Improving the compression and decompression performance of the MySQL storage engine: using the optimization method of the Archive engine

Improve the compression and decompression performance of the MySQL storage engine: using the optimization method of the Archive engine

Introduction:
In database applications, the choice of storage engine is very important for performance and storage space. MySQL provides a variety of storage engines, each with its own specific advantages and applicable scenarios. Among them, the Archive engine is famous for its excellent compression and decompression performance. This article will introduce how to further improve the compression and decompression performance of the Archive engine through some optimization methods.

1. Introduction to Archive engine
Archive engine is a storage engine of MySQL. Its design goal is to provide a high compression ratio and fast insertion and query performance. The Archive engine only supports insert and query operations, but does not support update and delete operations. Its compression algorithm is based on the zlib compression library and can achieve a very high compression ratio. The data of the Archive engine is stored by rows, not by pages, which is an important reason why it can provide high performance.

2. Optimization method

1. Specify the appropriate compression level: Archive engine provides different compression levels, and you can choose the appropriate level according to actual needs. The higher the compression level, the greater the compression ratio, but it also increases the time cost of compression and decompression. You can use the following statement to specify the compression level:

ALTER TABLE table_name ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=value;

where table_name is the table name, value is the compression level, and the optional values ​​are 0-9. 0 means no compression, 1 means the fastest compression (lowest compression rate), and 9 means the highest compression rate (longest compression time).

2. Turn off automatic submission: When inserting a large amount of data, you can significantly improve the insertion performance by turning off automatic submission. Automatic commit can be turned off using the following statement:

SET autocommit=0;

After the insertion is completed, the transaction can be manually committed using the following statement:

COMMIT;

3. Use batch insert: Archive engine supports multiple rows insert. By combining multiple insert statements into a single statement, you can reduce communication overhead and thereby improve insert performance. The following is an example:

INSERT INTO table_name(col1, col2) VALUES(value1, value2),(value3, value4),(value5, value6);

Where, table_name is the table name, col1, col2 is the column name, value1, value2, etc. are the inserted values.

4. Precompiled statements: Using precompiled statements can reduce syntax parsing time and improve query performance. You can use prepared statements to perform query operations. The following is an example:

PreparedStatement stmt = conn.prepareStatement("SELECT * FROM table_name WHERE condition");
ResultSet rs = stmt.executeQuery();

where table_name is the table name and condition is the query condition.

5. Optimize query statements: The Archive engine does not support indexes, so when performing query operations, you should try to avoid full table scans. Query performance can be improved by adding appropriate query conditions and using the LIMIT keyword to limit the number of query results.

3. Code Example
The following is a simple example using the Archive engine:

-- 创建表
CREATE TABLE my_table (
  id INT PRIMARY KEY AUTO_INCREMENT,
  data VARCHAR(255)
) ENGINE=ARCHIVE;

-- 指定压缩级别
ALTER TABLE my_table ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8;

-- 批量插入数据
INSERT INTO my_table(data) VALUES('data1'),('data2'),('data3'),('data4'),('data5');

-- 查询数据
SELECT * FROM my_table;

In this example, we first create a file named my_table table uses Archive engine. Then specify the compression level to 8 through the ALTER TABLE statement. Then use the INSERT INTO statement to insert 5 pieces of data in batches. Finally, the inserted data was queried through the SELECT statement.

Conclusion:
Through the above optimization methods, we can further improve the compression and decompression performance of the Archive engine. In practical applications, appropriate optimization methods need to be selected based on specific scenarios and needs. At the same time, you also need to pay attention to the performance losses that may occur during compression and decompression.

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