Scala immutable collection

Scala collections come into 2 categories mutable & immutable collections. Scala’s core power is the collection framework.. let see it’s diagram below.




Priority Queue – Data Structure

We know that Queue follows First-In-First-Out model but sometimes we need to process the objects in the queue based on the priority. That is when JavaPriorityQueue is used.

For example, let’s say we have an application that generates stocks reports for daily trading session. This application processes a lot of data and takes time to process it. So customers are sending request to the application that is actually getting queued but we want to process premium customers first and standard customers after them. So in this case PriorityQueue implementation in java can be really helpful.

PriorityQueue is an unbounded queue based on a priority heap and the elements of the priority queue are ordered by default in natural order. We can provide a Comparator for ordering at the time of instantiation of priority queue.

Java Priority Queue doesn’t allow null values and we can’t create PriorityQueue of Objects that are non-comparable. We use java Comparable and Comparator for sorting Objects and Priority Queue use them for priority processing of it’s elements.

The simplest way to implement a priority queue data type is to keep an associative array mapping each priority to a list of elements with that priority. If association lists or hash tables are used to implement the associative array, adding an element takes constant time but removing or peeking at the element of highest priority takes linear (O(n)) time, because we must search all keys for the largest one. If a self-balancing binary search tree is used, all three operations take O(log n) time; this is a popular solution in environments that already provide balanced trees but nothing more sophisticated.

There are a number of specialized heap data structures that either supply additional operations or outperform the above approaches. The binary heap uses O(log n) time for both operations, but allows peeking at the element of highest priority without removing it in constant time. Binomial heaps add several more operations, but require O(log n) time for peeking. Fibonacci heaps can insert elements, peek at the maximum priority element, and decrease an element’s priority in amortized constant time (deletions are still O(log n)).


// BinaryHeap class
// CONSTRUCTION: empty or with initial array.
// ******************PUBLIC OPERATIONS*********************
// void insert( x )       –> Insert x
// Comparable deleteMin( )–> Return and remove smallest item
// Comparable findMin( )  –> Return smallest item
// boolean isEmpty( )     –> Return true if empty; else false
// void makeEmpty( )      –> Remove all items
// ******************ERRORS********************************
// Throws UnderflowException for findMin and deleteMin when empty

* Implements a binary heap.
* Note that all “matching” is based on the compareTo method.
* @author Bhavesh Gadoya
public class BinaryHeap implements PriorityQueue {
* Construct the binary heap.
public BinaryHeap( ) {
currentSize = 0;
array = new Comparable[ DEFAULT_CAPACITY + 1 ];

* Construct the binary heap from an array.
* @param items the inital items in the binary heap.
public BinaryHeap( Comparable [ ] items ) {
currentSize = items.length;
array = new Comparable[ items.length + 1 ];

for( int i = 0; i < items.length; i++ )
array[ i + 1 ] = items[ i ];
buildHeap( );

* Insert into the priority queue.
* Duplicates are allowed.
* @param x the item to insert.
* @return null, signifying that decreaseKey cannot be used.
public PriorityQueue.Position insert( Comparable x ) {
if( currentSize + 1 == array.length )
doubleArray( );

// Percolate up
int hole = ++currentSize;
array[ 0 ] = x;

for( ; x.compareTo( array[ hole / 2 ] ) < 0; hole /= 2 )
array[ hole ] = array[ hole / 2 ];
array[ hole ] = x;

return null;

* @throws UnsupportedOperationException because no Positions are returned
* by the insert method for BinaryHeap.
public void decreaseKey( PriorityQueue.Position p, Comparable newVal ) {
throw new UnsupportedOperationException( “Cannot use decreaseKey for binary heap” );

* Find the smallest item in the priority queue.
* @return the smallest item.
* @throws UnderflowException if empty.
public Comparable findMin( ) {
if( isEmpty( ) )
throw new UnderflowException( “Empty binary heap” );
return array[ 1 ];

* Remove the smallest item from the priority queue.
* @return the smallest item.
* @throws UnderflowException if empty.
public Comparable deleteMin( ) {
Comparable minItem = findMin( );
array[ 1 ] = array[ currentSize– ];
percolateDown( 1 );

return minItem;

* Establish heap order property from an arbitrary
* arrangement of items. Runs in linear time.
private void buildHeap( ) {
for( int i = currentSize / 2; i > 0; i– )
percolateDown( i );

* Test if the priority queue is logically empty.
* @return true if empty, false otherwise.
public boolean isEmpty( ) {
return currentSize == 0;

* Returns size.
* @return current size.
public int size( ) {
return currentSize;

* Make the priority queue logically empty.
public void makeEmpty( ) {
currentSize = 0;

private static final int DEFAULT_CAPACITY = 100;

private int currentSize;      // Number of elements in heap
private Comparable [ ] array; // The heap array

* Internal method to percolate down in the heap.
* @param hole the index at which the percolate begins.
private void percolateDown( int hole ) {
int child;
Comparable tmp = array[ hole ];

for( ; hole * 2 <= currentSize; hole = child ) {
child = hole * 2;
if( child != currentSize &&
array[ child + 1 ].compareTo( array[ child ] ) < 0 )
if( array[ child ].compareTo( tmp ) < 0 )
array[ hole ] = array[ child ];
array[ hole ] = tmp;

* Internal method to extend array.
private void doubleArray( ) {
Comparable [ ] newArray;

newArray = new Comparable[ array.length * 2 ];
for( int i = 0; i < array.length; i++ )
newArray[ i ] = array[ i ];
array = newArray;

// Test program
public static void main( String [ ] args ) {
int numItems = 10000;
BinaryHeap h1 = new BinaryHeap( );
Integer [ ] items = new Integer[ numItems – 1 ];

int i = 37;
int j;

for( i = 37, j = 0; i != 0; i = ( i + 37 ) % numItems, j++ ) {
h1.insert( new Integer( i ) );
items[ j ] = new Integer( i );

for( i = 1; i < numItems; i++ )
if( ((Integer)( h1.deleteMin( ) )).intValue( ) != i )
System.out.println( “Oops! ” + i );

BinaryHeap h2 = new BinaryHeap( items );
for( i = 1; i < numItems; i++ )
if( ((Integer)( h2.deleteMin( ) )).intValue( ) != i )
System.out.println( “Oops! ” + i );

