I have a method called split
that accepts an RDD[T] and a splitSize and returns an Array[RDD[T]].
Now, one of the test cases I write for it should verify that this function also randomly shuffles the RDD.
So I create a sorted RDD, and then see the results:
it should "randomize shuffle" in {
val inputRDD = sc.parallelize((0 until 16))
val result = RDDUtils.split(inputRDD, 2)
result.foreach(rdd => {
rdd.collect.foreach(println)
})
// Asset result is not sorted
}
If the results are:
0 1 2 3 .. 15
Then it's not working as expected.
A good result can be something like:
11 3 9 14 ... 1 6
How can I assert the output Array[RDD[T]]] is not sorted?
You could try something like this
val resultOrder = result.sortBy(....)
assert(!resultOrder.sameElements(result))
or
val resultOrder = result.sortBy(....)
assert(!resultOrder.toList == result.toList)
It's important to note that the key is to know how to sort the Array. For an Integer data type it would be easy, but for a complex data type you could need an implicit Ordering for your data type. e.g:
implicit val ordering: Ordering[T] =
Ordering.fromLessThan[T]((sa: T, sb: T) => sa < sb)
// OR
implicit val ordering: Ordering[MyClass] =
Ordering.fromLessThan[MyClass]((sa: MyClass, sb: MyClass) => sa.field1 < sb.field1)
The exact code would depend of your data type.
As a full example of this
package tests
import org.apache.log4j.{Level, Logger}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
object SortArrayRDD {
val spark = SparkSession
.builder()
.appName("SortArrayRDD")
.master("local[*]")
.config("spark.sql.shuffle.partitions","4") //Change to a more reasonable default number of partitions for our data
.config("spark.app.id","SortArrayRDD") // To silence Metrics warning
.getOrCreate()
val sc = spark.sparkContext
def main(args: Array[String]): Unit = {
try {
Logger.getRootLogger.setLevel(Level.ERROR)
val arrRDD: Array[RDD[Int]] = Array(sc.parallelize(List(2,3)),sc.parallelize(List(10,11)),sc.parallelize(List(6,7)),sc.parallelize(List(8,9)),
sc.parallelize(List(4,5)),sc.parallelize(List(0,1)),sc.parallelize(List(12,13)),sc.parallelize(List(14,15)))
val aux = arrRDD
implicit val ordering: Ordering[RDD[Int]] = Ordering.fromLessThan[RDD[Int]]((sa: RDD[Int], sb: RDD[Int]) => sa.sum() < sb.sum())
aux.sorted.foreach(rdd => println(rdd.collect().mkString(",")))
val resultOrder = aux.sorted
assert(!resultOrder.sameElements(arrRDD))
println("It's unordered")
} finally {
sc.stop()
}
}
}