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Best Practices of Data Migration in the Shard Merge Scenario

This document describes the features and limitations of TiDB Data Migration (DM) in the shard merge scenario and provides a data migration best practice guide for your application (the default "pessimistic" mode is used).

Use a separate data migration task

In the Merge and Migrate Data from Sharded Tables document, the definition of "sharding group" is given: A sharding group consists of all upstream tables that need to be merged and migrated into the same downstream table.

The current sharding DDL mechanism has some usage restrictions to coordinate the schema changes brought by DDL operations in different sharded tables. If these restrictions are violated due to unexpected reasons, you need to handle sharding DDL locks manually in DM, or even redo the entire data migration task.

To mitigate the impact on data migration when an exception occurs, it is recommended to merge and migrate each sharding group as a separate data migration task. This might enable that only a small number of data migration tasks need to be handled manually while others remain unaffected.

Handle sharding DDL locks manually

You can easily conclude from Merge and Migrate Data from Sharded Tables that DM's sharding DDL lock is a mechanism for coordinating the execution of DDL operations to the downstream from multiple upstream sharded tables.

Therefore, when you find any sharding DDL lock on DM-master through show-ddl-locks command, or any unresolvedGroups or blockingDDLs on some DM-workers through query-status command, do not rush to manually release the sharding DDL lock through unlock-ddl-lock commands.

Instead, you can:

  • Follow the corresponding manual solution to handle the scenario if the failure of automatically releasing the sharding DDL lock is one of the listed abnormal scenarios.
  • Redo the entire data migration task if it is an unsupported scenario: First, empty the data in the downstream database and the dm_meta information associated with the migration task; then, re-execute the full and incremental data replication.

Handle conflicts of auto-increment primary key

DM offers the column mapping feature to handle conflicts that might occur in merging the bigint type of auto-increment primary key. However, it is strongly discouraged to choose this approach. If it is acceptable in the production environment, the following two alternatives are recommended.

Remove the PRIMARY KEY attribute from the column

Assume that the upstream schemas are as follows:

CREATE TABLE `tbl_no_pk` (
  `auto_pk_c1` bigint(20) NOT NULL,
  `uk_c2` bigint(20) NOT NULL,
  `content_c3` text,
  PRIMARY KEY (`auto_pk_c1`),
  UNIQUE KEY `uk_c2` (`uk_c2`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1

If the following requirements are satisfied:

  • The auto_pk_c1 column has no impact on the application and does not depend on the column's PRIMARY KEY attribute.
  • The uk_c2 column has the UNIQUE KEY attribute, and it is globally unique in all upstream sharded tables.

Then you can perform the following steps to fix the ERROR 1062 (23000): Duplicate entry '***' for key 'PRIMARY' error that is possibly caused by the auto_pk_c1 column when you merge sharded tables.

  1. Before the full data migration, create a table in the downstream database for merging and migrating data, and modify the PRIMARY KEY attribute of the auto_pk_c1 column to normal index.

    CREATE TABLE `tbl_no_pk_2` (
      `auto_pk_c1` bigint(20) NOT NULL,
      `uk_c2` bigint(20) NOT NULL,
      `content_c3` text,
      INDEX (`auto_pk_c1`),
      UNIQUE KEY `uk_c2` (`uk_c2`)
    ) ENGINE=InnoDB DEFAULT CHARSET=latin1
  2. Add the following configuration in task.yaml to skip the check of auto-increment primary key conflict:

    ignore-checking-items: ["auto_increment_ID"]
  3. Start the full and incremental data replication task.

  4. Run query-status to verify whether the data migration task is successfully processed and whether the data from the upstream has already been merged and migrated to the downstream database.

Use a composite primary key

Assume that the upstream schemas are as follows:

CREATE TABLE `tbl_multi_pk` (
  `auto_pk_c1` bigint(20) NOT NULL,
  `uuid_c2` bigint(20) NOT NULL,
  `content_c3` text,
  PRIMARY KEY (`auto_pk_c1`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1

If the following requirements are satisfied:

  • The application does not depend on the PRIMARY KEY attribute of the auto_pk_c1 column.
  • The composite primary key that consists of the auto_pk_c1 and uuid_c2 columns is globally unique.
  • It is acceptable to use a composite primary key in the application.

Then you can perform the following steps to fix the ERROR 1062 (23000): Duplicate entry '***' for key 'PRIMARY' error that is possibly caused by the auto_pk_c1 column when you merge sharded tables.

  1. Before the full data migration, create a table in the downstream database for merging and migrating data. Do not specify the PRIMARY KEY attribute for the auto_pk_c1 column, but use the auto_pk_c1 and uuid_c2 columns to make up a composite primary key.

    CREATE TABLE `tbl_multi_pk_c2` (
      `auto_pk_c1` bigint(20) NOT NULL,
      `uuid_c2` bigint(20) NOT NULL,
      `content_c3` text,
      PRIMARY KEY (`auto_pk_c1`,`uuid_c2`)
    ) ENGINE=InnoDB DEFAULT CHARSET=latin1
  2. Start the full and incremental data migration task.

  3. Run query-status to verify whether the data migration task is successfully processed and whether the data from upstream has already been merged and migrated to the downstream database.

Create/drop tables in the upstream

In Merge and Migrate Data from Sharded Tables, it is clear that the coordination of sharding DDL lock depends on whether the downstream database receives the DDL statements of all upstream sharded tables. In addition, DM currently does not support dynamically creating or dropping sharded tables in the upstream. Therefore, to create or drop sharded tables in the upstream, it is recommended to perform the following steps.

Create sharded tables in the upstream

If you need to create a new sharded table in the upstream, perform the following steps:

  1. Wait for the coordination of all executed sharding DDL in the upstream sharded tables to finish.

  2. Run stop-task to stop the data migration task.

  3. Create a new sharded table in the upstream.

  4. Make sure that the configuration in the task.yaml file allows the newly added sharded table to be merged in one downstream table with other existing sharded tables.

  5. Run start-task to start the task.

  6. Run query-status to verify whether the data migration task is successfully processed and whether the data from upstream has already been merged and migrated to the downstream database.

Drop sharded tables in the upstream

If you need to drop a sharded table in the upstream, perform the following steps:

  1. Drop the sharded table, run SHOW BINLOG EVENTS to fetch the End_log_pos corresponding to the DROP TABLE statement in the binlog events, and mark it as Pos-M.

  2. Run query-status to fetch the position (syncerBinlog) corresponding to the binlog event that has been processed by DM, and mark it as Pos-S.

  3. When Pos-S is greater than Pos-M, it means that DM has processed all of the DROP TABLE statements, and the data of the table before dropping has been migrated to the downstream, so the subsequent operation can be performed. Otherwise, wait for DM to finish migrating the data.

  4. Run stop-task to stop the task.

  5. Make sure that the configuration in the task.yaml file ignores the dropped sharded table in the upstream.

  6. Run start-task to start the task.

  7. Run query-status to verify whether the data migration task is successfully processed.

Speed limits and traffic flow control

When data from multiple upstream MySQL or MariaDB instances is merged and migrated to the same TiDB cluster in the downstream, every DM-worker corresponding to each upstream instance executes full and incremental data replication concurrently. This means that the default degree of concurrency (pool-size in full data migration and worker-count in incremental data replication) accumulates as the number of DM-workers increases, which might overload the downstream database. In this case, you need to conduct a preliminary performance analysis based on TiDB and DM monitoring metrics and adjust the value of each concurrency parameter. In the future, DM is expected to support partially automated traffic flow control.