// PriorityQueue interface
// ******************PUBLIC OPERATIONS*********************
// Position insert( x )   –> Insert x
// Comparable deleteMin( )–> Return and remove smallest item
// Comparable findMin( )  –> Return smallest item
// boolean isEmpty( )     –> Return true if empty; else false
// void makeEmpty( )      –> Remove all items
// int size( )            –> Return size
// void decreaseKey( p, v)–> Decrease value in p to v
// ******************ERRORS********************************
// Throws UnderflowException for findMin and deleteMin when empty

* PriorityQueue interface.
* Some priority queues may support a decreaseKey operation,
* but this is considered an advanced operation. If so,
* a Position is returned by insert.
* Note that all “matching” is based on the compareTo method.
* @author Bhavesh Gadoya
public interface PriorityQueue {
* The Position interface represents a type that can
* be used for the decreaseKey operation.
public interface Position {
* Returns the value stored at this position.
* @return the value stored at this position.
Comparable getValue( );

* Insert into the priority queue, maintaining heap order.
* Duplicates are allowed.
* @param x the item to insert.
* @return may return a Position useful for decreaseKey.
Position insert( Comparable x );

* Find the smallest item in the priority queue.
* @return the smallest item.
* @throws UnderflowException if empty.
Comparable findMin( );

* Remove the smallest item from the priority queue.
* @return the smallest item.
* @throws UnderflowException if empty.
Comparable deleteMin( );

* Test if the priority queue is logically empty.
* @return true if empty, false otherwise.
boolean isEmpty( );

* Make the priority queue logically empty.
void makeEmpty( );

* Returns the size.
* @return current size.
int size( );

* Change the value of the item stored in the pairing heap.
* This is considered an advanced operation and might not
* be supported by all priority queues. A priority queue
* will signal its intention to not support decreaseKey by
* having insert return null consistently.
* @param p any non-null Position returned by insert.
* @param newVal the new value, which must be smaller
*    than the currently stored value.
* @throws IllegalArgumentException if p invalid.
* @throws UnsupportedOperationException if appropriate.
void decreaseKey( Position p, Comparable newVal );

* Exception class for access in empty containers
* such as stacks, queues, and priority queues.
* @author Bhavesh Gadoya
public class UnderflowException extends RuntimeException {
* Construct this exception object.
* @param message the error message.
public UnderflowException( String message ) {
super( message );


In-built java implementation of priorityQueue :

package com.journaldev.collections;

public class Customer {
	private int id;
	private String name;
	public Customer(int i, String n){;;

	public int getId() {
		return id;

	public String getName() {
		return name;

We will use java random number generation to generate random customer objects. For natural ordering, I will use Integer that is also a java wrapper class.

Here is our final test code that shows how to use priority queue in java.

package com.journaldev.collections;

import java.util.Comparator;
import java.util.PriorityQueue;
import java.util.Queue;
import java.util.Random;

public class PriorityQueueExample {

	public static void main(String[] args) {
		//natural ordering example of priority queue
		Queue<Integer> integerPriorityQueue = new PriorityQueue<>(7);
		Random rand = new Random();
		for(int i=0;i<7;i++){
			integerPriorityQueue.add(new Integer(rand.nextInt(100)));
		for(int i=0;i<7;i++){
			Integer in = integerPriorityQueue.poll();
			System.out.println("Processing Integer:"+in);
		//PriorityQueue example with Comparator
		Queue<Customer> customerPriorityQueue = new PriorityQueue<>(7, idComparator);
	//Comparator anonymous class implementation
	public static Comparator<Customer> idComparator = new Comparator<Customer>(){
		public int compare(Customer c1, Customer c2) {
            return (int) (c1.getId() - c2.getId());

	//utility method to add random data to Queue
	private static void addDataToQueue(Queue<Customer> customerPriorityQueue) {
		Random rand = new Random();
		for(int i=0; i<7; i++){
			int id = rand.nextInt(100);
			customerPriorityQueue.add(new Customer(id, "Pankaj "+id));
	//utility method to poll data from queue
	private static void pollDataFromQueue(Queue<Customer> customerPriorityQueue) {
			Customer cust = customerPriorityQueue.poll();
			if(cust == null) break;
			System.out.println("Processing Customer with ID="+cust.getId());


Apache Graphx

GraphX is a new component in Spark for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing a new Graph abstraction: a directed multigraph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators

Spark GraphX is a graph processing framework built on top of Spark.

GraphX models graphs as property graphs where vertices and edges can have properties.

GraphX comes with its own package org.apache.spark.graphx.


Graph abstract class represents a collection of vertices and edges.

abstract class Graph[VD: ClassTag, ED: ClassTag]

vertices attribute is of type VertexRDD while edges is of type EdgeRDD.

Standard GraphX API

Graph class comes with a small set of API.

  • Transformations

    • mapVertices

    • mapEdges

    • mapTriplets

    • reverse

    • subgraph

    • mask
    • groupEdges

  • Joins

    • outerJoinVertices

  • Computation

    • aggregateMessages

Creating Graphs (Graph object)

Graph object comes with the following factory methods to create instances of Graph:

Main classes & interfaces in Graphx :
Class Description
A single directed edge consisting of a source id, target id, and the data associated with the edge.
Represents an edge along with its neighboring vertices and allows sending messages along the edge.
The direction of a directed edge relative to a vertex.
EdgeRDD[ED, VD] extends RDD[Edge[ED} by storing the edges in columnar format on each partition for performance.
An edge triplet represents an edge along with the vertex attributes of its neighboring vertices.
The Graph abstractly represents a graph with arbitrary objects associated with vertices and edges.
Registers GraphX classes with Kryo for improved performance.
Provides utilities for loading Graphs from files.
Contains additional functionality for Graph.
Assigns edges to partitions by hashing the source and destination vertex IDs in a canonical direction, resulting in a random vertex cut that colocates all edges between two vertices, regardless of direction.
Assigns edges to partitions using only the source vertex ID, colocating edges with the same source.
Assigns edges to partitions using a 2D partitioning of the sparse edge adjacency matrix, guaranteeing a 2 * sqrt(numParts) - 1 bound on vertex replication.
Assigns edges to partitions by hashing the source and destination vertex IDs, resulting in a random vertex cut that colocates all same-direction edges between two vertices.
Implements a Pregel-like bulk-synchronous message-passing API.
Represents a subset of the fields of an [[EdgeTriplet]] or [[EdgeContext]].
Extends RDD[(VertexId, VD)] by ensuring that there is only one entry for each vertex and by pre-indexing the entries for fast, efficient joins.

