CelerData Glossary

What is ClickHouse? A Deep Dive into Its Features and Advantages

Written by Admin | Feb 1, 2025 2:25:12 AM

What is ClickHouse?


Overview of ClickHouse

ClickHouse is a high-performance analytical database designed to handle massive datasets efficiently. It specializes in online analytical processing (OLAP), making it ideal for businesses that need fast insights from large-scale data. Unlike traditional databases, ClickHouse uses a column-oriented structure, which optimizes how data is stored and queried. This design allows you to retrieve only the data you need, speeding up analytical queries on large datasets.

Originally developed at Yandex and later open-sourced in 2016, ClickHouse was designed with efficiency and simplicity in mind. Alexey Milovidov, one of its key developers, and his team envisioned a system capable of delivering high-speed analytics while remaining resource-efficient. This design philosophy has made ClickHouse a popular choice for modern, data-intensive applications.

The creators of ClickHouse focused on simplicity and efficiency. They minimized unnecessary components to ensure reliability and ease of use. Alexey Milovidov, the database's creator, envisioned a system that could deliver real-time analytics while being resource-efficient. This vision has made ClickHouse a popular choice for modern data-driven applications.

How ClickHouse works for online analytical processing

ClickHouse excels in OLAP by leveraging several advanced technical components. These features enable it to process complex queries quickly and efficiently. Here's a breakdown of how it works:

Component

Description

Columnar Storage Architecture

Organizes data into columns, optimizing memory usage and improving query speed by allowing only necessary columns to be read, enhancing aggregation performance.

Data Compression Techniques

Uses high-performance compression algorithms like LZ4 (fast, lower compression) and ZSTD (higher compression, slightly slower) to minimize storage space and reduce I/O operations.

Distributed Architecture

Supports manual data partitioning and parallel query execution across multiple nodes, enabling scalability and high-speed analytics.

Efficient Query Execution

Uses a vectorized execution engine and query pipelines, processing multiple operations in parallel to maximize throughput. It also employs data skipping indexes instead of traditional indexing methods.

Merge Tree Storage Engine

ClickHouse’s default engine, optimized for insert-heavy workloads and fast aggregation, using partitioning, sorting, and background merging to maintain efficiency.

Why ClickHouse is a column-oriented database management system

ClickHouse organizes data by columns instead of rows, making it a true column-oriented database management system. This approach offers several advantages for analytical workloads:

  • Storing data in columns instead of rows significantly reduces I/O and speeds up analytical queries.

  • Columnar compression reduces disk space usage and improves query efficiency.

  • Because OLAP workloads focus on aggregations and scans rather than single-row updates, columnar storage is more efficient than traditional row-based databases (e.g., MySQL, PostgreSQL).

These components work together to ensure ClickHouse delivers exceptional performance for analytical queries on large datasets. Whether you're analyzing time-series data or building real-time dashboards, ClickHouse provides the speed and scalability needed for data-driven applications.

 

Key Features of ClickHouse

 

Columnar storage for optimized analytics

ClickHouse is a columnar database, meaning it stores data by columns rather than rows. This structure is particularly suited for analytical workloads because queries typically involve aggregations and scans over large datasets. By reading only the relevant columns instead of entire rows, ClickHouse reduces disk I/O and improves query performance.

Additionally, columnar storage allows for highly efficient compression since data within a column tends to have similar values. ClickHouse supports multiple compression algorithms, including LZ4 and ZSTD, to optimize storage space and retrieval speed. Performance is further enhanced by column pruning (skipping unnecessary columns) and predicate pushdown (applying filters as early as possible in the query execution process).

Real-time analytics and query performance

ClickHouse is optimized for low-latency analytical queries. It achieves high performance through:

  • Vectorized query execution – Processes multiple rows simultaneously at the CPU level, maximizing efficiency.
  • Parallel query processing – Distributes workloads across CPU cores and nodes to speed up complex queries.
  • Indexing via primary key sorting and data skipping – Unlike traditional databases, ClickHouse does not use B-trees or bitmap indexes. Instead, it organizes data by primary key order and applies sparse indexes to skip irrelevant data efficiently.
  • MergeTree storage engine – ClickHouse’s default engine optimizes data storage and retrieval, supporting partitioning, sorting, and background merging for efficient reads and writes.

