MapFunctionLambdaExample.java

/**
 * Copyright 2016 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.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KStreamBuilder;

import java.util.Properties;

/**
 * Demonstrates how to perform simple, state-less transformations via map functions. See also the
 * Scala variant {@link MapFunctionScalaExample}.
 *
 * 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.
 *
 * Note: This example uses lambda expressions and thus works with Java 8+ only.
 *
 * HOW TO RUN THIS EXAMPLE
 *
 * 1) Start Zookeeper and Kafka. Please refer to <a href='http://docs.confluent.io/3.0.0/quickstart.html#quickstart'>CP3.0.0
 * QuickStart</a>.
 *
 * 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 CP 3.0.0 only. For Apache Kafka it should be
 * `bin/kafka-topics.sh ...`.
 *
 * 3) Start this example application either in your IDE or on the command line.
 *
 * If via the command line please refer to <a href='https://github.com/confluentinc/examples/tree/master/kafka-streams#packaging-and-running'>Packaging</a>.
 * Once packaged you can then run:
 *
 * <pre>
 * {@code
 * $ java -cp target/streams-examples-3.0.0-standalone.jar io.confluent.examples.streams.MapFunctionLambdaExample
 * }
 * </pre>
 *
 * 4) Write some input data to the source topic (e.g. via `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 `kafka-console-consumer`.
 *
 * <pre>
 * {@code
 * $ bin/kafka-console-consumer --topic UppercasedTextLinesTopic --from-beginning \
 *                              --zookeeper localhost:2181
 * $ bin/kafka-console-consumer --topic OriginalAndUppercasedTopic --from-beginning \
 *                              --zookeeper localhost:2181
 * }
 * </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 `Ctrl-C`.  If needed,
 * also stop the Kafka broker (`Ctrl-C`), and only then stop the ZooKeeper instance (`Ctrl-C`).
 */
public class MapFunctionLambdaExample {

  public static void main(String[] args) throws Exception {
    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");
    // Where to find Kafka broker(s).
    streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    // Where to find the corresponding ZooKeeper ensemble.
    streamsConfiguration.put(StreamsConfig.ZOOKEEPER_CONNECT_CONFIG, "localhost:2181");
    // Specify default (de)serializers for record keys and for record values.
    streamsConfiguration.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.ByteArray().getClass().getName());
    streamsConfiguration.put(StreamsConfig.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.
    KStreamBuilder builder = new KStreamBuilder();

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

    // Variant 1: using `mapValues`
    KStream<byte[], String> uppercasedWithMapValues = textLines.mapValues(String::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)
    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.
    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(stringSerde, stringSerde, "OriginalAndUppercasedTopic");

    KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration);
    streams.start();
  }

}