I'm Writing Unit Tests for Spark Scala code and facing this issue.
When I run UnitTests files separately I'm good to go but, When I run all of UnitTests in module using maven Testcases fails.
How we can create local instance of spark or mock for UnitTests.
`
Cannot call methods on a stopped SparkContext. This stopped
SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:947)
`
Method I tried.
Tried using creating private spark session for each one UnitTest File.
Creating common spark session trait for all unit test file.
calling spark.Stop() at end of each file and removing it from all.
File are make two unit test files and try to execute them together. Both files should have spark session.
Class test1 extends AnyFlatSpec
{
val spark: SparkSession = SparkSession.builder
.master("local[*]")
.getOrCreate()
val sc: SparkContext = spark.sparkContext
val sqlCont: SQLContext = spark.sqlContext
"test1" should "take spark session spark context and sql context" in
{
//do something
}
}
Class test2 extends AnyFlatSpec
{
val spark: SparkSession = SparkSession.builder
.master("local[*]")
.getOrCreate()
val sc: SparkContext = spark.sparkContext`enter code here`
val sqlCont: SQLContext = spark.sqlContext
"test2" should "take spark session spark context and sql context" in
{
//do something
}
}
when you run those independently each file will work fine but when you run them together using mvn test they will failed.
Related
I am trying to test the behaviour of a class which eats and process DataFrames.
Following this previous questions: How to write unit tests in Spark 2.0+? I tried to use the loan pattern to run my tests in the following way:
I have a SparkSession provider trait:
/**
* This trait allows to use spark in Unit tests
* https://stackoverflow.com/questions/43729262/how-to-write-unit-tests-in-spark-2-0
*/
trait SparkSetup {
def withSparkSession(testMethod: SparkSession => Any) {
val conf = new SparkConf()
.setMaster("local")
.setAppName("Spark test")
val sparkSession = SparkSession
.builder()
.config(conf)
.enableHiveSupport()
.getOrCreate()
try {
testMethod(sparkSession)
}
// finally sparkSession.stop()
}
}
Which I use in my test class:
class InnerNormalizationStrategySpec
extends WordSpec
with Matchers
with BeforeAndAfterAll
with SparkSetup {
...
"A correct contact message" should {
"be normalized without errors" in withSparkSession{ ss => {
import ss.implicits._
val df = ss.createDataFrame(
ss.sparkContext.parallelize(Seq[Row](Row(validContact))),
StructType(List(StructField("value", StringType, nullable = false))))
val result = target.innerTransform(df)
val collectedResult: Array[NormalizedContactHistoryMessage] = result
.where(result.col("contact").isNotNull)
.as[NormalizedContactHistoryMessage]
.collect()
collectedResult.isEmpty should be(false) // There should be something
collectedResult.length should be(1) // There should be exactly 1 message...
collectedResult.head.contact.isDefined should be(true) // ... of type contact.
}}
}
...
}
When attempting to run my tests using IntelliJ facility, all tests written in this manner works (running the Spec class at once), however, the sbt test command from terminal makes all the tests fail.
I thought also it was because of parallelism, so i added
concurrentRestrictions in Global += Tags.limit(Tags.Test, 1)
in my sbt settings, but didn't work.
Here is the stack trace I receive: https://pastebin.com/LNTd3KGW
Any help?
Thanks
I have tried to write a transform method from DataFrame to DataFrame.
And I also want to test it by scalatest.
As you know, in Spark 2.x with Scala API, you can create SparkSession object as follows:
import org.apache.spark.sql.SparkSession
val spark = SparkSession.bulider
.config("spark.master", "local[2]")
.getOrCreate()
This code works fine with unit tests.
But, when I run this code with spark-submit, the cluster options did not work.
For example,
spark-submit --master yarn --deploy-mode client --num-executors 10 ...
does not create any executors.
I have found that the spark-submit arguments are applied when I remove config("master", "local[2]") part of the above code.
But, without master setting the unit test code did not work.
I tried to split spark (SparkSession) object generation part to test and main.
But there is so many code blocks needs spark, for example import spark.implicit,_ and spark.createDataFrame(rdd, schema).
Is there any best practice to write a code to create spark object both to test and to run spark-submit?
