com.hazelcast.jet.kafka

Class KafkaSources

java.lang.Object

com.hazelcast.jet.kafka.KafkaSources

public final class KafkaSources
extends Object

Contains factory methods for Apache Kafka sources.

Since:

Jet 3.0

Method Summary

Modifier and Type	Method and Description
static <K,V,T> StreamSource<T>	kafka(Properties properties, FunctionEx<org.apache.kafka.clients.consumer.ConsumerRecord<K,V>,T> projectionFn, String... topics) Returns a source that consumes one or more Apache Kafka topics and emits items from them as Map.Entry instances.
static <K,V> StreamSource<Map.Entry<K,V>>	kafka(Properties properties, String... topics) Convenience for kafka(Properties, FunctionEx, String...) wrapping the output in Map.Entry.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

kafka

@Nonnull
public static <K,V> StreamSource<Map.Entry<K,V>> kafka(@Nonnull
                                                                Properties properties,
                                                                @Nonnull
                                                                String... topics)

Convenience for kafka(Properties, FunctionEx, String...) wrapping the output in Map.Entry.

kafka

@Nonnull
public static <K,V,T> StreamSource<T> kafka(@Nonnull
                                                     Properties properties,
                                                     @Nonnull
                                                     FunctionEx<org.apache.kafka.clients.consumer.ConsumerRecord<K,V>,T> projectionFn,
                                                     @Nonnull
                                                     String... topics)

Returns a source that consumes one or more Apache Kafka topics and emits items from them as Map.Entry instances.

The source creates a KafkaConsumer for each Processor instance using the supplied properties. It assigns a subset of Kafka partitions to each of them using manual partition assignment (it ignores the group.id property). The Kafka's message timestamp will be used as a native timestamp.

If snapshotting is enabled, partition offsets are saved to the snapshot. After a restart, the source emits the events from the same offsets.

If you start a new job from an exported state, you can change the source parameters as needed:

• if you add a topic, it will be consumed from the default position
• if you remove a topic, restored offsets for that topic will be ignored (there will be a warning logged)
• if you connect to another cluster, the offsets will be used based on the equality of the topic name. If you want to start from default position, give different name to this source
• if the partition count is lower after a restart, the extra offsets will be ignored

The source can work in two modes:

1. if processing guarantee is enabled, offsets are stored to the snapshot and after a restart or failure, the reading continues from the saved offset. You can achieve exactly-once or at-least-once behavior.
2. if processing guarantee is disabled, the source will start reading from default offsets (based on the auto.offset.reset property). You can enable offset committing by assigning a group.id, enabling auto offset committing using enable.auto.commit and configuring auto.commit.interval.ms in the given properties. Refer to Kafka documentation for the descriptions of these properties.

If you add Kafka partitions at run-time, consumption from them will start after a delay, based on the metadata.max.age.ms Kafka property. Note, however, that events from them can be dropped as late if the allowed lag is not large enough.

The processor never completes, it can only fail in the case of an error. However, IO failures are generally handled by Kafka producer and do not cause the processor to fail. Kafka consumer also does not return from poll(timeout) if the cluster is down. If snapshotting is enabled, entire job might be blocked. This is a known issue of Kafka (KAFKA-1894, now fixed). Refer to Kafka documentation for details.

The default local parallelism for this processor is 4 (or less if less CPUs are available). Note that deserialization is done inside KafkaConsumer. If you have high traffic, the deserialization might become a bottleneck - increase the local parallelism or use byte[] for messages and deserialize manually in a subsequent mapping step.

Parameters:

properties - consumer properties broker address and key/value deserializers

projectionFn - function to create output objects from the Kafka record. If the projection returns a null for an item, that item will be filtered out.

topics - the topics to consume, at least one is required