SQLServer CDC Connector

The SQLServer CDC connector allows for reading snapshot data and incremental data from SQLServer database. This document describes how to setup the SQLServer CDC connector to run SQL queries against SQLServer databases.

Dependencies

In order to setup the SQLServer CDC connector, the following table provides dependency information for both projects using a build automation tool (such as Maven or SBT) and SQL Client with SQL JAR bundles.

Maven dependency

<dependency>
  <groupId>com.ververica</groupId>
  <artifactId>flink-connector-sqlserver-cdc</artifactId>
  <!-- The dependency is available only for stable releases, SNAPSHOT dependencies need to be built based on master or release- branches by yourself. -->
  <version>3.0-SNAPSHOT</version>
</dependency>

SQL Client JAR

Download link is available only for stable releases.

Download flink-sql-connector-sqlserver-cdc-3.0-SNAPSHOT.jar and put it under <FLINK_HOME>/lib/.

Note: flink-sql-connector-sqlserver-cdc-XXX-SNAPSHOT version is the code corresponding to the development branch. Users need to download the source code and compile the corresponding jar. Users should use the released version, such as flink-sql-connector-sqlserver-cdc-2.2.1.jar, the released version will be available in the Maven central warehouse.

Setup SQLServer Database

A SQL Server administrator must enable change data capture on the source tables that you want to capture. The database must already be enabled for CDC. To enable CDC on a table, a SQL Server administrator runs the stored procedure sys.sp_cdc_enable_table for the table.

Prerequisites:

  • CDC is enabled on the SQL Server database.

  • The SQL Server Agent is running.

  • You are a member of the db_owner fixed database role for the database.

Procedure:

  • Connect to the SQL Server database by database management studio.

  • Run the following SQL statement to enable CDC on the table.

USE MyDB
GO

EXEC sys.sp_cdc_enable_table
@source_schema = N'dbo',     -- Specifies the schema of the source table.
@source_name   = N'MyTable', -- Specifies the name of the table that you want to capture.
@role_name     = N'MyRole',  -- Specifies a role MyRole to which you can add users to whom you want to grant SELECT permission on the captured columns of the source table. Users in the sysadmin or db_owner role also have access to the specified change tables. Set the value of @role_name to NULL, to allow only members in the sysadmin or db_owner to have full access to captured information.
@filegroup_name = N'MyDB_CT',-- Specifies the filegroup where SQL Server places the change table for the captured table. The named filegroup must already exist. It is best not to locate change tables in the same filegroup that you use for source tables.
@supports_net_changes = 0
GO

  • Verifying that the user has access to the CDC table

--The following example runs the stored procedure sys.sp_cdc_help_change_data_capture on the database MyDB:
USE MyDB;
GO
EXEC sys.sp_cdc_help_change_data_capture
GO

The query returns configuration information for each table in the database that is enabled for CDC and that contains change data that the caller is authorized to access. If the result is empty, verify that the user has privileges to access both the capture instance and the CDC tables.

How to create a SQLServer CDC table

The SqlServer CDC table can be defined as following:

-- register a SqlServer table 'orders' in Flink SQL
CREATE TABLE orders (
    id INT,
    order_date DATE,
    purchaser INT,
    quantity INT,
    product_id INT,
    PRIMARY KEY (id) NOT ENFORCED
) WITH (
    'connector' = 'sqlserver-cdc',
    'hostname' = 'localhost',
    'port' = '1433',
    'username' = 'sa',
    'password' = 'Password!',
    'database-name' = 'inventory',
    'table-name' = 'dob.orders'
);

-- read snapshot and binlogs from orders table
SELECT * FROM orders;

