apache-flink - 在 Apache Flink 中为两个消息流使用相同的接收器

Question

我们有两种消息传入 Flink

控制消息 -> 仅滚动文件

数据消息 -> 将使用接收器存储在 S3 中

我们为这两个消息提供了单独的源流。并且我们将相同的接收器附加到了两个流上。
我们想要做的是广播控制消息，以便所有并行运行的接收器都应该收到它。

下面是相同的代码:

package com.ranjit.com.flinkdemo;

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.fs.DateTimeBucketer;
import org.apache.flink.streaming.connectors.fs.RollingSink;

import org.apache.flink.streaming.connectors.fs.StringWriter;;

public class FlinkBroadcast {
    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(2);

        DataStream<String> ctrl_message_stream = env.socketTextStream("localhost", 8088);

        ctrl_message_stream.broadcast();

        DataStream<String> message_stream = env.socketTextStream("localhost", 8087);

        RollingSink sink = new RollingSink<String>("/base/path");
        sink.setBucketer(new DateTimeBucketer("yyyy-MM-dd--HHmm"));
        sink.setWriter(new StringWriter<String>() );
        sink.setBatchSize(1024 * 1024 * 400); // this is 400 MB,

        ctrl_message_stream.broadcast().addSink(sink);
        message_stream.addSink(sink);

        env.execute("stream");
    }

}

但是我观察到的是，它正在创建 4 个接收器实例，并且控制消息仅广播到 2 个接收器(由控制消息流创建)。
所以我的理解是两个流都应该通过相同的运算符链来做到这一点，这是我们不想要的，因为数据消息会有多重转换。
我们已经编写了自己的接收器，如果它是控制消息，它将读取消息，然后它只会滚动文件。

示例代码:

package com.gslab.com.dataSets;
import java.io.File;
import java.util.ArrayList;
import java.util.List;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericData.Record;
import org.apache.avro.generic.GenericRecord;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

public class FlinkBroadcast {
    public static void main(String[] args) throws Exception {

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(2);

        List<String> controlMessageList = new ArrayList<String>();
        controlMessageList.add("controlMessage1");
        controlMessageList.add("controlMessage2");

        List<String> dataMessageList = new ArrayList<String>();
        dataMessageList.add("Person1");
        dataMessageList.add("Person2");
        dataMessageList.add("Person3");
        dataMessageList.add("Person4");

        DataStream<String> controlMessageStream  = env.fromCollection(controlMessageList);
        DataStream<String> dataMessageStream  = env.fromCollection(dataMessageList);

        DataStream<GenericRecord> controlMessageGenericRecordStream = controlMessageStream.map(new MapFunction<String, GenericRecord>() {
            @Override
            public GenericRecord map(String value) throws Exception {
                 Record gr = new GenericData.Record(new Schema.Parser().parse(new File("src/main/resources/controlMessageSchema.avsc")));
                 gr.put("TYPE", value);
                 return gr;
            }
        });

        DataStream<GenericRecord> dataMessageGenericRecordStream = dataMessageStream.map(new MapFunction<String, GenericRecord>() {
            @Override
            public GenericRecord map(String value) throws Exception {
                 Record gr = new GenericData.Record(new Schema.Parser().parse(new File("src/main/resources/dataMessageSchema.avsc")));
                 gr.put("FIRSTNAME", value);
                 gr.put("LASTNAME", value+": lastname");
                 return gr;
            }
        });

        //Displaying Generic records
        dataMessageGenericRecordStream.map(new MapFunction<GenericRecord, GenericRecord>() {
            @Override
            public GenericRecord map(GenericRecord value) throws Exception {
                System.out.println("data before union: "+ value);
                return value;
            }
        });

        controlMessageGenericRecordStream.broadcast().union(dataMessageGenericRecordStream).map(new MapFunction<GenericRecord, GenericRecord>() {
            @Override
            public GenericRecord map(GenericRecord value) throws Exception {
                System.out.println("data after union: " + value);
                return value;
            }
        });
        env.execute("stream");
    }
}

输出:

05/09/2016 13:02:12 Source: Collection Source(1/1) switched to FINISHED 
05/09/2016 13:02:12 Source: Collection Source(1/1) switched to FINISHED 
05/09/2016 13:02:13 Map(1/2) switched to FINISHED 
05/09/2016 13:02:13 Map(2/2) switched to FINISHED 
data after union: {"TYPE": "controlMessage1"}
data before union: {"FIRSTNAME": "Person2", "LASTNAME": "Person2: lastname"}
data after union: {"TYPE": "controlMessage1"}
data before union: {"FIRSTNAME": "Person1", "LASTNAME": "Person1: lastname"}
data after union: {"TYPE": "controlMessage2"}
data after union: {"TYPE": "controlMessage2"}
data after union: {"FIRSTNAME": "Person1", "LASTNAME": "Person1"}
data before union: {"FIRSTNAME": "Person4", "LASTNAME": "Person4: lastname"}
data before union: {"FIRSTNAME": "Person3", "LASTNAME": "Person3: lastname"}
data after union: {"FIRSTNAME": "Person2", "LASTNAME": "Person2"}
data after union: {"FIRSTNAME": "Person3", "LASTNAME": "Person3"}
05/09/2016 13:02:13 Map -> Map(2/2) switched to FINISHED 
data after union: {"FIRSTNAME": "Person4", "LASTNAME": "Person4"}
05/09/2016 13:02:13 Map -> Map(1/2) switched to FINISHED 
05/09/2016 13:02:13 Map(1/2) switched to FINISHED 
05/09/2016 13:02:13 Map(2/2) switched to FINISHED 
05/09/2016 13:02:13 Job execution switched to status FINISHED.

正如我们所看到的 LASTNAME 值不正确，它被每条记录的 FIRSTNAME 值替换

Answer 1

您的代码基本上使用您定义的接收器的自己的副本来终止两个流。你想要的是这样的:

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);

DataStream<String> ctrl_message_stream = env.socketTextStream("localhost", 8088);

DataStream<String> message_stream = env.socketTextStream("localhost", 8087);

RollingSink sink = new RollingSink<String>("/base/path");
sink.setBucketer(new DateTimeBucketer("yyyy-MM-dd--HHmm"));
sink.setWriter(new StringWriter<String>() );
sink.setBatchSize(1024 * 1024 * 400); // this is 400 MB,

ctrl_message_stream.broadcast().union(message_stream).addSink(sink);

env.execute("stream");