SparkSession Vs SparkContext – What Are The Differences?

The Apache Spark development community is growing at a rapid pace. Spark is a robust framework for large-scale data processing that offers speed, scalability, and dependability. Since its inception, they have evolved over the course of the new releases. Every Spark Application requires an entry point to interface with data sources and execute activities like reading and writing data. In earlier versions of Spark or Pyspark, SparkContext was an entry point for programming with RDD and connecting to Spark Cluster. With the introduction of Spark 2.0 SparkSession, it became an entry point for programming with DataFrame and Dataset. However, many of us are ignorant of the advantages that SparkSession has over SparkContext. To answer the question, why do you need a SparkSession when you already have a SparkContext, we have done a SparkSession vs SparkContext comparison in this post. Take a look!

SparkSession vs SparkContext: Basic Difference?

Spark 1.x comes with three entry points: SparkContext, SQLContext, and HiveContext. And with the introduction of Spark 2.x, a new entry point named SparkSession was added. As a result, this single entry point effectively combines all of the functionality available in the three aforementioned contexts. Let’s do the comparison between SparkSession vs SparkContext.

What Is SparkContext?

SparkContext is the primary point of entry for Spark capabilities. A SparkContext represents a Spark cluster’s connection that is useful in building RDDs, accumulators, and broadcast variables on the cluster. It enables your Spark Application to connect to the Spark Cluster using Resource Manager. Also, before the creation of SparkContext, SparkConf must be created.

After creating the SparkContext, you can use it to create RDDs, broadcast variables, and accumulators, as well as access Spark services and perform jobs. All of this can be done until SparkContext is terminated. Access to the other two contexts, SQLContext and HiveContext, is also possible through SparkContext. Since Spark 2.0, most SparkContext functions are also available in SparkSession. SparkContext’s default object sc is provided in Spark-Shell, and it can also be constructed programmatically using the SparkContext class. As a result, SparkContext provides numerous Spark functions. This includes getting the current status of the Spark Application, setting the configuration, canceling a task, canceling a stage, and more. It was a means to get started with all the Spark features prior to the introduction of SparkSession, as shown in this SparkSession Vs SparkContext comparison post.

What Is SparkSession?

Apache Spark 2.0 is the company’s next significant release. This is a significant shift in the degree of abstraction for the Spark API and libraries. Previously, as RDD was the major API, SparkContext was the entry point for Spark. It was constructed and modified with the help of context APIs. At that time, we have to use a distinct context for each API. We required StreamingContext for Streaming, SQLContext for SQL, and HiveContext for Hive. However, because the DataSet and DataFrame APIs are becoming new independent APIs, we require an entry-point construct for them. As a result, in Spark 2.0, we have a new entry point built for DataSet and DataFrame APIs called SparkSession.

It combines SQLContext, HiveContext, and StreamingContext. All of the APIs accessible in those contexts are likewise available in SparkSession, and SparkSession includes a SparkContext for real computation. It’s worth noting that the previous SQLContext and HiveContext are still present in updated versions, but only for backward compatibility. As a result, when comparing SparkSession vs SparkContext, as of Spark 2.0.0, it is better to use SparkSession because it provides access to all of the Spark features that the other three APIs do. Its Spark object comes by default in Spark-shell, and it can be generated programmatically using the SparkSession builder pattern.

Why Should You Use SparkSession Over SparkContext?

From Spark 2.0, SparkSession provides a common entry point for a Spark application. It allows you to interface with Spark’s numerous features with a less amount of constructs. Instead of SparkContext, HiveContext, and SQLContext, everything is now within a SparkSession. One aspect of the explanation why SparkSession is preferable over SparkContext in SparkSession Vs SparkContext battle is that SparkSession unifies all of Spark’s numerous contexts, removing the developer’s need to worry about generating separate contexts. Apart from this benefit, the Apache Spark developers have attempted to address the issue of numerous users sharing the same SparkContext.

Assume we have several users accessing the same notebook environment that has a shared SparkContext, and the requirement is to have an isolated environment that shares the same SparkContext. Prior to version 2.0, the method was to create several SparkContext, one for each isolated environment or user, which took time and money. Generally, one SparkContext per JVM. However, with the introduction of SparkSession, this problem has been resolved. Thus, in the SparkSession Vs SparkContext battle, SparkSession wins the race.

Read More: The Role of Apache Spark in the Big Data Industry

Enhancing Spark with Apache Cassandra for Big Data Processing

When comparing SparkSession and SparkContext, both play a crucial role in managing and orchestrating large-scale data processing in Apache Spark. While these elements of Spark handle data processing, the challenge of storing and managing huge datasets in real time requires a reliable and scalable database like Apache Cassandra.

At Ksolves, we offer top-tier Apache Cassandra development services, powered by a team of Datastax-certified experts and 50+ skilled developers. Our expertise ensures seamless integration of Cassandra with your Spark infrastructure for highly efficient, real-time data handling and analytics.

Watch this video to see how our Apache Cassandra services can take your big data projects to the next level: 

Integrating Apache Cassandra with SparkSession or SparkContext enables your system to handle massive amounts of data with ease, ensuring both fast processing and scalable storage.

Contact us at sales@ksolves.com to learn how we can help you implement a seamless Cassandra and Spark integration to elevate your big data capabilities.

How Ksolves Can Help You In Understanding & Leveraging Spark?

Apache Spark plays a significant part in opening up new prospects in the big data industry by making it simple to address many sorts of challenges. Spark has proven to be an interesting platform for data scientists due to its ability to manage a never-ending stream of low-latency data. The technology can also distribute data throughout a cluster and allow computers to analyze data in parallel. As a consequence, businesses can investigate both real-time and historical data. This will assist them in identifying business possibilities, detecting dangers, combating fraud, promoting preventative maintenance, and doing other pertinent duties to run their organization. Ksolves, being the top Apache Spark consulting company, provides Apache Spark services in order to create powerful solutions.

Ksolves’ Spark developers assist client businesses in rising to the top of the heap by providing Spark as a service. Our streamlined solutions and software installation assist in the complete elimination of company difficulties. We can also assist you in resolving setup issues. To uncover such setup issues and remove bottlenecks that slow down processing, our practitioners can analyze your existing Spark application, check workloads, and dive down into task execution specifics. We aim to create data-centric creative solutions that make use of the unique Spark feature while providing a better user experience. Hire our specialized Apache Spark developers to pursue technological excellence in Spark implementation. If you need any further information about SparkSession vs SparkContext, please connect with us. We would be happy to assist you further.