Configuring a Job for Synchronizing Data from to Hudi

Supported Source and Destination Database Versions

**Table 1** Supported database versions
Source Database	Destination Database
Kafka cluster (2.7 and 3.x)	MRS cluster (3.2.0-LTS.x and 3.5.x) Hudi (0.11.0 and 0.15.0)

Database Account Permissions

Before you use DataArts Migration for data synchronization, ensure that the source and destination database accounts meet the requirements in the following table. The required account permissions vary depending on the synchronization task type.

**Table 2** Database account permissions
Type	Required Permissions
Source database connection account	The MRS user must have the read and write permissions on corresponding Kafka topics, that is, the user must belong to the kafka/kafkaadmin/kafkasuperuser user group. NOTE: A common Kafka user can access a topic only after being granted the read and write permissions on the topic by the Kafka administrator.
Destination database connection account	The MRS user must have read and write permissions for the Hadoop and Hive components. You are advised to assign the roles and user groups shown in the following figure to the MRS user. Figure 1 Minimal permissions for MRS Hudi For details, see .

You are advised to create independent database accounts for DataArts Migration task connections to prevent task failures caused by password modification.
After changing the account passwords for the source or destination databases, modify the connection information in Management Center as soon as possible to prevent automatic retries after a task failure. Automatic retries will lock the database accounts.

Supported Synchronization Objects

The following table lists the objects that can be synchronized using different links in DataArts Migration.

**Table 3** Synchronization objects
Type	Note
Synchronization objects	All Kafka messages can be synchronized, and message bodies in JSON or CSV format can be parsed.

Important Notes

In addition to the constraints on supported data sources and versions, connection account permissions, and synchronization objects, you also need to pay attention to the notes in the following table.

**Table 4** Important notes
Type	Restriction
Database	Kerberos authentication can be enabled. Kafka instances using SASL_PLAINTEXT can be used in MRS clusters for which Kerberos authentication is disabled. The SCRAM-SHA-512 and PLAIN authentication mechanisms are supported. Kafka instances with SASL_SSL enabled are not supported. The names of the destination Hudi databases, tables, and fields can only contain digits, letters, and underscores (_). Field names must start with a letter or an underscore (). You are advised to use common characters in names.
Usage	General: During real-time synchronization, the IP addresses, ports, accounts, and passwords cannot be changed. If a Hudi table uses bucket indexes, the partition key cannot be updated. Otherwise, duplicate data may be generated. If a Hudi table uses bucket indexes, ensure that the primary key is unique in a single partition. Incremental synchronization phase: After incremental data is synchronized to Hudi MOR tables in MRS clusters earlier than 3.2.0-LTS1.5, CDM or Spark SQL cannot be used to write data. You need to perform compaction before writing data. Troubleshooting: If any problem occurs during task creation, startup, full synchronization, incremental synchronization, or completion, rectify the fault by referring to .
Other	N/A

Procedure

This section uses real-time synchronization from MRS Kafka to MRS Hudi as an example to describe how to configure a real-time data migration job. Before that, ensure that you have read the instructions described in Check Before Use and completed all the preparations.

Create a real-time migration job by following the instructions in Creating a Real-Time Migration Job and go to the job configuration page.
Select the data connection type. Select MRS Kafka for Source and Hudi for Destination.

Figure 2 Selecting the data connection type
Select a job type. The default migration type is Real-time. The migration scenario is Single table.

Figure 3 Setting the job type

For details about synchronization scenarios, see Synchronization Scenarios.
Configure network resources. Select the created and MRS Hudi data connections and the migration resource group for which the network connection has been configured.

Figure 4 Selecting data connections and a migration resource group

If no data connection is available, click Create to go to the Manage Data Connections page of the Management Center console and click Create Data Connection to create a connection. For details, see Configuring DataArts Studio Data Connection Parameters.
Configure source parameters.
- Topic
  Enter a Kafka topic to be migrated.
  Figure 5 Entering a Kafka topic
- Data Format
  Format of the message content in the source Kafka topic. DataArts Migration can process the following types of messages:
  - JSON: Messages can be parsed in JSON format.
  - CSV: Messages can be parsed using specified separators in CSV format.
  - TEXT: The entire message is synchronized as text.
- Consumer Group ID
  A consumer subscribes to a topic. A consumer group consists of one or more consumers. DataArts Migration allows you to specify the Kafka consumer group to which a consumption action belongs.
- Source Kafka Attributes
  You can set Kafka attributes and add the properties. prefix. The job will automatically remove the prefix and transfer the attributes to the Kafka client. For details about the parameters, see the configuration descriptions in the Kafka documentation.

