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A tutorial on Apache Spark Unit Testing

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Testing in Apache Spark - A Tutorial

A tutorial on how to write unit tests and do performance testing of Apache Spark code in Scala.

My New Year's resolution: write more tests! May be, this is the year when I finally move over to TDD (Test Driven Development) i.e. start any new work by writing tests first! Writing tests is a very good idea :) when you plan to use your code for making real-world decisions, e.g. which ads to show to which user, or extend how much credit to which customers, etc. We have been using Spark and MLlib increasingly for these types of problems at work. Unfortunately, pointers on best practices of testing Spark code are few and scattered, so I wrote this tutorial to have a single place for all Spark and MLlib related testing, show example code and relevant URLs. I will continue to add to it as I find new material.

Example: Vector Operations in Euclidean Space

As an example project, I chose to write some of the basic vector operations in Euclidean space, such as distance and cosine between two vectors, centroid of an array of vectors, etc. With these basic operations defined, I then wrote a method that takes an RDD of vectors and a centroid as input, and calculates the sum of distances between each vector and the centroid. You will recognize this as very similar to the convergence criteria calculation step in k-means clustering and the cosine operation as the similarity calculation step in a typical collaborative filtering algorithm. We will be writing unit tests and performance tests for these in this tutorial.

The basic vector operations are defined in: src/main/scala/ml/dolphin/testing/EuclideanVectorSpace.scala

Nothing special here. It defines two 2-vector operations: distance and cosine, and two N-vector operations: centroid and closest. Notice that these are not defined over RDDs. Rather, we will be using these within a partition of the RDD, e.g. using mapPartitions.

Next, let's look at: src/main/scala/ml/dolphin/testing/DistanceFromCentroid.scala

It has only one method: calcDistance(sc: SparkContext, vPoints: RDD[Vector], centroid: Vector)

  def calcDistance(sc: SparkContext, vPoints: RDD[Vector], centroid: Vector): Double = {

    // 1. Broadcast centroid to all partitions
    val bcCentroid = sc.broadcast(centroid)

    // 2. For each partition, calculate the sum of distances from centroid to each of 
    // the points in that partition. Then, sum up the partial sums from all the partitions.

    val accmDistance = vPoints.mapPartitions{ points => {
      var sum = 0.0
      points.foreach { point => {
        sum += EuclideanVectorSpace.distance(point, bcCentroid.value)
    }}.reduce(_ + _) // 3. Sum up all the partial sums from the partitions
  1. Broadcast the centroid to all partitions of the input RDD (i.e. vPoints) so that we can calculate the distance from this centroid to all the vectors within each partition locally.
  2. Inside mapPartitions, we calculate all the distances using EuclideanVectorSpace.distance() and generate a local "sum". Note that we wrap the local "sum" with an Iterator from each partition since mapPartitions returns an Iterator.
  3. Finally, we sum up all the local contributions using a "reduce".

We will now write some unit tests for these. Reminder to self: next time, start with the tests!

Apache Spark Unit Testing

There is no native library for unit testing in Spark as of yet. After researching this topic for a while, I feel that the best option is to use two libraries:

A little bit about ScalaTest. For Scala users, this is the most familiar unit testing framework (you can also use it for testing Java code and soon for JavaScript).

it supports a number of different testing styles, each designed to support a specific type of testing need. For details, see ScalaTest User Guide.

Although ScalaTest supports many styles, I find that the quickest way to get started is to use the following ScalaTest traits and write the tests in the [TDD style (Test Driven Development)] (

  1. FunSuite
  2. Assertions
  3. BeforeAndAfter

Feel free to browse the above URLs to learn more about these traits - that will make rest of this tutorial go smoothly.

FunSuite tests are function values and each test is specified with a strong denoting its name. Let's go over the syntax of a basic test: src/test/scala/ml/dolphin/testing/EuclideanVectorSpaceTests.scala

Notice that there are multiple tests defined in this file (corresponding to the methods defined in EuclideanVectorSpace.scala and described earlier). We define a "test fixture" consisting of the objects and other artifacts such as files, database connections, connections to services, etc that the test will utilize to do its work. If you have multiple tests in a test suite that share some of the same test fixtures and you need to set them up to the same values before each test begins or you need to clean them up after each test finishes, it is best to use BeforeAndAfter trait. If an exception occurs during before or after method executes, all tests in the suite are abandoned. Our class "EuclideanVectorSpaceTests" defines 4 tests in the suite and two of them use the same fixtures: x and y. We initialize them to the same values every time in the method "before":

class EuclideanVectorSpaceTests extends FunSuite with BeforeAndAfter {

  // 1. Define the instance variables
  var x: linalg.Vector = _
  var y: linalg.Vector = _

  // 2. Set the values at the beginning of each test
  before {
    x = Vectors.dense(1.0, 2.0, 3.0, 4.0)
    y = Vectors.dense(2.0, 3.0, 4.0, 5.0)

  // 3. Write the actual test
  test("L2 distance between 2 Vector's") {
      assert(EuclideanVectorSpace.distance(x, y) === 2.0)
  test("Cosine between 2 Vector's") {
    // expected value = 40.0 / (sqrt(54) * sqrt(30))
    assert(EuclideanVectorSpace.cosine(x, y) === 40.0 / (sqrt(54) * sqrt(30)))

  test("Vectors of 0's will have a zero distance") {
    assertResult(0.0) {
      EuclideanVectorSpace.distance(Vectors.dense(0.0, 0.0, 0.0), Vectors.dense(0.0, 0.0, 0.0))
  // 4. Use "pending" to write tests later.
  test ("Centroid of a set of vectors") (pending)

  1. Define the instances of Vectors x and y that will be used in the tests.
  2. Initialize x and y before each test
  3. Write the actual test. It uses "assert" to enforce whether the expected value (right hand side) is same as the value returned by "distance" method in EuclideanVectorSpace. Note the "===" (three "=" signs) in the assert statement. This syntax provides more detailed error messages. I prefer to use this for all my tests.
  4. Note the "pending" next to the last test with an empty body. This tells ScalaTest (and reminds us) that we will be writing future tests. For example, we have the method centroid defined in EuclideanVectorSpace but we are not testing it yet. (It is always a good idea to write all the tests in one go though...I may not come back to this again...)

