flink kafka 统计 代码

以下是使用Flink和Kafka进行统计的示例代码。

首先，需要导入相关的依赖：

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
</dependency>

接下来，创建一个Flink的StreamExecutionEnvironment：

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

然后，创建一个Kafka的消费者流：

Properties props = new Properties();
props.setProperty("bootstrap.servers", "localhost:9092");
props.setProperty("group.id", "test");
FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), props);
DataStream<String> stream = env.addSource(consumer);

接着，对数据流进行处理和转换，例如统计单词出现的次数：

DataStream<Tuple2<String, Integer>> counts = stream
    .flatMap((String line, Collector<Tuple2<String, Integer>> out) -> {
        for (String word : line.split(" ")) {
            out.collect(new Tuple2<>(word, 1));
        }
    })
    .keyBy(0)
    .sum(1);

最后，将结果写入到Kafka的一个新的Topic中：

Properties producerProps = new Properties();
producerProps.setProperty("bootstrap.servers", "localhost:9092");
FlinkKafkaProducer<Tuple2<String, Integer>> producer = new FlinkKafkaProducer<>("output-topic", new Tuple2SerializationSchema(), producerProps, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
counts.addSink(producer);

完整的代码示例：

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
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.util.serialization.SimpleStringSchema;
import org.apache.flink.util.Collector;

import java.util.Properties;

public class WordCount {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        Properties props = new Properties();
        props.setProperty("bootstrap.servers", "localhost:9092");
        props.setProperty("group.id", "test");

        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("input-topic", new SimpleStringSchema(), props);
        DataStream<String> stream = env.addSource(consumer);

        DataStream<Tuple2<String, Integer>> counts = stream
            .flatMap((String line, Collector<Tuple2<String, Integer>> out) -> {
                for (String word : line.split(" ")) {
                    out.collect(new Tuple2<>(word, 1));
                }
            })
            .keyBy(0)
            .sum(1);

        Properties producerProps = new Properties();
        producerProps.setProperty("bootstrap.servers", "localhost:9092");

        FlinkKafkaProducer<Tuple2<String, Integer>> producer = new FlinkKafkaProducer<>("output-topic", new Tuple2SerializationSchema(), producerProps, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
        counts.addSink(producer);

        env.execute("Word Count");
    }

    private static class Tuple2SerializationSchema implements KafkaSerializationSchema<Tuple2<String, Integer>> {
        @Override
        public ProducerRecord<byte[], byte[]> serialize(Tuple2<String, Integer> element, @Nullable Long timestamp) {
            return new ProducerRecord<>("output-topic", element.f0.getBytes(), element.f1.toString().getBytes());
        }
    }
}