This document discusses testing Spark programs. It recommends using parallelize and collect for unit testing Spark code on small datasets that fit in memory. For larger datasets that do not fit in memory, it suggests using distributed set operations or RDD comparisons. The document also discusses testing streaming applications using a manual clock and test input streams. It provides examples of testing DataFrames and Datasets using equality checks. Finally, it recommends packages like spark-testing-base and sscheck for Spark testing and mentions options beyond local mode for testing.

1. Beyond Parallelize & Collect
(Effective testing of Spark Programs)
Now
mostly
“works”*
*See developer for details. Does not imply warranty. :p

2. Who am I?
● My name is Holden Karau
● Prefered pronouns are she/her
● I’m a Software Engineer
● currently IBM and previously Alpine, Databricks, Google, Foursquare &
Amazon
● co-author of Learning Spark & Fast Data processing with Spark
● @holdenkarau
● Slide share http://www.slideshare.net/hkarau
● Linkedin https://www.linkedin.com/in/holdenkarau
● Spark Videos http://bit.ly/holdenSparkVideos

3. What is going to be covered:
● What I think I might know about you
● A bit about why you should test your programs
● Using parallelize & collect for unit testing (quick skim)
● Comparing datasets too large to fit in memory
● Considerations for Streaming & SQL (DataFrames & Datasets)
● Cute & scary pictures
○ I promise at least one panda and one cat
● “Future Work”
○ Integration testing lives here for now (sorry)
○ Some of this future work might even get done!

4. Who I think you wonderful humans are?
● Nice* people
● Like silly pictures
● Familiar with Apache Spark
○ If not, buy one of my books or watch Paco’s awesome video
● Familiar with one of Scala, Java, or Python
○ If you know R well I’d love to chat though
● Want to make better software
○ (or models, or w/e)

5. So why should you test?
● Makes you a better person
● Save $s
○ May help you avoid losing your employer all of their money
■ Or “users” if we were in the bay
○ AWS is expensive
● Waiting for our jobs to fail is a pretty long dev cycle
● This is really just to guilt trip you & give you flashbacks to your QA internships

6. So why should you test - continued
Results from: Testing with Spark survey http://bit.ly/holdenTestingSpark

7. So why should you test - continued
Results from: Testing with Spark survey http://bit.ly/holdenTestingSpark

8. Why don’t we test?
● It’s hard
○ Faking data, setting up integration tests, urgh w/e
● Our tests can get too slow
● It takes a lot of time
○ and people always want everything done yesterday
○ or I just want to go home see my partner
○ etc.

9. Cat photo from http://galato901.deviantart.com/art/Cat-on-Work-Break-173043455

10. An artisanal Spark unit test
@transient private var _sc: SparkContext = _
override def beforeAll() {
_sc = new SparkContext("local[4]")
super.beforeAll()
}
override def afterAll() {
if (sc != null)
sc.stop()
System.clearProperty("spark.driver.port") // rebind issue
_sc = null
super.afterAll()
}
Photo by morinesque

11. And on to the actual test...
test("really simple transformation") {
val input = List("hi", "hi holden", "bye")
val expected = List(List("hi"), List("hi", "holden"), List("bye"))
assert(tokenize(sc.parallelize(input)).collect().toList === expected)
}
def tokenize(f: RDD[String]) = {
f.map(_.split(" ").toList)
}
Photo by morinesque

12. Wait, where were the batteries?
Photo by Jim Bauer

13. Let’s get batteries!
● Spark unit testing
○ spark-testing-base - https://github.com/holdenk/spark-testing-base
○ sscheck - https://github.com/juanrh/sscheck
● Integration testing
○ spark-integration-tests (Spark internals) - https://github.com/databricks/spark-integration-tests
● Performance
○ spark-perf (also for Spark internals) - https://github.com/databricks/spark-perf
● Spark job validation
○ spark-validator - https://github.com/holdenk/spark-validator
Photo by Mike Mozart