One way is to create a trait which provides the SparkContext/SparkSession, and use that in your test cases, like so:
trait SparkTestContext {
private val master = "local[*]"
private val appName = "testing"
System.setProperty("hadoop.home.dir", "c:\\winutils\\")
private val conf: SparkConf = new SparkConf()
.setMaster(master)
.setAppName(appName)
.set("spark.driver.allowMultipleContexts", "false")
.set("spark.ui.enabled", "false")
val ss: SparkSession = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate()
val sc: SparkContext = ss.sparkContext
val sqlContext: SQLContext = ss.sqlContext
}
And your test class header then looks like this for example:
class TestWithSparkTest extends BaseSpec with SparkTestContext with Matchers{
I made a version where Spark will close correctly after tests.
import org.apache.spark.sql.{SQLContext, SparkSession}
import org.apache.spark.{SparkConf, SparkContext}
import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers}
trait SparkTest extends FunSuite with BeforeAndAfterAll with Matchers {
var ss: SparkSession = _
var sc: SparkContext = _
var sqlContext: SQLContext = _
override def beforeAll(): Unit = {
val master = "local[*]"
val appName = "MyApp"
val conf: SparkConf = new SparkConf()
.setMaster(master)
.setAppName(appName)
.set("spark.driver.allowMultipleContexts", "false")
.set("spark.ui.enabled", "false")
ss = SparkSession.builder().config(conf).getOrCreate()
sc = ss.sparkContext
sqlContext = ss.sqlContext
super.beforeAll()
}
override def afterAll(): Unit = {
sc.stop()
super.afterAll()
}
}
The spark-submit command with parameter --master yarn is setting yarn master.
And this will be conflict with your code master("x"), even using like master("yarn").
If you want to use import sparkSession.implicits._ like toDF ,toDS or other func,
you can just use a local sparkSession variable created like below:
val spark = SparkSession.builder().appName("YourName").getOrCreate()
without setting master("x") in spark-submit --master yarn, not in local machine.
I advice : do not use global sparkSession in your code. That may cause some errors or exceptions.
hope this helps you.
good luck!
How about defining an object in which a method creates a singleton instance of SparkSession, like MySparkSession.get(), and pass it as a paramter in each of your unit tests.
In your main method, you can create a separate SparkSession instance, which can have different configurations.
I'm struggling to write a basic unit test for creation of a data frame, using the example text file provided with Spark, as follows.
class dataLoadTest extends FunSuite with Matchers with BeforeAndAfterEach {
private val master = "local[*]"
private val appName = "data_load_testing"
private var spark: SparkSession = _
override def beforeEach() {
spark = new SparkSession.Builder().appName(appName).getOrCreate()
}
import spark.implicits._
case class Person(name: String, age: Int)
val df = spark.sparkContext
.textFile("/Applications/spark-2.2.0-bin-hadoop2.7/examples/src/main/resources/people.txt")
.map(_.split(","))
.map(attributes => Person(attributes(0),attributes(1).trim.toInt))
.toDF()
test("Creating dataframe should produce data from of correct size") {
assert(df.count() == 3)
assert(df.take(1).equals(Array("Michael",29)))
}
override def afterEach(): Unit = {
spark.stop()
}
}
I know that the code itself works (from spark.implicits._ .... toDF()) because I have verified this in the Spark-Scala shell, but inside the test class I'm getting lots of errors; the IDE does not recognise 'import spark.implicits._, or toDF(), and therefore the tests don't run.
I am using SparkSession which automatically creates SparkConf, SparkContext and SQLContext under the hood.
My code simply uses the example code from the Spark repo.
Any ideas why this is not working? Thanks!
NB. I have already looked at the Spark unit test questions on StackOverflow, like this one: How to write unit tests in Spark 2.0+?
I have used this to write the test but I'm still getting the errors.
I'm using Scala 2.11.8, and Spark 2.2.0 with SBT and IntelliJ. These dependencies are correctly included within the SBT build file. The errors on running the tests are:
Error:(29, 10) value toDF is not a member of org.apache.spark.rdd.RDD[dataLoadTest.this.Person]
possible cause: maybe a semicolon is missing before `value toDF'?
.toDF()
Error:(20, 20) stable identifier required, but dataLoadTest.this.spark.implicits found.
import spark.implicits._
IntelliJ won't recognise import spark.implicits._ or the .toDF() method.
