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Danny Kopping
9fa103929a
_Disclaimer: produced using Claude Opus 4.6, reviewed by me, and validated against Dogfood dataset._ The `ListAIBridgeSessions` query materialized and aggregated all matching interceptions before paginating, then ran expensive token/prompt lookups across the full dataset. For a page of 25 sessions against ~200k interceptions (our dogfood dataset), this meant: - Three CTEs scanning all rows (filtered_interceptions, session_tokens, session_root) - ARRAY_AGG(fi.id) collecting every interception ID per session - Lateral prompt lookup via ANY(array_of_all_ids) running for every session, not just the page - ~90MB of disk sorts and JIT compilation kicking in The improvement is to restructure to paginate first and enrich after: a single CTE groups interceptions into sessions with only cheap aggregates (MIN, MAX, COUNT), applies cursor pagination and LIMIT, then lateral joins fetch metadata, tokens, and prompts for just the ~25-row page. Measured against 220k interceptions / 160k sessions: | Metric | Before | After | |--------------------|--------|-------| | Execution time | 1800ms | 185ms | | Shared buffer hits | 737k | 2.6k | | Disk sort spill | 86MB | 16MB | | Lateral loops | 160k | 25 | https://grafana.dev.coder.com/goto/fbODPGtvR?orgId=1 the results are identical, just _much_ faster. --- Also includes some additional tests which I added prior to refactoring the query to ensure no regressions on edge-cases. --------- Signed-off-by: Danny Kopping <danny@coder.com>