Why ClickHouse Outperforms Other Databases: Core Features Unveiled

Background

Yandex.Metrica processes over 200 billion events daily, storing more than 20 trillion rows in ClickHouse; 90 % of custom queries return within one second across a cluster of over 400 servers. Originally built for Yandex.Metrica, ClickHouse is now used by many Yandex products because of its exceptional performance.

Core DBMS Features

ClickHouse is a full‑featured DBMS, offering DDL (create/alter/drop without restart), DML (dynamic query/insert/update/delete), fine‑grained permission control, backup/restore mechanisms, and built‑in distributed management.

Columnar Storage & Compression

Data is stored by column, allowing queries to read only required columns and dramatically reducing I/O. Columns are compressed by default with LZ4, achieving an 8:1 compression ratio in production (17 PB raw → 2 PB compressed). The columnar layout also enables vectorized execution. SELECT A1, A2, A3, A4, A5 FROM A When data is stored row‑wise, the engine would scan all 50 fields per row even if only five are needed; columnar storage avoids this waste.

Vectorized Execution Engine

ClickHouse leverages CPU SIMD instructions (SSE4.2) to execute the same operation on many rows simultaneously, turning loop‑based logic into data‑parallel pipelines. This hardware‑level parallelism yields orders‑of‑magnitude speedups, especially for operations like filtering, decoding, and JSON conversion.

Relational Model & SQL

Unlike many NoSQL systems, ClickHouse uses a relational model and standard SQL (supporting GROUP BY, ORDER BY, JOIN, IN, etc.). The SQL parser builds an AST, which the interpreter turns into a pipeline of IBlockInputStream and IBlockOutputStream objects.

Table Engines

ClickHouse abstracts storage behind table engines (MergeTree, TinyLog, Kafka, etc.). Users choose an engine that matches their workload, balancing cost and performance. The design allows simple engines for lightweight use‑cases and sophisticated engines for complex analytics.

Multithreading & Distributed Architecture

Queries run on multiple threads, exploiting modern multi‑core CPUs. Data is sharded horizontally and replicated for fault tolerance. A Distributed table acts as a proxy to local tables across shards, enabling seamless distributed queries.

Multi‑Master Cluster Design

Every node in a ClickHouse cluster is equal; any node can accept client connections, eliminating a single point of failure and simplifying multi‑data‑center deployments.

Design Philosophy

ClickHouse follows a bottom‑up approach: hardware constraints drive algorithm choices, the fastest algorithms are selected (e.g., Volnitsky for constant strings, SIMD‑accelerated brute force for variable strings), and specialized optimizations are applied per workload (e.g., different uniq implementations based on data size). Continuous testing on real Yandex traffic and rapid monthly releases ensure rapid iteration and performance gains.

Key Takeaways

ClickHouse’s speed stems from the synergy of columnar storage, aggressive compression, SIMD‑based vectorization, flexible table engines, and a thoughtfully engineered distributed, multi‑master architecture. Understanding these mechanisms helps practitioners leverage ClickHouse effectively for large‑scale analytical workloads.