Rewrite backfill chunk planning with multi-strategy smart chunking#108
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Rewrite backfill chunk planning with multi-strategy smart chunking#108
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ClickHouse Cloud enables parallel replicas by default, which inflates count() results by the replica count (observed 35x over-count). Add SETTINGS enable_parallel_replicas=0 to all count queries used during chunk planning. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extend the fix to min/max, GROUP BY prefix, and GROUP BY temporal bucket queries. Tested on ObsessionDB: GROUP BY counts are inflated by the replica count (16-35x), and min/max queries are ~5x slower with replicas on. Extract a shared DISABLE_PARALLEL_REPLICAS constant with a note that this is an ObsessionDB workaround. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extend ClickHouseExecutor.query() to accept per-query settings and thread querySettings through PlannerContext. The ObsessionDB workaround (enable_parallel_replicas=0) is now set once at the plugin call site instead of being appended to each SQL string. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The backfill moves uncompressed data, so chunk sizing should be based on uncompressed bytes rather than compressed. With ~8x compression ratios, using compressed bytes produced chunks ~8x larger than intended. All size comparisons, merge budgets, and row-target calculations now use bytesUncompressed. Test maxChunkBytes values doubled to match the 2x compression ratio in the test fixture. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Analysis showed 3x oversampling is sufficient for equal-width range splitting while reducing the number of estimation queries. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
At root level (depth 0), use equal-width EXPLAIN ESTIMATE for the initial split — fast metadata-only probes instead of a full GROUP BY scan. Oversized children re-enter at depth 1+ and get GROUP BY prefix refinement on their narrowed sub-ranges. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace prefix-based hot key discovery with a direct GROUP BY key approach for sub-ranges with ≤100 distinct values. One query gives exact per-key counts instead of recursive depth drilling (1→4 chars). When a sub-range contains a single key, narrow the range to an exact match and re-enter dispatch so focusedValue propagates to subsequent dimension splitting (e.g. temporal buckets on the hot key). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The BigInt string boundary computation used a hardcoded 8-byte width, silently truncating sort key values longer than 8 characters. This caused chunk boundaries to miss rows at the end of the value range — e.g. a partition with tenants "mega-corp" (9 chars) through "tenant-0199" (11 chars) would lose all rows past the truncated upper bound "tenant-0". Replace the fixed width with a dynamic width derived from the actual input range strings (max of rangeFrom/rangeTo length). Also strip trailing null bytes from bigIntToStr output to avoid inflating boundary strings beyond their original length, while preserving semantically meaningful nulls via a minLength parameter. Adds E2E test infrastructure: a seed script to populate ClickHouse with controlled datasets, and an E2E test for the skewed power-law scenario (80/20 tenant distribution) that verifies hot key detection, cross- dimension splitting, estimate accuracy, and full row coverage. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds scenario 2: three tenants at ~30% each with 10% long tail, verifying that multiple hot keys are independently detected and split on the secondary dimension. Renames seed script to .script.ts so bun test does not re-execute it on every test run. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Development artifacts: benchmark scripts for testing chunking strategies against live ClickHouse, query traces, and E2E scenario planning notes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
sdkentry point (@chkit/plugin-backfill/sdk), keeping the main export surface clean.Test plan
bun verifypasses (typecheck, lint, test, build — 33/33)plancommand test on a real dataset🤖 Generated with Claude Code