需求分析
目的
重构某个由定时任务调度的系统，升级为流式系统。
技术选型
kafka-stream 2.7.0
kafka 2.7.0
整体流程
- 消费source-topic的order数据
- 窗口聚合: windowBy,aggregate
- 若干中间处理器: map、filter...，最终组成task
- 扁平展开为多条数据: flatMap
- 将task数据发往下游sink-topic
stream-system.png
程序实现(demo)
-
kafka基础配置
private static Properties buildConfigProps() { Properties props = new Properties(); String applicationId = "test_33333"; props.put("bootstrap.servers", "192.168.10.152:9092"); props.put("application.id", applicationId); props.put("auto.commit.interval.ms", "1000"); props.put("session.timeout.ms", "30000"); props.put("max.poll.records", 1000); props.put("auto.offset.reset", "earliest"); props.put("key.deserializer", StringDeserializer.class.getName()); props.put("value.deserializer", StringDeserializer.class.getName()); props.put("key.serializer", StringSerializer.class.getName()); props.put("value.serializer", StringSerializer.class.getName()); return props; } -
fast-json实现的序列化处理器
import com.alibaba.fastjson2.JSON; import org.apache.kafka.common.serialization.Serializer; public class JSONSerializer<T> implements Serializer<T> { @Override public byte[] serialize(String topic, T data) { if (data == null) { return null; } return JSON.toJSONBytes(data); } }import com.alibaba.fastjson2.JSON; import org.apache.kafka.common.serialization.Deserializer; public class JSONDeserializer<T> implements Deserializer<T> { @Override public T deserialize(String topic, byte[] data) { if (data == null || data.length == 0) { return null; } return (T) JSON.parse(data); } } -
异常处理逻辑
public abstract class RetryExceptionHandler {
public static final String SOURCE_TOPIC_KEY = "sourceTopic";
public static final String PRODUCER_KEY = "producer";
protected String sourceTopic;
protected KafkaProducer<String, String> producer;
public void configure(Map<String, ?> config) {
this.sourceTopic = (String) config.get(SOURCE_TOPIC_KEY);
this.producer = (KafkaProducer<String, String>) config.get(PRODUCER_KEY);
}
}
@Slf4j
public class RetryDeserializationExceptionHandler extends RetryExceptionHandler implements DeserializationExceptionHandler {
@Override
public DeserializationHandlerResponse handle(ProcessorContext context,
ConsumerRecord<byte[], byte[]> record, Exception exception) {
log.error("Exception caught during Deserialization, sending to the source topic, " +
"taskId: {}, topic: {}, partition: {}, offset: {}",
context.taskId(), record.topic(), record.partition(), record.offset(),
exception);
byte[] value = record.value();
producer.send(new ProducerRecord<>(sourceTopic, new String(value, StandardCharsets.UTF_8)));
return DeserializationHandlerResponse.CONTINUE;
}
}
@Slf4j
public class RetryProductionExceptionHandler extends RetryExceptionHandler implements
ProductionExceptionHandler {
@Override
public ProductionExceptionHandlerResponse handle(ProducerRecord<byte[], byte[]> record,
Exception exception) {
log.error("Exception caught during Production, sending to the source topic, " +
"topic: {}, partition: {}, offset: {}", record.topic(), record.partition(), exception);
ProducerRecord<String, String> producerRecord = new ProducerRecord<>(
new String(record.key(), StandardCharsets.UTF_8), new String(record.value(), StandardCharsets.UTF_8));
producer.send(producerRecord);
return ProductionExceptionHandlerResponse.CONTINUE;
}
}
@Slf4j
public class RestartUncaughtExceptionHandler implements Thread.UncaughtExceptionHandler {
public static final int MAX_AGE = 3;
private StreamsBuilder streamsBuilder;
private Properties props;
private AtomicInteger age;
public RestartUncaughtExceptionHandler(StreamsBuilder streamsBuilder, Properties props) {
this.streamsBuilder = streamsBuilder;
this.props = props;
this.age = new AtomicInteger();
}
@Override
public void uncaughtException(Thread t, Throwable e) {
log.error("thread: {} process failed. age: {}", t.getName(), age, e);
if (age.get() > MAX_AGE) {
log.info("stop the stream application after retry times: {}", age);
return;
}
age.incrementAndGet();
KafkaStreams kafkaStreams = new KafkaStreams(streamsBuilder.build(), props);
kafkaStreams.setUncaughtExceptionHandler(this);
kafkaStreams.start();
}
}
-
kafka-stream核心逻辑
