Verifying the Consistency of Data Migrated from Delta Lake (with Metadata) to MRS Delta Lake

Preparations

Install the MgC Agent, an MgC tool used for data verification, in the source intranet environment and register an account for using the MgC Agent. For details, see Installing the MgC Agent for Linux.

Procedure

1. Sign in to the MgC console.
2. In the navigation pane on the left, choose Settings.
3. Under Migration Projects, click Create Project.
   Figure 1 Creating a project
   

4. Set Project Type to Complex migration (for big data), enter a project name, and click Create.
   Figure 2 Creating a big data migration project
   

5. Connect the MgC Agent to MgC. For more information, see Connecting the MgC Agent to MgC.
6. After the connection is successful, add the username/password pairs for accessing the source Delta Lake executor and the target MRS Delta Lake executor to the MgC Agent. For more information, see Adding Resource Credentials.

   

7. In the navigation pane, choose Migrate > Big Data Verification. In the navigation pane, under Project, select the project created in step 4.
8. If you are performing a big data verification with MgC for the first time, select your MgC Agent to enable this feature. Click Select MgC Agent. In the displayed dialog box, select the MgC Agent you connected to MgC from the drop-down list.
   CAUTION:

   Ensure that the selected MgC Agent is always Online and Enabled before your verification is complete.

9. In the Features area, click Preparations.
10. Choose Connection Management and click Create Connection.
    Figure 3 Creating a connection
    

11. Select Delta Lake (with metadata) and click Next.

    

12. Set connection parameters based on Table 1 and click Test. If the test is successful, the connection is set up.

    Table 1 Parameters for creating a connection to Delta Lake (with metadata)

    Parameter	Configuration
    Connection To	Select Source.
    Connection Name	The default name is Delta-Lake-with-metadata-4 random characters (including letters and numbers). You can also customize a name.
    MgC Agent	Select the MgC Agent connected to MgC in step 5.
    Executor Credential	Select the source Delta Lake executor credential added to the MgC Agent in step 6.
    Executor IP Address	Enter the IP address for connecting to the executor.
    Executor Port	Enter the port for connecting to the executor. The default port is 22.
    Spark Client Directory	Enter the absolute path of the bin directory on the Spark client.
    Environment Variable Address	Enter the absolute path of the environment variable file, for example, /opt/bigdata/client/bigdata_env. If this field is not left blank, the environment variable file is automatically sourced before commands are executed.
    SQL File Location	Enter a directory for storing the SQL files generated for consistency verification. You must have the read and write permissions for the folder. NOTICE: After the migration is complete, you need to manually clear the folders generated at this location to release storage space.

13. After the connection test is successful, click Confirm. The cloud service connection is set up.
14. Choose Metadata Management and click Create Data Lake Metadata Collection Task.
    Figure 4 Create Data Lake Metadata Collection Task
    

15. Set a data lake metadata collection task based on Table 2 and click Confirm.

    

    Table 2 Parameters for configuring a metadata collection task

    Parameter	Configuration
    Task Name	The default name is Data-Lake-Metadata-Collection-Task-4 random characters (including letters and numbers). You can also customize a name.
    Metadata Source	Select the connection created in step 12.
    Databases	Enter the names of the databases whose metadata needs to be collected. If no database name is specified, the metadata of all databases is collected.
    Concurrent Threads	Set the maximum number of threads for executing the collection. The default value is 3. The value ranges from 1 to 10. Configuring more concurrent threads means more efficient collection, but more connection and MgC Agent resources will be consumed.
    Custom Parameters	You can customize parameters to specify the tables and partitions to collect or set criteria to filter tables and partitions. If the metadata source is Alibaba Cloud EMR, add the following parameter: Parameter: conf Value: spark.sql.catalogImplementation=hive If the source is Alibaba Cloud EMR Delta Lake 2.2 and is accessed through Delta Lake 2.3, add the following parameters: Parameter: master Value: local If the source is Alibaba Cloud EMR Delta Lake 2.1.0 and Spark 2.4.8 is configured to process Delta Lake data, add the following parameters: Parameter: mgc.delta.spark.version Value: 2 If the source is Alibaba Cloud EMR and Spark 3 is configured to process Delta Lake data, add the following parameters: Parameter: jars Value: '/opt/apps/DELTALAKE/deltalake-current/spark3-delta/delta-core_2.12-*.jar,/opt/apps/DELTALAKE/deltalake-current/spark3-delta/delta-storage-*.jar' CAUTION: Replace the parameter values with the actual environment directory and Delta Lake version.

16. Under Tasks, you can review the created metadata collection task and its settings. You can modify the task by choosing More > Modify in the Operation column.
    Figure 5 Managing a metadata collection task
    

17. Click Execute Task in the Operation column to run the task. Each time the task is executed, a task execution is generated.
18. Click View Executions in the Operation column. Under Task Executions, you can view the execution records of the task and the status and collection result of each task execution. When a task execution enters a Completed status and the collection results are displayed, you can view the list of databases and tables extracted from collected metadata on the Tables tab.
    Figure 6 Managing task executions
    

19. In the Features area, click Table Management.
20. Under Table Groups, click Create. Configure the parameters for creating a table group and click Confirm.

    

    Table 3 Parameters for creating a table group

    Parameter	Description
    Table Group	Enter a name.
    Metadata Source	Select the connection created in step 12. CAUTION: A table group can only contain tables coming from the same metadata source.
    Verification Rule	Select a rule that defines the method for verifying data consistency and the inconsistency tolerance. MgC provides multiple verification rules for you to choose. For details about these rules, click View More.
    Description (Optional)	Enter a description to identify the table group.

21. On the Table Management page, click the Tables tab, select the data tables to be added to the same table group, and choose Option > Add Tables to Group above the list. In the displayed dialog box, select the desired table group and click Confirm.

    

    NOTICE:

    You can manually import information of incremental data tables to MgC. For details, see Creating a Table Group and Adding Tables to the Group.

22. Create a connection to the source and target executors separately. For details, see Creating an Executor Connection. Select the source and target executor credentials added to the MgC Agent in step 6.
23. Create a data verification task for the source and target Delta Lake clusters, respectively, and execute the tasks. For more information, see Creating and Executing Verification Tasks. During the task creation, select the table group created in step 20.
    • On the Select Task Type page, choose Delta Lake.

      

    • Select a verification method. For details about each verification method, see Verification Methods.

24. Wait until the task executions enter a Completed status. You can view and export the task execution results on the Verification Results page. For details, see Viewing and Exporting Verification Results.