Observed Signal · Apr 15, 2026 · Technical Release · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
ClickHouse JOINs Fast: 2022–2026 PR Analysis
This article is a PR‑level analysis of ClickHouse's join subsystem evolution from 2022 through early 2026. Examining 50+ merged GitHub pull requests, changelogs, and release blogs, the author documents that ClickHouse moved from a single, memory‑bound hash join to a mature join engine shipping sensible defaults. Key changes include six join algorithms, parallel hash join as the default, grace hash (disk‑spilling), cost‑based global join reordering (greedy + DPsize), equivalence‑set predicate pushdown, and runtime bloom filters enabled by default in February 2026. The analysis cites concrete PRs and measured speedups (e.g., 180× from predicate pushdown, 1,450× on TPC‑H SF100 from reordering, 2.1× from runtime filters) and emphasizes that these features are production defaults rather than experiments.
Substantive engineering changes to a major analytical database (ClickHouse) improve join performance, scaling, and reliability—relevant to teams using warehouses/lakehouses for analytics in AdTech and MarTech but not an industry‑shifting platform policy change.
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Key Takeaways & Evidence Grounding
- Author analyzed 50+ merged ClickHouse GitHub pull requests and official release notes from 2022–early 2026.
- ClickHouse now ships six join algorithms (hash, parallel_hash, grace_hash, full_sorting_merge, direct, paste) with automatic selection.
- Equivalence‑set predicate pushdown (PR #61216) produced reported 180×+ speedups by propagating filters across join sides.
- Global join reordering with column statistics (PR #86822) delivered a reported 1,450× speedup on TPC‑H SF100 workloads.
- Runtime bloom filters (PR #89314), enabled by default in February 2026 (v25.10), reported a 2.1× query speedup and 7× memory reduction on star‑schema workloads.
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