ClickHouse vs DuckDB
A detailed comparison
Compare ClickHouse and DuckDB for time series and OLAP workloads
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of ClickHouse and DuckDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how ClickHouse and DuckDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
ClickHouse vs DuckDB Breakdown
 |
 |
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| Database Model | Columnar database |
Columnar database |
| Architecture | ClickHouse can be deployed on-premises, in the cloud, or as a managed service. |
DuckDB is intended for use as an embedded database and is primariliy focused on single node performance. |
| License | Apache 2.0 |
MIT |
| Use Cases | Real-time analytics, big data processing, event logging, monitoring, IoT, data warehousing |
Embedded analytics, Data Science, Data processing, ETL pipelines |
| Scalability | Horizontally scalable, supports distributed query processing and parallel execution |
Embedded and single-node focused, with limited support for parallelism |
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ClickHouse Overview
ClickHouse is an open source columnar database management system designed for high-performance online analytical processing (OLAP) tasks. It was developed by Yandex, a leading Russian technology company. ClickHouse is known for its ability to process large volumes of data in real-time, providing fast query performance and real-time analytics. Its columnar storage architecture enables efficient data compression and faster query execution, making it suitable for large-scale data analytics and business intelligence applications.
DuckDB Overview
DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system. It is designed to be simple, fast, and feature-rich. DuckDB can be used for processing and analyzing tabular datasets, such as CSV or Parquet files. It provides a rich SQL dialect with support for transactions, persistence, extensive SQL queries, and direct querying of Parquet and CSV files. DuckDB is built with a vectorized engine that is optimized for analytics and supports parallel query processing. It is designed to be easy to install and use, with no external dependencies and support for multiple programming languages.
ClickHouse for Time Series Data
ClickHouse can be used for storing and analyzing time series data effectively, although it is not explicitly optimized for working with time series data. While ClickHouse can query time series data very quickly once ingested, it tends to struggle with very high write scenarios where data needs to be ingested in smaller batches so it can be analyzed in real time.
DuckDB for Time Series Data
DuckDB can be used effectively with time series data. It supports processing and analyzing tabular datasets, which can include time series data stored in CSV or Parquet files. With its optimized analytics engine and support for complex SQL queries, DuckDB can perform aggregations, joins, and other time series analysis operations efficiently. However, it’s important to note that DuckDB is not specifically designed for time series data management and may not have specialized features tailored for time series analysis like some dedicated time series databases.
ClickHouse Key Concepts
- Columnar storage: ClickHouse stores data in a columnar format, which means that data for each column is stored separately. This enables efficient compression and faster query execution, as only the required columns are read during query execution.
- Distributed processing: ClickHouse supports distributed processing, allowing queries to be executed across multiple nodes in a cluster, improving query performance and scalability.
- Data replication: ClickHouse provides data replication, ensuring data availability and fault tolerance in case of hardware failures or node outages.
- Materialized Views: ClickHouse supports materialized views, which are precomputed query results stored as tables. Materialized views can significantly improve query performance, as they allow for faster data retrieval by avoiding the need to recompute the results for each query.
DuckDB Key Concepts
- In-process: DuckDB operates in-process, meaning it runs within the same process as the application using it, without the need for a separate server.
- OLAP: DuckDB is an OLAP database, which means it is optimized for analytical query processing.
- Vectorized engine: DuckDB utilizes a vectorized engine that operates on batches of data, improving query performance.
- Transactions: DuckDB supports transactional operations, ensuring the atomicity, consistency, isolation, and durability (ACID) properties of data operations.
- SQL dialect: DuckDB provides a rich SQL dialect with advanced features such as arbitrary and nested correlated subqueries, window functions, collations, and support for complex types like arrays and structs
ClickHouse Architecture
ClickHouse’s architecture is designed to support high-performance analytics on large datasets. ClickHouse stores data in a columnar format. This enables efficient data compression and faster query execution, as only the required columns are read during query execution. ClickHouse also supports distributed processing, which allows for queries to be executed across multiple nodes in a cluster. ClickHouse uses the MergeTree storage engine as its primary table engine. MergeTree is designed for high-performance OLAP tasks and supports data replication, data partitioning, and indexing.
DuckDB Architecture
DuckDB follows an in-process architecture, running within the same process as the application. It is a relational table-oriented database management system that supports SQL queries for producing analytical results. DuckDB is built using C++11 and is designed to have no external dependencies. It can be compiled as a single file, making it easy to install and integrate into applications.
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ClickHouse Features
Real-time analytics
ClickHouse is designed for real-time analytics and can process large volumes of data with low latency, providing fast query performance and real-time insights.
Data compression
ClickHouse’s columnar storage format enables efficient data compression, reducing storage requirements and improving query performance.
Materialized views
ClickHouse supports materialized views, which can significantly improve query performance by precomputing and storing query results as tables.
DuckDB Features
Transactions and Persistence
DuckDB supports transactional operations, ensuring data integrity and durability. It allows for persistent storage of data between sessions.
Extensive SQL Support
DuckDB provides a rich SQL dialect with support for advanced query features, including correlated subqueries, window functions, and complex data types.
Direct Parquet & CSV Querying
DuckDB allows direct querying of Parquet and CSV files, enabling efficient analysis of data stored in these formats.
Fast Analytical Queries
DuckDB is designed to run analytical queries efficiently, thanks to its vectorized engine and optimization for analytics workloads.
Parallel Query Processing
DuckDB can process queries in parallel, taking advantage of multi-core processors to improve query performance.
ClickHouse Use Cases
Large-scale data analytics
ClickHouse’s high-performance query engine and columnar storage format make it suitable for large-scale data analytics and business intelligence applications.
Real-time reporting
ClickHouse’s real-time analytics capabilities enable organizations to generate real-time reports and dashboards, providing up-to-date insights for decision-making.
Log and event data analysis
ClickHouse’s ability to process large volumes of data in real-time makes it a suitable choice for log and event data analysis, such as analyzing web server logs or application events.
DuckDB Use Cases
Processing and Storing Tabular Datasets
DuckDB is well-suited for scenarios where you need to process and store tabular datasets, such as data imported from CSV or Parquet files. It provides efficient storage and retrieval mechanisms for working with structured data.
Interactive Data Analysis
DuckDB is ideal for interactive data analysis tasks, particularly when dealing with large tables. It enables you to perform complex operations like joining and aggregating multiple large tables efficiently, allowing for rapid exploration and extraction of insights from your data.
Large Result Set Transfer to Client
When you need to transfer large result sets from the database to the client application, DuckDB can be a suitable choice. Its optimized query processing and efficient data transfer mechanisms enable fast and seamless retrieval of large amounts of data.
ClickHouse Pricing Model
ClickHouse is an open source database and can be deployed on your own hardware. The developers of ClickHouse have also recently created ClickHouse Cloud which is a managed service for deploying ClickHouse.
DuckDB Pricing Model
DuckDB is a free and open-source database management system released under the permissive MIT License. It can be freely used, modified, and distributed without any licensing costs.
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