Connector Options

Option Required Default Type Description
connector required (none) String Specify what connector to use, here should be 'sqlserver-cdc'.
hostname required (none) String IP address or hostname of the SQLServer database.
username required (none) String Username to use when connecting to the SQLServer database.
password required (none) String Password to use when connecting to the SQLServer database.
database-name required (none) String Database name of the SQLServer database to monitor.
table-name required (none) String Table name of the SQLServer database to monitor, e.g.: "db1.table1"
port optional 1433 Integer Integer port number of the SQLServer database.
server-time-zone optional UTC String The session time zone in database server, e.g. "Asia/Shanghai".
scan.incremental.snapshot.enabled optional true Boolean Whether enable parallelism snapshot.
chunk-meta.group.size optional 1000 Integer The group size of chunk meta, if the meta size exceeds the group size, the meta will be divided into multiple groups.
chunk-key.even-distribution.factor.lower-bound optional 0.05d Double The lower bound of chunk key distribution factor. The distribution factor is used to determine whether the table is evenly distribution or not. The table chunks would use evenly calculation optimization when the data distribution is even, and the query for splitting would happen when it is uneven. The distribution factor could be calculated by (MAX(id) - MIN(id) + 1) / rowCount.
chunk-key.even-distribution.factor.upper-bound optional 1000.0d Double The upper bound of chunk key distribution factor. The distribution factor is used to determine whether the table is evenly distribution or not. The table chunks would use evenly calculation optimization when the data distribution is even, and the query for splitting would happen when it is uneven. The distribution factor could be calculated by (MAX(id) - MIN(id) + 1) / rowCount.
debezium.* optional (none) String Pass-through Debezium's properties to Debezium Embedded Engine which is used to capture data changes from SQLServer. For example: 'debezium.snapshot.mode' = 'initial_only'. See more about the Debezium's SQLServer Connector properties
scan.incremental.close-idle-reader.enabled optional false Boolean Whether to close idle readers at the end of the snapshot phase.
The flink version is required to be greater than or equal to 1.14 when 'execution.checkpointing.checkpoints-after-tasks-finish.enabled' is set to true.
If the flink version is greater than or equal to 1.15, the default value of 'execution.checkpointing.checkpoints-after-tasks-finish.enabled' has been changed to true, so it does not need to be explicitly configured 'execution.checkpointing.checkpoints-after-tasks-finish.enabled' = 'true'

Available Metadata

The following format metadata can be exposed as read-only (VIRTUAL) columns in a table definition.

Key DataType Description
table_name STRING NOT NULL Name of the table that contain the row.
schema_name STRING NOT NULL Name of the schema that contain the row.
database_name STRING NOT NULL Name of the database that contain the row.
op_ts TIMESTAMP_LTZ(3) NOT NULL It indicates the time that the change was made in the database.
If the record is read from snapshot of the table instead of the change stream, the value is always 0.

Limitation

Can’t perform checkpoint during scanning snapshot of tables

During scanning snapshot of database tables, since there is no recoverable position, we can’t perform checkpoints. In order to not perform checkpoints, SqlServer CDC source will keep the checkpoint waiting to timeout. The timeout checkpoint will be recognized as failed checkpoint, by default, this will trigger a failover for the Flink job. So if the database table is large, it is recommended to add following Flink configurations to avoid failover because of the timeout checkpoints:

execution.checkpointing.interval: 10min
execution.checkpointing.tolerable-failed-checkpoints: 100
restart-strategy: fixed-delay
restart-strategy.fixed-delay.attempts: 2147483647

The extended CREATE TABLE example demonstrates the syntax for exposing these metadata fields:

CREATE TABLE products (
    table_name STRING METADATA  FROM 'table_name' VIRTUAL,
    schema_name STRING METADATA  FROM 'schema_name' VIRTUAL,
    db_name STRING METADATA FROM 'database_name' VIRTUAL,
    operation_ts TIMESTAMP_LTZ(3) METADATA FROM 'op_ts' VIRTUAL,
    id INT NOT NULL,
    name STRING,
    description STRING,
    weight DECIMAL(10,3)
) WITH (
    'connector' = 'sqlserver-cdc',
    'hostname' = 'localhost',
    'port' = '1433',
    'username' = 'sa',
    'password' = 'Password!',
    'database-name' = 'inventory',
    'table-name' = 'dbo.products'
);

Features

Exactly-Once Processing

The SQLServer CDC connector is a Flink Source connector which will read database snapshot first and then continues to read change events with exactly-once processing even failures happen. Please read How the connector works.

