examples/ctable/real_world.py exists but is a fairly raw script. There is no self-contained example that clearly shows what performance gain SUMMARY indexes provide over a full scan, or how block size affects that gain — which is the most common question users will have after enabling auto-indexing.
Suggested work: Write bench/ctable/summary_index_perf.py that:
- Generates a synthetic CTable with a few million rows and numeric columns
- Runs the same where() query three ways: no index, SUMMARY at chunk granularity, SUMMARY at block granularity
- Prints a clean results table (rows scanned, time, speedup)
- Includes comments explaining the trade-offs
Ideally, it should work without any external dataset so it can be run immediately after install, but using an accessible dataset is also an option.
examples/ctable/real_world.pyexists but is a fairly raw script. There is no self-contained example that clearly shows what performance gain SUMMARY indexes provide over a full scan, or how block size affects that gain — which is the most common question users will have after enabling auto-indexing.Suggested work: Write
bench/ctable/summary_index_perf.pythat:Ideally, it should work without any external dataset so it can be run immediately after install, but using an accessible dataset is also an option.