> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/spark-notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/spark-notes/spark-execution-flow/sparkcontext-and-sparksession.md).

# SparkContext and SparkSession

## SparkContext

SparkContext is the first object that a Spark program must create to access the cluster. In spark-shell, it is directly accessible via spark.sparkContext.

Here's how you can programmatically create SparkContext in your Scala code:

`import org.apache.spark.SparkContext`

`import org.apache.spark.SparkConf`

`val conf = new SparkConf().setAppName("my app").setMaster("master url")`

`new SparkContext(conf)`

## SparkSession - Starting from Spark 2.x

SparkContext, though still supported, was more relevant in the case of RDD . As you will see, different libraries have different wrappers around SparkContext, for example, HiveContext/SQLContext for Spark SQL, StreamingContext for Streaming, and

so on.

As all the libraries are moving toward DataSet/DataFrame, it makes sense to have a unified entry point for all these libraries as well, and that is SparkSession. SparkSession is available as spark in the spark-shell.

Here's how you do it:

import org.apache.spark.SparkContext

import org.apache.spark.SparkConf

val sparkSession = SparkSession.builder.master("master url").appName("my app").getOrCreate()