Technology

ClickHouse

Summary

What it is

A column-oriented DBMS designed for real-time analytical queries, with native support for reading from and writing to S3.

Where it fits

ClickHouse occupies the performance tier above pure lakehouse queries. It can use S3 as a storage backend (S3-backed MergeTree) while maintaining its own columnar indexes for sub-second query performance — bridging the gap between S3 data lakes and dedicated analytics databases.

Misconceptions / Traps

  • ClickHouse with S3 storage is not the same as querying S3 directly. ClickHouse maintains local indexes and metadata for performance; it uses S3 for durability and cost.
  • The S3 table function (for ad-hoc S3 reads) and the S3-backed MergeTree engine (for persistent tables) are different features with different performance characteristics.

Key Connections

  • depends_on Apache Parquet — reads/writes Parquet for S3 interop
  • implements Separation of Storage and Compute — S3-backed storage with independent compute
  • scoped_to S3, Lakehouse

Definition

What it is

A column-oriented database management system designed for real-time analytical queries, with native support for reading from and writing to S3.

Why it exists

Some analytical workloads require sub-second query performance on recent data, which pure S3-backed query engines cannot consistently deliver. ClickHouse uses S3 as a storage backend while maintaining its own columnar indexes for speed.

Primary use cases

Real-time analytics dashboards backed by S3 storage, log analytics with S3 archival, hybrid hot/cold query patterns.

Relationships

Outbound Relationships

scoped_to
depends_on

Inbound Relationships

Resources