Overview of Functions¶

Let us get an overview of different functions that are available to process data in columns.

While Data Frame APIs work on the Data Frame, at times we might want to apply functions on column values.
Functions to process column values are available under pyspark.sql.functions. These are typically used in select or withColumn on top of Data Frame.
There are approximately 300 pre-defined functions available for us.
Some of the important functions can be broadly categorized into String Manipulation, Date Manipulation, Numeric Functions and Aggregate Functions.
String Manipulation Functions
- Concatenating Strings - concat
- Getting Length - length
- Trimming Strings - trim, rtrim, ltrim
- Padding Strings - lpad, rpad
- Extracting Strings - split, substring
Date Manipulation Functions
- Date Arithmetic - date_add, date_sub, datediff, add_months
- Date Extraction - dayofmonth, month, year
- Get beginning period - trunc, date_trunc
Numeric Functions - abs, greatest
Aggregate Functions - sum, min, max

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 - Data Processing - Overview'). \
    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, "united states"),
             (2, "Henry", "Ford", 1250.0, "India"),
             (3, "Nick", "Junior", 750.0, "united KINGDOM"),
             (4, "Bill", "Gomes", 1500.0, "AUSTRALIA")
            ]

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

employeesDF.printSchema()

employeesDF.show()

Tasks¶

Let us perform a task to understand how functions are typically used.

Project full name by concatenating first name and last name along with other fields excluding first name and last name.

from pyspark.sql.functions import lit, concat

employeesDF. \
    withColumn("full_name", concat("first_name", ", ", "last_name")). \
    drop("first_name", "last_name"). \
    show()

employeesDF. \
    withColumn("full_name", concat("first_name", lit(", "), "last_name")). \
    drop("first_name", "last_name"). \
    show()

employeesDF. \
    select("employee_id",
           concat("first_name", lit(", "), "last_name"),
           "salary",
           "nationality"
          ). \
    show()

employeesDF. \
    select("employee_id",
           concat("first_name", lit(", "), "last_name").alias("full_name"),
           "salary",
           "nationality"
          ). \
    show()

employeesDF. \
    selectExpr("employee_id",
               "concat(first_name, ', ', last_name) AS full_name",
               "salary",
               "nationality"
              ). \
    show()

We will explore most of the functions as we get into the data processing at a later point in time

Mastering Pyspark

Overview of Functions¶

Tasks¶