/**
* Copyright 2016 Confluent Inc.
* <p>
* 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
* <p>
* http://www.apache.org/licenses/LICENSE-2.0
* <p>
* 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.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.consumer.ConsumerConfig;
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 org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.TimeWindows;
import org.apache.kafka.streams.kstream.Windowed;
import java.io.InputStream;
import java.util.Properties;
import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig;
import io.confluent.kafka.streams.serdes.avro.GenericAvroSerde;
/**
* Demonstrates how to perform a join between a KStream and a KTable, i.e. an example of a stateful
* computation, using the generic Avro binding for serdes in Kafka Streams. Same as
* PageViewRegionExample but uses lambda expressions and thus only works on Java 8+.
* <p>
* In this example, we join a stream of page views (aka clickstreams) that reads from a topic named
* "PageViews" with a user profile table that reads from a topic named "UserProfiles" to compute the
* number of page views per user region.
* <p>
* Note: The generic Avro binding is used for serialization/deserialization. This means the
* appropriate Avro schema files must be provided for each of the "intermediate" Avro classes, i.e.
* whenever new types of Avro objects (in the form of GenericRecord) are being passed between
* processing steps.
* <p>
* <br>
* HOW TO RUN THIS EXAMPLE
* <p>
* 1) Start Zookeeper, Kafka, and Confluent Schema Registry. Please refer to <a href='http://docs.confluent.io/current/quickstart.html#quickstart'>QuickStart</a>.
* <p>
* 2) Create the input/intermediate/output topics used by this example.
* <pre>
* {@code
* $ bin/kafka-topics --create --topic PageViews \
* --zookeeper localhost:2181 --partitions 1 --replication-factor 1
* $ bin/kafka-topics --create --topic PageViewsByUser \
* --zookeeper localhost:2181 --partitions 1 --replication-factor 1
* $ bin/kafka-topics --create --topic UserProfiles \
* --zookeeper localhost:2181 --partitions 1 --replication-factor 1
* $ bin/kafka-topics --create --topic PageViewsByRegion \
* --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/examples/tree/master/kafka-streams#packaging-and-running'>Packaging</a>.
* Once packaged you can then run:
* <pre>
* {@code
* $ java -cp target/streams-examples-3.3.0-SNAPSHOT-standalone.jar io.confluent.examples.streams.PageViewRegionLambdaExample
* }</pre>
* 4) Write some input data to the source topics (e.g. via {@link PageViewRegionExampleDriver}).
* The already running example application (step 3) will automatically process this input data and
* write the results to the output topic.
* <pre>
* {@code
* # Here: Write input data using the example driver. Once the driver has stopped generating data,
* # you can terminate it via `Ctrl-C`.
* $ java -cp target/streams-examples-3.3.0-SNAPSHOT-standalone.jar io.confluent.examples.streams.PageViewRegionExampleDriver
* }</pre>
* 5) Inspect the resulting data in the output topic, e.g. via {@code kafka-console-consumer}.
* <pre>
* {@code
* $ bin/kafka-console-consumer --topic PageViewsByRegion --from-beginning \
* --new-consumer --bootstrap-server localhost:9092 \
* --property print.key=true \
* --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
* }</pre>
* You should see output data similar to:
* <pre>
* {@code
* [africa@1466515140000] 2
* [asia@1466514900000] 3
* ...
* }</pre>
* Here, the output format is "[REGION@WINDOW_START_TIME] COUNT".
* <p>
* 6) Once you're done with your experiments, you can stop this example via {@code Ctrl-C}. If needed,
* also stop the Confluent Schema Registry ({@code Ctrl-C}), then stop the Kafka broker ({@code Ctrl-C}), and
* only then stop the ZooKeeper instance ({@code Ctrl-C}).
*/
public class PageViewRegionLambdaExample {
public static void main(final String[] args) throws Exception {
final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092";
final String schemaRegistryUrl = args.length > 1 ? args[1] : "http://localhost:8081";
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, "pageview-region-lambda-example");
// Where to find Kafka broker(s).
streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
// Where to find the Confluent schema registry instance(s)
streamsConfiguration.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, schemaRegistryUrl);
// Specify default (de)serializers for record keys and for record values.
streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, GenericAvroSerde.class);
streamsConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
// Records should be flushed every 10 seconds. This is less than the default
// in order to keep this example interactive.
streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);
final Serde<String> stringSerde = Serdes.String();
final Serde<Long> longSerde = Serdes.Long();
final KStreamBuilder builder = new KStreamBuilder();
// Create a stream of page view events from the PageViews topic, where the key of
// a record is assumed to be null and the value an Avro GenericRecord
// that represents the full details of the page view event. See `pageview.avsc` under
// `src/main/avro/` for the corresponding Avro schema.
final KStream<String, GenericRecord> views = builder.stream("PageViews");
// Create a keyed stream of page view events from the PageViews stream,
// by extracting the user id (String) from the Avro value
final KStream<String, GenericRecord> viewsByUser = views
.map((dummy, record) ->
new KeyValue<>(record.get("user").toString(), record));
// Create a changelog stream for user profiles from the UserProfiles topic,
// where the key of a record is assumed to be the user id (String) and its value
// an Avro GenericRecord. See `userprofile.avsc` under `src/main/avro/` for the
// corresponding Avro schema.
final KTable<String, GenericRecord> userProfiles = builder.table("UserProfiles", "UserProfilesStore");
// Create a changelog stream as a projection of the value to the region attribute only
final KTable<String, String> userRegions = userProfiles.mapValues(record ->
record.get("region").toString());
// We must specify the Avro schemas for all intermediate (Avro) classes, if any.
// In this example, we want to create an intermediate GenericRecord to hold the view region.
// See `pageviewregion.avsc` under `src/main/avro/`.
final InputStream
pageViewRegionSchema =
PageViewRegionLambdaExample.class.getClassLoader()
.getResourceAsStream("avro/io/confluent/examples/streams/pageviewregion.avsc");
final Schema schema = new Schema.Parser().parse(pageViewRegionSchema);
final KTable<Windowed<String>, Long> viewsByRegion = viewsByUser
.leftJoin(userRegions, (view, region) -> {
GenericRecord viewRegion = new GenericData.Record(schema);
viewRegion.put("user", view.get("user"));
viewRegion.put("page", view.get("page"));
viewRegion.put("region", region);
return viewRegion;
})
.map((user, viewRegion) -> new KeyValue<>(viewRegion.get("region").toString(), viewRegion))
// count views by region, using hopping windows of size 5 minutes that advance every 1 minute
.groupByKey() // no need to specify explicit serdes because the resulting key and value types match our default serde settings
.count(TimeWindows.of(5 * 60 * 1000L).advanceBy(60 * 1000L), "GeoPageViewsStore");
// Note: The following operations would NOT be needed for the actual pageview-by-region
// computation, which would normally stop at `count` above. We use the operations
// below only to "massage" the output data so it is easier to inspect on the console via
// kafka-console-consumer.
final KStream<String, Long> viewsByRegionForConsole = viewsByRegion
// get rid of windows (and the underlying KTable) by transforming the KTable to a KStream
// and by also converting the record key from type `Windowed<String>` (which
// kafka-console-consumer can't print to console out-of-the-box) to `String`
.toStream((windowedRegion, count) -> windowedRegion.toString());
viewsByRegionForConsole.to(stringSerde, longSerde, "PageViewsByRegion");
final KafkaStreams streams = new KafkaStreams(builder, 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,
// http://docs.confluent.io/current/streams/developer-guide.html#application-reset-tool).
//
// 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));
}
}