Resilient Distributed Datasets(RDD) in Spark



RDD:

Resilient Distributed Datasets represents a collection of partitioned data elements that can be operated on in a parallel manner. RDD is the primary data abstraction mechanism in Spark and defined as an abstract class in Spark library it is similar to SCALA collection and it supports LAZY evaluation.

Characteristics of RDD:

1.Immutable :

RDD is an immutable data structure. Once created, it cannot be modified in-place. Basically, an operation that modifies RDD returns a new RDD.

2.Partitioned:

In RDD Data is split into partitions. These partitions are generally distributed across a cluster of nodes. When Spark is running on a single machine all the partitions are on that machine.

 

RDD Operations :

Applications in Spark process data using the same methods in RDD class. It referred to as operations

RDD operations are two types:

1.Transformations

2.Actions

 1.Transformations :

A transformation method of an RDD creates a new RDD by performing a computation on the source RDD.

RDD transformations are conceptually similar to SCALA collection methods.

The key difference is that the SCALA collection methods operate on data that can fit in the memory of a single machine, whereas RDD methods can operate on data distributed across a cluster of node RDD transformations are LAZY but SCALA collection methods are strict.

A) Map:

The map method is a higher order method that takes a function as input and applies it to each element in the source RDD to create a new RDD.

B) filter:

The filter method is a high order method that takes a Boolean function as input and applies it to each element in the source RDD to create a new RDD. A Boolean function takes an input and returns false or true. It returns a new RDD formed by selecting only those elements for which the input Boolean function returned true. The new RDD contains a subset of the elements in the original RDD.

c) flatMap:

This method is a higher order method that takes an input function in Spark, it returns a sequence for each input element passed to it. The flatMap method returns a new RDD formed by flattening this collection of the sequence.

D) mapPartitions :

It is a higher order method allows you to process data at a partition level. Instead of passing one element at a time to its input function, mapPartitions passes a partition in the form an iterator. The input function to the mapPartitions method takes an iterator as input and returns iterator as output.

E)Intersection:

Intersection method itakesRDD as input and returns a new RDD that contains the intersection of the element in the source RDD and the RDD passed to it as an input.

F)Union:

This method takes  RDD as input and returns a new RDD that contains a Union of the element in the resource RDD and the RDD passed to it as an input.


G)Subtract:

Subtract method takes RDD as input and returns a new RDD that contains elements in the source RDD but not in the input RDD.

 

H)Parallelize:

The Prallelized collections are created by calling Spark Context’s parallelize method on an existing collection in your driver program. The elements of the collection are copied to form a distributed data set that can be operated on in parallel.

 

I)Distinct:

Distinct method of an RDD returns a new RDD containing the distinct elements in the source RDD

 

J)Group By:

Group By is a higher order method it groups the elements of  RDD according to user-specified criteria. It takes as input a function that generates a key for each element in the source RDD. It is applicable to all the elements in the source RDD and returns an RDD of pairs.

 

K)Sort By:

The sortBy method is a higher order it returns RDD with sorted elements from the source RDD. It takes two input parameters. The first input is a function that generates a key for each element in the source RDD. The second input allows specifying ascending or descending order for sort.

 

L)Coalesce:

Coalesce method reduces the number of partitions in  RDD. It takes an integer input and returns new RDD with the specified number of partitions.

 

M)GroupByKey:

The GroupByKey method returns an RDD of pairs, where the first element in a pair is a key from the source RDD and the second element is a collection of all values that have the same key. It is the same as the groupBy method. The major difference is that groupBy is a higher order method that takes an input function that returns a key for each element in the source RDD. The groupByKey method operates in an RDD of key-value pairs.


N)ReduceByKey:

The higher-order reduceBy key method takes an associative binary operator as input and reduces values with the same key to a single value using specified binary operators.

2.Actions:

Actions are RDD methods that return a value to a driver program.

A)Collect:

The collect method returns the elements in the source RDD as an array. This method should be used with caution since it moves data from all the worker to the driver program.

B)Count:

This method returns a count of the elements in the source RDD.

C)Count By Value :

The countByValue method returns a count of each unique element in the source RDD. It returns an instance of the Map class containing each unique element and its count as a key-value pair.

D)First:

The first method returns the first element in the source RDD

E)Max:

The max method returns the largest element in  RDD

F)Min

The min method returns the smallest element in RDD

G)Top

The top method takes an integer N as input and returns an array containing the N largest elements in the source RDD.

H)Reduce

The high order reduces method aggregates the elements of the source RDD using an associative and commutative binary operator provided to it.

G)CountByKey

The countByKey methods count the occurrences of each unique key in the source RDD. It returns a Map of key count pairs.


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