Basic Transformations

As part of this section we will see basic transformations we can perform on top of Data Frames such as filtering, aggregations, joins etc using SQL. We will build end to end solution by taking a simple problem statement.

  • Spark SQL – Overview

  • Define Problem Statement

  • Preparing Tables

  • Projecting Data

  • Filtering Data

  • Joining Tables - Inner

  • Joining Tables - Outer

  • Perform Aggregations

  • Sorting Data

  • Conclusion - Final Solution

Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

val username = System.getProperty("user.name")

import org.apache.spark.sql.SparkSession

val username = System.getProperty("user.name")
val spark = SparkSession.
    builder.
    config("spark.ui.port", "0").
    config("spark.sql.warehouse.dir", s"/user/${username}/warehouse").
    enableHiveSupport.
    appName(s"${username} | Spark SQL - Basic Transformations").
    master("yarn").
    getOrCreate

If you are going to use CLIs, you can use Spark SQL using one of the 3 approaches.

Using Spark SQL

spark2-sql \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Scala

spark2-shell \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Pyspark

pyspark2 \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse
Understanding Linux File System Spark SQL – Overview