Why I love foldLeft :)

FoldLeft is the one of the my favourite function in Scala. In this blog,  I will explain capabilities of foldLeft. After reading this blog foldLeft will be your favourite function if you like Scala. In this blog I am taking example of List’s foldLeft. Of course, It’s also available on many Scala collection like Vector, Set, Map,Option.

Let’s see foldLeft definition from Scala doc:
According definition, foldLeft can do everything which required iteration of all elements of list. Really ?

Yes. Let’s understand by examples.

  1. Reverse the list:
    def reverse(list: List[Int]): List[Int] =
     list.foldLeft[List[Int]](Nil)((acc, element) => element :: acc)
  2. Remove duplicate element from list:
     def dedupe(list: List[Int]): List[Int] =
    list.foldLeft[List[Int]](Nil)((acc, element) => if (acc.contains(element)) acc else acc :+ element)
  3. Split into two list. first list contains all element which satisfies the predicate  and remaining into second list.
     def span(list: List[Int], p: Int => Boolean): (List[Int], List[Int]) =
     list.foldLeft[(List[Int], List[Int])]((Nil, Nil)) { case ((posList, negList), element) =>
     if (p(element)) (posList :+ element, negList) else (posList, negList :+ element)
  4. Splitting into two list not big deal 🙂 My use case is different. I have 4 predicate. That means split input list into four list according predicates. First list satisfy first predicate and second list   satisfy second predicate ..so on. discard otherwise.
     def span(list: List[Int], p1: Int => Boolean, p2: Int => Boolean, p3: Int => Boolean, p4: Int => Boolean): (List[Int], List[Int], List[Int], List[Int]) =
     list.foldLeft[(List[Int], List[Int], List[Int], List[Int])]((Nil, Nil, Nil, Nil)) { case ((p1List, p2List, p3List, p4List), element) =>
     if (p1(element)) p1List :+ element else p1List,
     if (p2(element)) p2List :+ element else p2List,
     if (p3(element)) p3List :+ element else p3List,
     if (p4(element)) p4List :+ element else p4List
  5. Combine two list:
     def combine(list1: List[Int], list2: List[Int]) =
     list2.foldLeft[List[Int]](list1)((acc, element) => acc :+ element)
  6. Zip two list:
      def zip(list1: List[Int], list2: List[String]): List[(Int, String)] =
        if (list1.length > list2.length) {
          list2.foldLeft[(List[Int], List[(Int, String)])]((list1, Nil)) {
            case ((list, zip), element) => (list.tail, zip :+ (list.head, element))
        } else {
          list1.foldLeft[(List[String], List[(Int, String)])]((list2, Nil)) {
            case ((list, zip), element) => (list.tail, zip :+ (element, list.head))
  7. Unzip the List :
      def unzip(list: List[(Int, String)]): (List[Int], List[String]) =
        list.foldLeft[(List[Int], List[String])]((Nil, Nil)) {
          case ((list1, list2), (number, str)) => (list1 :+ number, list2 :+ str)
  8. Sort the list:
      def sort(list: List[Int]): List[Int] =
        list.foldLeft[List[Int]](Nil) { (acc, element) => insert(element, acc) }
      def insert(elem: Int, list: List[Int]): List[Int] =
        list match {
          case head :: tail => if (head <= elem) head :: insert(elem, tail) else elem :: list
          case Nil => elem :: Nil
  9. Sum the List:
    def sum(list: List[Int]): Int = list.foldLeft(0)(_ + _)
  10. Implement own map using foldLeft:
        def map(list: List[Int], f: Int => Int): List[Int] =
        list.foldLeft[List[Int]](Nil)((acc, element) => acc :+ f(element))
  11. Of course you can implement flatMap using foldLeft 🙂
    def flatMap(list: List[Int], f: Int => List[Int]): List[Int] =
    list.foldLeft[List[Int]](Nil)((acc, element) => acc ++ f(element))

May be these examples make you happy and give the understanding of foldLeft.

All example are available here.
Happy Hacking!!! 🙂

Building Analytics Engine Using Akka, Kafka & ElasticSearch

Knoldus Blogs

In this blog , I will share my experience on building scalable, distributed and fault-tolerant  Analytics engine using Scala, Akka, Play, Kafka and ElasticSearch.

I would like to take you through the journey of  building an analytics engine which was primarily used for text analysis. The inputs were structured, unstructured and semi-structured data and we were doing a lot of data crunching using it. The Analytics engine was accessible by the rest-client and web-client(Built In with engine)  as shown in below diagram.

Here is a quick overview on technology stack :

  1. Play Framework  as Rest Server & Web Application  (Play is MVC  framework based on  lightweight, stateless and web friendly architecture.)
  2. Akka cluster as processing engine.(Akka is a toolkit and runtime for building highly concurrent,distributed, and resilient message driven applications on the JVM.)
  3. ClusterClient (It was contributed module) for communication with Akka cluster. It used to run on rest…

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