I have imported:
import org.apache.spark.sql.SparkSession
import org.scalatest.{BeforeAndAfterEach, FlatSpec, FunSuite, Matchers}
you need to assign sqlContext to a val for implicits to work . Since your sparkSession is a var, implicits won't work with it
So you need to do
val sQLContext = spark.sqlContext
import sQLContext.implicits._
Moreover you can write functions for your tests so that your test class looks as following
class dataLoadTest extends FunSuite with Matchers with BeforeAndAfterEach {
private val master = "local[*]"
private val appName = "data_load_testing"
var spark: SparkSession = _
override def beforeEach() {
spark = new SparkSession.Builder().appName(appName).master(master).getOrCreate()
}
test("Creating dataframe should produce data from of correct size") {
val sQLContext = spark.sqlContext
import sQLContext.implicits._
val df = spark.sparkContext
.textFile("/Applications/spark-2.2.0-bin-hadoop2.7/examples/src/main/resources/people.txt")
.map(_.split(","))
.map(attributes => Person(attributes(0), attributes(1).trim.toInt))
.toDF()
assert(df.count() == 3)
assert(df.take(1)(0)(0).equals("Michael"))
}
override def afterEach() {
spark.stop()
}
}
case class Person(name: String, age: Int)
There are many libraries for unit testing of spark, one of the mostly used is
spark-testing-base: By Holden Karau
This library have all with sc as the SparkContext below is a simple example
class TestSharedSparkContext extends FunSuite with SharedSparkContext {
val expectedResult = List(("a", 3),("b", 2),("c", 4))
test("Word counts should be equal to expected") {
verifyWordCount(Seq("c a a b a c b c c"))
}
def verifyWordCount(seq: Seq[String]): Unit = {
assertResult(expectedResult)(new WordCount().transform(sc.makeRDD(seq)).collect().toList)
}
}
Here, every thing is prepared with sc as a SparkContext
Another approach is to create a TestWrapper and use for the multiple testcases as below
import org.apache.spark.sql.SparkSession
trait TestSparkWrapper {
lazy val sparkSession: SparkSession =
SparkSession.builder().master("local").appName("spark test example ").getOrCreate()
}
And use this TestWrapper for all the tests with Scala-test, playing with BeforeAndAfterAll and BeforeAndAfterEach.
Hope this helps!
I would like to use spark SQL in an Intellij IDEA SBT project.
Even though I have imported the library the code does not seem to import it.
Spark Core seems to be working however.
You can't create a DataFrame from a scala List[A]. You need first to create an RDD[A], and then transform that to a DataFrame. You also need an SQLContext:
val conf = new SparkConf()
.setMaster("local[*]")
.setAppName("test")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
val test = sc.parallelize(List(1,2,3,4)).toDF
For reference this is how the Spark 2.0 boilerplate with spark sql should look like:
import org.apache.spark.sql.SparkSession
object Test {
def main(args: Array[String]) {
val spark = SparkSession.builder()
.master("local")
.appName("some name")
.getOrCreate()
import spark.sqlContext.implicits._
}
}
I am executing tests in Scala with Spark creating a SparkContext as follows:
val conf = new SparkConf().setMaster("local").setAppName("test")
val sc = new SparkContext(conf)
After the first execution there was no error. But now I am getting this message (and a failed test notification):
Only one SparkContext may be running in this JVM (see SPARK-2243).
It looks like I need to check if there is any running SparkContext and stop it before launching a new one (I do not want to allow multiple contexts).
How can I do this?
UPDATE:
I tried this, but there is the same error (I am running tests from IntellijIdea and I make the code before executing it):
val conf = new SparkConf().setMaster("local").setAppName("test")
// also tried: .set("spark.driver.allowMultipleContexts", "true")
UPDATE 2:
class TestApp extends SparkFunSuite with TestSuiteBase {
// use longer wait time to ensure job completion
override def maxWaitTimeMillis: Int = 20000
System.clearProperty("spark.driver.port")
System.clearProperty("spark.hostPort")
var ssc: StreamingContext = _
val config: SparkConf = new SparkConf().setMaster("local").setAppName("test")
.set("spark.driver.allowMultipleContexts", "true")
val sc: SparkContext = new SparkContext(config)
//...
test("Test1")
{
sc.stop()
}
}
To stop existing context you can use stop method on a given SparkContext instance.
import org.apache.spark.{SparkContext, SparkConf}
val conf: SparkConf = ???
val sc: SparkContext = new SparkContext(conf)
...
sc.stop()
To reuse existing context or create a new one you can use SparkContex.getOrCreate method.
val sc1 = SparkContext.getOrCreate(conf)
...
val sc2 = SparkContext.getOrCreate(conf)
When used in test suites both methods can be used to achieve different things:
stop - stopping context in afterAll method (see for example MLlibTestSparkContext.afterAll)
getOrCreate - to get active instance in individual test cases (see for example QuantileDiscretizerSuite)