Example Property Graph

Suppose we want to construct a property graph consisting of the various collaborators on the GraphX project. The vertex property might contain the username and occupation. We could annotate edges with a string describing the relationships between collaborators:

The Property Graph

The resulting graph would have the type signature:

val userGraph: Graph[(String, String), String]

There are numerous ways to construct a property graph from raw files, RDDs, and even synthetic generators and these are discussed in more detail in the section on graph builders. Probably the most general method is to use the Graph object. For example the following code constructs a graph from a collection of RDDs:

// Assume the SparkContext has already been constructed
val sc: SparkContext
// Create an RDD for the vertices
val users: RDD[(VertexId, (String, String))] =
  sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")),
                       (5L, ("franklin", "prof")), (2L, ("istoica", "prof"))))
// Create an RDD for edges
val relationships: RDD[Edge[String]] =
  sc.parallelize(Array(Edge(3L, 7L, "collab"),    Edge(5L, 3L, "advisor"),
                       Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi")))
// Define a default user in case there are relationship with missing user
val defaultUser = ("John Doe", "Missing")
// Build the initial Graph
val graph = Graph(users, relationships, defaultUser)

In the above example we make use of the Edge case class. Edges have a srcId and a dstId corresponding to the source and destination vertex identifiers. In addition, the Edge class has an attr member which stores the edge property.

We can deconstruct a graph into the respective vertex and edge views by using the graph.vertices and graph.edges members respectively.

val graph: Graph[(String, String), String] // Constructed from above
// Count all users which are postdocs
graph.vertices.filter { case (id, (name, pos)) => pos == "postdoc" }.count
// Count all the edges where src > dst
graph.edges.filter(e => e.srcId > e.dstId).count

Note that graph.vertices returns an VertexRDD[(String, String)] which extends RDD[(VertexId, (String, String))] and so we use the scala case expression to deconstruct the tuple. On the other hand, graph.edges returns an EdgeRDD containingEdge[String] objects. We could have also used the case class type constructor as in the following:

graph.edges.filter { case Edge(src, dst, prop) => src > dst }.count

In addition to the vertex and edge views of the property graph, GraphX also exposes a triplet view. The triplet view logically joins the vertex and edge properties yielding an RDD[EdgeTriplet[VD, ED]] containing instances of the EdgeTriplet class. This join can be expressed in the following SQL expression:

SELECT,, src.attr, e.attr, dst.attr
FROM edges AS e LEFT JOIN vertices AS src, vertices AS dst
ON e.srcId = src.Id AND e.dstId = dst.Id

or graphically as:

Edge Triplet

The EdgeTriplet class extends the Edge class by adding the srcAttr and dstAttr members which contain the source and destination properties respectively. We can use the triplet view of a graph to render a collection of strings describing relationships between users.

val graph: Graph[(String, String), String] // Constructed from above
// Use the triplets view to create an RDD of facts.
val facts: RDD[String] = =>
    triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1)

Graph Operators

Just as RDDs have basic operations like map, filter, and reduceByKey, property graphs also have a collection of basic operators that take user defined functions and produce new graphs with transformed properties and structure. The core operators that have optimized implementations are defined in Graph and convenient operators that are expressed as a compositions of the core operators are defined in GraphOps. However, thanks to Scala implicits the operators in GraphOps are automatically available as members of Graph. For example, we can compute the in-degree of each vertex (defined in GraphOps) by the following:

val graph: Graph[(String, String), String]
// Use the implicit GraphOps.inDegrees operator
val inDegrees: VertexRDD[Int] = graph.inDegrees

The reason for differentiating between core graph operations and GraphOps is to be able to support different graph representations in the future. Each graph representation must provide implementations of the core operations and reuse many of the useful operations defined in GraphOps.

Summary List of Operators

The following is a quick summary of the functionality defined in both Graph and GraphOps but presented as members of Graph for simplicity. Note that some function signatures have been simplified (e.g., default arguments and type constraints removed) and some more advanced functionality has been removed so please consult the API docs for the official list of operations.

/** Summary of the functionality in the property graph */
class Graph[VD, ED] {
  // Information about the Graph ===================================================================
  val numEdges: Long
  val numVertices: Long
  val inDegrees: VertexRDD[Int]
  val outDegrees: VertexRDD[Int]
  val degrees: VertexRDD[Int]
  // Views of the graph as collections =============================================================
  val vertices: VertexRDD[VD]
  val edges: EdgeRDD[ED]
  val triplets: RDD[EdgeTriplet[VD, ED]]
  // Functions for caching graphs ==================================================================
  def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
  def cache(): Graph[VD, ED]
  def unpersistVertices(blocking: Boolean = true): Graph[VD, ED]
  // Change the partitioning heuristic  ============================================================
  def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
  // Transform vertex and edge attributes ==========================================================
  def mapVertices[VD2](map: (VertexId, VD) => VD2): Graph[VD2, ED]
  def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2]
  def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2]): Graph[VD, ED2]
  def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2]
  def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2])
    : Graph[VD, ED2]
  // Modify the graph structure ====================================================================
  def reverse: Graph[VD, ED]
  def subgraph(
      epred: EdgeTriplet[VD,ED] => Boolean = (x => true),
      vpred: (VertexId, VD) => Boolean = ((v, d) => true))
    : Graph[VD, ED]
  def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED]
  def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED]
  // Join RDDs with the graph ======================================================================
  def joinVertices[U](table: RDD[(VertexId, U)])(mapFunc: (VertexId, VD, U) => VD): Graph[VD, ED]
  def outerJoinVertices[U, VD2](other: RDD[(VertexId, U)])
      (mapFunc: (VertexId, VD, Option[U]) => VD2)
    : Graph[VD2, ED]
  // Aggregate information about adjacent triplets =================================================
  def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexId]]
  def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[Array[(VertexId, VD)]]
  def aggregateMessages[Msg: ClassTag](
      sendMsg: EdgeContext[VD, ED, Msg] => Unit,
      mergeMsg: (Msg, Msg) => Msg,
      tripletFields: TripletFields = TripletFields.All)
    : VertexRDD[A]
  // Iterative graph-parallel computation ==========================================================
  def pregel[A](initialMsg: A, maxIterations: Int, activeDirection: EdgeDirection)(
      vprog: (VertexId, VD, A) => VD,
      sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId,A)],
      mergeMsg: (A, A) => A)
    : Graph[VD, ED]
  // Basic graph algorithms ========================================================================
  def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double]
  def connectedComponents(): Graph[VertexId, ED]
  def triangleCount(): Graph[Int, ED]
  def stronglyConnectedComponents(numIter: Int): Graph[VertexId, ED]

