OpenObserve vs ClickHouse: Which Is Better for Logs?

A vendor-authored, reproducible benchmark compared OpenObserve v0.92.0-rc2 and ClickHouse 26.7.1 on one billion synthetic Kubernetes log records totaling 2.04 TiB of raw JSON, using matched AWS hardware with both local NVMe and high-performance gp3 EBS storage. ClickHouse delivered faster warm-query performance on 15 of 19 tests, particularly for aggregations, rare-token searches, and many row fetches, generally by modest millisecond margins, while OpenObserve was substantially faster for common-token searches and scattered pod-name lookups, reaching a 13.9-fold warm advantage for pod-name counts. After cache drops, OpenObserve was faster on more queries, especially on gp3, where its cold-query latency remained relatively stable while ClickHouse’s heaviest cold queries became 2.3 to 2.8 times slower, including a pod-name search measured at 141 ms versus 14.16 seconds. OpenObserve used about 621 GiB on disk compared with ClickHouse’s 937 GiB, although the report cautions that ClickHouse used default rather than recommended compression codecs, making storage comparisons inconclusive. The comparison frames ClickHouse as a powerful analytical database requiring schema, indexing, and surrounding observability components to be designed or added, while OpenObserve provides integrated ingestion, indexing, search, dashboards, alerting, schema evolution, and an object-storage-oriented architecture; it does not evaluate object-storage performance or total cost and encourages independent reproduction of the results.

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