Trending September 2023 # Complete Guide To How Spark Architecture Shuffle Works # Suggested October 2023 # Top 9 Popular |

Trending September 2023 # Complete Guide To How Spark Architecture Shuffle Works # Suggested October 2023 # Top 9 Popular

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Introduction to Spark Shuffle

In Apache Spark, Spark Shuffle describes the procedure in between reduce task and map task. Shuffling refers to the shuffle of data given. This operation is considered the costliest. Parallelising effectively of the spark shuffle operation gives performance output as good for spark jobs. Spark data frames are the partitions of Shuffle operations. The original data frame partitions differ with the number of data frame partitions. The data moving from one partition to the other partition process in order to mat up, aggregate, join, or spread out in other ways is called a shuffle.


The syntax for Shuffle in Spark Architecture:

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Explanation: This is a Shuffle spark method of partition in FlatMap operation RDD where we create an application of word count where each word separated into a tuple and then gets aggregated to result.

How Spark Architecture Shuffle Works

Data is returned to disk and is transferred all across the network during a shuffle. The shuffle operation number reduction is to be done or consequently reduce the amount of data being shuffled.

More shufflings in numbers are not always bad. Memory constraints and other impossibilities can be overcome by shuffling.

– cogroup

These above Shuffle operations built in a hash table perform the grouping within each task. This is often huge or large. This can be fixed by increasing the parallelism level and the input task is so set to small.

Skewed keys.

Examples to Implement Spark Shuffle

Let us look into an example:

Example #1

( customerId: Int, destination: String, price: Double) case class CFFPurchase

Val purchasesRdd: RDD[CFFPurchase] = sc.textFile(…)

Goal: Let us calculate how much money has been spent by each individual person and see how many trips he has made in a month.


// Return an array – Array[(Int, (Int, Double))] .collect()

sample1 – sample1.txt:

(300, “Zurich”, 42.10))


Explanation: We have concrete instances of data. To create collections of values to go with each unique key-value pair we have to move key-value pairs across the network. We have to collect all the values for each key on the node that the key is hosted on. In this example, we have assumed that three nodes, each node will be home to one single key, So we put 100, 200, 300 on each of the nodes shown below. Then we move all the key-value pairs so that all purchase by customer number 100 on the first node and purchase by customer number 200 on second node and purchase by customer number 300 on the third node and they are all in this value which is a collection together. groupByKey part is where all of the data moves around the network. This operation is considered as Shuffle in Spark Architecture.

Important points to be noted about Shuffle in Spark

And to overcome such problems, the shuffling partitions in spark should be done dynamically.


We have seen the concept of Shuffle in Spark Architecture. Shuffle operation is pretty swift and sorting is not at all required. Sometimes no hash table is to be maintained. When included with a map, a small amount of data or files are created on the map side. Random Input-output operations, small amounts are required, most of it is sequential read and writes.

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