Synchronize data from a PolarDB for MySQL cluster to an Elasticsearch cluster

Precautions

• DTS uses read and write resources of the source and destination databases during initial full data synchronization. This may increase the database load. If the database performance is unfavorable, the specification is low, or the data volume is large, database services may become unavailable. For example, DTS occupies a large amount of read and write resources in the following cases: a large number of slow SQL queries are performed on the source database, the tables have no primary keys, or a deadlock occurs in the destination database. Before synchronizing data, you must evaluate the performance of the source and destination databases. We recommend that you synchronize data during off-peak hours. For example, you can synchronize data when the CPU usage of the source and destination databases is less than 30%.
• DTS does not synchronize data definition language (DDL) operations. If a DDL operation is performed on a table in the source database during data synchronization, you must perform the following steps: Remove the table from the required objects, remove the index for the table from the Elasticsearch cluster, and then add the table to the required objects. For more information, see Remove an object from a data synchronization task and Add an object to a data synchronization task.
• To add columns to the table that you want to synchronize, perform the following steps: Modify the mapping of the table in the Elasticsearch cluster, perform data definition language (DDL) operations in the PolarDB for MySQL cluster, and then pause and start the data synchronization task.

SQL operations that can be synchronized

INSERT, DELETE, and UPDATE

Data type conversion

The data types of the PolarDB for MySQL cluster and the Elasticsearch cluster do not have one-to-one correspondence. During initial schema synchronization, DTS converts the data types of the PolarDB for MySQL cluster into the data types of the Elasticsearch cluster. For more information, see Data type mappings for initial schema synchronization.

Procedure

1. Purchase a data synchronization instance. For more information, see Purchase procedure.
   Note On the buy page, set Source Instance to PolarDB, set Target Instance to Elasticsearch, and set Synchronization Topology to One-Way Synchronization.
2. Log on to the DTS console.
3. In the left-side navigation pane, click Data Synchronization.
4. At the top of the Synchronization Tasks page, select the region where the destination instance resides.
   
5. Find the data synchronization instance and click Configure Synchronization Channel in the Actions column.
6. Configure the source and destination instances.
   

   Section	Parameter	Description
   N/A	Synchronization Task Name	DTS automatically generates a task name. We recommend that you specify an informative name for easy identification. You do not need to use a unique task name.
   Source Instance Details	Instance Type	The value of this parameter is set to PolarDB Instance and cannot be changed.
   	Instance Region	The region of the source cluster. The region is the same as the source region that you selected on the buy page. You cannot change the value of this parameter.
   	PolarDB Instance ID	Select the ID of the source PolarDB for MySQL cluster.
   	Database Account	Enter the database account of the PolarDB for MySQL cluster. Note The account must have the read permission on the source database.
   	Database Password	Enter the password of the source database account.
   Destination Instance Details	Instance Type	The value of this parameter is set to Elasticsearch and cannot be changed.
   	Instance Region	The region of the destination cluster. The region is the same as the destination region that you selected on the buy page. You cannot change the value of this parameter.
   	Elasticsearch	Select the ID of the destination Elasticsearch cluster.
   	Database Account	Enter the account that is used to connect to the Elasticsearch cluster. The default account is elastic.
   	Database Password	Enter the password of the destination database account.

7. In the lower-right corner of the page, click Set Whitelist and Next.
   Note DTS adds the CIDR blocks of DTS servers to the whitelists of the PolarDB for MySQL cluster and the Elasticsearch cluster. This ensures that DTS servers can connect to the source and destination clusters.
8. Configure the index name, the processing mode of identical index names, and the objects to be synchronized.
   

   Parameter	Description
   Index Name	Table Name If you select Table Name, the name of the index that is created in the Elasticsearch cluster is the same as the name of the table. In this example, the index name is customer. DatabaseName_TableName If you select DatabaseName_TableName, the name of the index that is created in the Elasticsearch cluster is <Database name>_<Table name>. In this example, the index name is dtstestdata_customer.
   Processing Mode In Existed Target Table	Pre-check and Intercept: checks whether the destination database contains indexes that have the same names as tables in the source database. If the destination database does not contain indexes that have the same names as tables in the source database, the precheck is passed. Otherwise, an error is returned during precheck and the data synchronization task cannot be started. Note If indexes in the destination database have the same names as tables in the source database, and cannot be deleted or renamed, you can use the object name mapping feature. For more information, see Specify the name of an object in the destination instance. Ignore: skips the precheck for identical index names in the source and destination databases. Warning If you select Ignore, data consistency is not guaranteed and your business may be exposed to potential risks. If the source and destination databases have the same mappings and the primary key of a record in the destination database is the same as that in the source database, the record remains unchanged during initial data synchronization. However, the record is overwritten during incremental data synchronization. If the source and destination databases have different mappings, initial data synchronization may fail. In this case, only some columns are synchronized or the data synchronization task fails.
   Objects to be synchronized	Select objects from the Available section and click the icon to move the objects to the Selected section. You can select databases and tables as the objects to be synchronized.

9. In the Selected section, move the pointer over a table, and then click Edit. In the Edit Table dialog box, configure parameters for the table in the Elasticsearch cluster, such as the index name and type name.
   

   Parameter	Description
   Index Name	For more information, see Terms in the Elasticsearch documentation. Warning The only type of special characters that an index name and type name can contain is underscore (_). To synchronize multiple source tables with the same schema to a destination object, you must repeat this step to set the same index name and type name for the tables.
   Type Name
   Filter	Specify SQL conditions to filter data. Only data that meets the specified conditions is synchronized to the destination instance. For more information, see Use SQL conditions to filter data.
   IsPartition	Select whether to set partitions. If you select Yes, you must also specify the partition key column and number of partitions.
   _id value	Primary key column Composite primary key fields are merged into one column. Business key If you select a business key, you must also specify the business key column.
   add param	Select the column parameter and parameter value. For more information, see Mapping parameters in the Elasticsearch documentation.

10. In the lower-right corner of the page, click Precheck.
    Note

    • Before you can start the data synchronization task, a precheck is performed. You can start the data synchronization task only after the task passes the precheck.
    • If the task fails to pass the precheck, click the icon next to each failed item to view details. Troubleshoot the issues based on the causes and run the precheck again.
11. Close the Precheck dialog box after the following message is displayed: The precheck is passed.
12. Wait until the initial synchronization is complete and the data synchronization task is in the Synchronizing state.
    On the Synchronization Tasks page, view the status of the data synchronization task.

Check the index and data

If the data synchronization task is in the Synchronizing state, you can connect to the Elasticsearch cluster by using the Elasticsearch-Head plug-in. Then, you can check whether the index is created and data is synchronized as expected. For more information, see Use Cerebro to access an Elasticsearch cluster.