Examples with KStream org.apache.kafka.streams.kstream.KStream used on opensource projects

Example 26 with KStream

use of org.apache.kafka.streams.kstream.KStream in project kafka by apache.

the class CogroupedKStreamImplTest method shouldInsertRepartitionsTopicForUpstreamKeyModificationWithGroupedReusedInDifferentCogroups.

@Test
public void shouldInsertRepartitionsTopicForUpstreamKeyModificationWithGroupedReusedInDifferentCogroups() {
    final StreamsBuilder builder = new StreamsBuilder();
    final KStream<String, String> stream1 = builder.stream("one", stringConsumed);
    final KStream<String, String> stream2 = builder.stream("two", stringConsumed);
    final KStream<String, String> stream3 = builder.stream("three", stringConsumed);
    final KGroupedStream<String, String> groupedOne = stream1.map((k, v) -> new KeyValue<>(v, k)).groupByKey();
    final KGroupedStream<String, String> groupedTwo = stream2.groupByKey();
    final KGroupedStream<String, String> groupedThree = stream3.groupByKey();
    groupedOne.cogroup(STRING_AGGREGATOR).cogroup(groupedThree, STRING_AGGREGATOR).aggregate(STRING_INITIALIZER);
    groupedOne.cogroup(STRING_AGGREGATOR).cogroup(groupedTwo, STRING_AGGREGATOR).aggregate(STRING_INITIALIZER);
    final String topologyDescription = builder.build().describe().toString();
    assertThat(topologyDescription, equalTo("Topologies:\n" + "   Sub-topology: 0\n" + "    Source: KSTREAM-SOURCE-0000000000 (topics: [one])\n" + "      --> KSTREAM-MAP-0000000003\n" + "    Processor: KSTREAM-MAP-0000000003 (stores: [])\n" + "      --> COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter, COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-filter\n" + "      <-- KSTREAM-SOURCE-0000000000\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter (stores: [])\n" + "      --> COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-sink\n" + "      <-- KSTREAM-MAP-0000000003\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-filter (stores: [])\n" + "      --> COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-sink\n" + "      <-- KSTREAM-MAP-0000000003\n" + "    Sink: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-sink (topic: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition)\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter\n" + "    Sink: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-sink (topic: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition)\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-filter\n\n" + "  Sub-topology: 1\n" + "    Source: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-source (topics: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000015\n" + "    Source: KSTREAM-SOURCE-0000000001 (topics: [two])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000016\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000015 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011])\n" + "      --> COGROUPKSTREAM-MERGE-0000000017\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011-repartition-source\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000016 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011])\n" + "      --> COGROUPKSTREAM-MERGE-0000000017\n" + "      <-- KSTREAM-SOURCE-0000000001\n" + "    Processor: COGROUPKSTREAM-MERGE-0000000017 (stores: [])\n" + "      --> none\n" + "      <-- COGROUPKSTREAM-AGGREGATE-0000000015, COGROUPKSTREAM-AGGREGATE-0000000016\n\n" + "  Sub-topology: 2\n" + "    Source: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-source (topics: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000008\n" + "    Source: KSTREAM-SOURCE-0000000002 (topics: [three])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000009\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000008 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004])\n" + "      --> COGROUPKSTREAM-MERGE-0000000010\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-source\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000009 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004])\n" + "      --> COGROUPKSTREAM-MERGE-0000000010\n" + "      <-- KSTREAM-SOURCE-0000000002\n" + "    Processor: COGROUPKSTREAM-MERGE-0000000010 (stores: [])\n" + "      --> none\n" + "      <-- COGROUPKSTREAM-AGGREGATE-0000000008, COGROUPKSTREAM-AGGREGATE-0000000009\n\n"));
}

Example 27 with KStream

use of org.apache.kafka.streams.kstream.KStream in project kafka by apache.

the class CogroupedKStreamImplTest method shouldInsertRepartitionsTopicForUpstreamKeyModificationWithGroupedReusedInDifferentCogroupsWithOptimization.

