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Build: cmake --preset linux-ninja-release && cmake --build --preset linux-ninja-release

Temporal Module

Time-series and temporal query support for ThemisDB.

Module Purpose

Implements temporal and bitemporal data management for ThemisDB, enabling transaction-time and valid-time queries, time travel queries, and bitemporal data versioning.

Subsystem Scope

In scope: Transaction-time tracking, valid-time management, bitemporal query operators, time travel queries, temporal data versioning and retention.

Out of scope: Time series storage (handled by timeseries module), event sourcing (handled by cdc module).

Relevant Interfaces

  • temporal_query_engine.cpp — time-travel query execution (AS OF, FROM...TO, BETWEEN...AND, bitemporal joins, SEQUENCED/NON-SEQUENCED semantics)
  • system_versioned_table.cpp — automatic transaction-time versioning of all table rows
  • bi_temporal.cpp — bitemporal record management (system time + valid time axes)
  • temporal_index.cpp — period-based B-tree index for efficient time range queries
  • temporal_aggregator.cpp — temporal aggregations (tumbling and sliding window)
  • temporal_conflict_resolver.cpp — HLC-based conflict resolution for concurrent edits
  • snapshot_manager.cpp — temporal snapshot creation, querying, and release
  • retention_manager.cpp — automated expiry of old versions based on retention policy
  • interval_tree_index.cpp — augmented interval tree, O(log n + k) overlap detection for valid-time period predicates
  • temporal_compressor.cpp — DELTA/ZSTD/Gorilla/dictionary compression for historical version payloads
  • temporal_cdc.cpp — versioned change data capture; typed ChangeEvent (INSERT/UPDATE/DELETE/VERSION_CREATED), pub/sub subscriptions, ring-buffer replay

Current Delivery Status

Maturity: 🟢 Production-Ready — Transaction-time and valid-time tracking, time-travel queries, bitemporal joins, SEQUENCED/NON-SEQUENCED query semantics, temporal aggregations, conflict resolution, snapshot management, and retention policies are all fully implemented and production-ready. SQL PERIOD FOR DDL syntax is not yet supported.

Phase 4 (v1.5.0): IntervalTreeIndex, TemporalCompressor, and TemporalCDC are implemented and production-ready.

Components

  • Temporal Conflict Resolver: Resolves conflicts between temporal snapshots using HLC timestamps
  • System-versioned tables: Track historical changes automatically
  • Application-versioned tables: User-controlled time periods
  • Time-travel query engine: Query data as it existed at specific points in time
  • Temporal joins and aggregations: Join and aggregate across time dimensions
  • Retention policies: Automated historical data cleanup
  • IntervalTreeIndex: Augmented BST-based interval tree for efficient valid-time overlap detection
  • TemporalCompressor: Compresses historical version payloads using DELTA, ZSTD, Gorilla, and DICTIONARY algorithms
  • TemporalCDC: Version-aware change data capture; subscribe to table change streams and replay events by time range

Features

Temporal Tables

  • System-versioned tables: Automatic tracking of all changes with system timestamps
  • Application-versioned tables: User-defined valid time periods for bi-temporal support
  • Transaction-time tracking: Record when data was stored in the database
  • Valid-time tracking: Record when data is valid in the real world

Time-Travel Queries

  • AS OF queries: Retrieve data as it existed at a specific point in time
  • FROM...TO queries: Retrieve all versions of data within a time range
  • BETWEEN...AND queries: Query data valid during a specific period
  • Historical snapshots: Create point-in-time snapshots for analysis

Conflict Resolution

  • HLC-based ordering: Use Hybrid Logical Clocks for distributed timestamp ordering
  • Multiple policies: Last-write-wins, first-write-wins, node-priority, manual, CRDT-merge
  • Conflict detection: Automatic detection of concurrent modifications
  • Resolution logging: Track all conflict resolutions for audit purposes

