MapFunctionLambdaExample.java

/*
 * Copyright Confluent Inc.
 *
 * Licensed 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,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package io.confluent.examples.streams;

import org.apache.kafka.common.serialization.Serde;
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.Consumed;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.Produced;

import java.util.Properties;

/**
 * Demonstrates how to perform simple, state-less transformations via map functions. See also the
 * Scala variant {@code MapFunctionScalaExample}.
 * <p>
 * Use cases include e.g. basic data sanitization, data anonymization by obfuscating sensitive data
 * fields (such as personally identifiable information aka PII).  This specific example reads
 * incoming text lines and converts each text line to all-uppercase.
 * <p>
 * Note: This example uses lambda expressions and thus works with Java 8+ only.
 * <p>
 * <br>
 * HOW TO RUN THIS EXAMPLE
 * <p>
 * 1) Start Zookeeper and Kafka. Please refer to <a href='http://docs.confluent.io/current/quickstart.html#quickstart'>QuickStart</a>.
 * <p>
 * 2) Create the input and output topics used by this example.
 * <pre>
 * {@code
 * $ bin/kafka-topics --create --topic TextLinesTopic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * $ bin/kafka-topics --create --topic UppercasedTextLinesTopic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * $ bin/kafka-topics --create --topic OriginalAndUppercasedTopic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * }</pre>
 * Note: The above commands are for the Confluent Platform. For Apache Kafka it should be {@code bin/kafka-topics.sh ...}.
 * <p>
 * 3) Start this example application either in your IDE or on the command line.
 * <p>
 * If via the command line please refer to <a href='https://github.com/confluentinc/kafka-streams-examples#packaging-and-running'>Packaging</a>.
 * Once packaged you can then run:
 * <pre>
 * {@code
 * $ java -cp target/kafka-streams-examples-7.8.0-0-standalone.jar io.confluent.examples.streams.MapFunctionLambdaExample
 * }
 * </pre>
 * 4) Write some input data to the source topic (e.g. via {@code kafka-console-producer}). The already
 * running example application (step 3) will automatically process this input data and write the
 * results to the output topics.
 * <pre>
 * {@code
 * # Start the console producer.  You can then enter input data by writing some line of text, followed by ENTER:
 * #
 * #   hello kafka streams<ENTER>
 * #   all streams lead to kafka<ENTER>
 * #
 * # Every line you enter will become the value of a single Kafka message.
 * $ bin/kafka-console-producer --broker-list localhost:9092 --topic TextLinesTopic
 * }</pre>
 * 5) Inspect the resulting data in the output topics, e.g. via {@code kafka-console-consumer}.
 * <pre>
 * {@code
 * $ bin/kafka-console-consumer --topic UppercasedTextLinesTopic --from-beginning \
 *                              --bootstrap-server localhost:9092
 * $ bin/kafka-console-consumer --topic OriginalAndUppercasedTopic --from-beginning \
 *                              --bootstrap-server localhost:9092 --property print.key=true
 * }</pre>
 * You should see output data similar to:
 * <pre>
 * {@code
 * HELLO KAFKA STREAMS
 * ALL STREAMS LEAD TO KAFKA
 * }</pre>
 * 6) Once you're done with your experiments, you can stop this example via {@code Ctrl-C}.  If needed,
 * also stop the Kafka broker ({@code Ctrl-C}), and only then stop the ZooKeeper instance ({@code Ctrl-C}).
 */
public class MapFunctionLambdaExample {

  public static void main(final String[] args) {
    final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092";
    final Properties streamsConfiguration = new Properties();
    // Give the Streams application a unique name.  The name must be unique in the Kafka cluster
    // against which the application is run.
    streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "map-function-lambda-example");
    streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG, "map-function-lambda-example-client");
    // Where to find Kafka broker(s).
    streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    // Specify default (de)serializers for record keys and for record values.
    streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.ByteArray().getClass().getName());
    streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());

    // Set up serializers and deserializers, which we will use for overriding the default serdes
    // specified above.
    final Serde<String> stringSerde = Serdes.String();
    final Serde<byte[]> byteArraySerde = Serdes.ByteArray();

    // In the subsequent lines we define the processing topology of the Streams application.
    final StreamsBuilder builder = new StreamsBuilder();

    // Read the input Kafka topic into a KStream instance.
    final KStream<byte[], String> textLines = builder.stream("TextLinesTopic", Consumed.with(byteArraySerde, stringSerde));

    // Variant 1: using `mapValues`
    final KStream<byte[], String> uppercasedWithMapValues = textLines.mapValues(v -> v.toUpperCase());

    // Write (i.e. persist) the results to a new Kafka topic called "UppercasedTextLinesTopic".
    //
    // In this case we can rely on the default serializers for keys and values because their data
    // types did not change, i.e. we only need to provide the name of the output topic.
    uppercasedWithMapValues.to("UppercasedTextLinesTopic");

    // Variant 2: using `map`, modify value only (equivalent to variant 1)
    final KStream<byte[], String> uppercasedWithMap = textLines.map((key, value) -> new KeyValue<>(key, value.toUpperCase()));

    // Variant 3: using `map`, modify both key and value
    //
    // Note: Whether, in general, you should follow this artificial example and store the original
    //       value in the key field is debatable and depends on your use case.  If in doubt, don't
    //       do it.
    final KStream<String, String> originalAndUppercased = textLines.map((key, value) -> KeyValue.pair(value, value.toUpperCase()));

    // Write the results to a new Kafka topic "OriginalAndUppercasedTopic".
    //
    // In this case we must explicitly set the correct serializers because the default serializers
    // (cf. streaming configuration) do not match the type of this particular KStream instance.
    originalAndUppercased.to("OriginalAndUppercasedTopic", Produced.with(stringSerde, stringSerde));

    final KafkaStreams streams = new KafkaStreams(builder.build(), streamsConfiguration);
    // Always (and unconditionally) clean local state prior to starting the processing topology.
    // We opt for this unconditional call here because this will make it easier for you to play around with the example
    // when resetting the application for doing a re-run (via the Application Reset Tool,
    // https://docs.confluent.io/platform/current/streams/developer-guide/app-reset-tool.html).
    //
    // The drawback of cleaning up local state prior is that your app must rebuilt its local state from scratch, which
    // will take time and will require reading all the state-relevant data from the Kafka cluster over the network.
    // Thus in a production scenario you typically do not want to clean up always as we do here but rather only when it
    // is truly needed, i.e., only under certain conditions (e.g., the presence of a command line flag for your app).
    // See `ApplicationResetExample.java` for a production-like example.
    streams.cleanUp();
    streams.start();

    // Add shutdown hook to respond to SIGTERM and gracefully close Kafka Streams
    Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
  }

}