Benchmark results show that ClickHouse Cloud executes queries more than twice as fast as Snowflake while providing better compression efficiency (38% smaller storage footprint).

One practical example of its efficiency is Uber's logging infrastructure, which relies on ClickHouse to process and analyze massive log data in real time.

Scalability and distributed architecture

ClickHouse is designed for horizontal scaling. It can handle growing data volumes by distributing data across multiple nodes. Its shared-nothing architecture allows each node to operate independently, avoiding bottlenecks.

Key scalability features include:

  • Data partitioning – Large datasets are divided into smaller segments for more efficient queries.
  • Sharded and replicated clusters – ClickHouse supports both manual and automatic data sharding, improving fault tolerance and load distribution.
  • Parallel distributed queries – Queries can execute across multiple nodes simultaneously, reducing response times for large datasets.

These features make ClickHouse suitable for handling terabytes to petabytes of data while maintaining query speed and reliability.

SQL compatibility and ease of use

ClickHouse supports a SQL-like query language, allowing users familiar with SQL to write queries without needing to learn a new syntax. It includes:

  • Standard SQL operations – Joins, subqueries, and common table expressions (CTEs).
  • Advanced analytics functionsWindow functions, aggregation functions, and array manipulations.
  • Real-time materialized views – Automatically update query results for faster access to frequently needed aggregations.
  • User-defined functions (UDFs) – Extend functionality with custom logic.

This level of SQL compatibility makes ClickHouse an approachable yet powerful tool for analysts and engineers working with large-scale data.

Open-source and community-driven development

ClickHouse is open-source, with an active community contributing to its continuous improvement. Development is focused on performance optimizations, new features, and integrations.

Some notable community contributions include:

Contributor

Notable Contributions

Kseniia Sumarokova

Filesystem cache for object storage, asynchronous reading prefetching, streaming replication from PostgreSQL

Smita Kulkarni

Compressed marks, recursive directory reading, parameterized views

He Wen Ting

Map Data Type for unstructured data, LineAsString format for structured logs

Because ClickHouse is open-source, users can modify, extend, and optimize it to meet their specific needs. The ecosystem also includes third-party integrations, allowing ClickHouse to work seamlessly with Kafka, S3, PostgreSQL, and more.

 

Benefits of Using ClickHouse

 

High-Performance Query Execution

ClickHouse is designed for speed, making it well-suited for real-time analytics and large-scale data processing. Its columnar storage model and vectorized query execution minimize I/O overhead and maximize CPU efficiency, enabling rapid data retrieval even from extensive datasets.

Key performance optimizations:

  • Columnar storage allows reading only the necessary data, reducing disk I/O.
  • Parallel execution and query optimization speed up analytical workloads.
  • MergeTree engine supports data skipping and indexing for efficient scans.

These capabilities make ClickHouse a powerful choice for OLAP (Online Analytical Processing), handling time-series analysis, monitoring, and dashboarding with minimal latency.

Cost-Efficient Scaling for Large Datasets

ClickHouse is open-source, eliminating licensing costs while providing enterprise-level analytical performance. Its efficient resource utilization ensures lower infrastructure costs compared to traditional data warehouses.

Cost benefits include:

  • Compressed columnar storage reduces disk space usage while maintaining fast query performance.
  • Horizontal scalability enables cost-effective expansion by adding commodity hardware rather than requiring expensive high-end servers.
  • Optimized hardware usage lowers computational overhead, reducing operational expenses.

For businesses managing large-scale analytics, ClickHouse offers a balance between performance and cost-efficiency without sacrificing query speed.

