What is ClickHouse?

The Core Problem: Traditional row oriented databases like PostgreSQL or MySQL are optimized for transactions. They write and read entire rows at once, which makes sense for operations like "fetch user ID 12345 with all their profile fields." But for analytics queries like "what's the average latency across all requests in the last hour," you only need 2 columns (timestamp, latency) from billions of rows. Reading complete rows wastes IO and memory. Cloud data warehouses like BigQuery or Snowflake solved part of this with columnar storage, but they're priced for sporadic workloads and typically deliver query latency in the seconds to minutes range, not milliseconds. For always on, user facing analytics dashboards or real time monitoring systems, that's too slow. What ClickHouse Provides: ClickHouse focuses on three specific capabilities. First, extremely fast scans through columnar storage and vectorized execution, where the CPU processes thousands of values at once instead of row by row. Second, high ingestion throughput to continuously stream logs, metrics, and events at sustained rates. Third, predictable sub second response times for aggregations over massive datasets, so dashboards feel interactive. The storage engine family, called MergeTree, is log structured. New data arrives as immutable sorted blocks called parts, then background processes merge these parts to maintain order and optimize storage. This design allows parallel ingestion while keeping query performance high.