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若想要实现实现Exactly Once,kafka必须要0.11以上版本(可以支持事务)
直接代码
- package cn._51doit.flink.day01;
-
- import org.apache.flink.api.common.functions.RuntimeContext;
- import org.apache.flink.api.common.serialization.SimpleStringSchema;
- import org.apache.flink.configuration.Configuration;
- import org.apache.flink.streaming.api.datastream.DataStreamSource;
- import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
- import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
- import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
- public class KafkaSinkDemo {
- public static void main(String[] args) throws Exception {
- //local模式默认的并行度是当前节点逻辑核的数量
- Configuration configuration = new Configuration();
- StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(configuration);
-
- //DataStream的并行度
- int parallelism01 = env.getParallelism();
- System.out.println("执行环境默认的并行度是:"+parallelism01);
-
- DataStreamSource<String> lines = env.socketTextStream("192.168.242.102", 9999);
-
- //获取DataStream的并行度
- int parallelism = lines.getParallelism();
- System.out.println("SocketSink的并行度:"+parallelism);
-
- //FlinkKafkaProducer往kafka写数据
- FlinkKafkaProducer<String> kafkaproducer = new FlinkKafkaProducer<>(
- "Master:9092,Slave:9092,Slave:9092", "topic_log", new SimpleStringSchema());
-
- //把数据写到指定的Slink
- lines.addSink(kafkaproducer);
-
-
-
- env.execute();
- }
-
- //定义内部类
- public static class MyPrintSink extends RichSinkFunction<String > {
- private int indexOfThisSubtask;
-
- //最终把数据输出的方法(如:mysql、jdbc)
- @Override
- public void invoke(String value, Context context) throws Exception {
- //:拿到索引编号[从0开始]
- RuntimeContext runtimeContext = getRuntimeContext();
- int indexOfThisSubtask = runtimeContext.getIndexOfThisSubtask();
-
- System.out.println(indexOfThisSubtask+"> "+value);
- }
- }
-
- }
-
-

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