@Test
public void shouldInsertRepartitionsTopicForUpstreamKeyModificationWithGroupedReusedInDifferentCogroupsWithOptimization() {
    final StreamsBuilder builder = new StreamsBuilder();
    final Properties properties = new Properties();
    properties.setProperty(StreamsConfig.TOPOLOGY_OPTIMIZATION_CONFIG, StreamsConfig.OPTIMIZE);
    final KStream<String, String> stream1 = builder.stream("one", stringConsumed);
    final KStream<String, String> stream2 = builder.stream("two", stringConsumed);
    final KStream<String, String> stream3 = builder.stream("three", stringConsumed);
    final KGroupedStream<String, String> groupedOne = stream1.map((k, v) -> new KeyValue<>(v, k)).groupByKey();
    final KGroupedStream<String, String> groupedTwo = stream2.groupByKey();
    final KGroupedStream<String, String> groupedThree = stream3.groupByKey();
    groupedOne.cogroup(STRING_AGGREGATOR).cogroup(groupedThree, STRING_AGGREGATOR).aggregate(STRING_INITIALIZER);
    groupedOne.cogroup(STRING_AGGREGATOR).cogroup(groupedTwo, STRING_AGGREGATOR).aggregate(STRING_INITIALIZER);
    final String topologyDescription = builder.build(properties).describe().toString();
    assertThat(topologyDescription, equalTo("Topologies:\n" + "   Sub-topology: 0\n" + "    Source: KSTREAM-SOURCE-0000000000 (topics: [one])\n" + "      --> KSTREAM-MAP-0000000003\n" + "    Processor: KSTREAM-MAP-0000000003 (stores: [])\n" + "      --> COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter\n" + "      <-- KSTREAM-SOURCE-0000000000\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter (stores: [])\n" + "      --> COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-sink\n" + "      <-- KSTREAM-MAP-0000000003\n" + "    Sink: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-sink (topic: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition)\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-filter\n\n" + "  Sub-topology: 1\n" + "    Source: COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-source (topics: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000008, COGROUPKSTREAM-AGGREGATE-0000000015\n" + "    Source: KSTREAM-SOURCE-0000000001 (topics: [two])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000016\n" + "    Source: KSTREAM-SOURCE-0000000002 (topics: [three])\n" + "      --> COGROUPKSTREAM-AGGREGATE-0000000009\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000008 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004])\n" + "      --> COGROUPKSTREAM-MERGE-0000000010\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-source\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000009 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004])\n" + "      --> COGROUPKSTREAM-MERGE-0000000010\n" + "      <-- KSTREAM-SOURCE-0000000002\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000015 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011])\n" + "      --> COGROUPKSTREAM-MERGE-0000000017\n" + "      <-- COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000004-repartition-source\n" + "    Processor: COGROUPKSTREAM-AGGREGATE-0000000016 (stores: [COGROUPKSTREAM-AGGREGATE-STATE-STORE-0000000011])\n" + "      --> COGROUPKSTREAM-MERGE-0000000017\n" + "      <-- KSTREAM-SOURCE-0000000001\n" + "    Processor: COGROUPKSTREAM-MERGE-0000000010 (stores: [])\n" + "      --> none\n" + "      <-- COGROUPKSTREAM-AGGREGATE-0000000008, COGROUPKSTREAM-AGGREGATE-0000000009\n" + "    Processor: COGROUPKSTREAM-MERGE-0000000017 (stores: [])\n" + "      --> none\n" + "      <-- COGROUPKSTREAM-AGGREGATE-0000000015, COGROUPKSTREAM-AGGREGATE-0000000016\n\n"));
}

Example 28 with KStream

use of org.apache.kafka.streams.kstream.KStream in project kafka by apache.

the class StreamsUpgradeTest method main.

@SuppressWarnings("unchecked")
public static void main(final String[] args) throws Exception {
    if (args.length < 1) {
        System.err.println("StreamsUpgradeTest requires one argument (properties-file) but provided none");
    }
    final String propFileName = args[0];
    final Properties streamsProperties = Utils.loadProps(propFileName);
    System.out.println("StreamsTest instance started (StreamsUpgradeTest v3.1)");
    System.out.println("props=" + streamsProperties);
    final StreamsBuilder builder = new StreamsBuilder();
    final KStream dataStream = builder.stream("data");
    dataStream.process(printProcessorSupplier());
    dataStream.to("echo");
    final Properties config = new Properties();
    config.setProperty(StreamsConfig.APPLICATION_ID_CONFIG, "StreamsUpgradeTest");
    config.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000);
    config.putAll(streamsProperties);
    final KafkaStreams streams = new KafkaStreams(builder.build(), config);
    streams.start();
    Runtime.getRuntime().addShutdownHook(new Thread(() -> {
        streams.close();
        System.out.println("UPGRADE-TEST-CLIENT-CLOSED");
        System.out.flush();
    }));
}

