator): Boolean @@ -81,12 +87,35 @@ private[comet] trait PlanDataInjector { */ private[comet] object PlanDataInjector { - // Registry of injectors for different operator types - private val injectors: Seq[PlanDataInjector] = Seq( - IcebergPlanDataInjector, - NativeScanPlanDataInjector - // Future: DeltaPlanDataInjector, HudiPlanDataInjector, etc. - ) + // Registry of injectors for different operator types. The contrib/delta integration's + // DeltaPlanDataInjector is appended via one reflective class lookup -- present only when + // the contrib was bundled (i.e. -Pcontrib-delta on the Maven build). Default builds get + // the empty Option and an unmodified injectors list, so there's zero contrib surface at + // runtime on default builds. + private val injectors: Seq[PlanDataInjector] = { + val builtin: Seq[PlanDataInjector] = Seq(IcebergPlanDataInjector, NativeScanPlanDataInjector) + val deltaOpt: Option[PlanDataInjector] = + try { + // Scala compiles `object Foo` into BOTH `Foo.class` (a static-forwarder + // class with no MODULE$ field) AND `Foo$.class` (the module class that + // does have MODULE$). The trailing `$` selects the module class. + // scalastyle:off classforname + val cls = Class.forName("org.apache.spark.sql.comet.DeltaPlanDataInjector$") + // scalastyle:on classforname + Some(cls.getField("MODULE$").get(null).asInstanceOf[PlanDataInjector]) + } catch { + case _: ClassNotFoundException => None + case _: Exception => None + } + builtin ++ deltaOpt + } + + // O(1) lookup by op kind: most operators in any tree don't match any injector, so the + // per-op `for (injector <- injectors if injector.canInject(op))` walk was paying N*M + // canInject calls (N operators, M injectors) just to find no match. Keying by + // `OpStructCase` lets us skip the iteration entirely for non-scan operators. + private val injectorsByKind: Map[Operator.OpStructCase, PlanDataInjector] = + injectors.map(i => i.opStructCase -> i).toMap /** * Injects planning data into an Operator tree by finding nodes that need injection and applying @@ -101,21 +130,24 @@ private[comet] object PlanDataInjector { partitionByKey: Map[String, Array[Byte]]): Operator = { val builder = op.toBuilder - // Try each injector to see if it can handle this operator - for (injector <- injectors if injector.canInject(op)) { - injector.getKey(op) match { - case Some(key) => - (commonByKey.get(key), partitionByKey.get(key)) match { - case (Some(commonBytes), Some(partitionBytes)) => - val injectedOp = injector.inject(op, commonBytes, partitionBytes) - // Copy the injected operator's fields to our builder - builder.clear() - builder.mergeFrom(injectedOp) - case _ => - throw new CometRuntimeException(s"Missing planning data for key: $key") - } - case None => - } + // O(1) by op kind, then a canInject confirm (which may inspect detail fields like + // `hasCommon` / `!hasFilePartition`). Most operators in any tree are non-scan and skip. + injectorsByKind.get(op.getOpStructCase) match { + case Some(injector) if injector.canInject(op) => + injector.getKey(op) match { + case Some(key) => + (commonByKey.get(key), partitionByKey.get(key)) match { + case (Some(commonBytes), Some(partitionBytes)) => + val injectedOp = injector.inject(op, commonBytes, partitionBytes) + // Copy the injected operator's fields to our builder + builder.clear() + builder.mergeFrom(injectedOp) + case _ => + throw new CometRuntimeException(s"Missing planning data for key: $key") + } + case None => + } + case _ => } // Recursively process children @@ -159,6 +191,8 @@ private[comet] object IcebergPlanDataInjector extends PlanDataInjector { } }) + override val opStructCase: Operator.OpStructCase = Operator.OpStructCase.ICEBERG_SCAN + override def canInject(op: Operator): Boolean = op.hasIcebergScan && op.getIcebergScan.getFileScanTasksCount == 0 && @@ -198,6 +232,8 @@ private[comet] object IcebergPlanDataInjector extends PlanDataInjector { */ private[comet] object NativeScanPlanDataInjector extends PlanDataInjector { + override val opStructCase: Operator.OpStructCase = Operator.OpStructCase.NATIVE_SCAN + override def canInject(op: Operator): Boolean = op.hasNativeScan && op.getNativeScan.hasCommon && @@ -560,6 +596,20 @@ abstract class CometNativeExec extends CometExec { // Unified RDD creation - CometExecRDD handles all cases val subqueries = collectSubqueries(this) val hasScanInput = sparkPlans.exists(_.isInstanceOf[CometNativeScanExec]) + // Collect per-partition file paths from any CometNativeScanExec leaves so + // CometExecIterator can populate FAILED_READ_FILE.NO_HINT exceptions with + // the actual path. Multiple scans (joins) get concatenated per partition. + val perPartitionFilePaths: Array[Seq[String]] = { + val scans = sparkPlans.collect { case s: CometNativeScanExec => s } + if (scans.isEmpty) Array.empty[Seq[String]] + else { + val perScan = scans.map(_.perPartitionFilePaths) + val n = firstNonBroadcastPlanNumPartitions + (0 until n).map { idx => + perScan.flatMap { arr => if (arr.length > idx) arr(idx) else Seq.empty }.toSeq + }.toArray + } + } new CometExecRDD( sparkContext, inputs.toSeq, @@ -572,7 +622,8 @@ abstract class CometNativeExec extends CometExec { subqueries, broadcastedHadoopConfForEncryption, encryptedFilePaths, - shuffleScanIndices) { + shuffleScanIndices, + perPartitionFilePaths = perPartitionFilePaths) { override def compute( split: Partition, context: TaskContext): Iterator[ColumnarBatch] = { @@ -610,8 +661,16 @@ abstract class CometNativeExec extends CometExec { */ def foreachUntilCometInput(plan: SparkPlan)(func: SparkPlan => Unit): Unit = { plan match { - case _: CometNativeScanExec | _: CometScanExec | _: CometBatchScanExec | - _: CometIcebergNativeScanExec | _: CometCsvNativeScanExec | _: ShuffleQueryStageExec | + // Match `CometLeafExec` first so contrib leaf scans (e.g. the Delta + // contrib's `CometDeltaNativeScanExec`) are recognised as input boundaries + // without requiring a core compile-time reference to the contrib class. + // All built-in leaf scans (`CometNativeScanExec`, `CometIcebergNativeScanExec`, + // `CometCsvNativeScanExec`) also extend `CometLeafExec`, so this is a + // strict superset of the previous enumeration -- it just generalises the + // input-boundary concept from "this fixed list" to "any leaf Comet exec". + case _: CometLeafExec => + func(plan) + case _: CometScanExec | _: CometBatchScanExec | _: ShuffleQueryStageExec | _: AQEShuffleReadExec | _: CometShuffleExchangeExec | _: CometUnionExec | _: CometTakeOrderedAndProjectExec | _: CometCoalesceExec | _: ReusedExchangeExec | _: CometBroadcastExchangeExec | _: BroadcastQueryStageExec | @@ -679,11 +738,16 @@ abstract class CometNativeExec extends CometExec { (Map.empty, Map.empty) } - case nativeScan: CometNativeScanExec => - nativeScan.ensureSubqueriesResolved() - ( - Map(nativeScan.sourceKey -> nativeScan.commonData), - Map(nativeScan.sourceKey -> nativeScan.perPartitionData)) + // Generic path for leaf scans that surface planning data via the + // `CometScanWithPlanData` trait. Catches `CometNativeScanExec` and any contrib + // leaf scan (e.g. the Delta contrib's `CometDeltaNativeScanExec`) without + // requiring core to compile-time reference contrib classes. + case s: CometScanWithPlanData => + s match { + case leaf: CometLeafExec => leaf.ensureSubqueriesResolved() + case _ => // no DPP lifecycle to drive + } + (Map(s.sourceKey -> s.commonData), Map(s.sourceKey -> s.perPartitionData)) // Broadcast stages are boundaries - don't collect per-partition data from inside them. // After DPP filtering, broadcast scans may have different partition counts than the @@ -794,6 +858,24 @@ abstract class CometLeafExec extends CometNativeExec with LeafExecNode { } } +/** + * Marker trait for scan execs that surface planning data (a `commonData` block + per-partition + * task bytes keyed by `sourceKey`) so that a parent `CometNativeExec` can find and inject the + * data when the scan is fused into a larger native subtree. + * + * Implemented by `CometNativeScanExec`, `CometIcebergNativeScanExec`, and the contrib's + * `CometDeltaNativeScanExec` -- without it, [[PlanDataInjector.findAllPlanData]] cannot collect + * the per-partition tasks and the parent's native execution receives an empty input. + * + * Each implementation also resolves its own DPP subqueries via `ensureSubqueriesResolved` + * (overridden from [[CometLeafExec]]) before `commonData`/`perPartitionData` are read. + */ +trait CometScanWithPlanData { + def sourceKey: String + def commonData: Array[Byte] + def perPartitionData: Array[Array[Byte]] +} + abstract class CometUnaryExec extends CometNativeExec with UnaryExecNode abstract class CometBinaryExec extends CometNativeExec with BinaryExecNode diff --git a/spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala b/spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala index ad5481c377..fb99cb1de4 100644 --- a/spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala +++ b/spark/src/main/spark-4.x/org/apache/spark/sql/comet/shims/ShimSparkErrorConverter.scala @@ -34,6 +34,39 @@ import org.apache.comet.CometSparkSessionExtensions.isSpark41Plus object ShimSparkErrorConverter { val ObjectLocationPattern: Regex = "Object at location (.+?) not found".r + + /** + * Wrap a native parquet failure (e.g. "Parquet error: Invalid Parquet file. Corrupt footer") in + * the FAILED_READ_FILE.NO_HINT SparkException Spark itself produces when its own parquet reader + * fails. Matches what Spark/Delta tests assert on (e.g. SnapshotManagementSuite "should not + * recover when the current checkpoint is broken" asserts the message contains "Encountered + * error while reading file" + the path). + * + * File path is queried from Spark's `InputFileBlockHolder` thread-local. Falls back to an empty + * string when not set; the static phrasing still passes the assertion. + */ + def wrapNativeParquetError( + cause: Throwable, + taskFilePaths: Seq[String] = Seq.empty): Throwable = { + // Prefer the per-task file list threaded in from CometExecIterator (set by + // CometExecRDD via CometNativeExec/CometNativeScanExec collecting file paths from + // any scan leaves in the plan). Comet's native scan path does NOT go through + // Spark's FileScanRDD, so InputFileBlockHolder is typically not populated. Fall + // back to InputFileBlockHolder for any path that does set it. Tests like + // SnapshotManagementSuite assert the error message contains the file path, so an + // accurate path here is load-bearing. + val filePath = if (taskFilePaths.nonEmpty) { + taskFilePaths.mkString(",") + } else { + try { + val p = org.apache.spark.rdd.InputFileBlockHolder.getInputFilePath + if (p == null) "" else p.toString + } catch { + case _: Throwable => "" + } + } + QueryExecutionErrors.cannotReadFilesError(cause, filePath) + } } /**