ClickHouse Data Compression Techniques for Time-series Datasets


ClickHouse, a powerful columnar database management system, employs various techniques to optimize data storage and retrieval. Among these techniques, delta-encoding and dictionary encoding play a pivotal role in enhancing compression, particularly for time-series and repetitive data. In this article, we’ll explore how these encoding methods work and why they are particularly suitable for these specific data scenarios.

Delta-Encoding: A Technique for Sequential Data

Delta-encoding, also known as delta compression, is a technique where the difference between consecutive values in a column is stored, rather than the actual values themselves. This approach is particularly efficient for time-series data where values tend to change gradually over time.

For example, consider a dataset tracking the daily temperature. Instead of storing each temperature value, ClickHouse stores the difference between each day’s temperature and the previous day’s temperature. As temperatures usually change incrementally, this technique results in significantly smaller storage requirements.

Dictionary Encoding: Efficiently Handling Repetitive Data

Dictionary encoding is a method to efficiently store repetitive values. It creates a mapping, or dictionary, between unique values in a column and their corresponding codes. Instead of storing the actual values, ClickHouse stores the compact codes, effectively reducing storage overhead.

In the context of time-series data, where certain events or states might repeat frequently (such as status updates or sensor readings), dictionary encoding shines. For instance, in a dataset recording vehicle statuses (idle, moving, stopped), dictionary encoding replaces the repeated status values with short codes, leading to reduced storage consumption.

Advantages for Time-Series and Repetitive Data

  1. Reduced Storage: Both delta-encoding and dictionary encoding drastically reduce the amount of storage needed for time-series and repetitive data. This is crucial when dealing with large volumes of data common in these scenarios.
  2. Faster Retrieval: Smaller storage requirements mean fewer I/O operations, resulting in faster data retrieval. This is particularly important for time-series analysis where quick access to historical data is vital.
  3. Improved Compression Ratios: These encoding techniques lead to higher compression ratios, allowing organizations to store more data within the same storage infrastructure.
  4. Lower Costs: Efficient data storage translates to reduced hardware costs and optimized resource utilization.


ClickHouse’s adoption of delta-encoding and dictionary encoding showcases its commitment to maximizing compression efficiency for specific data scenarios. These techniques significantly benefit time-series and repetitive data, where gradual changes or recurring patterns are prevalent. By drastically reducing storage overhead and enhancing data retrieval speeds, ClickHouse empowers organizations to handle vast amounts of data with optimal efficiency, making it a compelling choice for real-time analytics and data-intensive applications.

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About Shiv Iyer 215 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.