Clickhouse is winning the Observability Wars
a day ago
- Logs are consistently the worst part of observability work due to volume, schema drift, and conflicting expectations between developers and non-technical consumers.
- ClickHouse's columnar storage allows queries to read only relevant columns, significantly reducing I/O and enabling 10–14x compression on observability data.
- At 1 TB/day, all major observability stacks (Elasticsearch, LGTM, Datadog, ClickHouse) are comparable in complexity and cost.
- At 5–10 TB/day, Elasticsearch requires Kafka, shard management, and multiple clusters; LGTM demands 180+ pods and dedicated teams; Datadog becomes financially untenable with six- to seven-figure monthly bills.
- ClickHouse scales linearly by simply adding shards, maintaining the same architecture, query language, and operational model regardless of data volume.
- The trade-off for ClickHouse is upfront schema design and query complexity, but this investment pays off as data grows by orders of magnitude without architectural shifts.