Versatile Analytical Workload Support

ClickHouse is highly adaptable to different analytical use cases, making it useful across industries for tasks such as:

  • Log and event data analysis – Process high-throughput event streams and logs for monitoring and security analytics.
  • Clickstream and user behavior analysis – Track and analyze web or application interactions in real time.
  • Business intelligence – Perform ad-hoc queries and generate reports on large datasets.
  • Time-series analytics – Efficiently process IoT sensor data, financial market trends, or application performance metrics.

Its ability to handle concurrent workloads ensures stable performance even as query complexity and data volume increase.

Active Community and Continuous Development

ClickHouse benefits from an active open-source community that continuously enhances its capabilities. Developers worldwide contribute optimizations, ensuring that the database remains cutting-edge.

Why community-driven development matters:

  • Frequent updates bring performance improvements, new features, and security patches.
  • Open discussions on forums, GitHub, and Slack provide solutions to real-world challenges.
  • Collaborative problem-solving enables users to refine ClickHouse for specific use cases.

Seamless Integration with Data Ecosystems

ClickHouse integrates with various tools, making it easy to incorporate into existing data pipelines:

Tool/Platform Use Case
Grafana Real-time dashboards for monitoring and analytics.
Apache Kafka Streaming data ingestion for real-time processing.
Tableau Business intelligence and data visualization.
DataGrip SQL query development and management.

These integrations ensure ClickHouse fits smoothly into modern ETL (Extract, Transform, Load) and real-time data workflows.

 

When to Use ClickHouse—and When Not To

 

Ideal Use Cases for ClickHouse

ClickHouse is optimized for high-performance analytical workloads, particularly those requiring fast queries on large datasets. Its columnar storage, parallel execution, and compression make it well-suited for the following scenarios:

1. Real-Time Analytics & Dashboards

ClickHouse delivers sub-second query performance, making it ideal for interactive dashboards that require real-time metrics and aggregations. Businesses use it to monitor KPIs, operational metrics, and performance indicators with minimal latency.

2. Log and Event Data Processing

ClickHouse handles high-throughput log ingestion and analysis, making it a strong choice for:

  • System monitoring (e.g., infrastructure logs, security event tracking).
  • Anomaly detection (e.g., fraud detection, cybersecurity monitoring).
  • Real-time debugging (e.g., tracing API requests or errors at scale).

Its MergeTree engine supports efficient partitioning and indexing, ensuring fast lookups even on multi-terabyte datasets.

3. Time-Series Data Analysis

ClickHouse is highly efficient for processing time-series data, such as:

  • IoT sensor readings (e.g., industrial telemetry, smart devices).
  • Financial market trends (e.g., stock price movements, trade execution analytics).

The engine’s compression and columnar storage minimize storage costs while ensuring high-speed aggregations and rollups.

4. Clickstream Analytics & User Behavior Tracking

ClickHouse enables businesses to track and analyze user interactions in real time, making it valuable for:

  • E-commerce and content recommendations (e.g., tracking customer journeys).
  • A/B testing platforms (e.g., analyzing feature adoption).
  • Advertising analytics (e.g., monitoring impressions, clicks, and conversions).

Its ability to handle highly concurrent queries with low latency makes it an excellent choice for tracking large-scale web traffic.

When Not to Use ClickHouse

Despite its strengths, ClickHouse has limitations that make it less suitable for certain workloads. Here’s when you should consider alternatives:

1. Workloads with Heavy Joins on Large Tables

ClickHouse provides basic join support, but it’s not optimized for large, complex joins across massive datasets.

  • Why? ClickHouse does not use traditional hash join strategies like row-based databases. Instead, it processes joins in-memory, making large multi-table joins inefficient and memory-intensive.
  • Best Alternative: StarRocks offers native multi-table joins and optimized vectorized query execution, making it a better choice for complex relational queries that require frequent joins.

 

2. Transactional Workloads (OLTP)

ClickHouse is an OLAP database, meaning it is not designed for transactional (OLTP) workloads that require frequent row-level updates.

  • No native ACID transactions. Instead of row-level updates, ClickHouse uses batch mutations, which are asynchronous and expensive.
  • Deletes and updates are costly. Updating or deleting records requires rebuilding entire partitions, making it impractical for transactional systems.
  • Best Alternative: Consider PostgreSQL, MySQL, or TiDB for workloads requiring frequent updates and ACID compliance.

