:Configure a batch synchronization node by using the code editor (2.0)

Go to the DataStudio page

1. Log on to the DataWorks console.
2. In the left-side navigation pane, click Workspaces.
3. In the top navigation bar, select the region where the desired workspace resides. On the Workspaces page, find the workspace and click DataStudio in the Actions column. The DataStudio page appears.

Procedure

1. Step 1: Create a batch synchronization node
2. Step 2: Configure the batch synchronization node
   1. Establish network connections between the exclusive resource group for Data Integration and the data sources
   2. Convert from the codeless user interface (UI) to the code editor and apply a script template
   3. Compile code to configure the batch synchronization node, such as configuring mappings between source fields and destination fields and configuring channel control policies
   4. Configure scheduling properties for the batch synchronization node
3. Step 3: Commit and deploy the batch synchronization node

Step 1: Create a batch synchronization node

1. Create a workflow. For more information, see Manage workflows.
2. Create a batch synchronization node.
   You can use one of the following methods to create a batch synchronization node:

   • Method 1: Log on to the DataWorks console. In the left-side navigation pane, click Workspaces. On the Workspaces page, find the workspace in which you want to create a batch synchronization node and click DataStudio in the Actions column. In the Scheduled Workflow pane of the DataStudio page, find the created workflow and click its name. Right-click Data Integration and choose Create Node > Batch Synchronization.
   • Method 2: Log on to the DataWorks console. In the left-side navigation pane, click Workspaces. On the Workspaces page, find the workspace in which you want to create a batch synchronization node and click DataStudio in the Actions column. In the Scheduled Workflow pane of the DataStudio page, find the created workflow and double-click its name. In the Data Integration section of the workflow editing tab that appears, click Batch Synchronization.
3. In the Create Node dialog box, configure the parameters to create a batch synchronization node.

Step 2: Configure the batch synchronization node

1. Establish network connections between the exclusive resource group for Data Integration and the data sources.
   Select the source, destination, and exclusive resource group for Data Integration, and establish network connections between the resource group and the data sources.

   Note

   • You can use a batch synchronization node to synchronize data from tables in sharded databases to a single table. For more information, see Scenario: Configure an offline synchronization task for database sharding and table sharding.
   • If network connections between the exclusive resource group for Data Integration and the data sources cannot be established, you can configure the network connectivity as prompted or by referring to the related topic. For more information, see Establish a network connection between a resource group and a data source.
2. Convert from the codeless UI to the code editor.
   Click the Switch to Script Mode icon in the top toolbar.

   If no script is configured, you can click the icon in the top toolbar to apply a script template.

3. Compile code in the code editor to configure the batch synchronization node.
   The following figure shows the common settings that are used to configure a batch synchronization node in the code editor.

   Note

   • DataWorks provides default settings for the type and version parameters. You cannot change the default settings.
   • You can ignore the processor-related settings in the code. The settings are not required.

   
   1. Configure basic information for the Reader and Writer plug-ins and configure mappings between source fields and destination fields.
      Parameters that need to be configured for different Reader plug-ins and Writer plug-ins vary. For information about data source types, Reader plug-ins, and Writer plug-ins that are supported by batch synchronization, see Supported data source types, readers, and writers.

      You can perform the following operations by configuring the related parameters:

