Apache Spark offers two fundamental entry points for interacting with the Spark engine: SparkContext and SparkSession. They serve different purposes and are used in different contexts. Here’s a breakdown of the key differences between SparkContext and SparkSession:
- Purpose:
- SparkContext:
- It was the primary entry point in earlier versions of Spark.
- SparkContext is primarily responsible for coordinating tasks and managing resources across a Spark cluster.
- It provides a low-level API for interacting with Spark, offering functionalities for RDD (Resilient Distributed Dataset) operations, job submission, and setting cluster-wide configurations.
- SparkContext is suitable for low-level, fine-grained control over Spark jobs and for applications that do not require structured data processing.
- SparkSession:
- It is introduced in Spark 2.0 and serves as a higher-level, unified entry point.
- SparkSession is designed to simplify working with structured data, including DataFrames and Datasets.
- It handles various aspects of a Spark application, including configuring Spark, managing the Spark application lifecycle, and providing a user-friendly interface for structured data processing.
- SparkSession is the recommended entry point for most Spark applications, especially those dealing with structured data.
- SparkContext:
- Data Processing:
- SparkContext:
- Primarily focuses on low-level operations on RDDs.
- Suitable for custom data processing tasks, such as machine learning algorithms and graph processing, where you need full control over the data.
- SparkSession:
- Specializes in working with structured data, such as DataFrames and Datasets.
- Provides a high-level API for reading, writing, querying, and processing structured data efficiently.
- Ideal for data analysis, ETL (Extract, Transform, Load) tasks, and SQL-like operations.
- SparkContext:
- Configuration:
- SparkContext:
- Requires manual configuration of Spark properties, such as cluster manager settings, memory allocation, and application name.
- SparkSession:
- Simplifies configuration by providing a builder pattern for setting Spark properties. You can easily configure SparkSession using methods like
.appName(),.config(), and others.
- Simplifies configuration by providing a builder pattern for setting Spark properties. You can easily configure SparkSession using methods like
- SparkContext:
- Application Lifecycle:
- SparkContext:
- You need to manually initialize and stop SparkContext, handling the entire application lifecycle yourself.
- SparkSession:
- Manages the application lifecycle, including initialization and cleanup. You typically create a SparkSession using
.getOrCreate()and rely on it for the entire duration of your application.
- Manages the application lifecycle, including initialization and cleanup. You typically create a SparkSession using
- SparkContext:
- Compatibility:
- SparkContext:
- Still available in Spark for backward compatibility and for applications that require RDD-based operations.
- SparkSession:
- The recommended entry point for modern Spark applications, especially those working with structured data.
- SparkContext:
Spark important urls to refer
