Kafka join streams filter by key - join

Question

I did some very easy example with join streams where two topics have the simple key value structure (integer/string) and it's works perfectly.
May I ask, how can I do something like:
SELECT * FROM stream1, stream2
WHERE stream1.key = stream2.key AND (stream1.key > 50 && stream1.key < 100) AND (stream2.key > 50 AND stream2.key < 100)
Kafka allows something like this?
Finally what I want to do is to filter 2 joined streams where key will be GenericRecord and it will looks somehow:
SELECT * FROM stream1, stream2
WHERE stream1.genericRecordkey.someId. = stream2.genericRecordkey.someId
My test example:
public void joinKStreamToKStreamWhereKeyValueIsIntegerString() throws Exception {
String uniqueKey = new Object() {
}.getClass().getEnclosingMethod().getName();
long timestamp = new Date().getTime();
String firstTopic = String.format("%1$s_1_%2$s", uniqueKey, timestamp);
String secondTopic = String.format("%1$s_2_%2$s", uniqueKey, timestamp);
String outputTopic = String.format("%1$s_output_%2$s", uniqueKey, timestamp);
String appIdConfig = String.format("%1$s_app_id_%2$s", uniqueKey, timestamp);
String groupIdConfig = String.format("%1$s_group_id_%2$s", uniqueKey, timestamp);
List<KeyValue<Integer, String>> ikv1 = Arrays.asList(
new KeyValue<>(1, "Bruce Eckel"),
new KeyValue<>(2, "Robert Lafore"),
new KeyValue<>(3, "Andrew Tanenbaum")
);
List<KeyValue<Integer, String>> ikv2 = Arrays.asList(
new KeyValue<>(3, "Modern Operating System"),
new KeyValue<>(1, "Thinking in Java"),
new KeyValue<>(3, "Computer Architecture"),
new KeyValue<>(4, "Programming in Scala")
);
List<KeyValue<Integer, String>> expectedResults = Arrays.asList(
new KeyValue<>(3, "Andrew Tanenbaum/Modern Operating System"),
new KeyValue<>(1, "Bruce Eckel/Thinking in Java"),
new KeyValue<>(3, "Andrew Tanenbaum/Computer Architecture")
);
Integer partitions = 1;
Integer replication = 1;
Properties topicConfig = new Properties();
TopicUtils.createTopic(firstTopic, partitions, replication, topicConfig);
TopicUtils.createTopic(secondTopic, partitions, replication, topicConfig);
TopicUtils.createTopic(outputTopic, partitions, replication, topicConfig);
final Serde<String> stringSerde = Serdes.String();
final Serde<Integer> integerSerde = Serdes.Integer();
Properties streamsConfiguration = new Properties();
streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, appIdConfig);
streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP_SERVERS_CONFIG);
streamsConfiguration.put(StreamsConfig.ZOOKEEPER_CONNECT_CONFIG, ZOOKEEPER_CONNECT_CONFIG);
streamsConfiguration.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.Integer().getClass().getName());
streamsConfiguration.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
// The commit interval for flushing records to state stores and downstream must be lower than
// this integration test's timeout (30 secs) to ensure we observe the expected processing results.
streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);
streamsConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
// Use a temporary directory for storing state, which will be automatically removed after the test.
streamsConfiguration.put(StreamsConfig.STATE_DIR_CONFIG, TestUtils.tempDirectory().getAbsolutePath());
KStreamBuilder builder = new KStreamBuilder();
KStream<Integer, String> firstStream = builder.stream(integerSerde, stringSerde, firstTopic);
KStream<Integer, String> secondStream = builder.stream(integerSerde, stringSerde, secondTopic);
KStream<Integer, String> outputStream = firstStream.join(secondStream, (l, r) -> {
return l + "/" + r;
}, JoinWindows.of(TimeUnit.SECONDS.toMillis(5)), integerSerde, stringSerde, stringSerde);
outputStream.to(integerSerde, stringSerde, outputTopic);
KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration);
streams.start();
Properties pCfg1 = new Properties();
pCfg1.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP_SERVERS_CONFIG);
pCfg1.put(ProducerConfig.ACKS_CONFIG, "all");
pCfg1.put(ProducerConfig.RETRIES_CONFIG, 0);
pCfg1.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class);
pCfg1.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
IntegrationTestUtils.produceKeyValuesSynchronously(firstTopic, ikv1, pCfg1);
Properties pCfg2 = new Properties();
pCfg2.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP_SERVERS_CONFIG);
pCfg2.put(ProducerConfig.ACKS_CONFIG, "all");
pCfg2.put(ProducerConfig.RETRIES_CONFIG, 0);
pCfg2.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class);
pCfg2.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
IntegrationTestUtils.produceKeyValuesSynchronously(secondTopic, ikv2, pCfg2);
Properties consumerConfig = new Properties();
consumerConfig.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP_SERVERS_CONFIG);
consumerConfig.put(ConsumerConfig.GROUP_ID_CONFIG, groupIdConfig);
consumerConfig.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
consumerConfig.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, IntegerDeserializer.class);
consumerConfig.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
List<KeyValue<Integer, String>> actualResults = IntegrationTestUtils.waitUntilMinKeyValueRecordsReceived(consumerConfig, outputTopic, expectedResults.size());
streams.close();
assertThat(actualResults).containsExactlyElementsOf(expectedResults);
}
Hope I explained well and thanks for any help.

Answer 1

You can just apply a filter before you do the join.
outputStream = firstStream.filter(...).join(secondStream.filter(...), ...);
If you want to join on stream1.genericRecordkey.someId you will need to extract someId first and set it as key:
firstStream.selectKey((k,v) -> v.someId) ).join(secondStream.selectKey((k,v) -> v.someId), ...);
For more details, check out the docs: http://docs.confluent.io/current/streams/developer-guide.html