Observed Signal · May 2, 2026 · Technical Guide · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
Why ClickHouse ReplacingMergeTree Drops Rows
A technical guide explains why ClickHouse can appear to silently drop rows when using the ReplacingMergeTree engine. ReplacingMergeTree deduplicates rows by an ORDER BY key and a version column (the row with the highest version wins). Duplicates inside the same INSERT block are resolved immediately; duplicates across separate INSERTs remain visible until background part merges run (or until an explicit OPTIMIZE TABLE ... FINAL). SELECT ... FINAL applies deduplication at read time but is expensive. Separately, ClickHouse also performs INSERT-block deduplication using checksums — identical INSERT blocks are skipped. The article includes a sample table schema, SQL examples, and recommended commands to force merges or read deduplicated data.
Practical explanation of ClickHouse deduplication behavior that affects data accuracy and analytics pipelines; useful for data engineers but not industry-shifting.
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Key Takeaways & Evidence Grounding
- ReplacingMergeTree removes duplicates based on a defined ORDER BY key and a version column (the row with the highest version survives).
- Duplicate rows within the same INSERT block are deduplicated immediately at insert time.
- Duplicate rows across separate INSERTs remain until ClickHouse merges data parts in the background; OPTIMIZE TABLE ... FINAL forces an immediate merge.
- SELECT ... FINAL applies ReplacingMergeTree deduplication at read time (expensive) and always returns deduplicated results.
- ClickHouse uses INSERT-block deduplication (by checksum) to skip identical repeated INSERT blocks.
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PREWHERE Optimization for ReplacingMergeTree FINAL
This technical blog explains how ClickHouse's PREWHERE optimization interacts with the ReplacingMergeTree engine and the FINAL keyword. PREWHERE can drastically reduce I/O by filtering data before reading non-filter columns, but ClickHouse disables automatic PREWHERE movement when FINAL is used because FINAL triggers read-time deduplication and PREWHERE applied beforehand can change which row survives. The post demonstrates the risk with a concrete example and states the safe rule of thumb: moving ORDER BY columns (the deduplication key) into PREWHERE is safe, while moving other columns is not. The author suggests upvoting an existing ClickHouse discussion proposing automatic PREWHERE application for ORDER BY columns when FINAL is present.
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.
ClickHouse vs PostgreSQL: OLAP vs OLTP Comparison
A developer-authored technical post comparing ClickHouse and PostgreSQL as part of a '100 Days of ClickHouse' series. The article explains that PostgreSQL is a row-oriented OLTP database optimized for transactional workloads with frequent inserts/updates/deletes and strong transactional guarantees, while ClickHouse is a column-oriented OLAP database designed for large-scale analytics, fast aggregations and time-series/event analytics. It outlines storage and compression differences, scaling considerations, and common deployment patterns where organizations use PostgreSQL for operational data and ClickHouse for analytical/reporting workloads. The piece aims to guide database selection based on workload requirements rather than popularity or benchmarks.
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