Apache Flink With Kafka - Consumer and Producer
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Apache Flink provides various connectors to integrate with other systems. In this article, I will share an example of consuming records from Kafka through FlinkKafkaConsumer and producing records to Kafka using FlinkKafkaProducer.
Setup
I installed Kafka locally and created two Topics, TOPIC-IN and TOPIC-OUT.
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# Create two topics
./bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic TOPIC-IN
./bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic TOPIC-OUT
# List all topics
./bin/kafka-topics.sh --zookeeper localhost:2181 --list
TOPIC-IN
TOPIC-OUT
__consumer_offsets
I wrote a very simple NumberGenerator, which will generate a number every second and send it to TOPIC_IN using a KafkaProducer object. The code for both is available on Github.
A sample run produces the following output:
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./bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic TOPIC-IN --property print.key=true --from-beginning
myKey [1]
myKey [2]
myKey [3]
myKey [4]
myKey [5]
myKey [6]
myKey [7]
myKey [8]
myKey [9]
FlinkKafkaConnector Example
First, define a FlinkKafkaConsumer, as shown below:
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String TOPIC_IN = "TOPIC-IN";
String TOPIC_OUT = "TOPIC-OUT";
String BOOTSTRAP_SERVER = "localhost:9092";
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// to use allowed lateness and timestamp from kafka message
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
Properties props = new Properties();
props.put("bootstrap.servers", BOOTSTRAP_SERVER);
props.put("client.id", "flink-kafka-example");
// consumer to get both key/values per Topic
FlinkKafkaConsumer<KafkaRecord> kafkaConsumer = new FlinkKafkaConsumer<>(TOPIC_IN, new MySchema(), props);
// for allowing Flink to handle late elements
kafkaConsumer.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<KafkaRecord>()
{
public long extractAscendingTimestamp(KafkaRecord record)
{
return record.timestamp;
}
});
kafkaConsumer.setStartFromLatest();
Line #5: Get a local Flink StreamExecutionEnvrionment.
Line #8: Required to use timestamp coming in the messages from Kafka. Otherwise, Flink will use the system clock.
Line #15: Create a FlinkKafkaConsumer<> object, which will act as a source for us. The class "KafkaRecord" is a wrapper for the key and value coming from Kafka, and the MySchema class implements KafkaDeserializationSchema<KafkaRecord> to provide deserialization logic used by Flink to convert byte[] from Kafka to String.
The code for both is available here. This is required because I want to read both the key and value of the Kafka messages.
Line #18 to #25: Required to inform Flink where it should read the timestamp. This is used to decide the start and end of a TumblingTimewindow.
After this, we need to define a FlinkKafkaProducer, as shown below:
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Properties prodProps = new Properties();
prodProps.put("bootstrap.servers", BOOTSTRAP_SERVER);
FlinkKafkaProducer<KafkaRecord> kafkaProducer =
new FlinkKafkaProducer<KafkaRecord>(TOPIC_OUT,
((record, timestamp) -> new ProducerRecord<byte[], byte[]>(TOPIC_OUT, record.key.getBytes(), record.value.getBytes())),
prodProps,
Semantic.EXACTLY_ONCE);
Now, we can define a simple pipeline, as shown below:
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DataStream<KafkaRecord> stream = env.addSource(kafkaConsumer);
stream
.filter((record) -> record.value != null && !record.value.isEmpty())
.keyBy(record -> record.key)
.timeWindow(Time.seconds(5))
.allowedLateness(Time.seconds(1))
.reduce(new ReduceFunction<KafkaRecord>()
{
KafkaRecord result = new KafkaRecord();
public KafkaRecord reduce(KafkaRecord record1, KafkaRecord record2) throws Exception
{
result.key = "outKey";
result.value = record1.value+record2.value;
return result;
}
})
.addSink(kafkaProducer);
// produce a number as string every second
new NumberGenerator(p, TOPIC_IN).start();
// start flink
env.execute();
Line #1: Create a DataStream from the FlinkKafkaConsumer object as the source.
Line #3: Filter out null and empty values coming from Kafka.
Line #5: Key the Flink stream based on the key present in Kafka messages. This will logically partition the stream and allow parallel execution on a per-key basis.
Line #6 to #7: Define a time window of five seconds and provide lateness of an extra second.
Line #8 to #19: Simple reduction logic that appends all the numbers collected in a window and sends the result using a new key "outKey".
Line #20: Sends the output of each window to the FlinkKafkaProducer object created above.
Line #23: Start the NumberGenerator.
Line #26: Start the Flink execution environment.
A sample run of this code produces the following output:
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./bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic TOPIC-OUT --property print.key=true --from-beginning
outKey [5][6]
outKey [7][8][9][10][11]
outKey [12][13][14][15][16]
outKey [17][18][19][20][21]
outKey [22][23][24][25][26]
Conclusion
The above example shows how to use Flink's Kafka connector API to consume as well as produce messages to Kafka and customized deserialization when reading data from Kafka.
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