14. A simple unit test re-visited (Scala)
class SampleRDDTest extends FunSuite with SharedSparkContext {
test("really simple transformation") {
val input = List("hi", "hi holden", "bye")
val expected = List(List("hi"), List("hi", "holden"), List("bye"))
assert(SampleRDD.tokenize(sc.parallelize(input)).collect().toList === expected)
}
}

15. Ok but what about problems @ scale
● Maybe our program works fine on our local sized input
● If we are using Spark our actual workload is probably huge
● How do we test workloads too large for a single machine?
○ we can’t just use parallelize and collect
Qfamily

16. Distributed “set” operations to the rescue*
● Pretty close - already built into Spark
● Doesn’t do so well with floating points :(
○ damn floating points keep showing up everywhere :p
● Doesn’t really handle duplicates very well
○ {“coffee”, “coffee”, “panda”} != {“panda”, “coffee”} but with set operations...
Matti Mattila

17. Or use RDDComparisions:
def compareWithOrderSamePartitioner[T: ClassTag](expected: RDD
[T], result: RDD[T]): Option[(T, T)] = {
expected.zip(result).filter{case (x, y) => x != y}.take(1).
headOption
}
Matti Mattila

18. Or use RDDComparisions:
def compare[T: ClassTag](expected: RDD[T], result: RDD[T]): Option
[(T, Int, Int)] = {
val expectedKeyed = expected.map(x => (x, 1)).reduceByKey(_ +
_)
val resultKeyed = result.map(x => (x, 1)).reduceByKey(_ + _)
expectedKeyed.cogroup(resultKeyed).filter{case (_, (i1, i2))
=>
i1.isEmpty || i2.isEmpty || i1.head != i2.head}.take(1).
headOption.
map{case (v, (i1, i2)) => (v, i1.headOption.getOrElse(0),
i2.headOption.getOrElse(0))}
}
Matti Mattila

19. But where do we get the data for those tests?
● If you have production data you can sample you are lucky!
○ If possible you can try and save in the same format
● If our data is a bunch of Vectors or Doubles Spark’s got tools :)
● Coming up with good test data can take a long time
Lori Rielly

20. QuickCheck / ScalaCheck
● QuickCheck generates tests data under a set of constraints
● Scala version is ScalaCheck - supported by the two unit testing libraries for
Spark
● sscheck
○ Awesome people*, supports generating DStreams too!
● spark-testing-base
○ Also Awesome people*, generates more pathological (e.g. empty partitions etc.) RDDs
*I assume
PROtara hunt

21. With spark-testing-base
test("map should not change number of elements") {
forAll(RDDGenerator.genRDD[String](sc)){
rdd => rdd.map(_.length).count() == rdd.count()
}
}

22. Testing streaming….
Photo by Steve Jurvetson

23. // Setup our Stream:
class TestInputStream[T: ClassTag](@transient var sc:
SparkContext,
ssc_ : StreamingContext, input: Seq[Seq[T]], numPartitions: Int)
extends FriendlyInputDStream[T](ssc_) {
def start() {}
def stop() {}
def compute(validTime: Time): Option[RDD[T]] = {
logInfo("Computing RDD for time " + validTime)
val index = ((validTime - ourZeroTime) / slideDuration - 1).
toInt
val selectedInput = if (index < input.size) input(index) else
Seq[T]()
// lets us test cases where RDDs are not created
if (selectedInput == null) {
return None
}
val rdd = sc.makeRDD(selectedInput, numPartitions)
logInfo("Created RDD " + rdd.id + " with " + selectedInput)
Some(rdd)
}
}
Artisanal Stream Testing Code
trait StreamingSuiteBase extends FunSuite with BeforeAndAfterAll with Logging
with SharedSparkContext {
// Name of the framework for Spark context
def framework: String = this.getClass.getSimpleName
// Master for Spark context
def master: String = "local[4]"
// Batch duration
def batchDuration: Duration = Seconds(1)
// Directory where the checkpoint data will be saved
lazy val checkpointDir = {
val dir = Utils.createTempDir()
logDebug(s"checkpointDir: $dir")
dir.toString
}
// Default after function for any streaming test suite. Override this
// if you want to add your stuff to "after" (i.e., don't call after { } )
override def afterAll() {
System.clearProperty("spark.streaming.clock")
super.afterAll()
}
Phot
o by
Stev
e
Jurv
etso
n

