ClickHouse

ClickHouse is the open-source, column-oriented OLAP database that powers real-time analytics for Cloudflare, Uber, eBay, Microsoft, GitLab, Lyft and thousands of other companies. We rate it 92/100 — the fastest analytical database you can run yourself, and the obvious default for log analytics, observability and product-analytics workloads at any meaningful scale.

What is ClickHouse?

ClickHouse is a column-oriented OLAP DBMS that started inside Yandex in 2009, originally built by Alexey Milovidov to power Yandex.Metrica's web-scale analytics. It was open-sourced under Apache 2.0 in June 2016, and the independent company ClickHouse Inc. spun out of Yandex in September 2021 with a $50M Series A and a $250M Series B at a $2B valuation a month later, both led by Index Ventures and Coatue. The repo at github.com/ClickHouse/ClickHouse now sits at roughly 45,000+ stars with more than 1,500 contributors.

The pitch is simple: traditional OLTP databases like Postgres and MySQL store data row-by-row, which is fine for transactions but catastrophic for aggregations over billions of rows. ClickHouse stores data in compressed columns, vectorizes its query execution and parallelizes across cores and shards. The result is queries that are routinely 100×–1000× faster than equivalent Postgres or MySQL queries, with linear scaling on commodity hardware. Cloudflare, for example, uses a 36-node ClickHouse cluster to ingest non-aggregated request logs at over 6 million requests per second.

Key Features of ClickHouse

• Column-oriented storage with vectorized execution: Data is stored and processed by column with SIMD-vectorized kernels — the architectural reason most queries run two to three orders of magnitude faster than on row stores.
• Best-in-class compression: Built-in LZ4, ZSTD and Delta codecs typically achieve 5×–10× compression ratios, which both reduces storage cost and feeds CPUs faster.
• Distributed and replicated by default: Sharded tables, ReplicatedMergeTree, ZooKeeper- or Keeper-coordinated multi-master replication and automatic failover are first-class.
• SQL with analytical extensions: Full ANSI SQL plus window functions, arrays, tuples, nested JSON, geo functions, approximate aggregations (HyperLogLog, T-Digest) and 1,000+ built-in functions.
• Materialized views and projections: Pre-aggregate billions of rows incrementally as data lands, so dashboards return sub-second even on petabyte tables.
• Object-storage tiering: S3, GCS and Azure Blob can be used as primary or cold-tier storage, decoupling compute from storage and making petabyte-scale workloads economical.
• 70+ table engines and integrations: Native engines for Kafka, RabbitMQ, MySQL, Postgres, MongoDB, S3, HDFS, Hive and Iceberg let you query external sources directly without ETL.
• ClickPipes managed ingestion: In ClickHouse Cloud, ClickPipes streams data from Kafka, Postgres CDC and S3 with no separate Flink or Kafka Connect to operate.

What Users Say About ClickHouse

Sentiment in the data engineering community is overwhelmingly positive. The most upvoted Hacker News thread on ClickHouse calls it "unreasonably fast", and a long-running r/dataengineering discussion concludes that "once you've used ClickHouse for log analytics it's hard to go back to anything else." On G2, ClickHouse Cloud holds a strong 4.6/5 rating with reviewers consistently calling out query speed, low TCO and the team's responsiveness on GitHub issues. Cloudflare, Uber, GitLab, Instacart, Lyft, eBay and Contentsquare have all published engineering posts crediting ClickHouse with handling workloads that previously required Vertica, Snowflake or Druid.

The recurring complaints fall into three buckets. First, ClickHouse is analytics-only — it is not a replacement for OLTP and you should not point your transactional app at it. Second, the operational learning curve is real: ReplicatedMergeTree, sharding keys, partition design and merge tuning all matter, and the docs assume you understand the storage engine. Third, on the cloud side, several reviewers note that the Basic tier's idle scale-down can introduce cold-start latency on first query and that the consumption-based pricing is hard to forecast for spiky workloads.

ClickHouse Pricing

The ClickHouse open-source server is free forever under Apache 2.0. ClickHouse Cloud, the fully-managed offering, uses consumption-based pricing across three service tiers. New accounts get $300 in credits and a 30-day trial.

