How LinkedIn extended ClickHouse from distributed tracing to metric discovery and analytics

LinkedIn’s observability team adopted ClickHouse first for an OpenTelemetry-based distributed tracing platform that processes roughly 800 billion sampled spans and 200 TB of uncompressed data daily across three data centers, meeting demanding ingestion and query-latency targets through a replicated, highly tuned architecture. Building on that experience, the team migrated its metric metadata discovery system from three aging services—a custom search index, Elasticsearch, and a key-value store—to a single ClickHouse index, while preserving compatibility for tools using legacy RRDtool-style metric names through a gateway that translates requests into SQL. The consolidated system uses continual change capture, deduplication, daily table refreshes, projections for aggregations, and directly queryable metric dimensions to support discovery and analytics across more than 13 billion metrics with about 30% daily turnover. It now serves over 150,000 queries per minute at an average latency of 68 milliseconds, reduces memory consumption to about one-fifth and compute use to roughly two-thirds of the prior stack, and has capacity to double metric volume while helping users replace inefficient regex-based queries and gradually move away from legacy metric conventions.

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