Key Features of ClickHouse: DBMS Capabilities, Columnar Storage, Vectorized Execution, and Distributed Architecture

ClickHouse is an MPP‑style columnar database that, despite its MPP and columnar design, offers a full DBMS feature set, including DDL/DML, fine‑grained permission control, backup/restore, and distributed management.

01. Complete DBMS Functions – ClickHouse supports dynamic creation, modification, and deletion of databases, tables, and views (DDL) without restarting, as well as data manipulation (DML), permission control, data backup, and cluster management.

02. Columnar Storage and Data Compression – Columnar storage reduces the amount of data scanned during queries, and ClickHouse uses LZ4 compression (up to 8:1 in production) to shrink data size, lower I/O, and improve cache efficiency. Examples illustrate how columnar layout and compression work together.

03. Vectorized Execution Engine – By leveraging CPU SIMD instructions (e.g., SSE4.2), ClickHouse executes operations on multiple data items with a single instruction, achieving hardware‑level parallelism that dramatically speeds up query processing.

04. Relational Model and SQL Queries – ClickHouse adopts a relational model, exposing databases, tables, views, and functions, and uses standard SQL (GROUP BY, ORDER BY, JOIN, IN, etc.) with case‑sensitive identifiers, making it familiar to developers.

05. Diverse Table Engines – Inspired by MySQL, ClickHouse abstracts storage via pluggable table engines (MergeTree, Memory, File, etc.), offering over 20 engine variants to suit different workloads.

06. Multithreading and Distributed Processing – ClickHouse utilizes both thread‑level parallelism and distributed execution (partitioning and sharding) to scale horizontally, pushing computation to the nodes where data resides.

07. Multi‑Master Architecture – Unlike traditional master‑slave systems, every ClickHouse node is equal, eliminating single points of failure and simplifying multi‑data‑center deployments.

08. Online Query Performance – ClickHouse delivers sub‑second responses for complex analytical queries without pre‑processing, outperforming many commercial and open‑source alternatives.

09. Data Sharding and Distributed Queries – ClickHouse distinguishes between local tables (single‑shard data) and distributed tables (proxies that query multiple shards), enabling flexible scaling from a single node to large clusters.

Core Modules – Internally, ClickHouse is built around Columns and Fields (basic data units), DataTypes (serialization logic), Blocks (column‑type‑name triples) and Block streams (IBlockInputStream/IBlockOutputStream), and Storage interfaces that implement various table engines and handle DDL, reads, and writes.