ClickHouse

ClickHouse is an open-source, column-oriented database management system built for high-performance online analytical processing. By storing data in columns rather than rows and executing vectorized queries, ClickHouse enables organizations to process analytical queries rapidly across petabyte-scale datasets.

The platform serves software developers, data engineers, and AI infrastructure teams who require low-latency queries on high-throughput data streams. Users can deploy ClickHouse as a fully managed cloud service across AWS, GCP, and Azure, operate self-managed open-source clusters, or execute embedded queries on local files using chDB and ClickHouse Local.

In addition to core analytical warehousing, ClickHouse supports vector search for AI workloads, observability via the ClickStack logging and tracing engine, and integrated LLM evaluation using Langfuse. It connects with business intelligence tools, data pipelines, and AI agent frameworks via the Model Context Protocol.

Key features

• Column-oriented storage: Stores values from the same column together to improve compression ratios and reduce disk reads during analytical queries.
• Vectorized query execution: Utilizes available CPU resources to process query blocks in parallel for sub-second responses on large datasets.
• Flexible deployment options: Runs as a managed cloud service, a self-hosted open-source database, or an in-process local SQL engine.
• Vector search capability: Performs vector comparisons alongside SQL analytical queries to support machine learning and generative AI applications.
• ClickStack observability: Integrates logging, metrics, tracing, and session replay storage into a single high-performance analytical environment.
• ClickPipes data ingestion: Ingests continuous data streams directly from external platforms and messaging systems with managed connectors.