Tuesday, January 20, 2015

Spark + Kafka + Cassandra (Part 1 - Spark Kafka Producer)


Welcome to the part one of the series 'Spark + Kafka + Cassandra'.

In this series we will look to build up a Spark, Kafka, Cassandra stack that can be used as the foundation for real projects on real clusters that do real work.

As is typical here at Spark-Fu! we will start with the most basic case and build on that incrementally.  Keeping with that, this first instalment will have you:
  • Run ZooKeeper local (as part of Kafka distribution)
  • Run Kafka local
  • Run a local Spark Cluster
  • Create a Kafka Producer wrapped up in a Spark application
  • Submit the application and consume the messages in a terminal
In subsequent parts of the series we'll look at consuming messages from Kafka using Spark Streaming, using custom encoders, adding Cassandra into the mix, and hopefully top it off with something that will run against a small cluster.


  • Java 1.7+ (Oracle JDK required)
  • Scala 2.10.4
  • SBT 0.13.x
  • A Spark cluster (how to do it here.)
  • ZooKeeper and Kafka running local 
  • git

Get ZooKeeper & Kafka

So, before we even clone the example application, we have to do a bit of work to get ZooKeeper and Kafka up and running on your machine.

At the time of this post, 0.8.2-beta is the latest release. The current stable version is  So let's use - start by downloading it here.  (NOTE: This is the Scala 2.10.x version!)  Other variants are all available here if you have other needs.

Once downloaded, extract the archive - for this example I'm a fan of putting things at:


The rest of the example will assume this is the case.

Run ZooKeeper Local

To be polite, setting up local ZooKeeper is about as fun as giving a bad tasting pill to a pissed of cat with claws.  Thankfully, the Kafka folks have done us a solid and bundled a mechanism to get up and running with ZooKeeper as simple as:

[bkarels@rev27 kafka_2.10-]$ bin/zookeeper-server-start.sh config/zookeeper.properties
Yes, there is an infinite number of ways to get ZK up and running - but the above is as far as I'm going here.  Simple and functional.

Run Kafka Local

The next set of steps is near exact copy of the Kafka Quickstart.  I have tuned the bits here that jive with the example application so do look at the Kafka docs, but do follow the steps below.

Assuming you have Kafka downloaded and extracted as above, lets start things up, create our topic, and get a list of topics.

Start the server:
[bkarels@rev27 kafka_2.10-]$ bin/kafka-server-start.sh config/server.properties &
Create topic `sparkfu`:
[bkarels@rev27 kafka_2.10-]$ bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic sparkfu &
List our topic(s) as an exercise:
[bkarels@rev27 kafka_2.10-]$ bin/kafka-topics.sh --list --zookeeper localhost:2181
All of the above commands spit out a goodly amount of "stuff" to your console, but just a bit of careful examination should have you feeling confident that everything is up and running correctly.   Most importantly, when you listed out your topics that somewhere in that output you saw sparkfu listed (will be on it's own line).

To be certain, we can test your sparkfu topic.  Open two terminals and navigate to ~/kafka_2.10-  In one terminal start up a consumer to sit and listen for messages on your topic:
bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic sparkfu --from-beginning
In your other terminal we'll fire up a producer:
bin/kafka-console-producer.sh --broker-list localhost:9092 --topic sparkfu
Once your producer is started and staring at you, you can enter any string and press [Enter] to produce the message.  Within a moment you should see your message parroted back to you in the terminal where you started up the consumer.  If you crave greater details review the Kafka documentation.

Kill your producer, but leave your consumer running - you'll need it in just a bit...

Clone the Example

Begin by cloning the example project from github - spark-kafka-producer, & cd into the project directory.

