Document SUMMARY indexes in Indexing CTables notebook - #729
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| "id": "d9fa565cab114c0d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Automatic SUMMARY indexes\n", |
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I sourced this text from the PR description of #643 and from the existing info about summary indexes in the Optimization Tips guide
| " blosc2.open(str(summary_path), mode=\"r\") as summary_readings,\n", | ||
| " blosc2.open(str(no_summary_path), mode=\"r\") as no_summary_readings,\n", | ||
| "):\n", | ||
| " def predicate(t):\n", |
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In this notebook, I only focused on the benefit to where() queries, since the min()/max() benefits were already covered in the Optimization Tips guide. However, I'm happy to add a min/max example here too, if desired.
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September 30, 2026 19:42
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This offers a clear and very simple explanation of what SUMMARY indexes offer. Thanks, it is exactly the kind of contributions that our current docs need. Looking forward for more like this!
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Summary
This PR adds a new section to the "Indexing CTables" tutorial notebook to describe SUMMARY indexes and provides an example benchmark to demonstrate the benefit.
Addresses #645.
Testing
AI Tool Use
I used Codex to generate the boilerplate code in the
Generate the datasetsection. I had also used it to generate theRun the benchmarksection, but ended up rewriting that code myself.