3. Small-Scale or Low-Frequency Data Processing

If your dataset is small, or queries are infrequent, ClickHouse may be overkill due to its:

  • Resource overhead (requires tuning and cluster management for efficiency).
  • Complex setup (self-hosted ClickHouse clusters need careful configuration).
  • Steep learning curve (advanced optimizations require deep knowledge of MergeTree settings and indexing strategies).
  • Best Alternative: If you only need occasional analytics, a simpler columnar database like DuckDB (for local analytics) or BigQuery (serverless cloud analytics) may be a better fit.

Choosing Between ClickHouse and StarRocks

Feature ClickHouse StarRocks
Performance on Aggregations 🚀 Fast 🚀 Fast
Performance on Joins ⚠️ Limited, memory-intensive Optimized for large joins
Vectorized Query Execution ⚠️ Partial Fully vectorized
Star Schema / Multi-table Queries ⚠️ Not optimized Efficient and scalable
Real-Time Data Ingestion Excellent Excellent
ACID Transactions Not supported Not supported

Verdict: If your workload relies heavily on joins and star schema queries, StarRocks is a better alternative due to its optimized join execution and vectorized engine. If your focus is on aggregations, logs, and time-series data, ClickHouse remains a top choice.

 

Getting Started with ClickHouse

 

Installation and Setup

Setting up ClickHouse is relatively straightforward, especially with Docker, which simplifies the installation process. Before you begin, ensure you meet the following prerequisites:

  • Docker and Docker Compose are installed on your system.
  • Your system meets the hardware and software requirements for running Docker.
  • You have a GitHub account and an SSH key set up for development purposes.

Once the prerequisites are in place, the easiest way to install ClickHouse is by using Docker. You can pull the official ClickHouse image from Docker Hub and run it as a container. This method ensures compatibility across different environments and eliminates the need for manual configuration.

For users who prefer not to use Docker, ClickHouse also supports installation on Linux-based systems and cloud platforms (e.g., AWS, GCP, or Azure). However, the Docker method is recommended for most use cases due to its simplicity and flexibility.

Writing basic SQL queries in ClickHouse

ClickHouse uses SQL, which makes it easy to query data and start analyzing your datasets. Below are some examples of basic SQL queries in ClickHouse:

1. Create a Table

To create a table in ClickHouse, use the following syntax. Note that ClickHouse typically uses the MergeTree engine, which is optimized for large-scale analytics:

CREATE TABLE table_name (
    column_name1 DataType,
    column_name2 DataType
) ENGINE = MergeTree()
ORDER BY column_name1;
  • ENGINE = MergeTree() specifies the table engine.
  • ORDER BY column_name1 defines the sorting key, which is important for query performance.

2. Insert Data

To insert data into the table:

INSERT INTO table_name VALUES (value1, value2);

Make sure to insert data in batches rather than individually. Inserting many small rows can degrade performance and increase query latency.

3. Retrieve Data

To retrieve all records from the table:

SELECT * FROM table_name;

If you only need specific columns, specify them:

SELECT column_name1, column_name2 FROM table_name;

4. Sorting Results

You can sort query results by a specific column using:

SELECT * FROM table_name ORDER BY column_name1;

For optimal performance, ensure that columns you frequently query are included in the ORDER BY clause when creating the table.

Tips for First-Time Users

As you start working with ClickHouse, here are some common pitfalls and tips to keep in mind to avoid performance issues:

1. Choosing Partition Keys

  • Avoid high-cardinality partition keys, like timestamps or user IDs, as they can result in too many small partitions, negatively impacting query performance.
  • Consider partitioning by time intervals (e.g., toYYYYMMDD(date_column)) for better performance and manageability.

2. Data Types

  • Use primitive data types (e.g., UInt32, Float64) instead of complex types like JSON, as they require more resources and reduce insertion efficiency.