      • Operations related to the source

        Operation	Description
        Specify a filter condition	DataWorks allows you to specify a filter condition used to implement incremental synchronization when you configure specific types of Reader plug-ins. For example, when you configure MySQL Reader, you can specify such a filter condition. You can use a filter condition together with scheduling parameters to implement incremental synchronization. For information about how to configure a data synchronization node to synchronize only incremental data, see Configure a batch synchronization node to synchronize only incremental data. Note Support of different Reader plug-ins for incremental synchronization and implementation of incremental synchronization vary based on the Reader plug-in type. For more information, see the related topic. If you do not specify a filter condition when you configure a Reader plug-in that supports parameters related to incremental synchronization, full data is synchronized by default. When you configure scheduling properties for a batch synchronization node, you can assign values to the variables that you specified in the filter condition. You can configure scheduling parameters for the batch synchronization node to enable full or incremental data in the source to be written to the related time-based partitions in the destination tables. For more information, see Overview of scheduling parameters. The syntax for the filter condition that is used to implement incremental synchronization is almost the same as the syntax supported by a database. During data synchronization, the batch synchronization node uses a complete SQL statement that is obtained based on the filter condition to extract data from the source.
        Specify a shard key used to shard data in a relational database	A shard key specifies a field based on which source data is sharded. After you specify a shard key, source data is sharded and distributed to multiple shards. This way, the batch synchronization node can run parallel threads to read the data in batches. Note We recommend that you specify the name of the primary key column of a source table as the shard key. This way, data can be evenly distributed to different shards based on the primary key column, instead of being intensively distributed only to specific shards. A shard key can be used to shard data only of an integer data type. If you use a shard key to shard data of an unsupported data type, the batch synchronization node ignores the shard key that you specified and uses a single thread to read data. If no shard key is specified, a data synchronization node uses a single thread to read data. Support of Reader plug-ins for the configuration of a shard key varies based on the Reader plug-in type. The instructions provided in this topic are for reference only. You can refer to the topic for a Reader plug-in to check whether the Reader plug-in supports the configuration of a shard key. For information about the data source types, Reader plug-ins, and Writer plug-ins that are supported by batch synchronization, see Supported data source types, readers, and writers.
        Add fields to a source table and assign values to the fields	You can add fields to a source table and assign values to the fields. When the batch synchronization node is run, the added fields are synchronized to the related destination table. The fields can be constants and variables that are enclosed in single quotation marks ('), such as '123' and '${Variable name}'. If you add variables to a source table as fields, you can assign values to the variables when you configure scheduling properties for the batch synchronization node. For more information about scheduling parameters, see Overview of scheduling parameters.
        Edit fields in a source table	You can use a function that is supported by the source to process fields in a source table. For example, you can use the Max(id) function to implement synchronization of data in the row with the largest ID in the source table. Note Functions are not supported if you configure a batch synchronization node that uses MaxCompute Reader.

      • Operations related to the destination

        Operation	Description
        Configure SQL statements that you want to execute before and after data synchronization	DataWorks allows you to execute SQL statements before and after data is written to specific types of destinations. For example, when you configure a batch synchronization node that uses MySQL Writer, you can configure the SQL statement truncate table tablename as a statement to be executed before data is written to the destination. This statement is used to delete existing data in a specified table. You can also configure an SQL statement as a statement to be executed after data is written to the destination.
        Specify the write mode that is used when a conflict occurs	You can specify the write mode that is used to write data to the destination when a conflict, such as a path conflict or primary key conflict, occurs. The configuration varies based on the attributes of destinations and the support of Writer plug-ins. To configure this item, refer to the topic for the related Writer plug-in.

   2. Configure channel control policies.
      You can configure channel control policies in the setting field, such as the maximum number of parallel threads that can be used for data synchronization, the maximum transmission rate, and settings for dirty data records.

