342612b65f
Updated the broken link pointing to the KafkaWordCount example to the correct one. Author: sigmoidanalytics <mayur@sigmoidanalytics.com> Closes #3877 from sigmoidanalytics/patch-1 and squashes the following commits: 3e19b31 [sigmoidanalytics] Updated broken links
60 lines
4 KiB
Markdown
60 lines
4 KiB
Markdown
---
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layout: global
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title: Spark Streaming + Kafka Integration Guide
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---
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[Apache Kafka](http://kafka.apache.org/) 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.
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1. **Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
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groupId = org.apache.spark
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artifactId = spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}
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version = {{site.SPARK_VERSION_SHORT}}
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2. **Programming:** In the streaming application code, import `KafkaUtils` and create input DStream as follows.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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import org.apache.spark.streaming.kafka._
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val kafkaStream = KafkaUtils.createStream(
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streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume])
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See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
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and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
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</div>
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<div data-lang="java" markdown="1">
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import org.apache.spark.streaming.kafka.*;
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JavaPairReceiverInputDStream<String, String> kafkaStream = KafkaUtils.createStream(
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streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume]);
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See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
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and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
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</div>
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</div>
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*Points to remember:*
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- 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.
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- Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers.
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3. **Deploying:** Package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
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Note that the Kafka receiver used by default is an
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[*unreliable* receiver](streaming-programming-guide.html#receiver-reliability) section in the
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programming guide). In Spark 1.2, we have added an experimental *reliable* Kafka receiver that
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provides stronger
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[fault-tolerance guarantees](streaming-programming-guide.html#fault-tolerance-semantics) of zero
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data loss on failures. This receiver is automatically used when the write ahead log
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(also introduced in Spark 1.2) is enabled
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(see [Deployment](#deploying-applications.html) section in the programming guide). This
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may reduce the receiving throughput of individual Kafka receivers compared to the unreliable
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receivers, but this can be corrected by running
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[more receivers in parallel](streaming-programming-guide.html#level-of-parallelism-in-data-receiving)
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to increase aggregate throughput. Additionally, it is recommended that the replication of the
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received data within Spark be disabled when the write ahead log is enabled as the log is already stored
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in a replicated storage system. This can be done by setting the storage level for the input
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stream to `StorageLevel.MEMORY_AND_DISK_SER` (that is, use
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`KafkaUtils.createStream(..., StorageLevel.MEMORY_AND_DISK_SER)`).
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