PHP Database Sharding: Capacity Management Guide

Database table partitioning is the process of dividing large database tables into multiple smaller tables according to certain rules in order to manage data capacity. In PHP, database table partitioning can be achieved through the following steps:

1. Establish a database connection using PHP’s database extension libraries such as MySQLi or PDO.
2. Create a structure of main table and subtables: Start by creating a main table to store all relevant information about the subtables, such as table name and creation time. Then, establish the structure of the subtables by defining the fields and indexes for each one.
3. Table partitioning rules: The partitioning rules are created based on requirements and can be divided according to dimensions such as time, region, and user. For example, partitioning by date can create table names with year and month as suffix.
4. Data insertion into partitioned tables: When inserting data, the data is inserted into the corresponding partitioned table according to the partitioning rules. PHP string handling functions or regular expressions can be used to generate the partitioned table names.
5. Data retrieval by partitioning: when querying data, determine the appropriate partition to query based on partitioning rules, then pass the query conditions to the corresponding partition for retrieval. String manipulation functions in PHP or regular expressions can be used to generate partition table names.
6. Deleting data by partition: When the data in a partition reaches a certain capacity, data deletion can be carried out based on the partitioning rules. This can be done using PHP’s database operation functions.
7. Data migration: When the data volume of a certain partition is too large or the data is not evenly distributed, data migration can be conducted to move data from one partition to another. Data migration can be done using PHP’s database operation functions.

By following the above steps, it is possible to achieve database sharding capacity management and effectively manage the data capacity of large databases.