24. and continued….
/**
* Create an input stream for the provided input sequence. This is done using
* TestInputStream as queueStream's are not checkpointable.
*/
def createTestInputStream[T: ClassTag](sc: SparkContext, ssc_ :
TestStreamingContext,
input: Seq[Seq[T]]): TestInputStream[T] = {
new TestInputStream(sc, ssc_, input, numInputPartitions)
}
// Default before function for any streaming test suite. Override this
// if you want to add your stuff to "before" (i.e., don't call before { } )
override def beforeAll() {
if (useManualClock) {
logInfo("Using manual clock")
conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.
TestManualClock") // We can specify our own clock
} else {
logInfo("Using real clock")
conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock")
}
super.beforeAll()
}
/**
* Run a block of code with the given StreamingContext and automatically
* stop the context when the block completes or when an exception is thrown.
*/
def withOutputAndStreamingContext[R](outputStreamSSC: (TestOutputStream
[R], TestStreamingContext))
(block: (TestOutputStream[R], TestStreamingContext) => Unit): Unit = {
val outputStream = outputStreamSSC._1
val ssc = outputStreamSSC._2
try {
block(outputStream, ssc)
} finally {
try {
ssc.stop(stopSparkContext = false)
} catch {
case e: Exception =>
logError("Error stopping StreamingContext", e)
}
}
}
}

25. and now for the clock
/*
* Allows us access to a manual clock. Note that the manual clock changed between
1.1.1 and 1.3
*/
class TestManualClock(var time: Long) extends Clock {
def this() = this(0L)
def getTime(): Long = getTimeMillis() // Compat
def currentTime(): Long = getTimeMillis() // Compat
def getTimeMillis(): Long =
synchronized {
time
}
def setTime(timeToSet: Long): Unit =
synchronized {
time = timeToSet
notifyAll()
}
def advance(timeToAdd: Long): Unit =
synchronized {
time += timeToAdd
notifyAll()
}
def addToTime(timeToAdd: Long): Unit = advance(timeToAdd) // Compat
/**
* @param targetTime block until the clock time is set or advanced to at least this
time
* @return current time reported by the clock when waiting finishes
*/
def waitTillTime(targetTime: Long): Long =
synchronized {
while (time < targetTime) {
wait(100)
}
getTimeMillis()
}
}

26. Testing streaming the happy panda way
● Creating test data is hard
○ ssc.queueStream works - unless you need checkpoints (1.4.1+)
● Collecting the data locally is hard
○ foreachRDD & a var
● figuring out when your test is “done”
Let’s abstract all that away into testOperation

27. We can hide all of that:
test("really simple transformation") {
val input = List(List("hi"), List("hi holden"), List("bye"))
val expected = List(List("hi"), List("hi", "holden"), List("bye"))
testOperation[String, String](input, tokenize _, expected, useSet = true)
}
Photo by An eye
for my mind

28. What about DataFrames?
● We can do the same as we did for RDD’s (.rdd)
● Inside of Spark validation looks like:
def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row])
● Sadly it’s not in a published package & local only
● instead we expose:
def equalDataFrames(expected: DataFrame, result: DataFrame) {
def approxEqualDataFrames(e: DataFrame, r: DataFrame, tol: Double) {

29. …. and Datasets
● We can do the same as we did for RDD’s (.rdd)
● Inside of Spark validation looks like:
def checkAnswer(df: Dataset[T], expectedAnswer: T*)
● Sadly it’s not in a published package & local only
● instead we expose:
def equalDatasets(expected: Dataset[U], result: Dataset[V]) {
def approxEqualDatasets(e: Dataset[U], r: Dataset[V], tol: Double) {

30. This is what it looks like:
test("dataframe should be equal to its self") {
val sqlCtx = sqlContext
import sqlCtx.implicits._// Yah I know this is ugly
val input = sc.parallelize(inputList).toDF
equalDataFrames(input, input)
}
*This may or may not be easier.

