Conclusion¶

As part of this module we have gone through list of functions that can be applied on top of columns for row level transformations.

There are approximately 300 pre-defined functions.
Functions can be broadly categorized into String Manipulation Functions, Date Manipulation Functions, Numeric Functions etc.
Typically when we read data from source, we get data in the form of strings and we need to apply functions to apply standardization rules, data type conversion, transformation rules etc.
Most of these functions can be used while projection using select, selectExpr, withColumn etc as well as part of filter or where, groupBy, orderBy or sort etc.
For selectExpr we need to use the functions using SQL Style syntax.
There are special functions such as col and lit. col is used to pass column names as column type for some of the functions while lit is used to pass literals as values as part of expressions (eg: concat($"first_name", lit(", "), $"last_name")).

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.

from pyspark.sql import SparkSession

import getpass
username = getpass.getuser()

spark = SparkSession. \
    builder. \
    config('spark.ui.port', '0'). \
    config("spark.sql.warehouse.dir", f"/user/{username}/warehouse"). \
    enableHiveSupport(). \
    appName(f'{username} | Python - Processing Column Data'). \
    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

employees = [(1, "Scott", "Tiger", 1000.0, 10,
                      "united states", "+1 123 456 7890", "123 45 6789"
                     ),
                     (2, "Henry", "Ford", 1250.0, None,
                      "India", "+91 234 567 8901", "456 78 9123"
                     ),
                     (3, "Nick", "Junior", 750.0, '',
                      "united KINGDOM", "+44 111 111 1111", "222 33 4444"
                     ),
                     (4, "Bill", "Gomes", 1500.0, 10,
                      "AUSTRALIA", "+61 987 654 3210", "789 12 6118"
                     )
                ]

employeesDF = spark. \
    createDataFrame(employees,
                    schema="""employee_id INT, first_name STRING, 
                    last_name STRING, salary FLOAT, bonus STRING, nationality STRING,
                    phone_number STRING, ssn STRING"""
                   )

employeesDF.show()

from pyspark.sql.functions import coalesce, lit

employeesDF.withColumn('payment', col('salary') + (col('salary') * coalesce(col('bonus').cast('int'), lit(0)) / 100)). \
  show()

Mastering Pyspark

Conclusion¶