Making life easier with Spark-Testing-Base

As I mentioned earlier, after trying a few different things, I find spark-testing-base to be the easiest and most functional unit testing framework for Spark so far. It surfaces some of the same test suites that the Spark committers use when testing internal Spark code. What you get out of the box:

  1. There are often multiple tests in a test suite, and we use "before" and "after" to set up and reset the test artifacts. That is fine for regular Scala code. When you are trying to do this for Spark code, each test needs to set up and stop a SparkContext, leading to a lot of boilerplate code replication. Also, in between SparkContext stops and starts, one has to clear spark.driver.port variable. When you use spart-testing-base, this is automatically taken care of so you can focus on writing the test iteself (i.e. the stuff in the "test() {}"). If you are writing lots of tests, this makes a big difference. More specifically, spark-testing-base allows you to share the same SparkContext for all the tests defined in the same test suite. We dig deeper into this below.
  2. If you are testing Spark Streaming or Spark DataFrames (i.e. not just basic RDD methods), there are other things to worry about, e.g. how long you have to wait for the test to finish. I will be adding more examples on these, especially DataFrames since there is not much info on testing currently. For now, you can read this excellent article by creator of spark-testing-base (Holden Karau).

Now let's look at a concrete example. src/test/scala/ml/dolphin/testing/DistanceFromCentroidTests.scala

class DistanceFromCentroidTests extends FunSuite with BeforeAndAfter with SharedSparkContext {

  var vPoints: Array[Vector] = _
  var centroid: Vector = _
  var vPointsRdd: RDD[Vector] = _

  // 1. Instantiate the variables for each test
  before {
    vPoints = Array(Vectors.dense(1.0, 2.0, 3.0, 4.0), Vectors.dense(2.0, 3.0, 4.0, 5.0),
    Vectors.dense(3.0, 9.0, 1.0, 7.0), Vectors.dense(1.0, 5.0, 6.0, 8.0))
    centroid = Vectors.dense(1.0, 1.0, 1.0, 1.0)
    vPointsRdd = sc.parallelize(vPoints, 3)
  // 2. an actual test
  test("Testing calcDistance using a shared Spark Context") {
    val sum = DistanceFromCentroid.calcDistance(sc, vPointsRdd, centroid)
    val expected = sqrt(14.0) + sqrt(30.0) + sqrt(104.0) + sqrt(90.0)
    assert(sum === expected)
  1. Note something special here. We are using a SparkContext ("sc") without instantiating it anywhere in this class:

    vPointsRdd = sc.parallelize(vPoints, 3) 

    This is done for you within spark-testing-base when you extend your class definition with "SharedSparkContext" trait (you need to import "com.holdenkarau.spark.testing.SharedSparkContext" in the file where you define your test suite).

    To see how it's handled, take a look at the internal of spark-testing-base, specifically SharedSparkContext.scala

    /** Shares a local `SparkContext` between all tests in a suite and closes it at the end. */
    trait SharedSparkContext extends BeforeAndAfterAll with SparkContextProvider {
      self: Suite =>
      @transient private var _sc: SparkContext = _
      // 1.1. SparkContext definition
      override def sc: SparkContext = _sc
      val appID = new Date().toString + math.floor(math.random * 10E4).toLong.toString
      override val conf = new SparkConf().
        set("spark.ui.enabled", "false").
        set("", appID)
      // 1.2. Instantiate new SparkContext and set logging level
      override def beforeAll() {
        _sc = new SparkContext(conf)
      // 1.3. stop SparkContext
      override def afterAll() {
        try {
          _sc = null
        } finally {
  • 1.1 sc is the SparkContext that you will use for your tests within the same suite. Internally, the trait uses a private variable "_sc" to manage the actual Spark Context so that you cannot (accidentally) modify it.
  • 1.2 "_sc" is instantiated within a "beforeAll()" method. The difference between "before()" that you have seen before and "beforeAll()" is that the latter is executed before executing the suite (difference here before the suite vs before a test in the suite). You can also have nested suites - slightly more advanced but a very handy approach when testing a large platform. You can read more about beforeAll and afterAll here:
  • 1.3 Similarly, Spark Context is stopped at the end of the test suite via a call to afterAll().
  1. Coming back to our test suite: DistanceFromCentroidTests, let's look at the actual test:
    // 2. an actual test
    test("Testing calcDistance using a shared Spark Context") {
      val sum = DistanceFromCentroid.calcDistance(sc, vPointsRdd, centroid)
      val expected = sqrt(14.0) + sqrt(30.0) + sqrt(104.0) + sqrt(90.0)
      assert(sum === expected)
    This is very similar to regular ScalaTest syntax. The only difference is that we are invoking a test on a RDD using the local shared SparkContext supplied to us by spark-testing-base. We are testing DistanceFromCentroid.calcDistance(...) method that takes a RDD of points, a centroid and calculates the sum of inidividual distances from the centroid. Notice how we expressed the expected value. Since there were 4 points defined in vPointsRdd, we put in sqrt(each point's distance from centroid) for each point. This is just an individual choice - it helps with code readability by another person as to how the expected value is calculated.

That's it. Happy testing!

Spark Performance Testing with spark-perf



Spark testing:

Scala Test

Spark Testing Base

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