clickhouse/clickhouse

ClickHouse® is a real-time analytics database management system

46,617 stars C++ 12 components 21 connections

7-stage pipeline · 12 components

Real-time column-oriented database management system for analytical queries

SQL queries flow through parsing, analysis, optimization, and vectorized execution against columnar storage, with results streamed back to clients

Under the hood, the system uses 3 feedback loops, 4 data pools, 4 control points to manage its runtime behavior.

A 12-component dashboard with 21 connections. 11104 files analyzed. Data flows through 7 distinct pipeline stages.

How Data Flows Through the System

SQL queries flow through parsing, analysis, optimization, and vectorized execution against columnar storage, with results streamed back to clients

Query Reception — Server receives SQL query via HTTP or TCP protocol
Parse SQL — Parser converts SQL text into Abstract Syntax Tree
Semantic Analysis — Analyzer validates query semantics and resolves references
Query Planning — Optimizer generates execution plan with processor pipeline
Data Access — Storage engines read columnar data using appropriate table engine
Vectorized Execution — Processors execute operations on column chunks using SIMD
Result Streaming — Results streamed back to client in requested format

System Behavior

How the system operates at runtime — where data accumulates, what loops, what waits, and what controls what.

Data Pools

ColumnStore (database)
Columnar data storage with compression and indexing

QueryCache (cache)
Cached query results and execution plans

CompilerCache (cache)
Cached compilation artifacts and build results

BufferEngine (buffer)
In-memory buffer for batching writes to storage

Feedback Loops

Query Optimization (recursive, balancing) — Trigger: Query execution statistics. Action: Adjust execution plan based on cardinality estimates. Exit: Optimal plan found.
Merge Process (convergence, balancing) — Trigger: Multiple data parts in storage. Action: Merge smaller parts into larger ones. Exit: Optimal part structure achieved.
Retry Logic (retry, balancing) — Trigger: Network or storage failures. Action: Retry operation with exponential backoff. Exit: Success or max retries reached.

Delays

Merge Delay (batch-window, ~configurable seconds) — Data parts accumulate before merging for efficiency
Buffer Flush (batch-window, ~configurable interval) — Writes accumulate in memory before flushing to disk
Query Rate Limit (rate-limit, ~per-user/per-query limits) — Queries may be throttled or queued
Async Insert (async-processing, ~background processing) — Insert operations processed asynchronously for throughput

Control Points

max_threads (threshold) — Controls: Maximum query execution parallelism
merge_tree_settings (runtime-toggle) — Controls: MergeTree engine behavior and performance tuning
query_cache_enabled (feature-flag) — Controls: Whether to cache query results
background_pool_size (threshold) — Controls: Background task thread pool sizing

Technology Stack

C++ (framework)
Primary implementation language for database engine

Rust (framework)
Performance utilities for caching and SQL transpilation

POCO (library)
C++ foundation library for networking and utilities

CMake (build)
Build system and dependency management

GitHub Actions (infra)
CI/CD pipeline with Praktika automation framework

Docker (infra)
Containerization and testing environments

Zlib (library)
Compression library for data storage

LLVM (library)
JIT compilation for query optimization

Key Components

StorageEngine (module) — Manages different table engines and storage formats for columnar data src/Storages/
QueryProcessor (module) — Vectorized query execution pipeline with streaming data processing src/Processors/
Parser (module) — SQL query parsing and AST construction src/Parsers/
Analyzer (module) — Query semantic analysis, optimization and execution planning src/Analyzer/
Functions (module) — Built-in SQL functions and expressions for data processing src/Functions/
IOSystem (module) — High-performance I/O operations for data reading and writing src/IO/
Server (module) — HTTP and TCP server implementation for client connections src/Server/
CompilerCache (cli-command) — Caches compilation artifacts for faster builds using local or ClickHouse storage rust/chcache/src/main.rs
PolyglotTranspiler (service) — Transpiles SQL queries from various dialects to ClickHouse SQL syntax rust/workspace/polyglot/src/lib.rs
Formats (module) — Data serialization formats including JSON, CSV, Parquet, and native formats src/Formats/
+2 more components

Explore the interactive analysis

See the full architecture map, data flow, and code patterns visualization.

Analyze on CodeSea

Related Dashboard Repositories

Frequently Asked Questions

What is ClickHouse used for?

Real-time column-oriented database management system for analytical queries clickhouse/clickhouse is a 12-component dashboard written in C++. Data flows through 7 distinct pipeline stages. The codebase contains 11104 files.

How is ClickHouse architected?

ClickHouse is organized into 5 architecture layers: Database Programs, Query Processing, Storage Engine, Core Infrastructure, and 1 more. Data flows through 7 distinct pipeline stages. This layered structure enables tight integration between components.

How does data flow through ClickHouse?

Data moves through 7 stages: Query Reception → Parse SQL → Semantic Analysis → Query Planning → Data Access → .... SQL queries flow through parsing, analysis, optimization, and vectorized execution against columnar storage, with results streamed back to clients This pipeline design reflects a complex multi-stage processing system.

What technologies does ClickHouse use?

The core stack includes C++ (Primary implementation language for database engine), Rust (Performance utilities for caching and SQL transpilation), POCO (C++ foundation library for networking and utilities), CMake (Build system and dependency management), GitHub Actions (CI/CD pipeline with Praktika automation framework), Docker (Containerization and testing environments), and 2 more. A focused set of dependencies that keeps the build manageable.

What system dynamics does ClickHouse have?

ClickHouse exhibits 4 data pools (ColumnStore, QueryCache), 3 feedback loops, 4 control points, 4 delays. The feedback loops handle recursive and convergence. These runtime behaviors shape how the system responds to load, failures, and configuration changes.

What design patterns does ClickHouse use?

5 design patterns detected: Vectorized Processing, Plugin Architecture, Query Pipeline, Multi-Language Integration, Configuration-Driven.

Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.