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Learning Scala Implicits With Spark

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Learning Scala Implicits With Spark

How to use Scala's more advanced features like Implicits. When the Scala compiler finds a variable or expression of the wrong type, it will look for an implicit function, expression or class to provide the correct type.

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A while back I wrote two posts on avoiding the use of the groupBy function in Spark. While I won’t re-hash both posts here, the bottom line was to take advantage of the combineByKey or aggreagateByKey functions instead. While both functions hold the potential for improved performance and efficiency in our Spark jobs, at times creating the required arguments over and over for basic use cases could get tedious. It got me to thinking is there a way of providing some level of abstraction for basic use cases? For example grouping values into a list or set. Simultaneously, I’ve been trying to expand my knowlege of Scala’s more advanced features including implicits and TypeClasses. What I came up with is the GroupingRDDFunctions class that provides some syntactic sugar for basic use cases of the aggregateByKey function by using Scala’s implicit class functionality.

Scala Implicits in Brief

While a full explanation of Scala’s implicits is beyond the scope of this post, here’s a quick description. When the Scala compiler finds a variable or expression of the wrong type, it will look for an implicit function, expression or class to provide the correct type. The implicitfunction (or class) needs to be in the current scope for the compiler to do it’s work. This is typically accomplished by importing a Scala object that contains the implicit definition(s). In this case the GroupingRDDFunctions class is wrapped in the GroupingRDDUtils object. Here’s the class declaration:

object GroupingRDDUtils {
  implicit class GroupingRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Logging with Serializable {
//... details left out for clarity

To use GroupingRDDFunctions just use the following import statement:

import bbejeck.implicits.GroupingRDDUtils._

Provided Functionality

The methods defined on GroupingRDDFunctions are:

  1. groupByKeyToList
  2. groupByKeyUnique
  3. countByKey
  4. sumWithTotal – provides a tuple with the sumation of numeric value as a Double along with the total count of items to create the sum
  5. averageByKey

Here’s some examples of using GroupingRDDFunctions:

//To do a grouping of unique values by key using aggregateByKey
val initialSet = mutable.HashSet.empty[String]
val addToSet = (s: mutable.HashSet[String], v: String) => s += v
val mergePartitionSets = (p1: mutable.HashSet[String], p2: mutable.HashSet[String]) => p1 ++= p2
val uniqueByKey = kv.aggregateByKey(initialSet)(addToSet, mergePartitionSets)

//Now can be accomplished doing
val uniqueByKey = kv.groupByKeyUnique()

//Computing Count By Key with aggregateByKey
val initialCount = 0;
val addToCounts = (n: Int, v: String) => n + 1
val sumPartitionCounts = (p1: Int, p2: Int) => p1 + p2
val counts = kv.aggregateByKey(initialCount)(addToCounts, sumPartitionCounts)

//Now becomes
val counts = kv.countByKey()

There is really nothing special happening here. We are simply wrapping an RDD instance and providing the ability to use the methods listed above on that RDD instance. Within the GroupingRDDFunctions class we are still leveraging the aggregateByKey function.

Implicit Parameter Conversion

As another example of implicit useage let’s take a look at the averageByKey function. In the code below, we compute the average by key by applying the avaragingFunction to the results returned from the sumWithTotal method. But if we look closely, our keys and values are generics of ‘K’ and ‘V’, but all of these functions work on doubles.

implicit def intToDouble(num: V): Double = {
        num match {
          case i: Int => i.toDouble
          case _ => num.asInstanceOf[Double]

def sumWithTotal(): RDD[(K, (Int, Double))] = {
      self.aggregateByKey((0, 0.0))(incrementCountSumValue, sumTuples)

def averageByKey(): RDD[(K, Double)] = {
    self.sumWithTotal().map(t => averagingFunction(t))


private def averagingFunction(t: (K, (Int, Double))): (K, Double) = {
      val (name, (numberScores, totalScore)) = t
      (name, totalScore / numberScores)

private def incrementCountSumValue(t: (Int, Double), v: V): (Int, Double) = {
      (t._1 + 1, t._2 + v)

private def sumTuples(t: (Int, Double), t2: (Int, Double)): (Int, Double) = {
      val (numScores1, totalScore1) = t
      val (numScores2, totalScore2) = t2
      (numScores1 + numScores2, totalScore1 + totalScore2)

So what happens if the values provided are integers instead of doubles? Also take a look at the incrementCountSumValue method, how can a value of type ‘V’ be added to the double value of the tuple? This is good example of using an implicit function. The compiler will look for and find the intToDouble function and apply to the parameter of the incrementCountSumValuemethod. If the value is an integer, it’s implicity converted to a double, otherwise we return a double.


While the use of implicits in Scala needs to be judicious, the example presented here represents a good use-case in my opinion. We are adding some useful behavior to a class, just by adding an import statement. Plus it’s very easy to inspect the implicit class to see what’s going on under the covers.


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spark ,scala ,java

Published at DZone with permission of Bill Bejeck, DZone MVB. See the original article here.

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