实现 Flink CDC MySQL Binlog
简介
Flink CDC（Change Data Capture）是 Apache Flink 提供的一种用于抓取和处理数据变化的功能。通过 Flink CDC，我们可以将数据源中的数据变化捕获并实时处理，从而实现数据的实时同步、ETL（Extract, Transform, Load）等应用场景。本文将介绍如何使用 Flink CDC 来处理 MySQL Binlog 变化数据。
步骤
| 步骤 | 描述 |
|---|---|
| 步骤1 | 设置 Flink CDC 环境 |
| 步骤2 | 导入依赖 |
| 步骤3 | 编写 Flink Job |
| 步骤4 | 提交 Flink Job |
| 步骤5 | 监控 Flink Job 运行情况 |
步骤详解
步骤1：设置 Flink CDC 环境
首先，我们需要在项目中引入 Flink CDC 的相关依赖，以及 MySQL Connector 的依赖。在项目的 pom.xml 文件中添加以下代码：
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-cdc</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>${mysql.version}</version>
</dependency>
其中 ${flink.version} 和 ${mysql.version} 根据实际情况替换为对应的版本号。
步骤2：导入依赖
在 Java 代码中导入所需的依赖：
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.cdc.CdcSource;
import org.apache.flink.streaming.connectors.cdc.debezium.DebeziumDeserializationSchema;
import org.apache.flink.streaming.connectors.cdc.debezium.DebeziumSourceFunction;
import org.apache.flink.streaming.connectors.cdc.debezium.DebeziumSourceFunctionWrapper;
import org.apache.flink.streaming.connectors.cdc.table.LookupConfig;
import org.apache.flink.streaming.connectors.cdc.table.LookupConfigFactory;
import org.apache.flink.streaming.connectors.cdc.table.TableLookupFunction;
import org.apache.flink.streaming.connectors.cdc.table.TableSchemaConverter;
import org.apache.flink.streaming.connectors.cdc.table.TableLookupFunctionFactory;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.bridge.java.table.LookupTableSource;
import org.apache.flink.table.api.bridge.java.table.TableEnvironmentExtension;
import org.apache.flink.table.api.bridge.java.table.TableEnvironmentFactory;
import org.apache.flink.table.api.bridge.java.table.TableLookupJoinUtil;
import org.apache.flink.table.api.bridge.java.table.TableSinkFactory;
import org.apache.flink.table.api.bridge.java.table.TableSourceFactory;
import org.apache.flink.table.api.config.ExecutionConfigOptions;
import org.apache.flink.table.catalog.Catalog;
import org.apache.flink.table.catalog.CatalogFactory;
import org.apache.flink.table.catalog.GenericInMemoryCatalog;
import org.apache.flink.table.connector.source.LookupTableSourceFunction;
import org.apache.flink.table.connector.source.LookupTableSourceFunctionFactory;
import org.apache.flink.table.types.DataType;
import org.apache.flink.table.types.logical.LogicalType;
import org.apache.flink.table.types.logical.RowType;
步骤3：编写 Flink Job
在 Flink Job 中，我们需要设置 MySQL 连接信息，并创建 CDC Source。以下是一个示例：
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.enableCheckpointing(10000);
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.seconds(5)));
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
EnvironmentSettings settings = EnvironmentSettings.newInstance()
.useBlinkPlanner()
.inStreamingMode()
.build();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, settings);
String server = "mysql-server";
int port = 3306;
String username = "root";
String password = "password";
String database = "my_database";
String serverId = "server-id";
CdcSource.Builder<byte[], DebeziumDeserialization
