Data table compression technology in MySQL

MySQL is a common relational database and a core component of many websites and applications. As the amount of data becomes larger and larger, how to optimize the performance of MySQL becomes particularly important. One of the key areas is the compression of data tables. In this article we will introduce the data table compression technology in MySQL.

1. Compressed tables and non-compressed tables

There are two types of data tables in MySQL: compressed tables and non-compressed tables.

Non-compressed table is the default table type of MySQL, which uses a fixed-length row format to store data. This means that the data will occupy a fixed length of space when stored, rather than adjusting the space size based on the size of the data. This makes uncompressed tables fast when writing and reading data, but can take up a lot of disk space when storing large amounts of data.

Compressed tables, on the other hand, use a variable-length row format that adjusts the space size based on the size of the data. This is useful for storing large data tables and historical data tables. Compressed tables reduce storage space but can in some cases be slower at writing and reading data than uncompressed tables.

You can use the following command to create a compressed table:

CREATE TABLE compressed_table (
id INT PRIMARY KEY,
name VARCHAR(50),
address VARCHAR(100 )
) ENGINE=InnoDB ROW_FORMAT=COMPRESSED;

In row format, COMPRESSED means using a compressed table.

2. Compression algorithm

MySQL supports a variety of compression algorithms, each algorithm has its advantages and disadvantages. Here are some commonly used compression algorithms:

• Zlib: Zlib is a general-purpose lossless compression algorithm that can achieve the right balance between CPU and disk I/O. Zlib is useful for writing large files to disk. In MySQL, it is used to compress InnoDB tables.
• LZ4: LZ4 is a faster compression algorithm that is fast for the CPU and can improve disk I/O when writing large files. In MySQL, it is used to compress MyISAM tables.
• Snappy: Snappy is a very fast lossless compression algorithm. Its speed is faster than other compression algorithms, but its compression rate is lower. In MySQL, it can be used to compress CSV tables or compress non-indexed columns.

In MySQL, you can use the following command to set the compression algorithm of the table:

ALTER TABLE my_table ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8;

Among them, KEY_BLOCK_SIZE is specified Compression algorithm to use, allowed values ​​are 1, 2, 4, 8, depending on the selected algorithm.

3. Performance and Limitations of Compression

While compression can reduce disk usage and improve performance, it also has some limitations and performance overhead.

First, compression can increase CPU usage, which means that when many concurrent queries are running, excessive CPU resources may be used. Therefore, compression should not be overused in environments with limited CPU resources.

Secondly, compression may also increase disk I/O latency. Whenever a compressed table is read or written, MySQL must decompress the data before performing the operation. This increases I/O latency and can cause queries to be slower in some cases.

Finally, compression may also have a negative impact on the index performance of the table. Specifically, compression can make indexes larger, resulting in more disk I/O and CPU usage. In some cases, this can cause queries to become slower.

4. Conclusion

The data table compression technology in MySQL can help us optimize database performance, reduce disk usage and increase query speed. However, we need to remember that compressing tables also has its limitations and performance overhead. We need to weigh the pros and cons and choose whether to use compression on a case-by-case basis.

When using compressed tables, it is recommended to choose an appropriate compression algorithm and row format to find a balance between CPU and disk I/O. Finally, we should always monitor performance metrics so we can optimize and adjust if necessary.

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