[bkarels@rev27 work]$ git clone https://github.com/bradkarels/spark-kafka-producer.git
Cloning into 'spark-kafka-producer'...
remote: Counting objects: 19, done.
remote: Compressing objects: 100% (13/13), done.
remote: Total 19 (delta 0), reused 15 (delta 0)
Unpacking objects: 100% (19/19), done.
[bkarels@rev27 work]$ cd spark-kafka-producer/
[bkarels@rev27 spark-kafka-producer]$

Prepare the Application

In a terminal, from the project root, fire up SBT and assembly the project to create your application jar file.
[bkarels@rev27 spark-kafka-producer]$ sbt
Picked up _JAVA_OPTIONS: -Xms1G -Xmx2G -XX:PermSize=512m -XX:MaxPermSize=1G
[info] Loading project definition from /home/bkarels/work/spark-kafka-producer/project
[info] Updating {file:/home/bkarels/work/spark-kafka-producer/project/}spark-kafka-producer-build...
[info] Resolving org.fusesource.jansi#jansi;1.4 ...
[info] Done updating.
[info] Set current project to Spark Fu Kafka Producer (in build file:/home/bkarels/work/spark-kafka-producer/)
> assembly
[info] SHA-1: 69db758e5dd205ae60875f00388cd5c935955773
[info] Packaging /home/bkarels/work/spark-kafka-producer/target/scala-2.10/sparkFuProducer.jar ...
[info] Done packaging.
[success] Total time: 11 s, completed Jan 20, 2015 12:08:44 PM
As per normal, take note of where your jar file gets put (highlighted above).  Also, note that we have set the resulting jar file name in assembly.sbt - here we have set it to sparkFuProducer.jar.

Spark it up!

If your local Spark cluster is not up and running, do that now.  It's not!?  Mon Dieu!  Go here to see about remedying that.

Make Sparks fly! (i.e. run it)

Assuming you have poked around in the code, you will have seen that the Messenger within MillaJovovich.scala (a few will see my humor...) will produce four messages for the topic sparkfu and send them.  So, when this application is submitted to the cluster it will have no clear output like our previous examples.  This is where the consumer you have running will come in.  If you stopped it no worries, just restart it as you did above.
bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic sparkfu --from-beginning
Truth be told you could start it up after the fact since we are telling it to read all messages from the start of time.  But isn't it more fun to see messages come in as your job is sending them?  Yes, it is.

OK, spark it up:
[bkarels@rev27 kafka_2.10-]$ $SPARK_HOME/bin/spark-submit --class com.bradkarels.simple.Messenger --master spark:// /home/bkarels/dev/spark-kafka-producer/target/scala-2.10/sparkFuProducer.jar
If all has gone well you should see output like the following in your consumer as the application runs:
[2015-01-20 12:28:09,547] INFO Closing socket connection to / (kafka.network.Processor)
What the foo?
What the bar?
What the baz?
No! No! NO! What the FU!
[2015-01-20 12:28:10,071] INFO Closing socket connection to / (kafka.network.Processor)

That's it.  Well, that's it as far as scraping ever so lightly at the surface of this topic.  In the following parts of this series we will dig a bit deeper.

What's next?

As I alluded to in the overview the next parts in this series will provide examples for:
  • Rudimentary Kafka consumer in Spark
  • Custom Kafka Message encoder
  • Consuming Kafka messages using Spark Streaming
  • A larger example pulling all these bits together
 Thanks for following along - stay tuned!


  1. Hi, I'm getting the following error, when i entered assembly command

    [trace] Stack trace suppressed: run last *:assembly for the full output.
    [error] (*:assembly) deduplicate: different file contents found in the following:
    [error] /Users/462522/.ivy2/cache/org.apache.avro/avro-ipc/jars/avro-ipc-1.7.7-tests.jar:META-INF/maven/org.apache.avro/avro-ipc/pom.properties
    [error] /Users/462522/.ivy2/cache/org.apache.avro/avro-ipc/jars/avro-ipc-1.7.7.jar:META-INF/maven/org.apache.avro/avro-ipc/pom.properties
    [error] Total time: 1321 s, completed 19 Aug, 2016 1:18:47 PM

    my set file is

    name := "Spark Fu Kafka Producer"

    version := "0.0.1"

    scalaVersion := "2.11.6"

    libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % "1.6.2",
    "org.apache.kafka" %% "kafka" % "",
    "joda-time" % "joda-time" % "2.7"

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