Question

当我使用聚合函数模拟单词计数大小写时，遇到了Serde转换问题。

Exception in thread "aggregation-transformation-application-43485635-2d3c-4edc-b13c-c6505a793d18-StreamThread-1" org.apache.kafka.streams.errors.StreamsException: Deserialization exception handler is set to fail upon a deserialization error. If you would rather have the streaming pipeline continue after a deserialization error, please set the default.deserialization.exception.handler appropriately.
    at org.apache.kafka.streams.processor.internals.RecordDeserializer.deserialize(RecordDeserializer.java:80)
    at org.apache.kafka.streams.processor.internals.RecordQueue.maybeUpdateTimestamp(RecordQueue.java:160)
    at org.apache.kafka.streams.processor.internals.RecordQueue.poll(RecordQueue.java:115)
    at org.apache.kafka.streams.processor.internals.PartitionGroup.nextRecord(PartitionGroup.java:100)
    at org.apache.kafka.streams.processor.internals.StreamTask.process(StreamTask.java:349)
    at org.apache.kafka.streams.processor.internals.AssignedStreamsTasks.process(AssignedStreamsTasks.java:199)
    at org.apache.kafka.streams.processor.internals.TaskManager.process(TaskManager.java:420)
    at org.apache.kafka.streams.processor.internals.StreamThread.runOnce(StreamThread.java:890)
    at org.apache.kafka.streams.processor.internals.StreamThread.runLoop(StreamThread.java:805)
    at org.apache.kafka.streams.processor.internals.StreamThread.run(StreamThread.java:774)
Caused by: org.apache.kafka.common.errors.SerializationException: Size of data received by IntegerDeserializer is not 4

尽管我为每个任务定义了Serdes，但它会引发SerializationException。

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.utils.Bytes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.*;
import org.apache.kafka.streams.state.KeyValueStore;

import java.util.Arrays;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;

public class AggregationTransformation {
    public static void main(String[] args) {
        //prepare config
        Properties config = new Properties();
        config.put(StreamsConfig.APPLICATION_ID_CONFIG, "aggregation-transformation-application");
        config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        config.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
        config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        StreamsBuilder builder = new StreamsBuilder();

        KStream<String, String> kStream = builder.stream("agg-table-source-topic");
        KStream<String, Integer> kStreamFormatted = kStream.flatMapValues((key, value) ->
                Arrays.asList(value.split("\\W+"))).selectKey((key, value) -> value)
                .mapValues(value -> 1);

        kStreamFormatted.groupByKey(Grouped.<String,Integer>as(null)
                .withValueSerde(Serdes.Integer()))
                .aggregate(() -> 0,
                        (aggKey, newValue, aggValue) -> aggValue + newValue,
                        Materialized.<String, Integer, KeyValueStore<Bytes, byte[]>>
                                as("aggregated-stream-store")
                                .withKeySerde(Serdes.String())
                                .withValueSerde(Serdes.Integer())
                ).toStream().to("agg-output-topic", Produced.with(Serdes.String(), Serdes.Integer()));

        Topology topology = builder.build();
        KafkaStreams kafkaStreams = new KafkaStreams(topology, config);

        CountDownLatch countDownLatch = new CountDownLatch(1);

        // attach shutdown handler to catch control-c
        Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
            @Override
            public void run() {
                kafkaStreams.close();
                countDownLatch.countDown();
            }
        });

        try {
            kafkaStreams.start();
            countDownLatch.await();
        } catch (Throwable e) {
            System.exit(1);
        }
        System.exit(0);
    }
}

对于作为“ John Smith”的第一个条目进入生产者控制台，我希望输出主题（agg-output-topic）应该具有

John 1
Smith 1

如果我向生产者输入相同的输入（agg-table-source-topic），则输出主题应进行汇总，结果应为

John 2
Smith 2

我请求您的帮助。

Answer 1

SerializationException意味着您的Deserializer（在您的情况下为IntegerDeserializer）无法反序列化消息-无法将字节转换为某些对象（Integer）

您如何撰写评论，您已将类型从Long更改为Integer。我认为您首先使用类型Long启动应用程序并处理几条消息，然后将类型更改为Integer。您的应用程序将中间结果保存在changelog主题中，然后使用 new 类型和反序列化器（Serdes）将其保存，无法对其进行反序列化并引发异常。

如果您在应用程序中更改类型，则必须删除所有在处理过程中创建的changelog主题。否则可能会发生SerializationException。

Answer 2

当我使用聚合函数[...]模拟字数大小写时

您的设置看起来非常复杂。您为什么不只执行以下操作？

final KTable<String, Long> aggregated = builder.stream("agg-table-source-topic");
  .flatMapValues(value -> Arrays.asList(value.split("\\W+")))
  .groupBy((keyIgnored, word) -> word)
  // Normally, you'd use `count()` here and be done with it.
  // But you mentioned you intentionally want to use `aggregate(...)`.
  .aggregate(
      () -> 0L,
      (aggKey, newValue, aggValue) -> aggValue + 1L,
      Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("aggregate-store").withValueSerde(Serdes.Long()))

aggregated.toStream().to("agg-output-topic", Produced.with(Serdes.String(), Serdes.Long()));

也就是说，与普通的WordCount示例相比，您要做的就是替换：

  .count()

使用

  .aggregate(
      () -> 0L,
      (aggKey, newValue, aggValue) -> aggValue + 1L,
      Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("aggregate-store").withValueSerde(Serdes.Long()))

请注意，上面的示例代码使用Long，而不是Integer，但是您当然可以更改它。

Kafka Streams：如何修复Serde投放错误

2 个答案: