How ShardingJDBC Splits Old Data

ShardingJDBC is an open-source Java-based middleware that can help partition and shard data to improve the horizontal scalability of databases. In ShardingJDBC, data sharding can be achieved by configuring data shard rules and partitioning strategies, as well as dividing old data into tables.

To partition old data, you can achieve it through the following steps:

1. To create a new sharded table: Start by creating a new sharded table with the same structure as the original table, but add a shard identifier to the table name, such as the year or month.
2. Importing old data: transfer old data to a new sharded table can be achieved using a database tool or writing a script.
3. Adjusting data access logic: By modifying the SQL query statements in the application, direct the query operations to the new sharded table in order to access the data in the new sharded table.
4. Progressive migration: data can be migrated gradually, first moving some old data to the new sharded table, and then gradually migrating the remaining data, avoiding performance issues caused by large-scale data migration.

By following the steps mentioned above, old data can be sharded to ensure data consistency and accuracy. When using ShardingJDBC for data sharding, it is important to carefully design sharding rules and database table partitioning strategies, as well as manage the data migration process effectively, to ensure the successful implementation of data sharding.