Property Operators

Like the RDD map operator, the property graph contains the following:

class Graph[VD, ED] {
  def mapVertices[VD2](map: (VertexId, VD) => VD2): Graph[VD2, ED]
  def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2]
  def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2]

Each of these operators yields a new graph with the vertex or edge properties modified by the user defined map function.

Note that in each case the graph structure is unaffected. This is a key feature of these operators which allows the resulting graph to reuse the structural indices of the original graph. The following snippets are logically equivalent, but the first one does not preserve the structural indices and would not benefit from the GraphX system optimizations:

val newVertices = { case (id, attr) => (id, mapUdf(id, attr)) }
val newGraph = Graph(newVertices, graph.edges)

Instead, use mapVertices to preserve the indices:

val newGraph = graph.mapVertices((id, attr) => mapUdf(id, attr))

These operators are often used to initialize the graph for a particular computation or project away unnecessary properties. For example, given a graph with the out degrees as the vertex properties (we describe how to construct such a graph later), we initialize it for PageRank:

// Given a graph where the vertex property is the out degree
val inputGraph: Graph[Int, String] =
  graph.outerJoinVertices(graph.outDegrees)((vid, _, degOpt) => degOpt.getOrElse(0))
// Construct a graph where each edge contains the weight
// and each vertex is the initial PageRank
val outputGraph: Graph[Double, Double] =
  inputGraph.mapTriplets(triplet => 1.0 / triplet.srcAttr).mapVertices((id, _) => 1.0)

Structural Operators

Currently GraphX supports only a simple set of commonly used structural operators and we expect to add more in the future. The following is a list of the basic structural operators.

class Graph[VD, ED] {
  def reverse: Graph[VD, ED]
  def subgraph(epred: EdgeTriplet[VD,ED] => Boolean,
               vpred: (VertexId, VD) => Boolean): Graph[VD, ED]
  def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED]
  def groupEdges(merge: (ED, ED) => ED): Graph[VD,ED]

The reverse operator returns a new graph with all the edge directions reversed. This can be useful when, for example, trying to compute the inverse PageRank. Because the reverse operation does not modify vertex or edge properties or change the number of edges, it can be implemented efficiently without data movement or duplication.

The subgraph operator takes vertex and edge predicates and returns the graph containing only the vertices that satisfy the vertex predicate (evaluate to true) and edges that satisfy the edge predicate and connect vertices that satisfy the vertex predicate. The subgraph operator can be used in number of situations to restrict the graph to the vertices and edges of interest or eliminate broken links. For example in the following code we remove broken links:

// Create an RDD for the vertices
val users: RDD[(VertexId, (String, String))] =
  sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")),
                       (5L, ("franklin", "prof")), (2L, ("istoica", "prof")),
                       (4L, ("peter", "student"))))
// Create an RDD for edges
val relationships: RDD[Edge[String]] =
  sc.parallelize(Array(Edge(3L, 7L, "collab"),    Edge(5L, 3L, "advisor"),
                       Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi"),
                       Edge(4L, 0L, "student"),   Edge(5L, 0L, "colleague")))
// Define a default user in case there are relationship with missing user
val defaultUser = ("John Doe", "Missing")
// Build the initial Graph
val graph = Graph(users, relationships, defaultUser)
// Notice that there is a user 0 (for which we have no information) connected to users
// 4 (peter) and 5 (franklin).
  triplet => triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1
// Remove missing vertices as well as the edges to connected to them
val validGraph = graph.subgraph(vpred = (id, attr) => attr._2 != "Missing")
// The valid subgraph will disconnect users 4 and 5 by removing user 0
  triplet => triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1

Note in the above example only the vertex predicate is provided. The subgraph operator defaults to true if the vertex or edge predicates are not provided.

The mask operator constructs a subgraph by returning a graph that contains the vertices and edges that are also found in the input graph. This can be used in conjunction with the subgraph operator to restrict a graph based on the properties in another related graph. For example, we might run connected components using the graph with missing vertices and then restrict the answer to the valid subgraph.

// Run Connected Components
val ccGraph = graph.connectedComponents() // No longer contains missing field
// Remove missing vertices as well as the edges to connected to them
val validGraph = graph.subgraph(vpred = (id, attr) => attr._2 != "Missing")
// Restrict the answer to the valid subgraph
val validCCGraph = ccGraph.mask(validGraph)

The groupEdges operator merges parallel edges (i.e., duplicate edges between pairs of vertices) in the multigraph. In many numerical applications, parallel edges can be added (their weights combined) into a single edge thereby reducing the size of the graph.

Join Operators

In many cases it is necessary to join data from external collections (RDDs) with graphs. For example, we might have extra user properties that we want to merge with an existing graph or we might want to pull vertex properties from one graph into another. These tasks can be accomplished using the join operators. Below we list the key join operators:

class Graph[VD, ED] {
  def joinVertices[U](table: RDD[(VertexId, U)])(map: (VertexId, VD, U) => VD)
    : Graph[VD, ED]
  def outerJoinVertices[U, VD2](table: RDD[(VertexId, U)])(map: (VertexId, VD, Option[U]) => VD2)
    : Graph[VD2, ED]

The joinVertices operator joins the vertices with the input RDD and returns a new graph with the vertex properties obtained by applying the user defined map function to the result of the joined vertices. Vertices without a matching value in the RDD retain their original value.

