AnalyticDB for MySQL:Use data synchronization to synchronize data to Data Lakehouse Edition (recommended)

Overview

AnalyticDB for MySQL Data Lakehouse Edition (V3.0) provides the data synchronization feature that allows you to synchronize data from ApsaraMQ for Kafka to a Data Lakehouse Edition (V3.0) cluster in real time from a specified point in time. This feature helps meet requirements such as near-real-time data ingestion, full data archiving, and elastic analysis.

Prerequisites

• An AnalyticDB for MySQL Data Lakehouse Edition (V3.0) cluster is created. For more information, see Create a cluster.

• A job resource group is created. For more information, see Create a resource group.

• A database account is created.

  • If you use an Alibaba Cloud account, you must create a privileged database account. For more information, see the "Create a privileged account" section of the Create a database account topic.

  • If you use a Resource Access Management (RAM) user, you must create a privileged account and a standard account and associate the standard account with the RAM user. For more information, see Create a database account and Associate or disassociate a database account with or from a RAM user.

• An ApsaraMQ for Kafka instance is created in the same region as the AnalyticDB for MySQL Data Lakehouse Edition (V3.0) cluster. In this instance, a topic is created and messages are sent. For more information, see Getting started with ApsaraMQ for Kafka.

Usage notes

After the data of a topic expires, the system automatically deletes the data. If the topic data expires and the data synchronization job fails, the system fails to read the data that is deleted when you restart the data synchronization job. As a result, the data may be lost. If your data synchronization job fails, we recommend that you increase the lifecycle of topic data and contact technical support.

Procedure

• Step 1: Create a data source.

• Step 2: Create a data synchronization job.

• Step 3: Start the data synchronization job.

• Step 4: Analyze data.

• Step 5: (Optional) Manage the data source.

Create a data source

1. Log on to the AnalyticDB for MySQL console. In the upper-left corner of the console, select a region. In the left-side navigation pane, click Clusters. On the Data Lakehouse Edition (V3.0) tab, find the cluster that you want to manage and click the cluster ID.

2. In the left-side navigation pane, choose Data Ingestion > Data Sources.

3. In the upper-right corner of the page, click Create Data Source.

4. On the Create Data Source page, configure the parameters that are described in the following table.

   Parameter	Description
   Data Source Type	The data source. Select Kafka.
   Data Source Name	The name of the data source. By default, the system generates a name based on the data source type and the current time. You can modify the name based on your business requirements.
   Data Source Description	The description of the data source. For example, you can enter the use case and business limits.
   Deployment Mode	Only Alibaba Cloud Instance is supported.
   Kafka Instance	The ID of the ApsaraMQ for Kafka instance. Log on to the ApsaraMQ for Kafka console and go to the Instances page to view the instance ID.
   Kafka Topic	The name of the topic that you create in the ApsaraMQ for Kafka instance. Log on to the ApsaraMQ for Kafka console and go to the Topics page of the instance to view the topic name.
   Message Data Format	The data format of messages. Only JSON is supported.

5. Click Create.

Create a data synchronization job

1. In the left-side navigation pane, click Data Synchronization.

2. In the upper-right corner of the page, click Create Synchronization Job.

3. On the Kafka Data Source tab of the Create Synchronization Job page, configure the parameters in the Source and Destination Settings, Destination Database and Table Settings, and Synchronization Settings sections.

   • The following table describes the parameters in the Source and Destination Settings section.

     Parameter	Description
     Data Job Name	The name of the data synchronization job. By default, the system generates a name based on the data source type and the current time. You can modify the name based on your business requirements.
     Data Source	The data source. You can select an existing ApsaraMQ for Kafka data source or create a data source.
     Destination Type	The data storage type in AnalyticDB for MySQL. Only Data Lake - OSS Storage is supported.
     OSS Path	The Object Storage Service (OSS) storage path of the AnalyticDB for MySQL cluster data. Important All buckets that reside in the same region as the AnalyticDB for MySQL cluster are displayed. You can select one of the buckets. Configure this parameter based on your business requirements. After you configure this parameter, it cannot be modified. We recommend that you select an empty directory that does not have nested relationships with the directories of other data synchronization jobs. This prevents historical data from being overwritten. For example, assume that the involved OSS storage paths of two data synchronization jobs are oss://adb_demo/test/sls1/ and oss://adb_demo/test/. In this case, data overwriting occurs during data synchronization because these two paths have nested relationships with each other.

   • The following table describes the parameters in the Destination Database and Table Settings section.

