What is ClickHouse?

ClickHouse is a columnar database management system (DBMS) for big data analytics. Developed by Yandex, it is open source, focusing on real-time generation of analytical data reports. It aids in querying large datasets while providing high-speed data processing, which is crucial for business intelligence applications.

Its columnar storage format reduces storage space and increases the efficiency of data access, making it suitable for various analytical workloads. ClickHouse’s architecture is optimized for handling complex queries using minimal resources.

ClickHouse can process gigabytes of data in milliseconds. Users can perform aggregate functions and complex calculations directly within ClickHouse, eliminating the need for additional data processing layers. Additionally, it includes features such as sharding, replication, and distributed querying, improving its scalability.

What Is PostgreSQL?

Postgres, officially known as PostgreSQL, is an open source relational database management system (RDBMS) that focuses on stability and performance. It supports both relational and non-relational datasets for diverse data management needs. Postgres features extensive data integrity capabilities, allowing for precise data transactions in enterprise environments.

Its extensibility permits custom functionalities, and the platform supports various data types, which improve its adaptability for complex application requirements. Developers favor Postgres for its ACID (atomicity, consistency, isolation, durability) compliance, ensuring data reliability even in the event of failures.

The DBMS offers an array of indexing techniques, such as B-tree, hash, GiST, SP-GiST, and GIN, optimizing it for varied query performance improvements. Its security features, such as authentication, access control, and data encryption, ensure data protection. Postgres has a strong community and numerous third-party tools.

Key features of ClickHouse

ClickHouse is built for high-performance analytical queries and excels in handling large-scale data efficiently. Its features support real-time analytics, scalability, and fault tolerance:

  • Columnar storage: Stores data in columns instead of rows, enabling faster query execution and efficient compression.
  • Real-time querying: Optimized for analytical queries, allowing millisecond-level query performance on massive datasets.
  • Distributed processing: Supports horizontal scaling with sharding and replication, ensuring high availability and parallel processing.
  • Compression algorithms: Uses compression techniques to reduce storage costs while maintaining fast read speeds.
  • Vectorized query execution: Executes queries in batches, leveraging CPU cache efficiency for improved performance.
  • Materialized views: Precomputes and stores query results to improve read performance and reduce computation overhead.
  • Fault tolerance: Ensures reliability through replication and data integrity mechanisms.
  • JSON and semi-structured data support: Allows efficient querying of semi-structured data formats.

Related content: Read our guide to ClickHouse architecture

Key features of PostgreSQL

PostgreSQL is a feature-rich relational database known for its data integrity, extensibility, and support for complex queries. It is widely used for transactional and analytical workloads:

  • ACID compliance: Guarantees reliable transactions by ensuring atomicity, consistency, isolation, and durability.
  • Extensible data types: Supports JSON, XML, hstore, arrays, and user-defined types for versatile data modeling.
  • Advanced indexing: Includes B-tree, hash, GiST, SP-GiST, GIN, and BRIN indexing for optimized query performance.
  • Foreign data wrappers (FDW): Allows integration with external databases and data sources.
  • Built-in replication: Supports streaming and logical replication for high availability and scalability.
  • Concurrency control: Uses multi-version concurrency control (MVCC) to enable high transaction throughput.
  • Stored procedures and triggers: Enhances automation and data integrity through procedural programming.
  • Security features: Provides role-based access control, authentication mechanisms, and data encryption for secure operations.

Tips from the expert

Perry Clark

Perry Clark

Professional Services Consultant

Perry Clark is a seasoned open source consultant with NetApp. Perry is passionate about delivering high-quality solutions and has a strong background in various open source technologies and methodologies, making him a valuable asset to any project.

