Spark Streaming + Kafka Integration Guide
Apache Kafka is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka.
Linking: In your SBT/Maven project definition, link your streaming application against the following artifact (see Linking section in the main programming guide for further information).
groupId = org.apache.spark artifactId = spark-streaming-kafka_2.10 version = 1.2.1
Programming: In the streaming application code, import
KafkaUtilsand create input DStream as follows.
import org.apache.spark.streaming.kafka._ val kafkaStream = KafkaUtils.createStream( streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume])
Points to remember:
Topic partitions in Kafka does not correlate to partitions of RDDs generated in Spark Streaming. So increasing the number of topic-specific partitions in the
KafkaUtils.createStream()only increases the number of threads using which topics that are consumed within a single receiver. It does not increase the parallelism of Spark in processing the data. Refer to the main document for more information on that.
Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers.
spark-streaming-kafka_2.10and its dependencies (except
spark-streaming_2.10which are provided by
spark-submit) into the application JAR. Then use
spark-submitto launch your application (see Deploying section in the main programming guide).
Note that the Kafka receiver used by default is an
unreliable receiver section in the
programming guide). In Spark 1.2, we have added an experimental reliable Kafka receiver that
fault-tolerance guarantees of zero
data loss on failures. This receiver is automatically used when the write ahead log
(also introduced in Spark 1.2) is enabled
(see Deployment section in the programming guide). This
may reduce the receiving throughput of individual Kafka receivers compared to the unreliable
receivers, but this can be corrected by running
more receivers in parallel
to increase aggregate throughput. Additionally, it is recommended that the replication of the
received data within Spark be disabled when the write ahead log is enabled as the log is already stored
in a replicated storage system. This can be done by setting the storage level for the input
StorageLevel.MEMORY_AND_DISK_SER (that is, use