31. Which has “built-in” large support :)

32. Photo by allison

33. Let’s talk about local mode
● It’s way better than you would expect*
● It does its best to try and catch serialization errors
● It’s still not the same as running on a “real” cluster
● Especially since if we were just local mode, parallelize and collect might be
fine
Photo by: Bev Sykes

34. Options beyond local mode:
● Just point at your existing cluster (set master)
● Start one with your shell scripts & change the master
○ Really easy way to plug into existing integration testing
● spark-docker - hack in our own tests
● YarnMiniCluster
○ https://github.
com/apache/spark/blob/master/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClu
sterSuite.scala
○ In Spark Testing Base extend SharedMiniCluster
■ Not recommended until after SPARK-10812 (e.g. 1.5.2+ or 1.6+)
Photo by Richard Masoner

35. Validation
● Validation can be really useful for catching errors before deploying a model
○ Our tests can’t catch everything
● For now checking file sizes & execution time seem like the most common best
practice (from survey)
● Accumulators have some challenges (see SPARK-12469 for progress) but
are an interesting option
● spark-validator is still in early stages and not ready for production use but
interesting proof of concept
Photo by:
Paul Schadler

36. Related talks & blog posts
● Testing Spark Best Practices (Spark Summit 2014)
● Every Day I’m Shuffling (Strata 2015) & slides
● Spark and Spark Streaming Unit Testing
● Making Spark Unit Testing With Spark Testing Base

37. Learning Spark
Fast Data
Processing with
Spark
(Out of Date)
Fast Data
Processing with
Spark
(2nd edition)
Advanced
Analytics with
Spark

38. Learning Spark
Fast Data
Processing with
Spark
(Out of Date)
Fast Data
Processing with
Spark
(2nd edition)
Advanced
Analytics with
Spark
Coming soon:
Spark in Action
Coming soon:
High Performance Spark

39. And the next book…..
Still being written - signup to be notified when it is available:
● http://www.highperformancespark.com
● https://twitter.com/highperfspark

40. Related packages
● spark-testing-base: https://github.com/holdenk/spark-testing-base
● sscheck: https://github.com/juanrh/sscheck
● spark-validator: https://github.com/holdenk/spark-validator *ALPHA*
● spark-perf - https://github.com/databricks/spark-perf
● spark-integration-tests - https://github.com/databricks/spark-integration-tests
● scalacheck - https://www.scalacheck.org/

41. And including spark-testing-base:
sbt:
"com.holdenkarau" %% "spark-testing-base" % "1.5.2_0.3.1"
maven:
<dependency>
<groupId>com.holdenkarau</groupId>
<artifactId>spark-testing-base</artifactId>
<version>${spark.version}_0.3.1</version>
<scope>test</scope>
</dependency>

42. “Future Work”
● Better ScalaCheck integration (ala sscheck)
● Testing details in my next Spark book
● Whatever* you all want
○ Testing with Spark survey: http://bit.ly/holdenTestingSpark
Semi-likely:
● integration testing (for now see @cfriegly’s Spark + Docker setup):
○ https://github.com/fluxcapacitor/pipeline
Pretty unlikely:
● Integrating into Apache Spark ( SPARK-12433 )
*That I feel like doing, or you feel like making a pull request for.
Photo by
bullet101

43. Cat wave photo by Quinn Dombrowski
k thnx bye!
If you want to fill out survey: http:
//bit.ly/holdenTestingSpark
Will use update results in
Strata Presentation & tweet
eventually at @holdenkarau