Explore the Architecture

ClickHouse is architected with a layered design for high performance and flexibility.

Core Layers

1. Query Processing Layer: This layer handles parsing, logical/physical plan building, and execution using a vectorized engine, similar to MonetDB/X100. It supports SQL, PRQL, and Kusto-like KQL. Runtime optimizations such as opportunistic compilation make it extremely efficient across diverse query types and data patterns.

2. Storage Layer: The storage backend is pluggable with multiple table engines:

   • MergeTree engines (e.g. ReplacingMergeTree, AggregatingMergeTree) form the core persistent format. Based on LSM-tree principles, they maintain sorted parts and continuously merge them in the background.

   • Special-purpose engines like Dictionaries support fast in-memory key-value lookups (even from external sources), and Distributed engines allow transparent sharding across nodes.

   • Virtual engines integrate with external systems like MySQL, Kafka, S3, Iceberg, and Redis for both read and write, enabling data federation and hybrid architectures.

3. Integration Layer: Facilitates connections to external systems via protocols (MySQL, PostgreSQL, HTTP), or direct engine bindings. It also includes observability, role-based access control, and backup hooks.

4. Access Layer: Manages sessions, authentication, and protocol-level communication with clients.

Performance Engineering

ClickHouse emphasizes low-level optimizations:

• It chooses from 30+ hash table implementations dynamically at runtime.

• Uses data-aware algorithms for sorting, filtering, and join operations.

• Employs column-level compression codecs (like ZSTD, Gorilla, Delta, AES) for high-speed storage and efficient I/O.

Sharding and Replication

• Sharding enables massive scalability by partitioning data horizontally across nodes.

• Replication is handled via ReplicatedMergeTree engines using a Raft-based coordination mechanism called Keeper (a C++ Zookeeper replacement).

• The Distributed engine provides a global view across shards.

Deployment Modes

• On-prem: Classic ClickHouse cluster or single-node server.

• Cloud: Fully-managed ClickHouse Cloud (autoscaling DBaaS).

• Standalone: CLI tool for local analytics, replacing tools like awk, grep.

• Embedded (chDB): In-process OLAP database, ideal for Jupyter/Pandas workflows. No IPC or serialization overhead.

For detailed information, see ClickHouse Architecture.

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