ClickHouse: A Comprehensive Guide for Data Engineers
As data engineers, we constantly deal with vast amounts of data that need to be ingested, stored, processed, and analyzed in real-time or batch jobs. Companies depend on fast and scalable systems to manage their data efficiently, and ClickHouse is a popular open-source database system that fits these requirements.
In this blog post, we will explore ClickHouse from its fundamental concepts and architecture to its features, management, and usage.
What is ClickHouse?
ClickHouse is a column-based, open-source, distributed analytical database management system that was developed by the Yandex team as a part of its in-house big data processing system. It is designed to run ad-hoc queries on petabytes of data and has become popular for its ability to facilitate real-time analytical processing over high-speed data streams. ClickHouse is known for its high performance for read-heavy workloads, fast ingestion rates, and low latency.
Why Use ClickHouse?
ClickHouse is gaining popularity among data engineers for several reasons:
- Fast Data Ingestion: ClickHouse is designed to support fast data ingestion at scale, which means that it can ingest large amounts of data quickly. It is a perfect solution for systems that require high-throughput data ingestion.
- Low Latency: ClickHouse has impressive performance with low query latencies, which makes it a popular database system for real-time analytical processing and ad-hoc analytics.
- Distributed architecture: ClickHouse is a distributed database system. It stores and processes data across a cluster of multiple computers, which can result in high availability, scalability, and fault tolerance.
- Optimized for Columnar Storage: ClickHouse optimizes the query performance by storing data in a columnar way, rather than the traditional row-based storage that SQL databases use.
ClickHouse Architecture
ClickHouse consists of multiple elements, as shown in the following figure:
The core component of ClickHouse is ClickHouse server, which stores data and performs query processing. It has a distributed architecture that enables it to scale horizontally with ease. The ClickHouse server can run on any Linux machine, and it supports both x86 and ARM processors.
ClickHouse's architecture is based on the shared-nothing storage model. Shared-nothing storage means that the nodes in a cluster are independent of one another and operate in isolation. Each node can hold a portion of the data and can perform computations independently.
The overall architecture can be broken down into four main components:
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Client: A client is the user interface that sends SQL queries to the ClickHouse server.
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Servers: The servers are responsible for storing data and processing queries. They can be run on any Linux machine and support both x86 and ARM processors. Each server can hold a portion of the data and can perform computations independently.
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Cluster Manager: A cluster manager is responsible for managing nodes in the ClickHouse cluster. It monitors the health of the nodes and balances the workload across the cluster.
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Zookeeper: Zookeeper is used to manage distributed systems, including ClickHouse. It is responsible for keeping track of the server status and cluster configuration.
Features of ClickHouse
ClickHouse comes packed with many features that make it a popular database system among data engineers. Here are some key features of ClickHouse:
Performance
ClickHouse is built with performance in mind, supporting multiple nodes and high query throughput. It is designed to handle queries over millions of rows, offering high-performance analytical processing. It has powerful compression techniques that enable efficient data storage on disk, leading to lower query processing times.
SQL Compatibility
ClickHouse supports the SQL standard, including advanced features that are useful for analytical processing, such as window functions and subquery expressions. ClickHouse has a comprehensive SQL parser that can handle complex queries, including joins and nested queries.
Compression
ClickHouse provides a range of data compression techniques that save disk space and improve query performance. These compression techniques include LZ4, ZSTD, and Brotli, among others. ClickHouse's powerful compression techniques help to reduce disk space and improve query performance.
Replication
ClickHouse provides different replication methods (such as asynchronous or synchronous) to improve the reliability and availability of data in distributed systems. It supports data replication across nodes, data centers, and geographic regions.
Security
ClickHouse provides several security features that help protect your data, including authentication, authorization, and encryption. You can configure users and roles to establish security policies and encrypt data at rest or in transit.
ClickHouse Management
Installation
ClickHouse can be installed on any Linux machine. Yandex provides pre-built binaries for many Linux distributions, including Ubuntu, Debian, CentOS, and others.
Configuration
ClickHouse's configuration file is called config.xml. It is located in the /etc/clickhouse-server/ directory. The configuration file includes various settings that determine