Temporal Operations

  • Temporal joins: Join tables based on temporal overlap or specific time points
  • Temporal aggregations: Aggregate data across time windows
  • Period operations: Union, intersection, and difference of time periods
  • Temporal predicates: OVERLAPS, CONTAINS, PRECEDES, SUCCEEDS

Retention and Optimization

  • Configurable retention policies: Automatically purge old historical data
  • Temporal indexes: Specialized indexes for time-based queries
  • Compression: Historical data compression to save storage
  • Partitioning: Time-based partitioning for performance

Architecture

TemporalModule
├─→ TemporalQueryEngine     (time-travel queries, bitemporal joins, SEQUENCED/NON-SEQUENCED semantics)
├─→ SystemVersionedTable    (automatic transaction-time versioning)
├─→ BiTemporalTable         (dual-axis: system time + valid time)
├─→ TemporalIndex           (period B-tree index for fast range lookups)
├─→ TemporalAggregator      (tumbling and sliding window aggregations)
├─→ TemporalConflictResolver (HLC-based conflict resolution with five policies)
├─→ TemporalSnapshotManager (consistent multi-table point-in-time snapshots)
├─→ RetentionManager        (time-based and count-based history cleanup)
├─→ IntervalTreeIndex       (augmented BST for O(log n + k) overlap detection)
├─→ TemporalCompressor      (DELTA/ZSTD/Gorilla/dictionary compression for history)
└─→ TemporalCDC             (pub/sub change data capture with ring-buffer replay)

Use Cases

Audit and Compliance

  • Track all changes to sensitive data
  • Comply with regulatory requirements (GDPR, HIPAA, SOX)
  • Provide complete audit trails
  • Support data lineage tracking

Historical Analysis

  • Analyze trends over time
  • Compare current vs. historical data
  • Identify patterns and anomalies
  • Generate historical reports

Point-in-Time Recovery

  • Restore data to any previous state
  • Undo unwanted changes
  • Investigate data corruption issues
  • Test against historical data

Temporal Data Modeling

  • Model real-world temporal relationships
  • Support bi-temporal data (transaction time + valid time)
  • Handle slowly changing dimensions
  • Track entity lifecycles

Performance Characteristics

  • Read queries: Historical queries incur additional overhead for version filtering
  • Write queries: System-versioned tables add ~10-20% write overhead for history tracking
  • Storage: Historical data requires additional storage (configurable with retention policies)
  • Indexes: Temporal indexes improve time-based query performance by 10-100x
  • Compression: Historical data compresses well (typical 3-5x compression ratio)

Configuration

Note: The SQL DDL syntax shown below (PERIOD FOR, WITH SYSTEM VERSIONING, FOR SYSTEM_TIME, ALTER TABLE … SET RETENTION_PERIOD) is not yet supported in the AQL parser. The same functionality is fully available through the C++ API (SystemVersionedTable, BiTemporalTable, RetentionManager).

System-Versioned Table (C++ API)

SystemVersionedTable employees("employees");
employees.insert("emp1", {{"name","Alice"},{"salary",90000}});
employees.update("emp1", {{"salary",95000}});
auto snapshot = employees.scan(as_of_timestamp);

Planned DDL Syntax (not yet supported)

-- Target: Q3 2026
CREATE TABLE employees (
    id INTEGER PRIMARY KEY,
    name TEXT,
    salary DECIMAL,
    PERIOD FOR SYSTEM_TIME
)
WITH SYSTEM VERSIONING;

Application-Versioned Table (C++ API)

BiTemporalTable contracts("contracts");
contracts.insert("c1", doc, valid_from, valid_to);
auto rows = contracts.scanBiTemporal(sys_as_of, valid_at);

Planned DDL Syntax (not yet supported)

-- Target: Q3 2026
CREATE TABLE contracts (
    id INTEGER PRIMARY KEY,
    customer_id INTEGER,
    valid_from DATE,
    valid_to DATE,
    PERIOD FOR APPLICATION_TIME (valid_from, valid_to)
);