3. Data Insertion Strategy

  • Perform batch inserts to minimize overhead. Frequent small inserts can lead to too many small table parts, which slows down query performance and increases maintenance costs.
  • Avoid the error: "Too many inactive parts (N). Parts cleaning are processing significantly slower than inserts." This happens when there are excessive small inserts or misconfigured partition keys. Batch your data to prevent this.

Resources for learning and community support

Learning ClickHouse becomes easier when you have access to the right resources. Whether you're a beginner or looking to deepen your expertise, several tools and platforms can guide you.

  • ClickHouse Academy: This platform offers structured courses to help you master ClickHouse. You can explore topics ranging from basic concepts to advanced techniques.

  • Free live training: Participate in live sessions hosted by experts. These events provide hands-on experience and allow you to ask questions in real time.

  • ClickHouse Certification: Validate your skills with official certification. This program helps you demonstrate your expertise to employers or clients.

  • Blog: Stay informed about the latest updates, features, and best practices by reading the official ClickHouse blog.

  • Videos: Watch tutorials and presentations to understand ClickHouse concepts visually. These videos cover a wide range of topics, making them suitable for all skill levels.

  • Demos: Explore interactive demos to see how ClickHouse works in real-world scenarios. These examples help you connect theory with practice.

The ClickHouse community also plays a vital role in your learning journey. Engage with other users through forums, GitHub discussions, and Slack channels. These platforms allow you to share ideas, ask questions, and solve challenges collaboratively. The community's active participation ensures you always have support when needed.

By leveraging these resources and connecting with the community, you can confidently navigate ClickHouse and unlock its full potential for your analytical needs.

 

FAQ

 

What makes ClickHouse different from traditional databases?

ClickHouse is a columnar database, meaning it stores data by columns instead of rows. This design drastically speeds up analytical queries by reading only the necessary columns, rather than scanning entire rows. Additionally, ClickHouse applies advanced compression techniques, reducing storage costs. Unlike traditional relational databases like PostgreSQL or MySQL, ClickHouse is optimized for real-time analytics and large-scale data processing rather than transactional workloads.

Can ClickHouse be used for transactional workloads?

No, ClickHouse is not designed for transactional workloads. It lacks native ACID compliance, meaning it does not support frequent row-level updates or deletes efficiently. Instead, ClickHouse handles data modifications asynchronously through mutations, which rewrite data in the background. If you require frequent inserts, updates, or deletes in a transactional system, consider using PostgreSQL, MySQL, or MariaDB instead.

How does ClickHouse handle large datasets?

ClickHouse excels at processing massive datasets due to its distributed architecture. It partitions data across multiple nodes and performs parallel query execution, ensuring high-speed processing even with billions of rows. The system scales horizontally, meaning you can add more nodes to accommodate growing data volumes while maintaining performance.


What are the hardware requirements for ClickHouse?

ClickHouse performs well on commodity hardware, but for optimal performance, consider the following:

  • Storage: SSDs are recommended for fast read/write speeds.
  • RAM: Allocate sufficient RAM for caching to improve query performance.
  • CPU: More CPU cores help handle parallel processing efficiently.
    The exact requirements depend on your dataset size and workload, but ClickHouse can start small and scale as needed.

What are the alternatives to ClickHouse?

While ClickHouse is a powerful choice for real-time analytics, other databases may be better suited for different use cases:

Alternative Best Use Cases
ClickHouse Real-time analytics, log processing, time-series data, large-scale event tracking.
StarRocks A ClickHouse alternative with better JOIN performance, optimized for mixed OLAP workloads.
Druid Event-driven analytics, dashboarding, and real-time ingestion with sub-second queries.
Apache Pinot High-speed, real-time OLAP queries for user-facing applications.
BigQuery Fully managed cloud analytics with seamless scalability, but higher cost.
Snowflake Cloud-native data warehousing with strong elasticity and ecosystem integration.

If your workload relies heavily on complex JOINs, StarRocks is an excellent alternative to ClickHouse, as it provides faster and more efficient multi-table joins while maintaining real-time analytical performance.