Introduction
ClickHouse is an open-source column-oriented database management system developed by Yandex. One of the critical features of ClickHouse is its ability to compress data in order to save storage space and increase query performance. This article will provide an overview of the different compression methods used in ClickHouse and how they impact performance and storage.
Compression algorithms & codecs used in ClickHouse
ClickHouse uses a variety of compression algorithms to compress data, including:
- ZSTD: This is a high-performance data compression algorithm that is used as the default compression method in ClickHouse. It offers a good balance between compression ratio and speed, making it well-suited for both storage and retrieval of large amounts of data.
- LZ4: This is a high-speed compression algorithm that is optimized for fast data compression and decompression. It is well-suited for use cases where speed is more important than compression ratio, such as real-time data processing.
- LZ4HC: This is a variation of the LZ4 algorithm that offers better compression ratios at the cost of increased compression and decompression times.
- Zlib: This is a general-purpose compression algorithm that offers a good balance between compression ratio and speed. It is well-suited for use cases where both storage and retrieval of data are important.
- None: This option disables compression and is useful in situations where data is already compressed or when the cost of compression is too high.
In addition to these compression methods, ClickHouse supports several other options that can fine-tune compression performance. For example, the “compression_level” setting can be used to adjust the compression ratio and speed of the selected algorithm, while the “min_compress_block_size” setting can be used to control the minimum size of a block that will be compressed.
Compressing data in ClickHouse is a straightforward process that can be accomplished through the use of the “COMPRESS” function. The basic syntax of the function is as follows:
COMPRESS(data, compression_algorithm, compression_level)
Where “data” is the column or expression that you want to compress, “compression_algorithm” is the algorithm that you want to use, and “compression_level” is an optional parameter that can be used to adjust the compression ratio and speed of the selected algorithm.
Here is an example of how to use the “COMPRESS” function to compress a column called “data_column” using the ZSTD algorithm with a compression level of 3:
ALTER TABLE my_table MODIFY COLUMN data_column COMPRESSED WITH (compression = 'zstd', level = 3);
It’s also possible to compress the data while inserting it into the table
INSERT INTO my_table (data_column) VALUES (COMPRESS('some_data', 'zstd', 3));
Note that it’s important to keep in mind that compression can increase the time it takes to insert and read data, so it’s essential to consider the trade-offs between compression ratio and performance when choosing a compression algorithm and level. Also, different algorithms might be more suitable for different types of data, and it’s important to test and see which one suits better to your use case.
It’s also important to mention that ClickHouse also supports partitioned table, which means that each partition can have a different compression algorithm, it gives more flexibility in terms of performance and storage optimization.
Conclusion
In conclusion, ClickHouse’s compressor is an important part of the database management system, enabling high performance and storage space savings. The flexibility of the different compression algorithms and options available in ClickHouse allows users to optimize performance and storage for their specific use case.
For more information, please visit the official ClickHouse Docs in here.
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