java flink 从kafka大主题拆分成多个小主题,根据key截取后相同的写入相同主题Kafka 和 MySQL ,实现程序
时间: 2023-12-10 18:41:06 浏览: 37
首先,我们需要使用 Flink Kafka Consumer 从大主题中读取数据。然后,我们可以使用 KeyedStream 将数据按照 key 分组,然后使用 Flink Kafka Producer 将数据写入多个小主题。同时,我们也可以将数据写入 MySQL 数据库。下面是一个简单的示例程序:
```java
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.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSink;
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;
import java.util.Properties;
public class KafkaSplitter {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 从大主题中读取数据
Properties kafkaProps = new Properties();
kafkaProps.setProperty("bootstrap.servers", "localhost:9092");
kafkaProps.setProperty("group.id", "my-group");
FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("big-topic", new SimpleStringSchema(), kafkaProps);
DataStream<String> stream = env.addSource(consumer);
// 根据 key 分组
DataStream<String> keyedStream = stream.keyBy(line -> line.split(",")[0]);
// 写入多个小主题
Properties producerProps = new Properties();
producerProps.setProperty("bootstrap.servers", "localhost:9092");
FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>("small-topic", new SimpleStringSchema(), producerProps, KafkaSerializationSchema::get);
keyedStream.addSink(kafkaProducer);
// 写入 MySQL 数据库
keyedStream.addSink(new MySQLSink());
env.execute("KafkaSplitter");
}
public static class MySQLSink extends RichSinkFunction<String> {
private Connection connection;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
Class.forName("com.mysql.jdbc.Driver");
connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/mydb", "username", "password");
}
@Override
public void close() throws Exception {
super.close();
if (connection != null) {
connection.close();
}
}
@Override
public void invoke(String line, Context context) throws Exception {
String[] fields = line.split(",");
String key = fields[0];
String value = fields[1];
PreparedStatement statement = connection.prepareStatement("INSERT INTO mytable (key, value) VALUES (?, ?)");
statement.setString(1, key);
statement.setString(2, value);
statement.executeUpdate();
}
}
}
```
在上面的示例程序中,我们使用了 Flink Kafka Consumer 和 Flink Kafka Producer 来读取和写入 Kafka 主题。我们还实现了一个自定义的 MySQLSink,在其中将数据写入 MySQL 数据库。注意,我们需要将 MySQL 驱动程序添加到 Maven 依赖中。
```xml
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.26</version>
</dependency>
```
使用这个程序,我们可以将一个大的 Kafka 主题拆分成多个小主题,并将数据写入 MySQL 数据库。