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

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:

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 functions – Window 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. The ecosystem also includes third-party integrations, allowing ClickHouse to work seamlessly with tools like Kafka, S3, and PostgreSQL.

When to Use ClickHouse—and When Not To

When to Use ClickHouse

ClickHouse is ideal for high-performance analytical workloads that require fast queries on large datasets. Some key scenarios include:

• Real-Time Analytics and Dashboards

  • ClickHouse is well-suited for scenarios requiring near-instantaneous query responses, such as monitoring KPIs in real-time dashboards for financial services, marketing analytics, and cybersecurity threat detection.

• Log and Event Data Processing

  • Companies dealing with high-velocity logs, such as infrastructure monitoring, system debugging, and security auditing, benefit from ClickHouse’s ability to rapidly ingest and analyze streaming data.

• Time-Series Data Analysis

  • ClickHouse performs well for time-series workloads, such as IoT sensor analytics, financial market trend analysis, and application performance monitoring.

• Clickstream and User Behavior Analytics

  • E-commerce and SaaS companies use ClickHouse for customer journey tracking, A/B testing analysis, and personalized recommendation systems based on large-scale user interactions.

• Large-Scale ETL Pipelines

  • ClickHouse is effective in processing and aggregating data for ETL workflows where batch processing is required for data warehousing and business intelligence applications.

When Not to Use ClickHouse

While ClickHouse is a powerful OLAP database, it is not suitable for every workload. Here are some of its limitations:

• Transactional (OLTP) Workloads

  • ClickHouse is not designed for frequent row-level updates or deletes, making it unsuitable for applications that require ACID transactions, such as e-commerce order management systems and banking applications.

  • Alternative: Consider PostgreSQL, MySQL, or TiDB for workloads requiring transactional consistency.

• Complex Multi-Table Joins

  • ClickHouse struggles with workloads requiring extensive joins across multiple large tables, as it is optimized for denormalized data.

  • For example, ERP systems or relational business intelligence applications that rely on complex star schemas may encounter performance bottlenecks.

  • Alternative: StarRocks or other databases optimized for multi-table joins provide better support.

• Frequent Schema Changes

  • Schema evolution in ClickHouse can be time-consuming, requiring full data reloads for structural modifications.

  • Companies that need to frequently modify schema structures, such as dynamically adding new fields in event-driven architectures, may face challenges.

  • Alternative: StarRocks offers more flexible schema evolution.

• Small-Scale or Infrequent Query Workloads

  • ClickHouse’s architecture introduces overhead that may be unnecessary for organizations with small datasets or occasional query needs.

  • Alternative: A lightweight columnar database like DuckDB (for local analytics) or BigQuery (serverless cloud analytics) may be more cost-effective.

• High Memory Overhead for Joins

  • ClickHouse processes joins in memory, leading to scalability issues when handling high-cardinality joins or extremely large datasets.

  • If joins are a frequent part of analytical workloads, query performance may degrade significantly.

  • Alternative: A system like StarRocks, designed with optimized distributed joins, is better suited for such cases.

Choosing Between ClickHouse and StarRocks

StarRocks: A Powerful Choice for Real-Time OLAP

StarRocks is a real-time OLAP database designed to handle high-concurrency analytical workloads, making it well-suited for customer-facing applications. It supports a broad range of analytics use cases, including multi-dimensional analysis, real-time insights, and ad-hoc querying, all while maintaining strong performance and scalability.

With its ability to process large-scale data efficiently, StarRocks provides a flexible and robust solution for businesses needing responsive analytics. As an open-source project under the Linux Foundation, it continues to evolve with contributions from a broad community, ensuring adaptability to modern data challenges.

How StarRocks Helped Demandbase Solve Their ClickHouse Challenges

Demandbase, a leading B2B marketing analytics platform, faced major challenges with ClickHouse, including poor join performance, costly denormalization, and operational complexity. By migrating to StarRocks powered CelerData Cloud, they achieved:

• Real-time analytics on normalized data with high-speed joins: Demandbase previously relied on heavily denormalized tables in ClickHouse due to poor join performance, leading to data explosion and excessive storage costs. With StarRocks' optimized join capabilities, they transitioned to a normalized data model, significantly reducing storage requirements and improving maintainability.
• Optimized query performance with distributed execution strategies: Demandbase leveraged StarRocks’ colocation joins and runtime filtering, ensuring that related data was kept together on the same nodes to reduce shuffle overhead and improve query speeds. Runtime filtering further enhanced efficiency by dynamically applying query constraints during execution, cutting down unnecessary data processing.
• Simplified infrastructure, replacing 40+ ClickHouse clusters with a single StarRocks cluster: ClickHouse required extensive cluster management, often requiring migrations to redistribute workloads. StarRocks’ shared-nothing architecture allowed Demandbase to consolidate their infrastructure into a single cluster, reducing maintenance complexity and operational costs.
• Faster schema evolution without requiring full data reloads: ClickHouse’s schema changes often required time-consuming data reprocessing, sometimes taking a week or more to propagate updates. StarRocks' flexible schema management enabled Demandbase to add columns and modify table structures without reloading all data, accelerating iteration cycles.
• Lakehouse integration for seamless querying of Iceberg data: Demandbase adopted Iceberg as its primary data lake format but previously had to duplicate data into ClickHouse for fast analytics. StarRocks’ native Iceberg support allowed direct querying from the data lake, eliminating the need for additional ETL pipelines and reducing data redundancy.

