We at VictoriaMetrics took another approach. We tried using ClickHouse as a database for metrics in 2017, but then decided implementing a specialized database for metrics. This database uses ClickHouse architecture ideas for achieving the best performance and the lowest resource usage. The main difference between ClickHouse and VictoriaMetrics is that VictoriaMetrics is optimized solely for typical observability tasks. It supports all the popular data ingestion protocols, it provides promql-compatible querying API, it provides Graphite-compatible querying API, it provides Prometheus-compatible service discovery and relabeling, it provides Prometheus-compatible alerting and recording rules. It provides built-in web UI for quick exploration and analysis of the ingested metrics, with the ability to investigate the source of high cardinality. It consists of a single small executable (~20MB) without external dependencies with minimum configs and minimum maintenance. See https://altinity.com/wp-content/uploads/2021/11/How-ClickHou... for more details.

We used the same approach for building VictoriaLogs - a specialized database for logs. It uses the most appropriate architecture ideas from ClickHouse for achieving high performance and low resource usage. It is schemaless and zero-config. It contains of a single small executable without external dependencies. It accepts logs via popular data ingestion protocols. It provides a specialized query language for typical queries over production logs - LogsQL. This language is much simpler to use than SQL for querying typical logs. It provides a built-in web UI for quick exploration of the ingested logs. It provides a Grafana plugin for building arbitrary complex dashboards from the stored logs. It provides the ability to build alerts and metrics from the stored logs. See https://docs.victoriametrics.com/victorialogs/faq/#what-is-t...