Managing the SparkSession, The DataFrame Entry Point

The SparkSession is used to create and read DataFrames. It’s used whenever you create a DataFrame in your test suite or whenever you read a Parquet / CSV data lake into a DataFrame.

This post explains how to create a SparkSession, share it throughout your program, and use it to create DataFrames.

Accessing the SparkSession

A SparkSession is automatically created and stored in the spark variable whenever you start the Spark console or open a Databricks notebook.

Your program should reuse the same SparkSession and you should avoid any code that creates and uses a different SparkSession.

Creating a RDD

Let’s open the Spark console and use the spark variable to create a RDD from a sequence.

Notice that the message Spark session available as 'spark' is printed when you start the Spark shell.

val data = Seq(2, 4, 6)
val myRDD = spark.sparkContext.parallelize(data)

The SparkSession is used to access the SparkContext, which has a parallelize method that converts a sequence into a RDD.

RDDs aren’t used much now that the DataFrame API has been released, but they’re still useful when creating DataFrames.

Creating a DataFrame

The SparkSession is used twice when manually creating a DataFrame:

  1. Converts a sequence into a RDD
  2. Converts a RDD into a DataFrame
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._

val rdd = spark.sparkContext.parallelize(
  Seq(
    Row("bob", 55)
  )
)

val schema = StructType(
  Seq(
    StructField("name", StringType, true),
    StructField("age", IntegerType, true)
  )
)

val df = spark.createDataFrame(rdd, schema)
df.show()

+----+---+
|name|age|
+----+---+
| bob| 55|
+----+---+

You will frequently use the SparkSession to create DataFrames when testing your code.

Reading a DataFrame

The SparkSession is also used to read CSV, JSON, and Parquet files.

Here are some examples.

val df1 = spark.read.csv("/mnt/my-bucket/csv-data")
val df2 = spark.read.json("/mnt/my-bucket/json-data")
val df3 = spark.read.parquet("/mnt/my-bucket/parquet-data")

There are separate posts on CSV, JSON, and Parquet files that do deep dives into the intracacies of each file format.

Creating a SparkSession

You can create a SparkSession in your applications with the getOrCreate method:

val spark = SparkSession.builder().master("local").appName("my cool app").getOrCreate()

You don’t need to manually create a SparkSession in programming environments that already define the variable (e.g. the Spark shell or a Databricks notebook). Creating your own SparkSession becomes vital when you start writing Spark code in a text editor.

A lot of Spark programmers have trouble writing high quality code because they cannot make the jump to writing Spark code in a text editor with tests. It’s hard to write good code without following best practices.

Wrapping the spark variable in a trait is the best way to share it across different classes and objects in your codebase.

import org.apache.spark.sql.SparkSession

trait SparkSessionWrapper extends Serializable {

  lazy val spark: SparkSession = {
    SparkSession.builder().master("local").appName("my cool app").getOrCreate()
  }

}

The getOrCreate() method will create a new SparkSession if one does not exist, but reuse an exiting SparkSession if it exists.

When your test suite is run, this code will create a SparkSession when the first spark variable is found. After the initial SparkSession is created, it will be reused for every subsequent reference to spark.

Your production environment will probably already define the spark variable, so getOrCreate() won’t ever both creating a SparkSession and will simply use the SparkSession already created by the environment.

Here is how the SparkSessionWrapper can be used in some example objects.

object transformations extends SparkSessionWrapper {

  def withSomeDatamart(
    coolDF: DataFrame = spark.read.parquet("/mnt/my-bucket/cool-data")
  )(df: DataFrame): DataFrame = {
    df.join(
      broadcast(coolDF),
      df("some_id") <=> coolDF("some_id")
    )
  }

}

The transformations.withSomeDatamart() method is injecting coolDF, so the code can easily be tested and intelligently grab the right file by default when run in production.

Notice how the spark variable is used to set our smart default.

We will use the SparkSessionWrapper trait and spark variable again when testing the withSomeDatamart method.

import utest._

object TransformsTest extends TestSuite with SparkSessionWrapper with ColumnComparer {

  val tests = Tests {

    'withSomeDatamart - {

      val coolDF = spark.createDF(
        List(

        ), List(

        )
      )

      val df = spark.createDF(
        List(

        ), List(

        )
      ).transform(transformations.withSomeDatamart())

    }

  }

}

The test leverages the createDF method, which is a SparkSession extension defined in spark-daria.

createDF is similar to createDataFrame, but more cocise. See this blog post on manually creating Spark DataFrames for more details.

Reusing the SparkSession in the test suite

Starting and stopping the SparkSession is slow, so you want to reuse the same SparkSession throughout your test suite. Don’t restart the SparkSession for every test file that is run – Spark tests run slowly enough as is and shouldn’t be made any slower.

The SparkSessionWrapper can be reused in your application code and the test suite.

SparkContext

The SparkSession encapsulates the SparkConf, SparkContext, and SQLContext.

Prior to Spark 2.0, developers needed to explicly create SparkConf, SparkContext, and SQLContext objects. Now Spark developers, can just create a SparkSession and access the other objects as needed.

The following code snippet uses the SparkSession to access the sparkContext, so the parallelize method can be used to create a DataFrame (we saw this same snippet earlier in the blog post).

spark.sparkContext.parallelize(
  Seq(
    Row("bob", 55)
  )
)

You shouldn’t have to access the sparkContext much – pretty much only when manually creating DataFrames. See the spark-daria createDF() method, so you don’t even need to explicitly call sparkContext when you want to create a DataFrame.

Read this blog post for more information.

Conclusion

You’ll need a SparkSession in your programs to create DataFrames.

Reusing the SparkSession in your application is critical for good code organization. Reusing the SparkSession in your test suite is vital to make your tests execute as quickly as possible.

1 Comment


  1. Great article . I have created Rdd using text file and data displayed in arrays. i want display data like in a tabular format without creating dataframe

    in file
    10,ACCOUNTING, NEW YORK
    20,RESEARCh, DALLAS
    30,SALES,CHICAGO
    40,OPERATIONS,BOSTONG

    val deptdf = sc.textFile(“H:/dept.txt”)

    scala> deptdf.collect
    res1: Array[String] = Array(10, ‘ACCOUNTING’, ‘NEW YORK’, 20, ‘RESEARCH’, ‘DALLAS’, 30, ‘SALES’, ‘CHICAGO’, 40, ‘OPERATIONS’, ‘BOSTON’)

    i want data to be like this. can you please help me .

    deptno dname loc
    10 ACCOUNTING NEW YORK
    20 RESEARCh DALLAS
    30 SALES CHICAGO
    40 OPERATIONS BOSTONG

    Reply

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