ClickHouse for time series metrics, rollups, and SLOs

ClickHouse can support large-scale time-series analytics despite not being a dedicated TSDB by using columnar storage, compression, MergeTree engines, carefully selected sort keys, and materialized-view rollups. The approach recommends narrow metric schemas for high-cardinality telemetry, distinguishes counter rates from gauge aggregations, batches ingestion efficiently, stores timestamps consistently, and uses tiered raw, minute, hourly, and daily tables with TTL-based retention to balance drill-down detail against long-range query cost. It describes SQL patterns for latest values, percentile latency, SLO monitoring, window comparisons, gap handling, late data, duplicate delivery, tenant-scoped APIs, and query-log analysis, while emphasizing that dashboard filters should drive partitioning and sort-key design. ClickHouse is positioned as especially useful for long-term analytics combining metrics with logs, events, and dimensions, whereas dedicated TSDBs remain preferable for native PromQL, service discovery, and scrape-centric operations; managed platforms such as Tinybird can reduce operational work by providing ingestion, transformations, APIs, and schema-management features.

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