Note that if the RDD contains more than one value for a given vertex only one will be used. It is therefore recommended that the input RDD be made unique using the following which will also pre-index the resulting values to substantially accelerate the subsequent join.

val nonUniqueCosts: RDD[(VertexId, Double)]
val uniqueCosts: VertexRDD[Double] =
  graph.vertices.aggregateUsingIndex(nonUnique, (a,b) => a + b)
val joinedGraph = graph.joinVertices(uniqueCosts)(
  (id, oldCost, extraCost) => oldCost + extraCost)

The more general outerJoinVertices behaves similarly to joinVertices except that the user defined map function is applied to all vertices and can change the vertex property type. Because not all vertices may have a matching value in the input RDD the map function takes an Option type. For example, we can setup a graph for PageRank by initializing vertex properties with their outDegree.

val outDegrees: VertexRDD[Int] = graph.outDegrees
val degreeGraph = graph.outerJoinVertices(outDegrees) { (id, oldAttr, outDegOpt) =>
  outDegOpt match {
    case Some(outDeg) => outDeg
    case None => 0 // No outDegree means zero outDegree

You may have noticed the multiple parameter lists (e.g., f(a)(b)) curried function pattern used in the above examples. While we could have equally written f(a)(b) as f(a,b) this would mean that type inference on b would not depend on a. As a consequence, the user would need to provide type annotation for the user defined function:

val joinedGraph = graph.joinVertices(uniqueCosts,
  (id: VertexId, oldCost: Double, extraCost: Double) => oldCost + extraCost)

Neighborhood Aggregation

A key step in many graph analytics tasks is aggregating information about the neighborhood of each vertex. For example, we might want to know the number of followers each user has or the average age of the the followers of each user. Many iterative graph algorithms (e.g., PageRank, Shortest Path, and connected components) repeatedly aggregate properties of neighboring vertices (e.g., current PageRank Value, shortest path to the source, and smallest reachable vertex id).

To improve performance the primary aggregation operator changed from graph.mapReduceTriplets to the newgraph.AggregateMessages. While the changes in the API are relatively small, we provide a transition guide below.

Aggregate Messages (aggregateMessages)

The core aggregation operation in GraphX is aggregateMessages. This operator applies a user defined sendMsg function to each edge triplet in the graph and then uses the mergeMsg function to aggregate those messages at their destination vertex.

class Graph[VD, ED] {
  def aggregateMessages[Msg: ClassTag](
      sendMsg: EdgeContext[VD, ED, Msg] => Unit,
      mergeMsg: (Msg, Msg) => Msg,
      tripletFields: TripletFields = TripletFields.All)
    : VertexRDD[Msg]

The user defined sendMsg function takes an EdgeContext, which exposes the source and destination attributes along with the edge attribute and functions (sendToSrc, and sendToDst) to send messages to the source and destination attributes. Think of sendMsg as the map function in map-reduce. The user defined mergeMsg function takes two messages destined to the same vertex and yields a single message. Think of mergeMsg as the reduce function in map-reduce. The aggregateMessages operator returns a VertexRDD[Msg] containing the aggregate message (of type Msg) destined to each vertex. Vertices that did not receive a message are not included in the returned VertexRDDVertexRDD.

In addition, aggregateMessages takes an optional tripletsFields which indicates what data is accessed in the EdgeContext (i.e., the source vertex attribute but not the destination vertex attribute). The possible options for the tripletsFields are defined in TripletFields and the default value is TripletFields.All which indicates that the user defined sendMsg function may access any of the fields in the EdgeContext. ThetripletFields argument can be used to notify GraphX that only part of the EdgeContext will be needed allowing GraphX to select an optimized join strategy. For example if we are computing the average age of the followers of each user we would only require the source field and so we would use TripletFields.Src to indicate that we only require the source field

In earlier versions of GraphX we used byte code inspection to infer the TripletFields however we have found that bytecode inspection to be slightly unreliable and instead opted for more explicit user control.

In the following example we use the aggregateMessages operator to compute the average age of the more senior followers of each user.

import org.apache.spark.graphx.{Graph, VertexRDD}
import org.apache.spark.graphx.util.GraphGenerators

// Create a graph with "age" as the vertex property.
// Here we use a random graph for simplicity.
val graph: Graph[Double, Int] =
  GraphGenerators.logNormalGraph(sc, numVertices = 100).mapVertices( (id, _) => id.toDouble )
// Compute the number of older followers and their total age
val olderFollowers: VertexRDD[(Int, Double)] = graph.aggregateMessages[(Int, Double)](
  triplet => { // Map Function
    if (triplet.srcAttr > triplet.dstAttr) {
      // Send message to destination vertex containing counter and age
      triplet.sendToDst(1, triplet.srcAttr)
  // Add counter and age
  (a, b) => (a._1 + b._1, a._2 + b._2) // Reduce Function
// Divide total age by number of older followers to get average age of older followers
val avgAgeOfOlderFollowers: VertexRDD[Double] =
  olderFollowers.mapValues( (id, value) =>
    value match { case (count, totalAge) => totalAge / count } )
// Display the results
Find full example code at “examples/src/main/scala/org/apache/spark/examples/graphx/AggregateMessagesExample.scala” in the Spark repo.

The aggregateMessages operation performs optimally when the messages (and the sums of messages) are constant sized (e.g., floats and addition instead of lists and concatenation).

Map Reduce Triplets Transition Guide (Legacy)

In earlier versions of GraphX neighborhood aggregation was accomplished using the mapReduceTriplets operator:

class Graph[VD, ED] {
  def mapReduceTriplets[Msg](
      map: EdgeTriplet[VD, ED] => Iterator[(VertexId, Msg)],
      reduce: (Msg, Msg) => Msg)
    : VertexRDD[Msg]

The mapReduceTriplets operator takes a user defined map function which is applied to each triplet and can yield messages which are aggregated using the user defined reduce function. However, we found the user of the returned iterator to be expensive and it inhibited our ability to apply additional optimizations (e.g., local vertex renumbering). In aggregateMessages we introduced the EdgeContext which exposes the triplet fields and also functions to explicitly send messages to the source and destination vertex. Furthermore we removed bytecode inspection and instead require the user to indicate what fields in the triplet are actually required.