Retention Policy (C++ API)

RetentionManager rm;
rm.setPolicy("employees", {RetentionPolicy::Type::TIME_BASED, 365 /* days */});
rm.enforceRetention("employees");

Planned DDL Syntax (not yet supported)

-- Target: Q3 2026
ALTER TABLE employees
SET RETENTION_PERIOD = INTERVAL '1 YEAR';

Time-Travel Query (C++ API)

TemporalQueryEngine engine(table);
auto rows = engine.queryAsOf(target_ts);
auto history = engine.queryFromTo(t_start, t_end);

Planned SQL Syntax (not yet supported)

-- Target: Q3 2026
SELECT * FROM employees
FOR SYSTEM_TIME AS OF '2024-01-01 00:00:00';

SELECT * FROM employees
FOR SYSTEM_TIME FROM '2024-01-01' TO '2024-12-31';

Integration Points

  • Storage Layer: Extended key schema for version tracking
  • Query Engine: Temporal query operators and predicates
  • Index Layer: Specialized temporal indexes
  • Replication: Temporal conflict resolution for distributed scenarios
  • Backup/Recovery: Point-in-time restore capabilities

Thread Safety

  • Thread-safe conflict resolution with concurrent snapshot handling
  • Lock-free temporal query execution for read-heavy workloads
  • Coordinated version creation to prevent conflicts
  • Safe retention policy enforcement with background cleanup

Dependencies

  • RocksDB: Underlying storage for temporal data
  • HLC (Hybrid Logical Clock): Distributed timestamp ordering
  • Replication Module: Multi-master conflict resolution
  • Index Module: Temporal index support

Documentation

For detailed implementation documentation, see:

Version History

  • v1.0.0: HLC-based temporal conflict resolver with five resolution policies
  • v1.1.0: System-versioned table with automatic transaction-time history
  • v1.2.0: BiTemporalTable (system time + valid time), TemporalIndex (period B-tree), TemporalQueryEngine (AS OF, FROM...TO, BETWEEN...AND)
  • v1.3.0: RetentionManager (time-based and count-based policies), TemporalAggregator (tumbling/sliding window), TemporalSnapshotManager
  • v1.4.0: Bitemporal joins (joinBiTemporal), SEQUENCED/NON-SEQUENCED query semantics (queryWithSemantics)
  • v1.5.0: IntervalTreeIndex (augmented BST, max-end tracking, O(log n + k) overlap queries), TemporalCompressor (DELTA/ZSTD/Gorilla/dictionary compression), TemporalCDC (pub/sub change events with ring-buffer replay)

See Also

Scientific References

  1. Jensen, C. S., & Snodgrass, R. T. (1999). Temporal Data Management. IEEE Transactions on Knowledge and Data Engineering, 11(1), 36–44. https://doi.org/10.1109/69.755613

  2. Snodgrass, R. T. (1987). The Temporal Query Language TQuel. ACM Transactions on Database Systems, 12(2), 247–298. https://doi.org/10.1145/22952.22956

  3. Kulkarni, K., & Michels, J.-E. (2012). Temporal Features in SQL:2011. ACM SIGMOD Record, 41(3), 34–43. https://doi.org/10.1145/2380776.2380786

  4. Lamport, L. (1978). Time, Clocks, and the Ordering of Events in a Distributed System. Communications of the ACM, 21(7), 558–565. https://doi.org/10.1145/359545.359563

  5. Dalgaard, P., & Jensen, C. S. (2001). On the Representation of Valid Time in a Temporal Relational Database. VLDB Journal, 10(2–3), 188–205. https://doi.org/10.1007/s007780100041

Installation

This module is built as part of ThemisDB. See the root CMakeLists.txt for build configuration.

Usage

The implementation files in this module are compiled into the ThemisDB library. See ../../include/temporal/README.md for the public API.