如何解决Kafka暴风雨，只有消费者从Kafka的一半数据？

Question

<dependency>
        <groupId>org.apache.storm</groupId>
        <artifactId>storm-core</artifactId>
        <version>1.0.2</version>
        <scope>provided</scope>
</dependency>
<dependency>
        <groupId>org.apache.kafka</groupId>
        <artifactId>kafka-clients</artifactId>
        <version>0.10.0.0</version>
        <scope>compile</scope>
</dependency>

我在Apache/Storm/external/storm-kafka-client中使用Storm Kafka Spout的storm-kafka-client和新的Kafka Consumer API。我的拓扑如下所示：

public class AnalyseTopo {
private static final Logger LOG = LoggerFactory.getLogger(AnalyseTopo.class);


private static final String[] STREAMS = new String[]{"test_stream"};
private static final String[] TOPICS = new String[]{"online"};

public static void main(String[] args) throws Exception {
    new AnalyseTopo().runMain(args);
}

protected void runMain(String[] args) throws Exception {
    if (args.length == 0) {
        submitTopologyLocalCluster(getTopologyKafkaSpout(), getConfig());
    } else {
        submitTopologyRemoteCluster(args[0], getTopologyKafkaSpout(), getConfig());
    }
}

protected void submitTopologyLocalCluster(StormTopology topology, Config config) throws InterruptException {
    LocalCluster cluster = new LocalCluster();
    cluster.submitTopology("KafkaTest", config, topology);
    stopWaitingForInput();
}

protected void submitTopologyRemoteCluster(String arg, StormTopology topology, Config config) throws Exception {
    StormSubmitter.submitTopology(arg, config, topology);
}

protected void stopWaitingForInput() {
    try {
        System.out.println("PRESS ENTER TO STOP Now");
        new BufferedReader(new InputStreamReader(System.in)).readLine();
        System.exit(0);
    } catch (IOException e) {
        e.printStackTrace();
    }
}

protected StormTopology getTopologyKafkaSpout() {
    final TopologyBuilder builder = new TopologyBuilder();
    builder.setSpout("kafka_spout", new KafkaSpout<>(getKafkaSpoutConfig(getKafkaSpoutStreams())), 1);

    // 1. 先用fastjson解析每条日志记录
    builder.setBolt("json_parse", new JsonParseBolt()).shuffleGrouping("kafka_spout", STREAMS[0]);

    // 2. 每隔60s去计算一下应用频道的统计量，固定窗口为时间增加60s
    Duration oneMinite = new Duration(60, TimeUnit.SECONDS);// 60 -> 2
    IWindowedBolt appChannelBolt = new AppChannelStatBolt()
            .withTimestampField("timestamp")
            .withLag(oneMinite)
            .withWatermarkInterval(oneMinite)
            .withTumblingWindow(oneMinite);
    builder.setBolt("app_channel", appChannelBolt, 3)
            .fieldsGrouping("json_parse", new Fields("timestamp"));   //from app_channel change to timestamp

    // 3. 将这些统计给到app整体统计，channel整体统计
    IWindowedBolt appStatBolt = new AppStatBolt()
            .withTimestampField("timestamp")
            .withLag(oneMinite)
            .withWatermarkInterval(oneMinite)
            .withTumblingWindow(oneMinite);
    builder.setBolt("app_stat", appStatBolt, 1)
            .fieldsGrouping("app_channel", "stat", new Fields("appid"));

    IWindowedBolt channelStatBolt = new ChannelStatBolt()
            .withTimestampField("timestamp")
            .withLag(oneMinite)
            .withWatermarkInterval(oneMinite)
            .withTumblingWindow(oneMinite);
    builder.setBolt("channel_stat", channelStatBolt, 1)
            .fieldsGrouping("app_channel", "stat", new Fields("channel"));

    // 4. 写道mysql持久化保存
    IWindowedBolt batchWriteBolt = new BatchWriteBolt()
            .withTumblingWindow(new BaseWindowedBolt.Count(10));
    builder.setBolt("batch_write", batchWriteBolt, 1)
            .shuffleGrouping("app_channel", "sql")
            .shuffleGrouping("app_stat", "sql")
            .shuffleGrouping("channel_stat", "sql");

    return builder.createTopology();
}


protected Config getConfig() {
    Config config = new Config();
    config.setDebug(true);
    config.put("topology.message.timeout.secs", 1000);
    return config;
}

protected KafkaSpoutConfig<String, String> getKafkaSpoutConfig(KafkaSpoutStreams kafkaSpoutStreams) {
    return new KafkaSpoutConfig.Builder<>(getKafkaConsumerProps(), kafkaSpoutStreams, getTuplesBuilder(), getRetryService())
            .setOffsetCommitPeriodMs(2000)
            .setFirstPollOffsetStrategy(UNCOMMITTED_EARLIEST)
            .setMaxUncommittedOffsets(50000)
            .setPollTimeoutMs(2000)
            .build();
}

protected KafkaSpoutRetryService getRetryService() {
    return new KafkaSpoutRetryExponentialBackoff(TimeInterval.microSeconds(500),
            TimeInterval.microSeconds(2), 35, TimeInterval.seconds(10));//change Integer.MAXVALUE to 3->50
}

protected Map<String, Object> getKafkaConsumerProps() {
    Map<String, Object> props = new HashMap<>();
    props.put(KafkaSpoutConfig.Consumer.BOOTSTRAP_SERVERS, "cstr-01:9092,cstr-02:9092,cstr-03:9092");
    props.put(KafkaSpoutConfig.Consumer.GROUP_ID, "storm2");
    props.put(KafkaSpoutConfig.Consumer.KEY_DESERIALIZER, "org.apache.kafka.common.serialization.StringDeserializer");
    props.put(KafkaSpoutConfig.Consumer.VALUE_DESERIALIZER, "org.apache.kafka.common.serialization.StringDeserializer");
    //props.put(KafkaSpoutConfig.Consumer.ENABLE_AUTO_COMMIT, "true");
    //props.put(KafkaSpoutConfig.Consumer.AUTO_COMMIT_INTERVAL_MS, "5000");

    // add resolve commit failure param
    //props.put("session.timeout.ms", "50000");   //increase
    //props.put("max.poll.records", "50000");     //reduce
    return props;
}

protected KafkaSpoutTuplesBuilder<String, String> getTuplesBuilder() {
    return new KafkaSpoutTuplesBuilderNamedTopics.Builder<String, String>(
            new OnlineTupleBuilder<>(TOPICS[0]))
            .build();
}

protected KafkaSpoutStreams getKafkaSpoutStreams() {
    final Fields outputFields = new Fields("topic", "partition", "offset", "value");
    return new KafkaSpoutStreamsNamedTopics.Builder(outputFields, STREAMS[0], new String[]{TOPICS[0]})
            .build();
}

当我将kafkaspout.java更改为打印consumerRecords的偏移量时，我发现跳过了一些偏移量。跳过http://7xtjbx.com1.z0.glb.clouddn.com/stack.png中的偏移量图像

对于这个问题我该怎么做？是storm-kafka-client使用新消费者有问题吗？谢谢！

Answer 1

我在使用自动提交时解决了这个问题。

props.put(KafkaSpoutConfig.Consumer.ENABLE_AUTO_COMMIT, "true");
props.put(KafkaSpoutConfig.Consumer.AUTO_COMMIT_INTERVAL_MS, "5000");

您可能会发现一个NullPointException并使用try catch is ok来解决它，并且需要删除kafkaspout.java 297中的numuncommittedoffsets++。