In my experience, here are tips that can help you better choose and optimize between ClickHouse and PostgreSQL:

  1. Identify use cases relevant to database strengths: ClickHouse thrives in real-time analytics, log processing, and data warehousing projects due to its speed with aggregate functions. It’s the go-to for scenarios requiring rapid insights from large datasets. PostgreSQL is better suited for web applications, transactional systems, and use cases where complex joins or procedural logic are critical.
  2. Use ClickHouse for append-only workloads to maximize efficiency: ClickHouse is optimized for bulk inserts rather than frequent updates or deletes. If the workload involves immutable event logs or time-series data, ClickHouse will significantly outperform PostgreSQL.
  3. Leverage PostgreSQL’s BRIN indexes for large, sequential data: To run analytical queries on large datasets in PostgreSQL, BRIN (Block Range INdexes) can speed up queries by reducing the amount of data scanned, making it more columnar-like in performance.
  4. Optimize ClickHouse with MergeTree table engines: ClickHouse’s MergeTree engine is powerful but requires proper tuning. Adjust merge_with_ttl_timeout and background_merges_mutations_concurrency to avoid performance issues with frequent inserts.
  5. Use PostgreSQL’s jsonb for semi-structured data instead of ClickHouse: If the application relies on flexible, dynamic schemas with frequent updates to JSON documents, PostgreSQL’s jsonb storage is more efficient than ClickHouse’s approach to semi-structured data.
  6. Utilize ClickHouse’s built-in compression to reduce storage costs: ClickHouse automatically applies compression, but choosing the right algorithm (ZSTD, LZ4, or Delta) based on the workload can further optimize storage and performance.

ClickHouse vs. PostgreSQL: The key differences

Here an overview of how these DBMSs compare in several main areas.

1. Architecture

ClickHouse’s architecture is based on columnar storage, optimizing it for read-heavy analytical workloads. This structure enables high data compression and efficient query execution on specific columns rather than entire rows. PostgreSQL uses a row-based storage architecture, fitting transaction-heavy operations and complex joins.

While ClickHouse focuses on scalable, distributed query processing, PostgreSQL is designed to handle intricate transaction processing with its relational model foundation. ClickHouse scales horizontally with ease, allowing distributed querying across multiple nodes. It is optimized for handling large volumes of concurrent reads. PostgreSQL, while capable of horizontal scaling, typically excels in environments where data integrity and complex transactions are priorities.

2. Analytical workloads

For analytical workloads, ClickHouse outshines PostgreSQL due to its columnar storage and fast read performance. It’s tailored for running complex analytical queries over large datasets efficiently. Its in-built aggregate functions and support for complex expressions make it a favorite in analytics and reporting tasks. Postgres can perform such analytical tasks but with less efficiency, especially on very large datasets.

PostgreSQL can be equipped with extensions to improve its analytical capabilities, but these add-ons cannot fully match ClickHouse’s inherent speed and efficiency for massive data processing. ClickHouse’s ability to process real-time analytical queries makes it highly suitable for scenarios requiring up-to-the-minute analytics.

3. Deployment options

ClickHouse supports a range of deployment options, enabling easy integration into existing infrastructure. It works efficiently in on-premises setups as well as cloud-based deployments. Integration with various orchestration systems like Kubernetes allows ClickHouse to scale with operational demands.

PostgreSQL also offers a multitude of deployment options, from traditional on-premises infrastructure to cloud implementations. It supports containerized deployments and can integrate smoothly with cloud-native services. Both ClickHouse and PostgreSQL cater to modern cloud practices, but PostgreSQL often provides more polished integration with major cloud platforms’ managed services.

4. Performance

ClickHouse’s performance benefits stem from its column-oriented design and data compression strategies, enabling superb read speeds and efficient storage utilization. It’s engineered for high parallelism and supports large-scale data operations with minimal latency.

PostgreSQL delivers strong transaction processing capabilities, maintaining high reliability and stability. While its row-oriented design might limit read speeds in large-scale analytical contexts, it excels in workload scenarios demanding complex transactions and joins. PostgreSQL’s indexing and query planning ensure optimal performance for diverse operations.

5. Availability and maintenance

ClickHouse supports features like replication and sharding to improve availability, making sure data integrity is maintained even during node failures. Its design allows for easy maintenance, with tools and community support available to aid in effective database management. Features like automatic failover improve its operational resilience.

PostgreSQL provides solutions for high availability, including features like replication and auto-failover mechanisms. It is often appreciated for its strong data integrity and automatic backup systems, reducing maintenance burdens. PostgreSQL’s active community and extensive documentation aid administrators in troubleshooting and maintaining deployments.

Considerations when choosing ClickHouse or PostgreSQL

When selecting between ClickHouse and PostgreSQL, it’s important to consider the use case and workload requirements. Here are some considerations to keep in mind:

  • Workload type: ClickHouse is optimized for analytical workloads with fast read performance, while PostgreSQL is better suited for transactional applications requiring complex joins and high data integrity.
  • Query complexity: PostgreSQL excels at handling complex SQL queries, including multi-table joins, subqueries, and intricate business logic, whereas ClickHouse prioritizes fast analytical queries and may require denormalization for optimal performance.
  • Write performance: ClickHouse is designed for bulk inserts and performs best with batched writes. PostgreSQL, on the other hand, supports high-frequency transactional inserts and updates efficiently.
  • Data integrity and ACID compliance: PostgreSQL provides full ACID compliance, ensuring strong consistency, whereas ClickHouse prioritizes speed and efficiency, with eventual consistency in some cases.
  • Scalability and distribution: ClickHouse scales horizontally with built-in sharding and distributed querying, making it suitable for large-scale analytics. PostgreSQL supports scaling but often requires extensions or external tools for sharding and distributed workloads.
  • Indexing mechanisms: PostgreSQL has a rich set of indexing options, optimizing a variety of query patterns. ClickHouse relies more on columnar storage and primary key sorting to improve read performance, reducing the need for traditional indexes.
  • Storage efficiency: ClickHouse’s columnar storage and compression algorithms minimize disk usage for analytical data. PostgreSQL stores data row-wise, which may require more storage but is beneficial for transactional processing.
  • Ecosystem & extensions: PostgreSQL has a vast ecosystem of extensions and integrations, allowing customization for various workloads. ClickHouse has a growing ecosystem but is more specialized toward analytics.
  • Operational complexity: ClickHouse requires a different approach to schema design and query optimization compared to traditional databases. PostgreSQL follows a more conventional relational model, making it easier to adopt for those familiar with SQL databases.
  • Use case fit: ClickHouse is suitable for real-time analytics, event tracking, and time-series data, while PostgreSQL is a strong choice for OLTP applications, financial systems, and enterprise applications requiring strict data consistency.

Unlock the power of open source data solutions

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For businesses seeking to leverage PostgreSQL, Instaclustr simplifies database management by handling the heavy lifting of provisioning, maintenance, scaling, and monitoring. Known for its versatility and compatibility, PostgreSQL is perfect for managing intricate data relationships and powering everything from transactional applications to analytics workloads. With Instaclustr, customers can rely on comprehensive support and streamlined operations, allowing teams to focus on building applications and driving business value instead of wrangling database complexities.

If speed and scalability for real-time analytics are your priorities, ClickHouse is a standout solution. Known for processing billions of rows per second, ClickHouse enables businesses to perform lightning-fast aggregation and analysis on massive datasets. With Instaclustr’s fully managed ClickHouse offering, you gain access to seamless deployment, 24/7 monitoring, and expert optimization, ensuring your analytics platforms perform at peak efficiency.

Instaclustr empowers customers with the performance, flexibility, and scalability they need to succeed. Our customer-centric approach combines open source freedom with trusted expertise, making us the go-to partner for organizations that depend on PostgreSQL, ClickHouse, and other leading data technologies. Reach new heights with Instaclustr today and discover the difference a truly open source data platform can make.

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