How to configure SparkSession in PySpark

This recipe helps you configure SparkSession in PySpark

Recipe Objective - How to configure SparkSession in PySpark?

The Delta Lake table, defined as the Delta table, is both a batch table and the streaming source and sink. The Streaming data ingest, batch historic backfill, and interactive queries all work out of the box. Delta Lake provides the ability to specify the schema and also enforce it, which further helps ensure that data types are correct and the required columns are present, which also helps in building the delta tables and also preventing the bad data from causing data corruption in both delta lake and delta table. The Delta can write the batch and the streaming data into the same table, allowing a simpler architecture and quicker data ingestion to the query result. Also, the Delta provides the ability to infer the schema for data input which further reduces the effort required in managing the schema changes. The Spark session is the unified entry point of the spark application and provides a way to interact with various spark functionality with a lesser number of constructs. The Spark context, Hive context, SQL context, etc., are all encapsulated in the Spark session.

Learn Spark SQL for Relational Big Data Procesing

System Requirements

  • Python (3.0 version)
  • Apache Spark (3.1.1 version)

This recipe explains what is Delta lake and Explaining SparkSession in PySpark.

Implementing SparkSession in PySpark

# Importing package from pyspark.sql import SparkSession

Databricks-1

The PySparkSQL package is imported into the environment to configure SparkSession in Databricks in PySpark.

# Implementing SparkSession in PySpark SparkSe = SparkSession \ .builder \ .appName("...") \ .master("...") \ .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \ .getOrCreate()

Databricks-2

The "SparkSe" value is defined so as to initiate Spark Session in PySpark which uses "SparkSession" keyword with "spark.sql.extensions" and "io.delta.sql.DeltaSparkSessionExtension" configurations with "spark.sql.catalog.spark_catalog" and "org.apache.spark.sql.delta.catalog.DeltaCatalog" also as configurations. Finally, .getOrCreate() function is used which is used to further initiate the SparkSession.

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I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

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