java - 为什么 Apache Flink 从数据流中删除事件？

Question

在下面的单元测试用例中，生成了 numberOfElements 指定的一些事件，并将其作为数据流提供。该单元案例在生产线上随机失败。

assertEquals(numberOfElements, CollectSink.values.size());

任何解释 Apache Flink 跳过事件的原因。

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.api.functions.sink.SinkFunction;
import org.junit.Before;
import org.junit.Test;

import java.util.ArrayList;
import java.util.List;

import static java.lang.Thread.sleep;
import static org.junit.Assert.assertEquals;

public class FlinkTest {

StreamExecutionEnvironment env;

@Before
public void setup() {
    env = StreamExecutionEnvironment.createLocalEnvironment();
}

@Test
public void testStream1() throws Exception {
    testStream();
}

@Test
public void testStream2() throws Exception {
    testStream();
}

@Test
public void testStream3() throws Exception {
    testStream();
}

@Test
public void testStream4() throws Exception {
    testStream();
}


@Test
public void testStream() throws Exception {

    final int numberOfElements = 50;

    DataStream<Tuple2<String, Integer>> tupleStream = env.fromCollection(getCollectionOfBucketImps(numberOfElements));
    CollectSink.values.clear();
    tupleStream.addSink(new CollectSink());
    env.execute();
    sleep(2000);

    assertEquals(numberOfElements, getCollectionOfBucketImps(numberOfElements).size());
    assertEquals(numberOfElements, CollectSink.values.size());
}


public static List<Tuple2<String, Integer>> getCollectionOfBucketImps(int numberOfElements) throws InterruptedException {
    List<Tuple2<String, Integer>> records = new ArrayList<>();
    for (int i = 0; i < numberOfElements; i++) {
        records.add(new Tuple2<>(Integer.toString(i % 10), i));
    }
    return records;
}

// create a testing sink
private static class CollectSink implements SinkFunction<Tuple2<String, Integer>> {

    public static final List<Tuple2<String, Integer>> values = new ArrayList<>();

    @Override
    public synchronized void invoke(Tuple2<String, Integer> value, Context context) throws Exception {
        values.add(value);
    }
 }
}

例如，testStreamX 案例中的任何一个都会随机失败。

上下文:代码以 8 作为并行度设置运行，因为它运行的 cpu 有 8 个核心

Answer 1

我不知道你的工作的并行性(我想这是Flink可以分配的最大值)。看起来您可以在水槽的附加值上设置竞争条件。

解决方案

我已经运行了您的示例代码，将环境并行度设置为 1，一切正常。有关测试的文档示例使用此解决方案 link to documentation .

@Before
public void setup() {
    env = StreamExecutionEnvironment.createLocalEnvironment();
    env.setParallelism(1);
}

更好

您可以仅在接收器运算符上将并行度设置为 1，并保持管道其余部分的并行度。在下面的示例中，我添加了一个额外的映射函数，其映射运算符的强制并行度为 8。

public void testStream() throws Exception {

    final int numberOfElements = 50;

    DataStream<Tuple2<String, Integer>> tupleStream = env.fromCollection(getCollectionOfBucketImps(numberOfElements));
    CollectSink.values.clear();
    tupleStream
            .map(new MapFunction<Tuple2<String,Integer>, Tuple2<String,Integer>>() {
                @Override
                public Tuple2<String,Integer> map(Tuple2<String, Integer> stringIntegerTuple2) throws Exception {

                    stringIntegerTuple2.f0 += "- concat something";

                    return stringIntegerTuple2;
                }
            }).setParallelism(8)
            .addSink(new CollectSink()).setParallelism(1);
    env.execute();
    sleep(2000);

    assertEquals(numberOfElements, getCollectionOfBucketImps(numberOfElements).size());
    assertEquals(numberOfElements, CollectSink.values.size());
}