Profiles / ClickHouse
ClickHouse
A column-oriented database built for one job: answering aggregate queries over billions of rows in milliseconds while new rows keep arriving. It is the fastest system on hot queries in both of our ClickBench groups, the most compact on disk among the engines you can run yourself, and the only one here that is equally at home on a laptop, a single server or a managed cloud service.
Best overall for real-time analyticsApache 2.0
Strengths
- +Lowest ClickBench hot-run score in both groups: 1.03 on c6a.4xlarge, 1.06 in the managed group.
- +Smallest on-disk footprint of the self-hosted engines: 9.4 GB for 100M rows, under half of DuckDB or StarRocks.
- +Sustains 2.4–3× StarRocks' concurrent throughput on the same bare-metal hosts.
- +Built for continuous inserts: Kafka and S3 table engines, async inserts and materialized views that update on write.
- +Open source under Apache 2.0, with the same engine behind its managed cloud, so there is no lock-in.
Weaknesses
- −Joins between very large tables need more care than in a warehouse built around them.
- −Updates and deletes are possible but are not its strength; it favours append-mostly data.
- −Schema design (sort keys, partitions) matters a lot and takes some learning.
Capabilities
| Real-time ingestion | Kafka & S3 engines, async inserts, materialized views on write |
|---|---|
| Columnar storage | MergeTree; per-column codecs (LZ4, ZSTD, Delta, Gorilla) |
| Open source | Apache 2.0 |
| Managed cloud | ClickHouse Cloud on AWS, GCP and Azure |
| Query speed at scale | 1.03 self-hosted · 1.06 managed |
| High concurrency | 20–38 QPS on bare metal in ClickBench |
| Cost model | Free to self-host; usage-based compute and storage in the cloud |