Example 29 with KStream

use of org.apache.kafka.streams.kstream.KStream in project kafka by apache.

the class SmokeTestClient method getTopology.

public Topology getTopology() {
    final StreamsBuilder builder = new StreamsBuilder();
    final Consumed<String, Integer> stringIntConsumed = Consumed.with(stringSerde, intSerde);
    final KStream<String, Integer> source = builder.stream("data", stringIntConsumed);
    source.filterNot((k, v) -> k.equals("flush")).to("echo", Produced.with(stringSerde, intSerde));
    final KStream<String, Integer> data = source.filter((key, value) -> value == null || value != END);
    data.process(SmokeTestUtil.printProcessorSupplier("data", name));
    // min
    final KGroupedStream<String, Integer> groupedData = data.groupByKey(Grouped.with(stringSerde, intSerde));
    final KTable<Windowed<String>, Integer> minAggregation = groupedData.windowedBy(TimeWindows.ofSizeAndGrace(Duration.ofDays(1), Duration.ofMinutes(1))).aggregate(() -> Integer.MAX_VALUE, (aggKey, value, aggregate) -> (value < aggregate) ? value : aggregate, Materialized.<String, Integer, WindowStore<Bytes, byte[]>>as("uwin-min").withValueSerde(intSerde).withRetention(Duration.ofHours(25)));
    streamify(minAggregation, "min-raw");
    streamify(minAggregation.suppress(untilWindowCloses(BufferConfig.unbounded())), "min-suppressed");
    minAggregation.toStream(new Unwindow<>()).filterNot((k, v) -> k.equals("flush")).to("min", Produced.with(stringSerde, intSerde));
    final KTable<Windowed<String>, Integer> smallWindowSum = groupedData.windowedBy(TimeWindows.ofSizeAndGrace(Duration.ofSeconds(2), Duration.ofSeconds(30)).advanceBy(Duration.ofSeconds(1))).reduce(Integer::sum);
    streamify(smallWindowSum, "sws-raw");
    streamify(smallWindowSum.suppress(untilWindowCloses(BufferConfig.unbounded())), "sws-suppressed");
    final KTable<String, Integer> minTable = builder.table("min", Consumed.with(stringSerde, intSerde), Materialized.as("minStoreName"));
    minTable.toStream().process(SmokeTestUtil.printProcessorSupplier("min", name));
    // max
    groupedData.windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofDays(2))).aggregate(() -> Integer.MIN_VALUE, (aggKey, value, aggregate) -> (value > aggregate) ? value : aggregate, Materialized.<String, Integer, WindowStore<Bytes, byte[]>>as("uwin-max").withValueSerde(intSerde)).toStream(new Unwindow<>()).filterNot((k, v) -> k.equals("flush")).to("max", Produced.with(stringSerde, intSerde));
    final KTable<String, Integer> maxTable = builder.table("max", Consumed.with(stringSerde, intSerde), Materialized.as("maxStoreName"));
    maxTable.toStream().process(SmokeTestUtil.printProcessorSupplier("max", name));
    // sum
    groupedData.windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofDays(2))).aggregate(() -> 0L, (aggKey, value, aggregate) -> (long) value + aggregate, Materialized.<String, Long, WindowStore<Bytes, byte[]>>as("win-sum").withValueSerde(longSerde)).toStream(new Unwindow<>()).filterNot((k, v) -> k.equals("flush")).to("sum", Produced.with(stringSerde, longSerde));
    final Consumed<String, Long> stringLongConsumed = Consumed.with(stringSerde, longSerde);
    final KTable<String, Long> sumTable = builder.table("sum", stringLongConsumed);
    sumTable.toStream().process(SmokeTestUtil.printProcessorSupplier("sum", name));
    // cnt
    groupedData.windowedBy(TimeWindows.ofSizeWithNoGrace(Duration.ofDays(2))).count(Materialized.as("uwin-cnt")).toStream(new Unwindow<>()).filterNot((k, v) -> k.equals("flush")).to("cnt", Produced.with(stringSerde, longSerde));
    final KTable<String, Long> cntTable = builder.table("cnt", Consumed.with(stringSerde, longSerde), Materialized.as("cntStoreName"));
    cntTable.toStream().process(SmokeTestUtil.printProcessorSupplier("cnt", name));
    // dif
    maxTable.join(minTable, (value1, value2) -> value1 - value2).toStream().filterNot((k, v) -> k.equals("flush")).to("dif", Produced.with(stringSerde, intSerde));
    // avg
    sumTable.join(cntTable, (value1, value2) -> (double) value1 / (double) value2).toStream().filterNot((k, v) -> k.equals("flush")).to("avg", Produced.with(stringSerde, doubleSerde));
    // test repartition
    final Agg agg = new Agg();
    cntTable.groupBy(agg.selector(), Grouped.with(stringSerde, longSerde)).aggregate(agg.init(), agg.adder(), agg.remover(), Materialized.<String, Long>as(Stores.inMemoryKeyValueStore("cntByCnt")).withKeySerde(Serdes.String()).withValueSerde(Serdes.Long())).toStream().to("tagg", Produced.with(stringSerde, longSerde));
    return builder.build();
}