The following code block using mapReduceTriplets:

val graph: Graph[Int, Float] = ...
def msgFun(triplet: Triplet[Int, Float]): Iterator[(Int, String)] = {
  Iterator((triplet.dstId, "Hi"))
def reduceFun(a: String, b: String): String = a + " " + b
val result = graph.mapReduceTriplets[String](msgFun, reduceFun)

can be rewritten using aggregateMessages as:

val graph: Graph[Int, Float] = ...
def msgFun(triplet: EdgeContext[Int, Float, String]) {
def reduceFun(a: String, b: String): String = a + " " + b
val result = graph.aggregateMessages[String](msgFun, reduceFun)

Computing Degree Information

A common aggregation task is computing the degree of each vertex: the number of edges adjacent to each vertex. In the context of directed graphs it is often necessary to know the in-degree, out-degree, and the total degree of each vertex. The GraphOps class contains a collection of operators to compute the degrees of each vertex. For example in the following we compute the max in, out, and total degrees:

// Define a reduce operation to compute the highest degree vertex
def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {
  if (a._2 > b._2) a else b
// Compute the max degrees
val maxInDegree: (VertexId, Int)  = graph.inDegrees.reduce(max)
val maxOutDegree: (VertexId, Int) = graph.outDegrees.reduce(max)
val maxDegrees: (VertexId, Int)   = graph.degrees.reduce(max)

Collecting Neighbors

In some cases it may be easier to express computation by collecting neighboring vertices and their attributes at each vertex. This can be easily accomplished using the collectNeighborIds and the collectNeighbors operators.

class GraphOps[VD, ED] {
  def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexId]]
  def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[ Array[(VertexId, VD)] ]

These operators can be quite costly as they duplicate information and require substantial communication. If possible try expressing the same computation using the aggregateMessages operator directly.

Graphx is more faster then Spark naive when graph computation is needed.

CueSheet – Easy spark application deployment guide

CueSheet is a framework for writing Apache Spark 2.x applications more conveniently, designed to neatly separate the concerns of the business logic and the deployment environment, as well as to minimize the usage of shell scripts which are inconvenient to write and do not support validation. To jump-start, check out cuesheet-starter-kit which provides the skeleton for building CueSheet applications. CueSheet is featured in Spark Summit East 2017.

An example of a CueSheet application is shown below. Any Scala object extending CueSheet becomes a CueSheet application; the object body can then use the variables like sc, sqlContext, and spark to write the business logic, as if it is inside spark-shell:

import com.kakao.cuesheet.CueSheet

object Example extends CueSheet {{
  val rdd = sc.parallelize(1 to 100)
  println(s"sum = ${rdd.sum()}")
  println(s"sum2 = ${ + 1).sum()}")

CueSheet will take care of creating SparkContext or SparkSession according to the configuration given in a separate file, so that your application code can contain just the business logic. Furthermore, CueSheet will launch the application locally or to a YARN cluster by simply running your object as a Java application, eliminating the need to use spark-submit and accompanying shell scripts.

CueSheet also supports Spark Streaming applications, via ssc. When it is used in the object body, it automatically becomes a Spark Streaming application, and ssc provides access to the StreamingContext.

Importing CueSheet

libraryDependencies += "com.kakao.cuesheet" %% "cuesheet" % "0.10.0"

CueSheet can be used in Scala projects by configuring SBT as above. Note that this dependency is not specified as"provided", which makes it possible to launch the application right in the IDE, and even debug using breakpoints in driver code when launched in client mode.


Configurations for your CueSheet application, including Spark configurations and the arguments in spark-submit, are specified using the HOCON format. It is by default application.conf in your classpath root, but an alternate configuration file can be specified using -Dconfig.resource or -Dconfig.file. Below is an example configuration file.

spark {
  master = "yarn:classpath:com.kakao.cuesheet.launcher.test"
  deploy.mode = cluster = "cloudera"

  executor.instances = 2
  executor.memory = 1g
  driver.memory = 1g

  streaming.blockInterval = 10000
  eventLog.enabled = false
  eventLog.dir = "hdfs:///user/spark/applicationHistory"
  yarn.historyServer.address = "http://history.server:18088"

  driver.extraJavaOptions = "-XX:MaxPermSize=512m"

Unlike the standard spark configuration, spark.master for YARN should include an indicator for finding YARN/Hive/Hadoop configurations. It is the easiest to put the XML files inside your classpath, usually by putting them undersrc/main/resources, and specify the package classpath as above. Alternatively, spark.master can contain a URL to download the configuration in a ZIP file, e.g. yarn:http://cloudera.manager/hive/, copied from Cloudera Manager’s ‘Download Client Configuration’ link. The usual local or local[8] can also be used asspark.master.

deploy.mode can be either client or cluster, and should be the username to be used as the Hadoop user. CueSheet assumes that this user has the write permission to the home directory.

Using HDFS

While submitting an application to YARN, CueSheet will copy Spark and CueSheet’s dependency jars to HDFS. This way, in the next time you submit your application, CueSheet will analyze your classpath to find and assemble only the classes that are not part of the already installed jars.

One-Liner for Easy Deployment

When given a tag name as system property cuesheet.install, CueSheet will print a rather long shell command which can launch your application from anywhere hdfs command is available. Below is an example of the one-liner shell command that CueSheet produces when given -Dcuesheet.install=v0.0.1 as a JVM argument.

rm -rf SimpleExample_2.10-v0.0.1 && mkdir SimpleExample_2.10-v0.0.1 && cd SimpleExample_2.10-v0.0.1 &&
echo '<configuration><property><name>dfs.ha.automatic-failover.enabled</name><value>false</value></property><property><name>fs.defaultFS</name><value>hdfs://quickstart.cloudera:8020</value></property></configuration>' > core-site.xml &&
hdfs --config . dfs -get hdfs:///user/cloudera/.cuesheet/applications/com.kakao.cuesheet.SimpleExample/v0.0.1/SimpleExample_2.10.jar \!SimpleExample_2.10.jar &&
hdfs --config . dfs -get hdfs:///user/cloudera/.cuesheet/lib/0.10.0-SNAPSHOT-scala-2.10-spark-2.1.0/*.jar &&
java -classpath "*" com.kakao.cuesheet.SimpleExample "hello" "world" && cd .. && rm -rf SimpleExample_2.10-v0.0.1

What this command does is to download the CueSheet and Spark jars as well as your application assembly from HDFS, and launch the application in the same environment that was launched in the IDE. This way, it is not required to haveHADOOP_CONF_DIR or SPARK_HOME properly installed and set on every node, making it much easier to use it in distributed schedulers like Marathon, Chronos, or Aurora. These schedulers typically allow a single-line shell command as their job specification, so you can simply paste what CueSheet gives you in the scheduler’s Web UI.