      Parameter	Description
      executeMode	Specifies whether to enable the distributed execution mode for the batch synchronization node. Valid values: distribute: Enables the distributed execution mode. If you enable the distributed execution mode for the batch synchronization node, the system splits the node into slices and distributes them to multiple Elastic Compute Service (ECS) instances for parallel running. In this case, the more ECS instances, the higher the data synchronization speed. null: Disables the distributed execution mode. If you do not enable the distributed execution mode for the batch synchronization node, the specified maximum number of parallel threads is used only for a single ECS instance to run the node. Important If your exclusive resource group contains only one ECS instance, we recommend that you do not run your batch synchronization node in distributed execution mode. If one ECS instance can meet your business requirements for data transmission speed, you do not need to enable the distributed execution mode. This can simplify the execution mode of your node. The distributed execution mode can be enabled only if the maximum number of parallel threads that you specified is greater than or equal to 8. Whether a batch synchronization node supports the distributed execution mode varies based on the data source type. For more information, see the topics for Reader plug-ins and Writer plug-ins.
      concurrent	The maximum number of parallel threads that the batch synchronization node uses to read data from the source or write data to the destination. Note The actual number of parallel threads that are used during data synchronization may be less than or equal to the specified threshold due to the specifications of the exclusive resource group for Data Integration. You are charged for the exclusive resource group for Data Integration based on the number of parallel threads that are used. For more information, see Performance metrics.
      throttle	Specifies whether to enable throttling. true: Enables throttling. If you enable throttling, you can specify a maximum transmission rate to prevent heavy read workloads on the source. The minimum value of this parameter is 1 MB/s. Note If you set the throttle parameter to true, you must also configure the mbps parameter. The mbps parameter specifies the maximum transmission rate that is allowed. false: Disables throttling. If you do not enable throttling, data is transmitted at the maximum transmission rate allowed by the hardware based on the specified maximum parallel threads. Note The bandwidth is a metric provided by Data Integration and does not represent the actual traffic of an elastic network interface (ENI). In most cases, the ENI traffic is one to two times the channel traffic. The actual ENI traffic depends on the serialization of the data storage system.
      errorLimit	The maximum number of dirty data records allowed. Important If a large amount of dirty data is generated during data synchronization, the overall data synchronization speed is affected. If this parameter is not configured, dirty data records are allowed during data synchronization, and the batch synchronization node can continue to run if dirty data records are generated. If you set this parameter to 0, no dirty data records are allowed. If dirty data records are generated during data synchronization, the batch synchronization node fails. If you specify a value that is greater than 0 for this parameter, the following situations occur: If the number of dirty data records that are generated during data synchronization is less than or equal to the value that you specified, the dirty data records are ignored and are not written to the destination, and the batch synchronization node continues to run. If the number of dirty data records that are generated during data synchronization is greater than the value that you specified, the batch synchronization node fails. Note Dirty data indicates data that is meaningless to business, does not match the specified data type, or leads to an exception during data synchronization. If an exception occurs when a single data record is written to the destination, the data record is considered as dirty data. Data records that fail to be written to a destination are considered as dirty data. For example, when a batch synchronization node attempts to write VARCHAR-type data in a source to an INT-type field in a destination, a data conversion error occurs, and the data fails to be written to the destination. In this case, the data is dirty data. When you configure a batch synchronization node, you can control whether dirty data is allowed. You can also specify the maximum number of dirty data records that are allowed during data synchronization. If the number of generated dirty data records exceeds the upper limit that you specified, the synchronization node fails.

      Note In addition to the preceding configurations, the overall data synchronization speed of a batch synchronization node is also affected by factors such as the performance of the source and the network environment for data synchronization. For information about the data synchronization speed and performance tuning of a batch synchronization node, see Scenario: Optimize the performance of batch synchronization nodes.
4. Click Next Step to configure scheduling properties for the batch synchronization node.
   If you want DataWorks to periodically schedule your batch synchronization node, you must configure scheduling properties for the node. This substep describes how to configure scheduling properties for a batch synchronization node. For information about how to use scheduling parameters, see Overview of the batch synchronization feature.

   • Configure scheduling parameters: If you define variables in the preceding configurations, the scheduling parameters are assigned to the variables as values.
   • Configure time properties: The time properties define the mode in which the batch synchronization node is scheduled in the production environment. In the section in which you configure time properties for the batch synchronization node, you can configure attributes such as the instance generation mode, scheduling type, and scheduling cycle for the node.
   • Configure the resource property: The resource property defines the exclusive resource group for scheduling that is used to issue the batch synchronization node to the related exclusive resource group for Data Integration. You can select the exclusive resource group for scheduling that you want to use.
     Note DataWorks uses resource groups for scheduling to issue batch synchronization nodes in Data Integration to resource groups for Data Integration and uses the resource groups for Data Integration to run the nodes. You are charged for using the resource groups for scheduling to schedule batch synchronization nodes. For more information about the node issuing mechanism, see Mechanism for issuing nodes.

Step 3: Commit and deploy the batch synchronization node

If you want DataWorks to periodically run the batch synchronization node, you must deploy the node to the production environment. For more information about how to deploy a node, see Deploy nodes.