     Parameter	Description
     Database Name	The name of the destination database in the AnalyticDB for MySQL cluster. If no database that uses the same name exists, a database is created. If a database that uses the same name already exists, data is synchronized to the existing database. For information about the naming conventions for databases, see Limits.
     Table Name	The name of the destination table in the AnalyticDB for MySQL cluster. If no table that uses the same name exists in the database, a table is created. If a table that uses the same name already exists in the database, data fails to be synchronized. For information about the naming conventions for tables, see Limits.
     Sample Data	The latest data that is automatically obtained from the ApsaraMQ for Kafka topic. Note Data in the ApsaraMQ for Kafka topic must be in the JSON format. Otherwise, an error is reported during data synchronization.
     Parsed JSON Layers	The number of layers that are parsed for nested JSON fields. Valid values: 0: Nested JSON fields are not parsed. 1 (default): One layer is parsed. 2: Two layers are parsed. 3: Three layers are parsed. 4: Four layers are parsed. For information about how nested JSON fields are parsed, see the "Examples of schema fields parsed with different numbers of layers" section of this topic.
     Schema Field Mapping	The information about schema fields that are parsed from the sample data. You can modify the destination field name or type and add or remove fields.
     Partition Key Settings	The partition field settings for the destination table. To ensure that data is ingested and queries are executed as expected, we recommend that you configure partitions based on the log time or business logic. If you do not configure partitions, no partitions exist in the destination table. Valid values for the Format Processing Method field: Formatted Time: You must specify a time field value such as a timestamp. The destination table is partitioned based on the specified time field and the format of the time field. For example, if the source partition field is gmt_created and the format of the source field is yyyy-MM-dd, you can set the destination partition field to year and the format of the destination partition field to yyyy. This specifies that the destination table is partitioned by year. Specified Partition Field: The destination table is partitioned based on the specified partition field.

   • The following table describes the parameters in the Synchronization Settings section.

     Parameter	Description
     Starting Consumer Offset for Incremental Synchronization	The point in time from which the system consumes ApsaraMQ for Kafka data when the data synchronization job starts. Valid values: Earliest Offset (begin_cursor): The system consumes ApsaraMQ for Kafka data from the point in time at which the earliest data record was generated. Latest Offset (end_cursor): The system consumes ApsaraMQ for Kafka data from the point in time at which the latest data record was generated. Custom Offset: You can select a point in time. Then, the system consumes ApsaraMQ for Kafka data from the first data entry that is generated as of the selected point in time.
     Job Resource Group	The job resource group that runs the data synchronization job.
     ACUs for Incremental Synchronization	The number of AnalyticDB compute units (ACUs) that are required for the job resource group to run the data synchronization job. The value ranges from 2 to the maximum number of computing resources that are available in the job resource group. To improve the stability and performance of data ingestion, we recommend that you specify a larger number of ACUs. Note When you create a data synchronization job in a job resource group, elastic resources in the resource group are used, and the system deducts the used resources from the resource group. For example, assume that a job resource group has 48 ACUs of reserved computing resources and a synchronization job that consumes 8 ACUs is created. When you create another synchronization job in the resource group, you can select up to 40 ACUs.
     Advanced Settings	The personalized settings for the data synchronization job. If you want to configure personalized settings, contact technical support.

4. Click Submit.

Start the data synchronization job

1. On the Data Synchronization page, find the data synchronization job that you created and click Start in the Actions column.

2. In the upper-right corner of the page, click Search. If the state of the job changes to Starting, the data synchronization job is started.

Analyze data

After the data synchronization job is completed, you can use Spark JAR Development to analyze the data that is synchronized to the AnalyticDB for MySQL cluster. For more information about Spark development, see Spark editor and Overview of Spark application development.

1. In the left-side navigation pane, choose Job Development > Spark JAR Development.

2. Enter SQL statements in the default template and click Run Now.

   -- Here is just an example of SparkSQL. Modify the content and run your spark program.
   
   conf spark.driver.resourceSpec=medium;
   conf spark.executor.instances=2;
   conf spark.executor.resourceSpec=medium;
   conf spark.app.name=Spark SQL Test;
   conf spark.adb.connectors=oss;
   
   -- Here are your sql statements
   show tables from lakehouse20220413156_adbTest;

3. (Optional) On the Applications tab, find an application and click Log in the Actions column to view the Spark SQL running log of the application.

Manage the data source

Examples of schema fields parsed with different numbers of layers

{
  "name" : "zhangle",
  "age" : 18,
  "device" : {
    "os" : {
        "test":lag,
        "member":{
             "fa":zhangsan,
             "mo":limei
       }
     },
    "brand" : "none",
    "version" : "11.4.2"
  }
}

The preceding JSON data can be parsed with different numbers of layers.

No parsing

One-layer parsing

Two-layer parsing

Three-layer parsing

Four-layer parsing