kafka WordCountTransformerDemo 源码

  • 2022-10-20
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kafka WordCountTransformerDemo 代码


 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements. See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *    http://www.apache.org/licenses/LICENSE-2.0
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * See the License for the specific language governing permissions and
 * limitations under the License.
package org.apache.kafka.streams.examples.wordcount;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.Transformer;
import org.apache.kafka.streams.kstream.TransformerSupplier;
import org.apache.kafka.streams.processor.ConnectedStoreProvider;
import org.apache.kafka.streams.processor.PunctuationType;
import org.apache.kafka.streams.processor.api.Processor;
import org.apache.kafka.streams.processor.api.ProcessorContext;
import org.apache.kafka.streams.processor.api.ProcessorSupplier;
import org.apache.kafka.streams.processor.api.Record;
import org.apache.kafka.streams.state.KeyValueIterator;
import org.apache.kafka.streams.state.KeyValueStore;
import org.apache.kafka.streams.state.StoreBuilder;
import org.apache.kafka.streams.state.Stores;

import java.io.FileInputStream;
import java.io.IOException;
import java.time.Duration;
import java.util.Collections;
import java.util.Locale;
import java.util.Properties;
import java.util.Set;
import java.util.concurrent.CountDownLatch;

 * Demonstrates, using a {@link Transformer} which combines the low-level Processor APIs with the high-level Kafka Streams DSL,
 * how to implement the WordCount program that computes a simple word occurrence histogram from an input text.
 * <p>
 * <strong>Note: This is simplified code that only works correctly for single partition input topics.
 * Check out {@link WordCountDemo} for a generic example.</strong>
 * <p>
 * In this example, the input stream reads from a topic named "streams-plaintext-input", where the values of messages
 * represent lines of text; and the histogram output is written to topic "streams-wordcount-processor-output" where each record
 * is an updated count of a single word.
 * <p>
 * This example differs from {@link WordCountProcessorDemo} in that it uses a {@link Transformer} to define the word
 * count logic, and the topology is wired up through a {@link StreamsBuilder}, which more closely resembles the high-level DSL.
 * Additionally, the {@link TransformerSupplier} specifies the {@link StoreBuilder} that the {@link Transformer} needs
 * by implementing {@link ConnectedStoreProvider#stores()}.
 * <p>
 * Before running this example you must create the input topic and the output topic (e.g. via
 * {@code bin/kafka-topics.sh --create ...}), and write some data to the input topic (e.g. via
 * {@code bin/kafka-console-producer.sh}). Otherwise you won't see any data arriving in the output topic.
public final class WordCountTransformerDemo {

    static class MyProcessorSupplier implements ProcessorSupplier<String, String, String, String> {

        public Processor<String, String, String, String> get() {
            return new Processor<String, String, String, String>() {
                private KeyValueStore<String, Integer> kvStore;

                public void init(final ProcessorContext<String, String> context) {
                    context.schedule(Duration.ofSeconds(1), PunctuationType.STREAM_TIME, timestamp -> {
                        try (final KeyValueIterator<String, Integer> iter = kvStore.all()) {
                            System.out.println("----------- " + timestamp + " ----------- ");

                            while (iter.hasNext()) {
                                final KeyValue<String, Integer> entry = iter.next();

                                System.out.println("[" + entry.key + ", " + entry.value + "]");
                                context.forward(new Record<>(entry.key, entry.value.toString(), timestamp));
                    this.kvStore = context.getStateStore("Counts");

                public void process(final Record<String, String> record) {
                    final String[] words = record.value().toLowerCase(Locale.getDefault()).split("\\W+");

                    for (final String word : words) {
                        final Integer oldValue = this.kvStore.get(word);

                        if (oldValue == null) {
                            this.kvStore.put(word, 1);
                        } else {
                            this.kvStore.put(word, oldValue + 1);

                public void close() {}

        public Set<StoreBuilder<?>> stores() {
            return Collections.singleton(Stores.keyValueStoreBuilder(

    public static void main(final String[] args) throws IOException {
        final Properties props = new Properties();
        if (args != null && args.length > 0) {
            try (final FileInputStream fis = new FileInputStream(args[0])) {
            if (args.length > 1) {
                System.out.println("Warning: Some command line arguments were ignored. This demo only accepts an optional configuration file.");
        props.putIfAbsent(StreamsConfig.APPLICATION_ID_CONFIG, "streams-wordcount-transformer");
        props.putIfAbsent(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.putIfAbsent(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
        props.putIfAbsent(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        props.putIfAbsent(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        // setting offset reset to earliest so that we can re-run the demo code with the same pre-loaded data
        props.putIfAbsent(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");

        final StreamsBuilder builder = new StreamsBuilder();

        builder.<String, String>stream("streams-plaintext-input")
                .process(new MyProcessorSupplier())

        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-wordcount-shutdown-hook") {
            public void run() {

        try {
        } catch (final Throwable e) {


kafka 源码目录


kafka WordCountDemo 源码

kafka WordCountProcessorDemo 源码

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