ClickHouse
ClickHouse is an open-source, column-oriented database designed for real-time analytics on large datasets. It excels at fast query performance on read-heavy workloads and supports distributed deployments across clusters.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ClickHouse/ClickHouse |
| Owner | ClickHouse |
| Primary language | C++ |
| License | Apache-2.0 — OSI-approved |
| Stars | 48.5k |
| Forks | 8.6k |
| Open issues | 6.3k |
| Latest release | v25.8.28.1-lts (2026-07-05) |
| Last updated | 2026-07-08 |
| Source | https://github.com/ClickHouse/ClickHouse |
What ClickHouse is
Apache 2.0–licensed C++ OLAP database with columnar storage, distributed query execution, and SQL interface. Designed for petabyte-scale analytics; supports embedded, self-hosted, and cloud-managed deployment modes.
Get the ClickHouse source
Clone the repository and explore it locally.
git clone https://github.com/ClickHouse/ClickHouse.gitcd ClickHouse# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Cluster topology (shards, replicas) must be planned upfront; resharding is complex and requires careful orchestration.
- Schema design and data partitioning strategy are critical; poor choices severely impact query latency and storage efficiency.
- Ingestion pipeline (Kafka, file uploads, INSERT batches) must batch writes appropriately to avoid excessive part proliferation.
- Query optimization requires understanding columnar execution; naive queries on denormalized data can be slow despite the engine's speed potential.
- Memory and CPU provisioning depends heavily on query concurrency and result set size; monitoring and tuning are ongoing tasks.
When to avoid it — and what to weigh
- Transactional ACID Requirements — ClickHouse is optimized for read-heavy analytics. Complex multi-row transactions with strong consistency guarantees are not a primary design goal.
- Small Datasets or Single-Node Only — Distributed architecture adds operational overhead; for <100 GB datasets or purely local use, simpler databases (PostgreSQL, SQLite) may be more appropriate.
- Real-Time Row Updates — Mutation operations (UPDATE/DELETE) are computationally expensive; workloads requiring frequent updates to individual records suit row-oriented databases better.
- Minimal Infrastructure Expertise — Cluster setup, replication, and distributed query debugging require substantial database operations knowledge; fully managed cloud services reduce this burden.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive open-source license allowing use, modification, and distribution with straightforward attribution and liability disclaimers.
Apache 2.0 permits commercial use, derivative works, and private modifications. No license fees. Evaluate trademark/branding constraints separately. A commercial managed service (ClickHouse Cloud) exists, but the OSS version has no built-in commercial use restrictions. Legal review of your use case is recommended.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
C++ codebase with regular updates; no known endemic vulnerability class disclosed in provided data. Network security (TLS, authentication) and access controls are configurable. Self-hosted deployments require hardened infrastructure. Distributed systems add attack surface. Security audit and threat modeling recommended for sensitive workloads.
Alternatives to consider
Apache Druid
Time-series and real-time OLAP; operationally simpler Kubernetes-native deployment but narrower data model than ClickHouse.
Snowflake
Fully managed cloud data warehouse with strong governance and ACID transactions; higher cost and less suitable for ultra-high-volume streaming ingest.
Apache Iceberg + Spark
Open lakehouse stack with flexible compute; requires more engineering effort than ClickHouse for operational deployment and query optimization.
Build on ClickHouse with DEV.co software developers
Our engineers can assess architectural fit, deployment strategy, and operational readiness. Start with a cloud pilot or discuss self-hosted infrastructure needs.
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ClickHouse FAQ
Is ClickHouse suitable for OLTP (transactional) workloads?
Can I run ClickHouse on a single server?
What is the difference between self-hosted and ClickHouse Cloud?
How do I handle schema migrations in production?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like ClickHouse into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source devops stack.
Evaluating ClickHouse for Your Analytics Stack?
Our engineers can assess architectural fit, deployment strategy, and operational readiness. Start with a cloud pilot or discuss self-hosted infrastructure needs.