This post is for those who already understand the concept of stream processing and basic functionality of Kafka and Spark.
And this post is also for people who are just dying to try Kafka and Spark right away. :)

Purpose

Setup a simple pipeline for stream processing on your local machine.
Integrate Spark consumer to the Kafka.
Implement a word frequency processing pipeline.

The versions of components in this post will be:

Kafka: 0.10.1.0 with scala 2.11
Spark streaming: 2.10
Spark streaming Kafka: 2.10

Setup

Step 1 Install Kafka:

wget http://ftp.meisei-u.ac.jp/mirror/apache/dist/kafka/0.10.1.0/kafka_2.10-0.10.1.0.tgz
tar -xzf kafka_2.10-0.10.1.0.tgz
cd kafka_2.10-0.10.1.0.tgz

Step 2 Start a zookeeper for kafka

bin/zookeeper-server-start.sh config/zookeeper.properties

Step 3 Start kafka

bin/kafka-server-start.sh config/server.properties

Step 4 Create a topic test on Kafka

bin/kafka-topics.sh \
--create \
--zookeeper localhost:2181 \
--replication-factor 1 \
--partitions 1 \
--topic test

Step 5 Program a spark consumer for word frequency using scala:

sbt file:

name := "spark-playground"

version := "1.0"

scalaVersion := "2.11.8"


libraryDependencies += "org.apache.spark" %% "spark-core" % "2.1.0"

libraryDependencies += "org.apache.spark" %% "spark-streaming" % "2.1.0"

libraryDependencies += "org.apache.spark" %% "spark-streaming-kafka-0-10" % "2.1.0"

main file:

import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe


object AQuickExample extends App {
  val conf = new SparkConf().setMaster("local[2]").setAppName("AQuickExample")

  val kafkaParams = Map[String, Object](
    "bootstrap.servers" -> "localhost:9092",
    "key.deserializer" -> classOf[StringDeserializer],
    "value.deserializer" -> classOf[StringDeserializer],
    "group.id" -> "spark-playground-group",
    "auto.offset.reset" -> "latest",
    "enable.auto.commit" -> (false: java.lang.Boolean)
  )
  val ssc = new StreamingContext(conf, Seconds(1))


  val inputStream = KafkaUtils.createDirectStream(ssc, PreferConsistent, Subscribe[String, String](Array("test"), kafkaParams))
  val processedStream = inputStream
    .flatMap(record => record.value.split(" "))
    .map(x => (x, 1))
    .reduceByKey((x, y) => x + y)

  processedStream.print()
  ssc.start()
  ssc.awaitTermination()

}

Step 6 Start the Spark consumer you wrote in step 5.

sbt run

Step 7 Start a console Kafka producer and fire some message to the Kafka using the topic test.

Start the console producer:

bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test

Send some message to the Kafka:

hello world

Step 8 Once you send the message, there will be something showing up in your Spark consumer terminal representing the word frequency:

-------------------------------------------
Time: 1485135467000 ms
-------------------------------------------
(hello,1)
(world,1)

...

Stream Processing - Simple Get-Start with Kafka and Spark

Purpose

Setup