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How to Parallelise in Spark Parallel Processing? [Using RDD]

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3rd Sep, 2020
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How to Parallelise in Spark Parallel Processing? [Using RDD]

Data generation and consumption have increased n-folds over the last few years. With so many platforms coming to life, handling and managing data carefully have become crucial. AI (Artificial Intelligence) and ML (Machine Learning) are making our digital experiences even smoother by finding better solutions to our problems. Therefore, companies are now moving towards treating data and finding insights from it. 

Simultaneously, the data generated by companies, network players, and mobile giants are enormous. Due to which, the concept of big data was introduced. Since big data came into the picture, the tools to manage and manipulate big data also started gaining popularity and importance. 

Apache Spark is one of those tools that manipulate and processes massive datasets to gain insights from these data. These big datasets cannot be processed or managed in one single pass since the computational power required is too intense. 

That is where parallel processing comes into the picture. We will start by understanding parallel processing in short and then move on to understand how to parallelise in spark.

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What is Parallel Processing?

Parallel processing is one of the essential operations of a big data system. When your task is significant, you happen to break into smaller tasks and then solve each one independently. The parallel processing of big data involves the same process. 

Technically speaking, parallel processing is a method of running two or more parts of a single big problem in different processors. This reduces the processing time and enhances performance.

Since you cannot perform operations on big datasets on one machine, you need something very solid. That is precisely where parallelising in Spark comes into the picture. We will now take you through Spark Parallel Processing and how to parallelise in spark to get the right output from big datasets.

Spark Parallel Processing

Spark applications run in the form of independent processes that reside on clusters and are coordinated by SparkContext in the main program. 

The first step in running a Spark program is by submitting the job using Spark-submit. The spark-submit script is used to launch the program on a cluster. 

Once you have submitted the job using a spark-submit script, the job is forwarded to the sparkcontext drivers. Sparkcontext driver program is the entry point to Spark. Sparkcontext routes the program to the modules like Cluster Master Node and RDDs are also created by these Sparkcontext driver programs. 

The program is then given to the Cluster Master Node. Every cluster has one master node which carries out all the necessary processing. It forwards the program further to worker nodes. 

Worker node is the one that solves the problems. Master nodes contain executors that execute with Sparkcontext driver.

Spark Parallel Processing Source

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What is Resilient Distributed Dataset (RDD)?

RDD is the fundamental data structure of Apache Spark. This data structure is an immutable collection of objects that compute on different nodes of a cluster. Every dataset in Spark RDD is logically partitioned across various servers so the computations can be run smoothly on each node.

Let us understand RDD in a little more detail, as it forms the basis of parallelizing in spark. We can break the name into three parts and know why the data structure is named so. 

  • Resilient: It means the data structure is fault-tolerant with the help of the RDD lineage graph, and hence, it can recompute the missing partitions or damaged partitions caused due to node failures. 
  • Distributed: This stands true for all the systems that use a distributed environment. It is called distributed because data is available on different/multiple nodes.
  • Dataset: Dataset represents the data that you work with it. You can import any of the datasets available in any format like .csv, .json, a text file, or a database. You can do that by using JDBC with no specific structure. 

Once you import or load your dataset, the RDDs will logically partition your data into multiple nodes across many servers, to keep the operation running.

Also Read: Apache Spark Features

Now that you know RDD, it will be easier for you to understand Spark Parallel Processing. 

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Parallelise in Spark Using RDD

The parallel processing is carried out in 4 significant steps in Apache Spark. RDD is used on a major level to parallelise in spark to perform parallel processing.  

Step 1

RDD is usually created from an external data source. It could be a CSV file, JSON file, or simply a database for that matter. In most cases, it is an HDFS or a local file. 

Step 2

After the first step, RDD would go through a few parallel transformations like filter, map, groupBy, and join. Each of these transformations provides a different RDD that goes forward to the next transformation. 

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Step 3

The last stage is about action; it always is. The RDD, in this stage, is exported as an external output to external data sources. 

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Check out: Apache Spark Tutorial for Beginners


Parallel processing is gaining popularity among data enthusiasts, as the insights are helping companies and OTTs earn big. Spark, on the other hand, is one of the tools helping out big giants to make decisions by performing parallel processing on big and bigger data.

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If you are looking forward to making big data processing faster, Apache spark is your way to go. And, RDDs in Spark, is delivering the best performance ever since it is known. 

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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Frequently Asked Questions (FAQs)

1What is Spark in Big Data?

Apache Spark is a general-purpose engine designed for distributed data processing, which can be used in an extensive range of circumstances. Spark helps data scientists and developers quickly integrate it with other applications to analyze, query and transform data on a large scale. Libraries for graph computation, SQL, machine learning and stream processing are present over the core data processing engine of Spark, and all of them can be used together in a particular application. Spark supports several programming languages such as Scala, Python, Java, and R. Spark is mostly used to process streaming data from sensors, financial systems and IoT, and also with ETL batch jobs involving massive sets of data.

2 What is the difference between Hadoop and Teradata?

Hadoop is a software development framework that can store a huge volume of data to facilitate computational tasks. It is built using Java and is based on master-slave architecture. Teradata is a relational database management system which is used to support data warehousing tasks. Teradata is based on an MPP (massively parallel processing) system and can accept multiple requests from client applications. Hadoop is designed for Big Data and can process and store various types of data. At the same time, Teradata is executed in a single RDBMS and can only store structured data in tabular format.

3Can Spark be used along with MongoDB?

Yes, you can use Apache Spark with MongoDB. Spark has been designed for ease of use, high speed and support of advanced analytics, and MongoDB is designed mainly for real-time analytics tasks using operational enterprise data. MongoDB is very powerful, and along with Spark, it easily extends its analytics capabilities to offer a more enriched analytics output. With MongoDB and Spark, you can develop advanced functional applications in lesser time with the help of one database technology. Since both are designed for Big Data technology, they can save a lot of time and effort, increase operational efficiency, and reduce risks and expenses.

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