private static final String SOURCE_TOPIC = "sourceTopic"; private static final String SINK_TOPIC = "sinkTopic"; @Test void helloWorld() { // kafka config Properties props = buildConfigProps(); Serde<String> stringSerde = Serdes.String(); props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, stringSerde.getClass().getName()); props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, stringSerde.getClass().getName()); props.put(StreamsConfig.PROCESSING_GUARANTEE_CONFIG, StreamsConfig.EXACTLY_ONCE_BETA); props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10000); props.put(StreamsConfig.DEFAULT_DESERIALIZATION_EXCEPTION_HANDLER_CLASS_CONFIG, RetryDeserializationExceptionHandler.class.getName()); props.put(StreamsConfig.DEFAULT_PRODUCTION_EXCEPTION_HANDLER_CLASS_CONFIG, RetryProductionExceptionHandler.class.getName()); props.put(RetryExceptionHandler.PRODUCER_KEY, producer); props.put(RetryExceptionHandler.SOURCE_TOPIC_KEY, SOURCE_TOPIC); Serde<List<String>> jsonSerde = Serdes.serdeFrom(new JSONSerializer<>(), new JSONDeserializer<>()); StreamsBuilder streamsBuilder = new StreamsBuilder(); KStream<String, String> kStream = streamsBuilder.stream(SOURCE_TOPIC, Consumed.with(stringSerde, stringSerde)); Duration windowSize = Duration.ofSeconds(10); Materialized<String, List<String>, WindowStore<Bytes, byte[]>> storeMaterialized = Materialized.<String, List<String>, WindowStore<Bytes, byte[]>>as( "time-windowed-aggregated-stream-store").withKeySerde(stringSerde).withValueSerde(jsonSerde) .withRetention(Duration.ofMinutes(5)); ConcurrentHashMap<String, Long> aggRecordMap = new ConcurrentHashMap<>(); String lastMsgTimeKey = "lastMsgTimeKey"; String signal = "signal"; KTable<Windowed<String>, List<String>> kTable = kStream.groupBy((k, v) -> "defaultKey") .windowedBy(TimeWindows.of(windowSize).grace(Duration.ZERO)) .aggregate(() -> new ArrayList<>(), (k, v, agg) -> { System.out.println("========== aggregate record =========="); log.info("k: {}, v: {}, agg: {}", k, v, agg); if (!signal.equals(v)) { agg.add(v); } aggRecordMap.put(lastMsgTimeKey, System.currentTimeMillis()); return agg; }, storeMaterialized).suppress(Suppressed.untilWindowCloses(BufferConfig.unbounded())); String backFlow = "backFlow"; KStream<String, JSONObject>[] branches = kTable.mapValues(list -> list).mapValues(list -> list) .toStream().flatMap( (k, v) -> { List<KeyValue<String, JSONObject>> keyValues = new ArrayList<>(v.size()); System.out.println("========== flatMap record =========="); log.info("k: {}, v: {}", k, v); v.stream().forEach(str -> { JSONObject jsonObject = JSON.parseObject(str); int index = jsonObject.getIntValue("index"); boolean backFlowFlag = jsonObject.getBooleanValue(backFlow); if (!backFlowFlag && index % 2 == 0) { jsonObject.put(backFlow, true); } else { jsonObject.remove(backFlow); } keyValues.add(new KeyValue<>(String.valueOf(index), jsonObject)); }); log.info("keyValues: {}", keyValues); return keyValues; }) .branch((k, v) -> !v.getBooleanValue(backFlow), (k, v) -> v.getBooleanValue(backFlow)); branches[0].mapValues(v -> v.toJSONString()) .to(SINK_TOPIC, Produced.with(stringSerde, stringSerde)); KafkaProducer<String, String> producer = new KafkaProducer<>(buildConfigProps()); branches[1].map((k, v) -> new KeyValue<>(k, new ProducerRecord<>(SOURCE_TOPIC, k, v.toJSONString()))) .foreach((k, v) -> producer.send(v)); KafkaStreams kafkaStreams = new KafkaStreams(streamsBuilder.build(), props); kafkaStreams.setUncaughtExceptionHandler( new RestartUncaughtExceptionHandler(streamsBuilder, props)); kafkaStreams.start(); while (true) { System.out.println("运行中......"); Long lastModifiedKey = aggRecordMap.getOrDefault(lastMsgTimeKey, 0L); if (lastModifiedKey > 0 && System.currentTimeMillis() - lastModifiedKey > windowSize.toMillis()) { producer.send(new ProducerRecord<>(SOURCE_TOPIC, lastModifiedKey.toString(), signal)); } try { TimeUnit.SECONDS.sleep(2); } catch (InterruptedException e) { throw new RuntimeException(e); } } }
遇到的坑
-
实验发现，TimeWindow在生产者持续生产消息时，可以按照预期工作。但生产者停止发送消息后，最后一次窗口无法闭合，直到生产者再次发送消息。
尝试过各种修改，搞不定，怀疑kafka-stream本来就是这么设计的，无界数据，不需要考虑停止...
在发送邮件给kafka开发者社区users@kafka.apache.org询问后，我得到了大佬John Roesler(vvcephei@apache.org)的答复: kafka事件时间基于生产者推动，生产者停止，时钟也就停止了。
为了解决这个问题，只能写个轮巡任务去定期发假消息(dummy record). -
某些场景，部分记录需要回流到源端，下个周期重新处理，所以demo中使用了branch操作。
实验中发现发送直接to到源主题中的消息，无法再次进入stream中，可能是kafka规避死循环的某种机制。但可以直接使用Producer发送到源端。 -
kafka中流动的是orderId，而不是整个order，是因为业务上order可能会非常大，可能会超出kafka单条消息限制，并且造成网络拥堵。
暂时实现为传递orderId的半流式系统，待后续重构order结构。
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