自定義kafka streams的processor

序

本文來解析一下kafka streams的KStreamBuilder以及舉例如何自定義kafka streams的processor

實例

KStreamBuilder builder = new KStreamBuilder();
KStream<String, String> source = builder.stream("demo-topic");
KafkaStreams streams = new KafkaStreams(builder, props);
streams.start();

KStreamBuilder裏頭隱藏着Topology

KStreamBuilder

kafka-streams-0.10.2.1-sources.jar!/org/apache/kafka/streams/kstream/KStreamBuilder.java

public class KStreamBuilder extends TopologyBuilder {

    public <K, V> KStream<K, V> stream(final String... topics) {
        return stream(null, null, null, topics);
    }
    public <K, V> KStream<K, V> stream(final AutoOffsetReset offsetReset,
                                       final Serde<K> keySerde,
                                       final Serde<V> valSerde,
                                       final String... topics) {
        final String name = newName(KStreamImpl.SOURCE_NAME);

        addSource(offsetReset, name,  keySerde == null ? null : keySerde.deserializer(), valSerde == null ? null : valSerde.deserializer(), topics);

        return new KStreamImpl<>(this, name, Collections.singleton(name), false);
    }
}

這裏的addSource就是調用TopologyBuilder的方法

TopologyBuilder

kafka-streams-0.10.2.1-sources.jar!/org/apache/kafka/streams/processor/TopologyBuilder.java

public synchronized final TopologyBuilder addSource(final AutoOffsetReset offsetReset, final String name, final Deserializer keyDeserializer, final Deserializer valDeserializer, final String... topics) {
        if (topics.length == 0) {
            throw new TopologyBuilderException("You must provide at least one topic");
        }
        Objects.requireNonNull(name, "name must not be null");
        if (nodeFactories.containsKey(name))
            throw new TopologyBuilderException("Processor " + name + " is already added.");

        for (String topic : topics) {
            Objects.requireNonNull(topic, "topic names cannot be null");
            validateTopicNotAlreadyRegistered(topic);
            maybeAddToResetList(earliestResetTopics, latestResetTopics, offsetReset, topic);
            sourceTopicNames.add(topic);
        }

        nodeFactories.put(name, new SourceNodeFactory(name, topics, null, keyDeserializer, valDeserializer));
        nodeToSourceTopics.put(name, Arrays.asList(topics));
        nodeGrouper.add(name);

        return this;
    }

processor topology

這個topology是否是很熟悉呢，storm也有topology

TopologyBuilder builder = new TopologyBuilder();
        //併發度10
        builder.setSpout("spout", new TestWordSpout(), 10);
        builder.setBolt("count", new WordCountBolt(), 5).fieldsGrouping("spout", new Fields("word"));
        builder.setBolt("print", new PrintBolt(), 1).shuffleGrouping("count");

        String topologyName = "DemoTopology";
        Config conf = new Config();
        conf.setDebug(true);

        try {
            LocalCluster cluster = new LocalCluster();
            cluster.submitTopology(topologyName, conf,builder.createTopology());
            Thread.sleep(60 * 1000);
            cluster.shutdown();
        } catch (Exception e) {
            e.printStackTrace();
        }

Processor Topology定義了數據在各個處理單元（在Kafka Stream中被稱做Processor）間的流動方式，或者說定義了數據的處理邏輯。

具體看kafka-streams-0.10.2.1-sources.jar!/org/apache/kafka/streams/kstream/internals/KStreamImpl.java

@Override
    public KStream<K, V> filter(Predicate<? super K, ? super V> predicate) {
        Objects.requireNonNull(predicate, "predicate can't be null");
        String name = topology.newName(FILTER_NAME);

        topology.addProcessor(name, new KStreamFilter<>(predicate, false), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, this.repartitionRequired);
    }

    @Override
    public KStream<K, V> filterNot(final Predicate<? super K, ? super V> predicate) {
        Objects.requireNonNull(predicate, "predicate can't be null");
        String name = topology.newName(FILTER_NAME);

        topology.addProcessor(name, new KStreamFilter<>(predicate, true), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, this.repartitionRequired);
    }

    @Override
    public <K1> KStream<K1, V> selectKey(final KeyValueMapper<? super K, ? super V, ? extends K1> mapper) {
        Objects.requireNonNull(mapper, "mapper can't be null");
        return new KStreamImpl<>(topology, internalSelectKey(mapper), sourceNodes, true);
    }
    @Override
    public <K1, V1> KStream<K1, V1> map(KeyValueMapper<? super K, ? super V, ? extends KeyValue<? extends K1, ? extends V1>> mapper) {
        Objects.requireNonNull(mapper, "mapper can't be null");
        String name = topology.newName(MAP_NAME);

        topology.addProcessor(name, new KStreamMap<K, V, K1, V1>(mapper), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, true);
    }


    @Override
    public <V1> KStream<K, V1> mapValues(ValueMapper<? super V, ? extends V1> mapper) {
        Objects.requireNonNull(mapper, "mapper can't be null");
        String name = topology.newName(MAPVALUES_NAME);

        topology.addProcessor(name, new KStreamMapValues<>(mapper), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, this.repartitionRequired);
    }
    
