SparkContext and SparkSession
SparkContext and SparkSession are two fundamental components in Apache Spark. They serve different purposes but are closely related.
SparkContext
Here are key responsibilities of SparkContext:
- Cluster Communication - connects to the Spark cluster and manages the distribution of tasks across the cluster nodes;
- Resource Management - handles resource allocation by communicating with the cluster manager (like YARN, Mesos, or Kubernetes);
- Job Scheduling - distributes the execution of jobs and tasks among the worker nodes;
- RDD Creation - facilitates the creation of RDDs;
- Configuration - manages the configuration parameters for Spark applications.
SparkSession
Practically, it's an abstraction that combines SparkContext, SQLContext, and HiveContext.
Here are some of the key features:
Key Functions:
- Unified API - it provides a single interface to work with Spark SQL, DataFrames, Datasets, and also integrates with Hive and other data sources;
- DataFrame and Dataset Operations - SparkSession allows you to create DataFrames and Datasets, perform SQL queries, and manage metadata;
- Configuration - it manages the application configuration and provides options for Spark SQL and Hive.
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SparkContext and SparkSession are two fundamental components in Apache Spark. They serve different purposes but are closely related.
SparkContext
Here are key responsibilities of SparkContext:
- Cluster Communication - connects to the Spark cluster and manages the distribution of tasks across the cluster nodes;
- Resource Management - handles resource allocation by communicating with the cluster manager (like YARN, Mesos, or Kubernetes);
- Job Scheduling - distributes the execution of jobs and tasks among the worker nodes;
- RDD Creation - facilitates the creation of RDDs;
- Configuration - manages the configuration parameters for Spark applications.
SparkSession
Practically, it's an abstraction that combines SparkContext, SQLContext, and HiveContext.
Here are some of the key features:
Key Functions:
- Unified API - it provides a single interface to work with Spark SQL, DataFrames, Datasets, and also integrates with Hive and other data sources;
- DataFrame and Dataset Operations - SparkSession allows you to create DataFrames and Datasets, perform SQL queries, and manage metadata;
- Configuration - it manages the application configuration and provides options for Spark SQL and Hive.
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