Configure destination parameters.

Figure 6 Hudi destination parameters
Click to enlarge

Basic Configuration
Set the database to which the destination Hudi table belongs.
Destination Table
Set the name of the destination Hudi table.
Data Storage Path
Warehouse path when tables are automatically created in Hudi. A subdirectory is created in the warehouse path for each table. You can enter an HDFS or OBS path. The path format is as follows:
- OBS path: obs://bucket/warehouse
- HDFS path: /tmp/warehouse

Global Hudi Table Attribute Configuration

Some advanced functions can be configured using parameters. For details, see Hudi advanced parameters.

**Table 5** Hudi advanced parameters
Parameter	Type	Default Value	Unit	Description
index.type	string	BLOOM	N/A	Index type of the Hudi table BLOOM and BUCKET indexes are supported. If a large amount of data need to be migrated, BUCKET indexes are recommended for better performance.
hoodie.bucket.index.num.buckets	int	256	Count	Number of buckets within a Hudi table partition NOTE: When using Hudi BUCKET tables, you need to set the number of buckets for a table partition. The number of buckets affects the table performance. Number of buckets for a non-partitioned table = MAX(Data volume of the table (GB)/2 GB x 2, rounded up, 4) Number of buckets for a partitioned table = MAX(Data volume of a partition (GB)/2 GB x 2, rounded up, 1) Pay attention to the following: The total data volume of a table, instead of the compressed size, is used. An even number of buckets is recommended. The minimum number of buckets should be 4 for a non-partitioned table and 1 for a partitioned table.
changelog.enabled	boolean	false	N/A	Whether to enable the Hudi ChangeLog function. If this function is enabled, the migration job can output DELETE and UPDATE BEFORE data.
logical.delete.enabled	boolean	true	N/A	Whether to enable logical deletion. If the ChangeLog function is enabled, logical deletion must be disabled.
hoodie.write.liststatus.optimized	boolean	true	N/A	Whether to enable liststatus optimization when log files are written. If the migration job involves large tables or a large amount of partitioned data, the list operation is time-consuming during startup, which may cause job startup timeout. You are advised to disable this function.
hoodie.index.liststatus.optimized	boolean	false	N/A	Whether to enable liststatus optimization during data locating. If the migration job involves large tables or a large amount of partitioned data, the list operation is time-consuming during startup, which may cause job startup timeout. You are advised to disable this function.
compaction.async.enabled	boolean	true	N/A	Whether to enable asynchronous compaction. The compaction operation affects the writing performance of real-time jobs. If you use an external compaction operation, you can set this parameter to false to disable compaction for real-time processing migration jobs.
compaction.schedule.enabled	boolean	true	N/A	Whether to generate compaction plans. Compaction plans must be generated by this service and can be executed by Spark.
compaction.delta_commits	int	5	Count	Compaction request generation frequency Lowering the compaction request generation frequency reduces the compaction frequency and improves job performance. If there is a small volume of incremental data to be synchronized to Hudi, you can set a larger value for this parameter. NOTE: For example, if this parameter is set to 40, a compaction request is generated for every 40 commits. Since DataArts Migration generates a commit every minute, the interval between compaction requests is 40 minutes.
clean.async.enabled	boolean	true	N/A	Whether to clear data files of historical versions
clean.retain_commits	int	30	Count	Number of commits to retain Data files related to these commits will be retained for a period calculated by multiplying the number of specified commits by the interval between commits. You are advised to set this parameter to twice the value of compaction.delta_commits. NOTE: For example, if this parameter is set to 80 and since DataArts Migration generates a commit every minute, data files related to commits generated 80 minutes earlier are cleaned, and data files related to the recent 80 commits are retained.
hoodie.archive.automatic	boolean	true	N/A	Whether to age Hudi commit files
archive.min_commits	int	40	Count	Number of recent commits to keep when historical commits are archived to log files You are advised to set this parameter to one greater than clean.retain_commits. NOTE: For example, if this parameter is set to 81, the files related to the recent 81 commits are retained when an archive operation is triggered.
archive.max_commits	int	50	Count	Number of commits that triggers an archive operation You are advised to set this parameter to 20 greater than archive.min_commits. NOTE: For example, if the parameter is set to 101, an archive operation is triggered when the files of 101 commits are generated.