     @Override
    public <K1, V1> KStream<K1, V1> flatMap(KeyValueMapper<? super K, ? super V, ? extends Iterable<? extends KeyValue<? extends K1, ? extends V1>>> mapper) {
        Objects.requireNonNull(mapper, "mapper can't be null");
        String name = topology.newName(FLATMAP_NAME);

        topology.addProcessor(name, new KStreamFlatMap<>(mapper), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, true);
    }

    @Override
    public <V1> KStream<K, V1> flatMapValues(ValueMapper<? super V, ? extends Iterable<? extends V1>> mapper) {
        Objects.requireNonNull(mapper, "mapper can't be null");
        String name = topology.newName(FLATMAPVALUES_NAME);

        topology.addProcessor(name, new KStreamFlatMapValues<>(mapper), this.name);

        return new KStreamImpl<>(topology, name, sourceNodes, this.repartitionRequired);
    }
    
    @Override
    public void foreach(ForeachAction<? super K, ? super V> action) {
        Objects.requireNonNull(action, "action can't be null");
        String name = topology.newName(FOREACH_NAME);

        topology.addProcessor(name, new KStreamForeach<>(action), this.name);
    }

能夠看到各類流式操做，都是往topology添加processor

ProcessorSupplier

kafka-streams-0.10.2.1-sources.jar!/org/apache/kafka/streams/processor/ProcessorSupplier.java
具體的流式操做processor能夠看這個ProcessorSupplier的實現類，好比kafka-streams-0.10.2.1-sources.jar!/org/apache/kafka/streams/kstream/internals/KStreamFilter.java

class KStreamFilter<K, V> implements ProcessorSupplier<K, V> {

    private final Predicate<K, V> predicate;
    private final boolean filterNot;

    public KStreamFilter(Predicate<K, V> predicate, boolean filterNot) {
        this.predicate = predicate;
        this.filterNot = filterNot;
    }

    @Override
    public Processor<K, V> get() {
        return new KStreamFilterProcessor();
    }

    private class KStreamFilterProcessor extends AbstractProcessor<K, V> {
        @Override
        public void process(K key, V value) {
            if (filterNot ^ predicate.test(key, value)) {
                context().forward(key, value);
            }
        }
    }
}

word count的processor實例

public class WordCountProcessorSupplier implements ProcessorSupplier<String, Processor> {
    @Override
    public Processor get() {
        return new WordCountProcessor();
    }

    private class WordCountProcessor implements Processor<String, String>{

        private ProcessorContext context;

        private KeyValueStore<String, Integer> kvStore;

        @Override
        public void init(ProcessorContext context) {
            this.context = context;
            this.context.schedule(1000);
            this.kvStore = (KeyValueStore<String, Integer>) context.getStateStore("Counts");
        }

        @Override
        public void process(String key, String value) {
            String[] words = value.toLowerCase(Locale.getDefault()).split(" ");
            for (String word : words) {
                Integer oldValue = this.kvStore.get(word);
                if (oldValue == null) {
                    this.kvStore.put(word, 1);
                } else {
                    this.kvStore.put(word, oldValue + 1);
                }
            }
        }

        /**
         * Perform any periodic operations
         * @param timestamp
         */
        @Override
        public void punctuate(long timestamp) {
            try (KeyValueIterator<String, Integer> itr = this.kvStore.all()) {
                while (itr.hasNext()) {
                    KeyValue<String, Integer> entry = itr.next();
                    System.out.println("[" + entry.key + ", " + entry.value + "]");
                    context.forward(entry.key, entry.value.toString());
                }
                context.commit();
            }
        }

        @Override
        public void close() {
            this.kvStore.close();
        }
    }
}

配置

Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "word-count-demo");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        props.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        // setting offset reset to earliest so that we can re-run the demo code with the same pre-loaded data
        // Note: To re-run the demo, you need to use the offset reset tool:
        // https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Streams+Application+Reset+Tool
        props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");


        TopologyBuilder builder = new TopologyBuilder();
        builder.addSource("SOURCE", "wc-input");
        builder.addProcessor("PROCESSOR1", new WordCountProcessorSupplier(), "SOURCE");
        builder.addStateStore(Stores.create("Counts").withStringKeys().withIntegerValues().inMemory().build(),
                "PROCESSOR1");
//        builder.addSink("SINK", OUTPUT_TOPIC_NAME, "PROCESSOR1");


        KafkaStreams streams = new KafkaStreams(builder, props);
        final CountDownLatch latch = new CountDownLatch(1);

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

        try {
            streams.start();
            latch.await();
        } catch (Throwable e) {
            e.printStackTrace();
        }

輸出實例

[aaa, 2]
[bb, 1]
[ccc, 1]
[ddd, 1]
[hello, 2]
[kafka, 1]
[world, 2]
2017-10-16 22:33:01.835  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing task StreamTask 0_0
2017-10-16 22:33:08.775  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing all tasks because the commit interval 30000ms has elapsed
2017-10-16 22:33:08.776  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing task StreamTask 0_0
2017-10-16 22:33:08.776  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing task StreamTask 0_1
2017-10-16 22:33:08.776  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing task StreamTask 0_2
[aaa, 1]
2017-10-16 22:33:11.621  INFO   --- [ StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [StreamThread-1] Committing task StreamTask 0_2
[bb, 1]

doc

• Kafka設計解析（七）- 流式計算的新貴 Kafka Stream