Additional Features

Being started as a library of reusable Spark functions, CueSheet contains a number of additional features, not in an extremely coherent manner. Many parts of CueSheet including these features are powered by Mango library, another open-source project by Kakao.

One additional quirk is the “stop” tab CueSheet adds to the Spark UI. As shown below, it features three buttons with an increasing degree of seriousness. To stop a Spark Streaming application, to possibly trigger a restart by a scheduler like Marathon, one of the left two buttons will do the job. If you need to halt a Spark application ASAP, the red button will immediately kill the Spark driver.

Complexity analysis – Big o notation table


Algorithm Data Structure Time Complexity Space Complexity
Average Worst Worst
Depth First Search (DFS) Graph of |V| vertices and |E| edges - O(|E| + |V|) O(|V|)
Breadth First Search (BFS) Graph of |V| vertices and |E| edges - O(|E| + |V|) O(|V|)
Binary search Sorted array of n elements O(log(n)) O(log(n)) O(1)
Linear (Brute Force) Array O(n) O(n) O(1)
Shortest path by Dijkstra,
using a Min-heap as priority queue
Graph with |V| vertices and |E| edges O((|V| + |E|) log |V|) O((|V| + |E|) log |V|) O(|V|)
Shortest path by Dijkstra,
using an unsorted array as priority queue
Graph with |V| vertices and |E| edges O(|V|^2) O(|V|^2) O(|V|)
Shortest path by Bellman-Ford Graph with |V| vertices and |E| edges O(|V||E|) O(|V||E|) O(|V|)


Algorithm Data Structure Time Complexity Worst Case Auxiliary Space Complexity
Best Average Worst Worst
Quicksort Array O(n log(n)) O(n log(n)) O(n^2) O(log(n))
Mergesort Array O(n log(n)) O(n log(n)) O(n log(n)) O(n)
Heapsort Array O(n log(n)) O(n log(n)) O(n log(n)) O(1)
Bubble Sort Array O(n) O(n^2) O(n^2) O(1)
Insertion Sort Array O(n) O(n^2) O(n^2) O(1)
Select Sort Array O(n^2) O(n^2) O(n^2) O(1)
Bucket Sort Array O(n+k) O(n+k) O(n^2) O(nk)
Radix Sort Array O(nk) O(nk) O(nk) O(n+k)

Data Structures

Data Structure Time Complexity Space Complexity
Average Worst Worst
Indexing Search Insertion Deletion Indexing Search Insertion Deletion
Basic Array O(1) O(n) - - O(1) O(n) - - O(n)
Dynamic Array O(1) O(n) O(n) - O(1) O(n) O(n) - O(n)
Singly-Linked List O(n) O(n) O(1) O(1) O(n) O(n) O(1) O(1) O(n)
Doubly-Linked List O(n) O(n) O(1) O(1) O(n) O(n) O(1) O(1) O(n)
Skip List O(n) O(log(n)) O(log(n)) O(log(n)) O(n) O(n) O(n) O(n) O(n log(n))
Hash Table - O(1) O(1) O(1) - O(n) O(n) O(n) O(n)
Binary Search Tree - O(log(n)) O(log(n)) O(log(n)) - O(n) O(n) O(n) O(n)
B-Tree - O(log(n)) O(log(n)) O(log(n)) - O(log(n)) O(log(n)) O(log(n)) O(n)
Red-Black Tree - O(log(n)) O(log(n)) O(log(n)) - O(log(n)) O(log(n)) O(log(n)) O(n)
AVL Tree - O(log(n)) O(log(n)) O(log(n)) - O(log(n)) O(log(n)) O(log(n)) O(n)


Heaps Time Complexity
Heapify Find Max Extract Max Increase Key Insert Delete Merge
Linked List (sorted) - O(1) O(1) O(n) O(n) O(1) O(m+n)
Linked List (unsorted) - O(n) O(n) O(1) O(1) O(1) O(1)
Binary Heap O(log(n)) O(1) O(log(n)) O(log(n)) O(log(n)) O(log(n)) O(m+n)
Binomial Heap - O(log(n)) O(log(n)) O(log(n)) O(log(n)) O(log(n)) O(log(n))
Fibonacci Heap - O(1) O(log(n))* O(1)* O(1) O(log(n))* O(1)


Node / Edge Management Storage Add Vertex Add Edge Remove Vertex Remove Edge Query
Adjacency list O(|V|+|E|) O(1) O(1) O(|V| + |E|) O(|E|) O(|V|)
Incidence list O(|V|+|E|) O(1) O(1) O(|E|) O(|E|) O(|E|)
Adjacency matrix O(|V|^2) O(|V|^2) O(1) O(|V|^2) O(1) O(1)
Incidence matrix O(|V| ⋅ |E|) O(|V| ⋅ |E|) O(|V| ⋅ |E|) O(|V| ⋅ |E|) O(|V| ⋅ |E|) O(|E|)

Reference :

Re-partitioning & partition in spark

  In Hadoop, partitioning a data allows processing of huge volume of data in parallel such that it takes minimum amount of time to process entire dataset. Apache spark decides partitioning based on different factors. Factor that decide default partitioning

  1. On hadoop split by HDFS cores.
  2. Filter or map function don’t change partitioning
  3. Number of cpu cores in cluster when running on non-hadoop mode.

Re-partitioning : increases partition , it re-balance the partition  after filter &it increases parallelism.

 You can define partition in spark at the time of creating RDD as follow :

val users = sc.textFile(“hdfs://at-r3p11:8020/project/users.csv”,1);

where 2nd argument is nothing but number of partition.

