What is SparkSession?

SparkSession is the single entry point to Spark functionality in modern Spark (2.x+). It unifies what used to be separate contexts (SparkContext, SQLContext, HiveContext).

What it gives you

• Create DataFrames / Datasets
• Run Spark SQL
• Configure the app (shuffle partitions, AQE, etc.)
• Read/write data sources
• Access the underlying SparkContext when needed (spark.sparkContext)

Create one

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder
    .appName("orders-etl")
    .config("spark.sql.shuffle.partitions", "200")
    .config("spark.sql.adaptive.enabled", "true")
    .getOrCreate()
)

print(spark.version)
spark.sql("SELECT 1 AS ok").show()

Diagram

SparkSession
   |-- spark.read / spark.write
   |-- spark.sql(...)
   |-- spark.catalog / spark.udf
   +-- spark.sparkContext  (low-level RDD / cluster APIs)

Interview tip

In notebooks and Databricks, a spark session is often pre-created. In production jobs, you still build and configure it explicitly so settings are versioned with the code.