Example 30 with KStream

use of org.apache.kafka.streams.kstream.KStream in project kafka by apache.

the class PageViewTypedDemo method main.

public static void main(final String[] args) {
    final Properties props = new Properties();
    props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-pageview-typed");
    props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(StreamsConfig.DEFAULT_TIMESTAMP_EXTRACTOR_CLASS_CONFIG, JsonTimestampExtractor.class);
    props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, JSONSerde.class);
    props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, JSONSerde.class);
    props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
    props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000L);
    // setting offset reset to earliest so that we can re-run the demo code with the same pre-loaded data
    props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
    final StreamsBuilder builder = new StreamsBuilder();
    final KStream<String, PageView> views = builder.stream("streams-pageview-input", Consumed.with(Serdes.String(), new JSONSerde<>()));
    final KTable<String, UserProfile> users = builder.table("streams-userprofile-input", Consumed.with(Serdes.String(), new JSONSerde<>()));
    final Duration duration24Hours = Duration.ofHours(24);
    final KStream<WindowedPageViewByRegion, RegionCount> regionCount = views.leftJoin(users, (view, profile) -> {
        final PageViewByRegion viewByRegion = new PageViewByRegion();
        viewByRegion.user = view.user;
        viewByRegion.page = view.page;
        if (profile != null) {
            viewByRegion.region = profile.region;
        } else {
            viewByRegion.region = "UNKNOWN";
        }
        return viewByRegion;
    }).map((user, viewRegion) -> new KeyValue<>(viewRegion.region, viewRegion)).groupByKey(Grouped.with(Serdes.String(), new JSONSerde<>())).windowedBy(TimeWindows.ofSizeAndGrace(Duration.ofDays(7), duration24Hours).advanceBy(Duration.ofSeconds(1))).count().toStream().map((key, value) -> {
        final WindowedPageViewByRegion wViewByRegion = new WindowedPageViewByRegion();
        wViewByRegion.windowStart = key.window().start();
        wViewByRegion.region = key.key();
        final RegionCount rCount = new RegionCount();
        rCount.region = key.key();
        rCount.count = value;
        return new KeyValue<>(wViewByRegion, rCount);
    });
    // write to the result topic
    regionCount.to("streams-pageviewstats-typed-output", Produced.with(new JSONSerde<>(), new JSONSerde<>()));
    final KafkaStreams streams = new KafkaStreams(builder.build(), props);
    final CountDownLatch latch = new CountDownLatch(1);
    // attach shutdown handler to catch control-c
    Runtime.getRuntime().addShutdownHook(new Thread("streams-pipe-shutdown-hook") {

        @Override
        public void run() {
            streams.close();
            latch.countDown();
        }
    });
    try {
        streams.start();
        latch.await();
    } catch (final Throwable e) {
        e.printStackTrace();
        System.exit(1);
    }
    System.exit(0);
}