Plan	Compute	Storage	Best For
Open Source	Free (your hardware)	Free	Self-hosted on bare metal, k8s or VMs — Apache 2.0
Cloud Basic	From $0.2181 / unit-hour	$25.30 / TB-month	Single replica dev/test, scale-to-zero
Cloud Scale	From $0.2986 / unit-hour	$25.30 / TB-month	Production HA workloads, 3+ replicas
Cloud Enterprise	From $0.3903 / unit-hour	$25.30 / TB-month	Single-tenant, custom cloud, SOC 2 / HIPAA / PCI

Who Should Use ClickHouse?

Best for: Engineering teams running observability, log analytics, ad-tech, real-time product analytics, IoT or any time-series workload at billions-of-rows scale who need sub-second queries on commodity hardware. Especially strong as a Druid, Pinot, Vertica, BigQuery or Snowflake replacement for read-heavy analytical workloads.

Not ideal for: OLTP workloads (point lookups, single-row updates, multi-row transactions) — keep using Postgres or MySQL for that. Also overkill for sub-100GB analytical datasets, where DuckDB or even SQLite will be simpler.

Pros and Cons

Pros:

• Genuinely the fastest open-source analytical database — independent benchmarks consistently put ClickHouse at the top of the column-store leaderboard.
• Apache 2.0 licensed with a healthy contributor community (1,500+ contributors, 45K+ GitHub stars).
• Production-proven at internet scale at Cloudflare, Uber, eBay, Microsoft, GitLab and many more.
• Compression and storage tiering keep cost-per-TB dramatically lower than Snowflake or BigQuery for analytics.
• Optional fully-managed cloud (ClickHouse Cloud) on AWS, GCP and Azure with ClickPipes ingestion.
• Rich ecosystem: official drivers in Python, Go, Java, JS/TS, Rust and a strong Grafana / Superset / Metabase integration story.

Cons:

• Not an OLTP database — single-row updates and deletes are expensive and not the design point.
• Operational learning curve: ReplicatedMergeTree, partitioning, ORDER BY and merge tuning are all decisions you must get right.
• Cloud Basic tier scale-to-zero introduces cold-start latency on the first query after idle.
• Consumption-based pricing in ClickHouse Cloud can be hard to forecast for highly variable workloads.

Alternatives to ClickHouse

The closest competitors are Apache Druid and Apache Pinot, both purpose-built for real-time analytics but with steeper operational complexity; Snowflake and Google BigQuery, which are easier to operate but multiples more expensive at high scale; DuckDB, an excellent embedded analytical engine for sub-100GB workloads; and StarRocks, a newer ClickHouse-style entrant. ClickHouse is the most mature and broadly deployed of the open-source column-store options.

Verdict: Is ClickHouse Worth It?

Yes — for the workloads it was designed for, ClickHouse is the obvious choice. If you are doing log analytics, observability, ad-tech, product analytics, IoT or any other read-heavy aggregation over hundreds of millions of rows or more, ClickHouse will be faster, cheaper and more flexible than almost anything else in the market, especially against managed warehouses like Snowflake or BigQuery. Skip it only if you are in OLTP territory or your dataset is small enough that DuckDB or Postgres is enough. We rate it 92/100.

Frequently Asked Questions

Is ClickHouse free?

Yes. The ClickHouse server is free and open source under the Apache 2.0 License with no usage caps, user limits, or feature paywalls. Commercial use is allowed. ClickHouse Cloud, the managed service, is paid and starts at consumption-based pricing with a $300 free trial.

How does ClickHouse compare to Snowflake?

ClickHouse is typically faster on aggregation-heavy queries and dramatically cheaper at high scale, especially when self-hosted. Snowflake is easier to start with for non-engineers, has a more polished UI and a richer warehouse feature set (governance, data sharing, marketplace). For pure analytics performance per dollar, ClickHouse wins.

Can ClickHouse replace Postgres?

No — ClickHouse is an OLAP database, not OLTP. It is not designed for high-concurrency single-row reads or writes, foreign-key constraints or transactions. Use Postgres or MySQL for transactional workloads and ClickHouse for analytics on top of them.

Is ClickHouse open source?

Yes. The full source code is on GitHub at ClickHouse/ClickHouse under the Apache 2.0 License.

Who uses ClickHouse in production?

Cloudflare, Uber, eBay, Microsoft, GitLab, Lyft, Instacart, Contentsquare, Alibaba, Adevinta and thousands of other companies use ClickHouse for log analytics, observability, ad-tech and real-time analytics workloads.

What clouds does ClickHouse Cloud run on?

ClickHouse Cloud is available on AWS, Google Cloud Platform and Microsoft Azure across multiple regions in North America, Europe and Asia-Pacific.