Real-time OLAP databases · ClickHouse, Inc.

ClickHouse

Open-source columnar database built for sub-second aggregate queries over billions of rows.

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ClickHouse is an open-source columnar database management system designed for online analytical processing (OLAP). It stores data by column rather than row, uses vectorized query execution, and applies aggressive compression, which lets it scan and aggregate billions of rows per second on a single server. It is commonly used for real-time dashboards, product and log analytics, and observability backends where queries must return in sub-second time against constantly growing event data. ClickHouse can be run self-managed on any Linux server or Kubernetes cluster, or consumed as the managed service ClickHouse Cloud. It supports standard SQL with extensions, materialized views for pre-aggregation, and native connectors for Kafka, S3 and most BI tools. Compared to Druid or Pinot, ClickHouse favors a simpler single-binary architecture over separated ingestion/serving tiers.

At a glance

Vendor ClickHouse, Inc.
Pricing model Open source + paid options
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Engineering teams building real-time dashboards or log/event analytics who want maximum query throughput on commodity hardware.

Pricing

The self-hosted database is free open source; the managed ClickHouse Cloud service is billed separately (see clickhouse-cloud).

Pricing has not been verified yet — see the vendor's site.

Features

  • Columnar storage with vectorized execution
  • Sub-second aggregate queries over billions of rows
  • Materialized views for pre-aggregation
  • Native Kafka, S3 and MySQL/Postgres table engines
  • High compression ratios via column-level codecs
  • Distributed sharding and replication
  • Standard SQL dialect with array and JSON extensions

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Apache Druid Open-source real-time analytics database that ingests streaming and batch data for interactive dashboards. Apache Pinot Open-source distributed OLAP database built by LinkedIn for low-latency analytics serving at high query volume. StarRocks Open-source MPP database for sub-second analytics across real-time data and data-lake tables. Apache Doris Open-source MPP database combining real-time analytics with sub-second query response at large scale.

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Terms to know

OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis. Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Columnar storage A file and database layout that stores each column's values together, instead of storing each row together. Real-time OLAP An analytical database that ingests streaming data continuously and answers aggregation queries with sub-second latency.

Related guides

How to choose a real-time OLAP database Real-time OLAP databases trade operational complexity for speed on fresh data — the decisions are architecture, query concurrency, and who runs it.