ClickHouse Analytics Database

ClickHouse is an open-source column-oriented database management system created by Alexey Milovidov at Yandex, the Russian search engine, in 2009, designed for online analytical processing workloads requiring high-speed analytical queries on large datasets, achieving query performance of billions of rows per second through vectorized execution, sparse indexes, and data compression, processing analytical queries ten to one thousand times faster than traditional row-oriented databases, adopted by Cloudflare, Uber, Spotify, eBay, Cisco, and over one thousand companies for real-time analytics, logging, and business intelligence applications. The columnar storage layout stores each table column separately on disk, enabling the query engine to read only the columns needed for a query rather than entire rows, dramatically reducing disk input and output for analytical queries that typically access a small subset of columns from wide tables, with the storage using compression algorithms including LZ4, ZSTD, and Delta that achieve typical compression ratios of seven to one, further reducing storage costs and improving query performance through better cache utilization. The vectorized query execution engine processes data in batches of thousands of values at a time using SIMD CPU instructions, achieving high throughput through efficient CPU cache utilization and branch prediction, with the query optimizer automatically choosing optimal join strategies, aggregate combinator functions, and execution plans based on table statistics and data distribution. The sparse primary index provides fast point lookups. The materialized views enable pre-computed aggregations. The MergeTree family of table engines provides data replication. The TTL for automatic data expiration. The distributed tables for querying across shards. Designed for data engineers and analysts.