---
layout: global
title: Spark Streaming + Flume Integration Guide
---

[Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Here we explain how to configure Flume and Spark Streaming to receive data from Flume. There are two approaches to this.

## Approach 1: Flume-style Push-based Approach
Flume is designed to push data between Flume agents. In this approach, Spark Streaming essentially sets up a receiver that acts an Avro agent for Flume, to which Flume can push the data. Here are the configuration steps.

#### General Requirements
Choose a machine in your cluster such that

- When your Flume + Spark Streaming application is launched, one of the Spark workers must run on that machine.

- Flume can be configured to push data to a port on that machine.

Due to the push model, the streaming application needs to be up, with the receiver scheduled and listening on the chosen port, for Flume to be able push data.

#### Configuring Flume
Configure Flume agent to send data to an Avro sink by having the following in the configuration file.

	agent.sinks = avroSink
	agent.sinks.avroSink.type = avro
    agent.sinks.avroSink.channel = memoryChannel
    agent.sinks.avroSink.hostname = <chosen machine's hostname>
	agent.sinks.avroSink.port = <chosen port on the machine>

See the [Flume's documentation](https://flume.apache.org/documentation.html) for more information about
configuring Flume agents.

#### Configuring Spark Streaming Application
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).

		groupId = org.apache.spark
		artifactId = spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}
		version = {{site.SPARK_VERSION_SHORT}}

2. **Programming:** In the streaming application code, import `FlumeUtils` and create input DStream as follows.

	<div class="codetabs">
	<div data-lang="scala" markdown="1">
		import org.apache.spark.streaming.flume._

		val flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])

	See the [API docs](api/scala/index.html#org.apache.spark.streaming.flume.FlumeUtils$)
	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala).
	</div>
	<div data-lang="java" markdown="1">
		import org.apache.spark.streaming.flume.*;

		JavaReceiverInputDStream<SparkFlumeEvent> flumeStream =
        	FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port]);

	See the [API docs](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html)
	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java).
	</div>
	<div data-lang="python" markdown="1">
		from pyspark.streaming.flume import FlumeUtils

		flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])

	By default, the Python API will decode Flume event body as UTF8 encoded strings. You can specify your custom decoding function to decode the body byte arrays in Flume events to any arbitrary data type. 
	See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.flume.FlumeUtils)
	and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/flume_wordcount.py).
	</div>
	</div>

	Note that the hostname should be the same as the one used by the resource manager in the
    cluster (Mesos, YARN or Spark Standalone), so that resource allocation can match the names and launch
    the receiver in the right machine.

3. **Deploying:** As with any Spark applications, `spark-submit` is used to launch your application. However, the details are slightly different for Scala/Java applications and Python applications.

	For Scala and Java applications, if you are using SBT or Maven for project management, then package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).

	For Python applications which lack SBT/Maven project management, `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies can be directly added to `spark-submit` using `--packages` (see [Application Submission Guide](submitting-applications.html)). That is,

	    ./bin/spark-submit --packages org.apache.spark:spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...

	Alternatively, you can also download the JAR of the Maven artifact `spark-streaming-flume-assembly` from the
	[Maven repository](http://search.maven.org/#search|ga|1|a%3A%22spark-streaming-flume-assembly_{{site.SCALA_BINARY_VERSION}}%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) and add it to `spark-submit` with `--jars`.

## Approach 2: Pull-based Approach using a Custom Sink
Instead of Flume pushing data directly to Spark Streaming, this approach runs a custom Flume sink that allows the following.

- Flume pushes data into the sink, and the data stays buffered.
- Spark Streaming uses a [reliable Flume receiver](streaming-programming-guide.html#receiver-reliability)
  and transactions to pull data from the sink. Transactions succeed only after data is received and
  replicated by Spark Streaming.

This ensures stronger reliability and
[fault-tolerance guarantees](streaming-programming-guide.html#fault-tolerance-semantics)
than the previous approach. However, this requires configuring Flume to run a custom sink.
Here are the configuration steps.

#### General Requirements
Choose a machine that will run the custom sink in a Flume agent. The rest of the Flume pipeline is configured to send data to that agent. Machines in the Spark cluster should have access to the chosen machine running the custom sink.

#### Configuring Flume
Configuring Flume on the chosen machine requires the following two steps.

1. **Sink JARs**: Add the following JARs to Flume's classpath (see [Flume's documentation](https://flume.apache.org/documentation.html) to see how) in the machine designated to run the custom sink .

	(i) *Custom sink JAR*: Download the JAR corresponding to the following artifact (or [direct link](http://search.maven.org/remotecontent?filepath=org/apache/spark/spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}/{{site.SPARK_VERSION_SHORT}}/spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}-{{site.SPARK_VERSION_SHORT}}.jar)).

		groupId = org.apache.spark
		artifactId = spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}
		version = {{site.SPARK_VERSION_SHORT}}

	(ii) *Scala library JAR*: Download the Scala library JAR for Scala {{site.SCALA_VERSION}}. It can be found with the following artifact detail (or, [direct link](http://search.maven.org/remotecontent?filepath=org/scala-lang/scala-library/{{site.SCALA_VERSION}}/scala-library-{{site.SCALA_VERSION}}.jar)).

		groupId = org.scala-lang
		artifactId = scala-library
		version = {{site.SCALA_VERSION}}

	(iii) *Commons Lang 3 JAR*: Download the Commons Lang 3 JAR. It can be found with the following artifact detail (or, [direct link](http://search.maven.org/remotecontent?filepath=org/apache/commons/commons-lang3/3.3.2/commons-lang3-3.3.2.jar)).

		groupId = org.apache.commons
		artifactId = commons-lang3
		version = 3.3.2

2. **Configuration file**: On that machine, configure Flume agent to send data to an Avro sink by having the following in the configuration file.

		agent.sinks = spark
		agent.sinks.spark.type = org.apache.spark.streaming.flume.sink.SparkSink
		agent.sinks.spark.hostname = <hostname of the local machine>
		agent.sinks.spark.port = <port to listen on for connection from Spark>
		agent.sinks.spark.channel = memoryChannel

	Also make sure that the upstream Flume pipeline is configured to send the data to the Flume agent running this sink.

See the [Flume's documentation](https://flume.apache.org/documentation.html) for more information about
configuring Flume agents.

#### Configuring Spark Streaming Application
1. **Linking:** In your SBT/Maven project definition, link your streaming application against the `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide).

2. **Programming:** In the streaming application code, import `FlumeUtils` and create input DStream as follows.

	<div class="codetabs">
	<div data-lang="scala" markdown="1">
		import org.apache.spark.streaming.flume._

		val flumeStream = FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port])
	</div>
	<div data-lang="java" markdown="1">
		import org.apache.spark.streaming.flume.*;

		JavaReceiverInputDStream<SparkFlumeEvent>flumeStream =
			FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port]);
	</div>
	<div data-lang="python" markdown="1">
		from pyspark.streaming.flume import FlumeUtils

		addresses = [([sink machine hostname 1], [sink port 1]), ([sink machine hostname 2], [sink port 2])]
		flumeStream = FlumeUtils.createPollingStream(streamingContext, addresses)

	By default, the Python API will decode Flume event body as UTF8 encoded strings. You can specify your custom decoding function to decode the body byte arrays in Flume events to any arbitrary data type.
	See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.flume.FlumeUtils).
	</div>
	</div>

	See the Scala example [FlumePollingEventCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala).

	Note that each input DStream can be configured to receive data from multiple sinks.

3. **Deploying:** This is same as the first approach.