To achieve optimal performance for the migration job, you are advised to use an MOR table that uses Hudi BUCKET indexes and configure the number of buckets based on the actual data volume.
To ensure the stability of the migration job, you are advised to split the Hudi Compaction job into Spark jobs and execute them by MRS, and enable compaction plans to be generated for this migration job. For details, see

Refresh and check the mapping between the source and destination tables. In addition, you can modify table attributes, add additional fields, and use the automatic table creation capability to create tables in the destination Hudi database.

Figure 7 Mapping between source and destination tables
Click to enlarge

Synchronization Primary Key
The primary key must be set for Hudi tables. If the source table has no primary key, you must manually select the primary key during field mapping.
Partition Field
If no Hudi table is available at the destination, you can select a maximum of five mapped destination fields as partition fields. DataArts Migration automatically specifies partitions for a Hudi table when creating the table, and directories are automatically generated for the partitions when data is written to the Hudi table.

The field selection sequence affects the partition level. For example, if par1 and par2 are selected as partition fields, par1 is a level-1 partition and par2 is a level-2 partition. A maximum of five levels of partitions are supported.

Edit Destination Field

DataArts Migration automatically parses source messages based on the selected source message format and generates corresponding fields. You can customize the names, types, and values of the fields.

Field Name: name of the new field in the destination Hudi table
Field Type: Type of the new field in the destination Hudi table

Field Value: Value source of the new field in the destination Hudi table

**Table 6** Destination field value obtaining mode
Type	Example
Manually assigned value	Any character
Built-in variable	Kafka metadata, including six fields: __key__, __value__, __Topic__, __partition__, __offset__, and __timestamp__.
Field variable	Any field parsed from the source Kafka topic message
UDF	Flink built-in function used to transform data. The following are examples: CONCAT(CAST(NOW() as STRING), `col_name`) DATE_FORMAT(NOW(), 'yy') The field name must be enclosed in backquotes. For details about the built-in functions of Flink, see the official Flink documentation.

Configure task parameters.

**Table 7** Task parameters
Parameter	Description	Default Value
Execution Memory	Memory allocated for job execution, which automatically changes with the number of CPU cores	8GB
CPU Cores	Value range: 2 to 32 For each CPU core added, 4 GB execution memory and one concurrency are automatically added.	2
Maximum Concurrent Requests	Maximum number of jobs that can be concurrently executed. This parameter does not need to be configured and automatically changes with the number of CPU cores.	1
Custom attributes	You can add custom attributes to modify some job parameters and enable some advanced functions. For details, see Job Performance Optimization.	-

Submit and run the job.

After configuring the job, click Submit in the upper left corner to submit the job.

Figure 8 Submitting the job
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After submitting the job, click Start on the job development page. In the displayed dialog box, set required parameters and click OK.

Figure 9 Starting the job
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**Table 8** Parameters for starting the job
Parameter	Description
Offset Parameter	Earliest: Data consumption starts from the earliest offset of the Kafka topic. Latest: Data consumption starts from the latest offset of the Kafka topic. Time Range: Data consumption starts from the offset of the Kafka topic obtained based on the time.
Minimum Timestamp	This parameter is required if Offset Parameter is set to Time Range. It specifies the start time of synchronization. NOTE: If you set a time earlier than the earliest offset of Kafka messages, data consumption starts from the earliest offset by default.

Monitor the job.

On the job development page, click Monitor to go to the Job Monitoring page. You can view the status and log of the job, and configure alarm rules for the job. For details, see Real-Time Migration Job O&M.

Figure 10 Monitoring the job