By default if not used hdfs spark creates partition based on number of cores. & if used hdfs path it will create partition based on input split (default block size of hdfs).

To know the partition size , just enter in spark-shell


Spark can only run 1 concurrent task for every partition of an RDD, up to the number of cores in your cluster. So if you have a cluster with 50 cores, you want your RDDs to at least have 50 partitions (and probably 2-3x times that).

As far as choosing a “good” number of partitions, you generally want at least as many as the number of executors for parallelism. You can get this computed value by calling sc.defaultParallelism.

Also, the number of partitions determines how many files get generated by actions that save RDDs to files.

The maximum size of a partition is ultimately limited by the available memory of an executor.

In the first RDD transformation, e.g. reading from a file using sc.textFile(path, partition), thepartition parameter will be applied to all further transformations and actions on this RDD.

When using textFile with compressed files (file.txt.gz not file.txt or similar), Spark disables splitting that makes for an RDD with only 1 partition (as reads against gzipped files cannot be parallelized). In this case, to change the number of partitions you should do repartitioning.

Some operations, e.g. map, flatMap, filter, don’t preserve partitioning.

map, flatMap, filter operations apply a function to every partition.

rdd = sc.textFile('demo.gz')
rdd = rdd.repartition(100)

With the lines, you end up with rdd to be exactly 100 partitions of roughly equal in size.

  • rdd.repartition(N) does a shuffle to split data to match N

  • partitioning is done on round robin basis

Note :
If partitioning scheme doesn’t work for you, you can write your own custom partitioner.

coalesce Transformation :

The coalesce transformation is used to change the number of partitions. It can trigger RDD shufflingdepending on the shuffle flag (disabled by default, i.e. false).

In the following sample, you parallelize a local 10-number sequence and coalesce it first without and then with shuffling (note the shuffle parameter being false and true, respectively).
scala> val rdd = sc.parallelize(0 to 10, 8)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at :24

scala> rdd.partitions.size
res0: Int = 8

scala> rdd.coalesce(numPartitions=8, shuffle=false)   (1)
res1: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[1] at coalesce at :27

  1. shuffle is false by default and it’s explicitly used here for demo purposes. Note the number of partitions that remains the same as the number of partitions in the source RDD rdd.

Asynchronous processing in java

Asynchronous programming is very popular these days, primarily because of its ability to improve the overall throughput on a multi-core system. Asynchronous programming is a programming paradigm that facilitates fast and responsive user interfaces. The asynchronous programming model in Java provides a consistent programming model to write programs that support asynchrony.

Asynchronous programming provides a non-blocking, event-driven programming model. This programming model leverages the multiple cores in your system to provide parallelization by using multiple CPU cores to execute the tasks, thus increasing the application’s throughput. Note that throughput is a measure of the amount of work done in unit time. In this programming paradigm, a unit of work would execute separately from the main application thread and notify the calling thread about its execution state: success, in progress or failure.

Application of asynchronous can be a situation where we want to execute multiple things in parellel without waiting for 1 task to finish such that it increase the throughput of the system. Consider we want to send email to 100k+ users and at the same time need to process other data, such that we don’t want to wait for email task to complete to proceed.

Another good example of this can be logging frameworks: You typically would want to log exceptions and errors into your log targets; in other words, file, database, or something similar. There is no point for your application to wait till the logging tasks are over. In doing so, the application’s responsiveness would be affected. On the contrary, if the call to the logging framework can be made asynchronously, the application can proceed with other tasks concurrently, without having to wait. This is an example of a non-blocking mode of execution.

1. Future is a base interface and defines abstraction of an object which promises result to be available in future while FutureTask is an implementation of the Future interface.

2. Future is a parametric interface and type-safe written as Future<V>, where V denotes value.

3. Future provides get() method to get result, which is blocking method and blocks until result is available to Future.

4. Future interface also defines cancel() method to cancel task.

5. isDone() and isCancelled() method is used to query Future task states. isDone() returns true if task is completed and result is available to Future. If you call get() method, after isDone() returned true then it should return immediately. On the other hand, isCancelled() method returns true, if this task is cancelled before its completion.

6. Future has four sub interfaces, each with additional functionality e.g. Response, RunnableFuture, RunnableScheduledFuture and ScheduledFuture. RunnableFuture also implements Runnable and successful finish of run() method cause completion of this Future.

7. FutureTask and SwingWorker are two well known implementation of Future interface. FutureTask also implements RunnableFuture interface, which means this can be used as Runnable and can be submitted to ExecutorService for execution.

8. Though most of the time ExecutorService creates FutureTask for you, i.e. when you submit() Callable or Runnable object. You can also created it manually.

9. FutureTask is normally used to wrap Runnable or Callable object and submit them to ExecutorService for asynchronous execution.

import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.logging.Level;
import java.util.logging.Logger; /** * Java program to show how to use Future in Java. Future allows to write * asynchronous code in Java, where Future promises result to be available in * future * * @author Javin */
public class FutureDemo {
private static final ExecutorService threadpool = Executors.newFixedThreadPool(2);
public static void main(String args[]) throws InterruptedException, ExecutionException {
FactorialCalculator task = new FactorialCalculator(1000);

System.out.println(“Submitting Task …”);
Future future = threadpool.submit(task);
System.out.println(“Task is submitted”);
while (!future.isDone()) {
System.out.println(“Task is not completed yet….”);
Thread.sleep(1); //sleep for 1 millisecond before checking again
System.out.println(“Task is completed, let’s check result”);
long factorial = (long) future.get();
System.out.println(“Factorial of 1000000 is : ” + factorial);
private static class FactorialCalculator implements Callable {
private final int number;
public FactorialCalculator(int number) {
this.number = number;

@Override public Long call() {
long output = 0;
try {
output = factorial(number);
} catch (InterruptedException ex) {
//Logger.getLogger(Test.class.getName()).log(Level.SEVERE, null, ex);
return output;

private long factorial(int number) throws InterruptedException {
if (number < 0) {
throw new IllegalArgumentException(“Number must be greater than zero”);
long result = 1;
while (number > 0) {
Thread.sleep(1); // adding delay for example
result = result * number;
return result;


Usage in spring framework is given in below link :