flink通过flink-connector-tidb-cdc使用streamapi方式连接tidb, 并展示相关代码
时间: 2024-01-19 08:02:53 浏览: 306
下面是使用 Flink Connector TiDB CDC 通过 Stream API 连接 TiDB 的示例代码:
```java
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.connectors.tidb.JdbcConnectionOptions;
import org.apache.flink.streaming.connectors.tidb.TiDBOptions;
import org.apache.flink.streaming.connectors.tidb.TiDBSink;
import org.apache.flink.streaming.connectors.tidb.TiDBSource;
import org.apache.flink.streaming.connectors.tidb.TransactionIsolation;
import org.apache.flink.streaming.connectors.tidb.TiDBCatalog;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.bridge.java.internal.StreamTableEnvironmentImpl;
import org.apache.flink.types.Row;
import java.util.Properties;
public class TiDBStreamExample {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, settings);
// Define TiDB catalog
TiDBCatalog catalog = new TiDBCatalog("tidb_catalog", "default_database",
JdbcConnectionOptions.builder()
.withUrl("jdbc:mysql://tidb_ip:tidb_port/tidb_database_name?useSSL=false&useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC")
.withUsername("tidb_username")
.withPassword("tidb_password")
.build(),
TiDBOptions.builder().withDatabaseUrl("jdbc:mysql://tidb_ip:tidb_port/tidb_database_name").build());
tEnv.registerCatalog("tidb_catalog", catalog);
tEnv.useCatalog("tidb_catalog");
// Define TiDB source
TiDBSource source = TiDBSource.builder()
.setDatabaseName("tidb_database_name")
.setTableName("tidb_table_name")
.setOptions(TiDBOptions.builder()
.withDatabaseUrl("jdbc:mysql://tidb_ip:tidb_port/tidb_database_name")
.build())
.setPrimaryKey("id")
.setTransactionIsolation(TransactionIsolation.READ_COMMITTED)
.build();
// Create a data stream from TiDB source
DataStream<Row> stream = env.addSource(source);
// Define Flink Kafka producer
Properties props = new Properties();
props.setProperty("bootstrap.servers", "kafka_ip:kafka_port");
FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<String>(
"kafka_topic",
new SimpleStringSchema(),
props);
// Map the data stream to a string stream and send it to Kafka
DataStream<String> stringStream = stream.map(new MapFunction<Row, String>() {
@Override
public String map(Row row) throws Exception {
return row.toString();
}
});
stringStream.addSink(kafkaProducer);
// Define Flink Kafka consumer
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<String>(
"kafka_topic",
new SimpleStringSchema(),
props);
// Create a data stream from Kafka
DataStream<String> kafkaStream = env.addSource(kafkaConsumer);
// Convert the Kafka stream to a table and register it in the table environment
tEnv.createTemporaryView("kafka_table", kafkaStream, "value");
// Query the table and print the result to console
tEnv.sqlQuery("SELECT * FROM kafka_table").execute().print();
// Define TiDB sink
TiDBSink sink = TiDBSink.builder()
.setDatabaseName("tidb_database_name")
.setTableName("tidb_table_name")
.setOptions(TiDBOptions.builder()
.withDatabaseUrl("jdbc:mysql://tidb_ip:tidb_port/tidb_database_name")
.build())
.setPrimaryKey("id")
.build();
// Convert the Kafka stream back to a data stream of rows and write it to TiDB
DataStream<Row> rowStream = kafkaStream.map(new MapFunction<String, Row>() {
@Override
public Row map(String value) throws Exception {
String[] fields = value.split(",");
return Row.of(Integer.parseInt(fields[0]), fields[1], Double.parseDouble(fields[2]));
}
});
rowStream.addSink(sink);
env.execute("TiDB Stream Example");
}
}
```
在上面的示例代码中,我们首先定义了一个 TiDBCatalog 对象,用于连接 TiDB 数据库。然后,我们使用 TiDBSource.builder() 方法定义了一个 TiDB 数据源,用于从 TiDB 中读取数据。接着,我们使用 env.addSource(source) 方法创建了一个 Flink 数据流。我们还定义了一个 Flink Kafka 生产者,用于将数据流发送到 Kafka。为了将数据流转换为字符串流,我们使用了 map() 方法。然后,我们将字符串流发送到 Kafka。接着,我们定义了一个 Flink Kafka 消费者,用于从 Kafka 中读取数据。我们还将 Kafka 数据流转换为表,并在表环境中注册它。最后,我们使用 TiDBSink.builder() 方法定义了一个 TiDB 汇聚器,用于将数据流写入 TiDB 中。
请注意,在上面的示例代码中,我们使用了 TiDBCatalog 和 TiDBSource 类来连接 TiDB 数据库。这些类需要 TiDB Connector JAR 包的支持。如果您没有安装该 JAR 包,请按照以下步骤安装:
1. 访问 TiDB Connector JAR 包的下载页面:https://github.com/pingcap/tidb/releases/tag/v4.0.8
2. 下载适用于您的操作系统的 JAR 包
3. 将 JAR 包添加到您的项目依赖中
最后,记得将代码中的 tidb_ip、tidb_port、tidb_database_name、tidb_table_name、tidb_username 和 tidb_password 替换为实际的值。同样,将代码中的 kafka_ip、kafka_port 和 kafka_topic 替换为实际的值。
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