Real Time Data Processing with

Spark Streaming

Brandon O’BrienOct 26th, 2016

Spark Streaming: Intro

1. Intro2. Demo + high level walkthrough3. Spark in detail4. Detailed demo walkthrough and/or

workshop

Spark Streaming

Spark experience level?

Select one: Beginner Intermediate Expert

Spark Streaming: Demo

DEMO

Spark Streaming: Demo Info

• Data Source:• Data Producer Thread• Redis

• Data Consumer• Spark as Stream Consumer• Redis Publish

• Dashboard:• Node.js/Redis Integration• Socket.io Publish• AngularJS + JavaScript

Spark Streaming: Spark in detail

SPARK IN DETAIL

Spark Streaming: Concepts

Application:• Driver program• RDD• Partition• Elements• DStream• InputReceiver• 1 JVM for driver

program• 1 JVM per executor

Cluster:• Master• Executors• Resources• Cores• Gigs RAM

• Cluster Types:• Standalone• Mesos• YARN

Spark Streaming: Lazy execution

//Allocate resources on clusterval conf = new SparkConf().setAppName(appName).setMaster(master) val sc = new SparkContext(conf)

//Lazy definition of logical processing (transformations)val textFile = sc.textFile("README.md")

.filter(line=> {line.length> 10})

//foreachPartition() triggers execution (actions)textFile.foreachPartition(partition=> {

partition.foreach(line => {println(line)

})})• Use rdd.persist() when multiple actions are called on the same RDD

Spark Streaming: Execution Env

• Distributed data, distributed code• RDD partitions are distributed across executors• Actions trigger execution and return results to the driver program• Code is executed on either the driver or executors• Be careful of function closures!

//Function arguments to transformations executed on executorsval textFile = sc.textFile("README.md")

.filter(line=> {line.length> 10})

//collect() triggers execution (actions)//executed on driver. foreachPartition executed on executorstextFile.collect().foreach(line => {

println(line)})

Spark Streaming: Execution Env

Spark Streaming: Parallelism

• RDD partitions are processed in parallel• Elements in a single partition are processed serially• You control the number of partitions in an RDD• If you need to guarantee any particular ordering of processing, use

groupByKey() to force all elements with the same key onto the same partitions

• Be careful of shuffles

val textFile = sc.textFile("README.md”)val singlePartitionRDD = textFile.repartition(1)

val linesByKey = shopResultsEnriched.map(line => (getPartitionKey(line), line)).groupByKey()

Spark Streaming: DStreams

• Receiver Types• Kafka (Receiver + Direct)• Flume• Kinesis• TCP Socket• Custom (Ex: redis.receiver.RedisReceiver.scala)

• Note: Kafka receiver will consume an entire core (no context switch)

Real Time Data Processing with

Spark Streaming

Brandon O’BrienOct 26th, 2016