What are the data compression and acceleration techniques for learning MySQL?

What are the data compression and acceleration techniques for learning MySQL?

As a commonly used relational database management system, MySQL has been widely used in large-scale data storage and processing. However, as data volume grows and query load increases, database performance optimization becomes an important task. Among them, data compression and acceleration techniques are one of the key factors to improve database performance. This article will introduce some commonly used MySQL data compression and acceleration techniques and provide relevant code examples.

Data compression skills:

1. Compression storage engine: MySQL provides a variety of storage engines, such as InnoDB, MyISAM, etc. Among them, InnoDB supports row-level compression, and you can reduce the data storage space by configuring the compression algorithm (such as Zlib or LZ4). The following is an example of setting compression for an InnoDB table:

   ALTER TABLE table_name ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8;

   Adjust the balance between compression ratio and performance by setting the value of KEY_BLOCK_SIZE.

2. Partition compression: MySQL supports partitioned tables. By partitioning the table according to rules such as range or list, you can only operate the necessary partitioned data during query, reducing query time. The following is an example:

   CREATE TABLE sales (
     id INT,
     amount INT,
     sale_date DATE
   ) PARTITION BY RANGE (YEAR(sale_date)) (
     PARTITION p2019 VALUES LESS THAN (2020),
     PARTITION p2020 VALUES LESS THAN (2021)
   );

   In this way, querying the sales data in 2020 will only involve the partition table named p2020.

Data acceleration skills:

1. Create index: Index is an important means to improve query speed. Appropriate indexes can be created according to the characteristics of the query field. . The following is an example of creating an index:

   CREATE INDEX index_name ON table_name (column_name);

2. Using caching: MySQL provides a query caching mechanism that can cache query results to avoid repeatedly querying the same data. The following is an example of enabling query caching:

   SET GLOBAL query_cache_size = 1000000;

   By setting an appropriate cache size, query performance can be improved based on actual conditions.

3. Split database and table: If the amount of data is very large, you can consider splitting the data into databases and tables, and spreading the data into multiple databases or tables to ease the query of a single database. pressure. The following is an example of a split table:

   CREATE TABLE sales_2020 LIKE sales;
   ALTER TABLE sales_2020 ADD PRIMARY KEY (id);
   INSERT INTO sales_2020 SELECT * FROM sales WHERE YEAR(sale_date) = 2020;

   In this way, when querying the sales data in 2020, you only need to query the table named sales_2020.

To sum up, MySQL’s data compression and acceleration techniques cover storage engine compression, partition compression, indexing, caching and sub-database and sub-table. Depending on the specific business needs and data size, you can choose appropriate techniques to improve database performance.

Reference:

• [MySQL::Compression](https://dev.mysql.com/doc/refman/8.0/en/innodb-compression.html)
• [MySQL::Partition Management](https://dev.mysql.com/doc/refman/8.0/en/partitioning-management.html)
• [MySQL::Query Cache] (https://dev.mysql.com/doc/refman/8.0/en/query-cache.html)
• [MySQL::InnoDB Index](https://dev.mysql.com/doc /refman/8.0/en/innodb-index-types.html)

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