Startup Reading Position

The config option scan.startup.mode specifies the startup mode for SQLServer CDC consumer. The valid enumerations are:

  • initial (default): Takes a snapshot of structure and data of captured tables; useful if topics should be populated with a complete representation of the data from the captured tables.

  • initial-only: Takes a snapshot of structure and data like initial but instead does not transition into streaming changes once the snapshot has completed.

  • latest-offset: Takes a snapshot of the structure of captured tables only; useful if only changes happening from now onwards should be propagated to topics.

Note: the mechanism of scan.startup.mode option relying on Debezium’s snapshot.mode configuration. So please do not use them together. If you specific both scan.startup.mode and debezium.snapshot.mode options in the table DDL, it may make scan.startup.mode doesn’t work.

Single Thread Reading

The SQLServer CDC source can’t work in parallel reading, because there is only one task can receive change events.

DataStream Source

The SQLServer CDC connector can also be a DataStream source. You can create a SourceFunction as the following shows:

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import com.ververica.cdc.connectors.sqlserver.SqlServerSource;

public class SqlServerSourceExample {
  public static void main(String[] args) throws Exception {
    SourceFunction<String> sourceFunction = SqlServerSource.<String>builder()
      .hostname("localhost")
      .port(1433)
      .database("sqlserver") // monitor sqlserver database
      .tableList("dbo.products") // monitor products table
      .username("sa")
      .password("Password!")
      .deserializer(new JsonDebeziumDeserializationSchema()) // converts SourceRecord to JSON String
      .build();

    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    env
      .addSource(sourceFunction)
      .print().setParallelism(1); // use parallelism 1 for sink to keep message ordering

    env.execute();
  }
}

The SQLServer CDC incremental connector (after 2.4.0) can be used as the following shows:

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import com.ververica.cdc.connectors.base.options.StartupOptions;
import com.ververica.cdc.connectors.sqlserver.source.SqlServerSourceBuilder;
import com.ververica.cdc.connectors.sqlserver.source.SqlServerSourceBuilder.SqlServerIncrementalSource;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;

public class SqlServerIncrementalSourceExample {
    public static void main(String[] args) throws Exception {
        SqlServerIncrementalSource<String> sqlServerSource =
                new SqlServerSourceBuilder()
                        .hostname("localhost")
                        .port(1433)
                        .databaseList("inventory")
                        .tableList("dbo.products")
                        .username("sa")
                        .password("Password!")
                        .deserializer(new JsonDebeziumDeserializationSchema())
                        .startupOptions(StartupOptions.initial())
                        .build();

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // enable checkpoint
        env.enableCheckpointing(3000);
        // set the source parallelism to 2
        env.fromSource(
                        sqlServerSource,
                        WatermarkStrategy.noWatermarks(),
                        "SqlServerIncrementalSource")
                .setParallelism(2)
                .print()
                .setParallelism(1);

        env.execute("Print SqlServer Snapshot + Change Stream");
    }
}

Note: Please refer Deserialization for more details about the JSON deserialization.

Data Type Mapping

SQLServer type Flink SQL type
char(n) CHAR(n)
varchar(n)
nvarchar(n)
nchar(n) 		VARCHAR(n)
text
ntext
xml 		STRING
decimal(p, s)
money
smallmoney 		DECIMAL(p, s)
numeric NUMERIC
float
real 		DOUBLE
bit BOOLEAN
int INT
tinyint SMALLINT
smallint SMALLINT
bigint BIGINT
date DATE
time(n) TIME(n)
datetime2
datetime
smalldatetime 		TIMESTAMP(n)
datetimeoffset TIMESTAMP_LTZ(3)