Getting Started with ClickHouse

Installation and Setup

Setting up ClickHouse is straightforward, with multiple installation methods available. The most common approach is using Docker, which simplifies deployment.

Prerequisites

Before installing ClickHouse, ensure that:

• Docker and Docker Compose (if using Docker) are installed on your system.

• Your system meets the hardware requirements (SSD storage, multi-core CPU, and sufficient RAM are recommended).

• If installing directly on Linux, you have root or sudo access.

Installation Using Docker

The easiest way to install ClickHouse is via Docker:

docker run -d --name clickhouse-server --ulimit nofile=262144:262144 -p 8123:8123 -p 9000:9000 clickhouse/clickhouse-server

This method ensures compatibility across different environments and eliminates the need for manual configuration.

Alternative Installation Methods

• Linux Packages: Install ClickHouse on Debian, Ubuntu, or CentOS using the official repository.

• Cloud Platforms: ClickHouse is available for deployment on AWS, GCP, and Azure.

Writing Basic SQL Queries in ClickHouse

ClickHouse uses SQL, making it easy to query and analyze data.

1. Creating a Table

Use the following syntax to create a table optimized for analytical queries:

CREATE TABLE table_name (
    id UInt32,
    name String,
    event_time DateTime
) ENGINE = MergeTree()
ORDER BY id
PARTITION BY toYYYYMM(event_time);

• ORDER BY id ensures fast lookups.

• PARTITION BY toYYYYMM(event_time) optimizes query performance for time-based data.

2. Inserting Data

INSERT INTO table_name VALUES (1, 'Alice', '2024-01-01 10:00:00');

Batch inserts are recommended over frequent small inserts to maintain performance.

3. Retrieving Data

SELECT * FROM table_name;

For specific columns:

SELECT id, name FROM table_name;

4. Sorting Results

SELECT * FROM table_name ORDER BY event_time DESC;

Using indexed columns in ORDER BY helps improve performance.

Tips for First-Time Users

Choosing Partition Keys

• Avoid high-cardinality partition keys like raw timestamps.

• Use toYYYYMMDD(date_column) for better query efficiency.

Data Types

• Prefer simple types like UInt32, Float64, instead of complex structures like JSON, which can reduce query performance.

Data Insertion Strategy

• Batch inserts improve efficiency.

• Avoid too many small inserts to prevent excess table parts and slow query speeds.

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 optimizes analytical queries by reading only the necessary columns, rather than scanning entire rows, reducing disk I/O and improving query speed. Additionally, ClickHouse applies advanced compression techniques to optimize storage and performance.

Unlike traditional relational databases such as PostgreSQL or MySQL, ClickHouse is specifically designed for OLAP (Online Analytical Processing) workloads. It is built for high-speed analytical queries over large-scale datasets and is not optimized for OLTP (Online Transaction Processing) workloads, which require frequent updates and transactions.

Can ClickHouse be used for transactional workloads?

No, ClickHouse is not designed for transactional workloads. It lacks native ACID compliance and does not efficiently support row-level updates or deletes. Instead, ClickHouse handles mutations asynchronously, meaning updates and deletes are processed in batches rather than being immediately applied.

For workloads requiring frequent inserts, updates, and deletes with strict transactional guarantees, consider PostgreSQL, MySQL, or TiDB, which are optimized for OLTP use cases.

How does ClickHouse handle large datasets?

ClickHouse is designed to process massive datasets efficiently through its distributed architecture. It achieves high-speed query performance by:

• Partitioning data across multiple nodes, allowing for efficient parallel processing.
• Executing queries in parallel across CPU cores to minimize response times.
• Applying data skipping indexes and columnar compression to reduce I/O and storage overhead.

This makes ClickHouse an excellent choice for real-time analytics, log processing, and time-series data analysis even at petabyte-scale.

What are the hardware requirements for ClickHouse?

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

• Storage: SSDs are highly recommended for fast read/write speeds. HDDs can be used, but they significantly impact performance.
• RAM: Allocate sufficient RAM for caching frequently accessed data. The general recommendation is at least 64GB for large-scale workloads.
• CPU: ClickHouse scales well with more CPU cores, as queries are processed in parallel. A multi-core processor with a high clock speed improves performance.

ClickHouse can start small and scale horizontally as data volume grows, making it adaptable to different workloads.

What are the alternatives to ClickHouse?

While ClickHouse is a powerful OLAP database, other solutions may be better suited depending on the use case:

If your workload relies heavily on complex multi-table joins, StarRocks is an excellent alternative to ClickHouse. It provides better support for relational queries, improved query execution on normalized data, and native optimizations for star schema queries, making it a strong choice for business intelligence workloads.