Using LIKE Operator or like Function¶

Let us understand the usage of LIKE operator or like function while filtering the data in Data Frames.

like is primarily used for partial comparison (e.g.: Search for names which starts with Sco).
We can use like to get results which starts with a pattern or ends with a pattern or contain the pattern.
We can also use negation with like.
Spark also provides rlike to take care of partial comparison using regular expression.

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 - 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

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()

+-----------+----------+---------+------+-----+--------------+----------------+-----------+
|employee_id|first_name|last_name|salary|bonus|   nationality|    phone_number|        ssn|
+-----------+----------+---------+------+-----+--------------+----------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10| united states| +1 123 456 7890|123 45 6789|
|          2|     Henry|     Ford|1250.0| null|         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|
+-----------+----------+---------+------+-----+--------------+----------------+-----------+

Get employees whose first name starts with Sco

employeesDF. \
    filter("first_name LIKE 'Sco%'"). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

employeesDF. \
    filter("upper(first_name) LIKE 'SCO%'"). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

API Style

from pyspark.sql.functions import col

c = col('x')

help(c.like)

Help on method _ in module pyspark.sql.column:

_(other) method of pyspark.sql.column.Column instance
    SQL like expression. Returns a boolean :class:`Column` based on a SQL LIKE match.
    
    :param other: a SQL LIKE pattern
    
    See :func:`rlike` for a regex version
    
    >>> df.filter(df.name.like('Al%')).collect()
    [Row(age=2, name='Alice')]

# % at the end is mandatory
employeesDF. \
    filter(col('first_name').like('Sco%')). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

from pyspark.sql.functions import upper

employeesDF. \
    filter(upper(col('first_name')).like('SCO%')). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

Get employees where first name contain ott irrespective of case.

employeesDF. \
    filter("upper(first_name) LIKE '%OTT%'"). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

employeesDF. \
    filter(upper(col('first_name')).like('%OTT%')). \
    show()

+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|   phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states|+1 123 456 7890|123 45 6789|
+-----------+----------+---------+------+-----+-------------+---------------+-----------+

Get employees whose phone number does not start with +44

employeesDF. \
    filter("phone_number NOT LIKE '+44%'"). \
    show()

+-----------+----------+---------+------+-----+-------------+----------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|    phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+----------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states| +1 123 456 7890|123 45 6789|
|          2|     Henry|     Ford|1250.0| null|        India|+91 234 567 8901|456 78 9123|
|          4|      Bill|    Gomes|1500.0|   10|    AUSTRALIA|+61 987 654 3210|789 12 6118|
+-----------+----------+---------+------+-----+-------------+----------------+-----------+

employeesDF. \
    filter(~ col('phone_number').like('+44%')). \
    show()

+-----------+----------+---------+------+-----+-------------+----------------+-----------+
|employee_id|first_name|last_name|salary|bonus|  nationality|    phone_number|        ssn|
+-----------+----------+---------+------+-----+-------------+----------------+-----------+
|          1|     Scott|    Tiger|1000.0|   10|united states| +1 123 456 7890|123 45 6789|
|          2|     Henry|     Ford|1250.0| null|        India|+91 234 567 8901|456 78 9123|
|          4|      Bill|    Gomes|1500.0|   10|    AUSTRALIA|+61 987 654 3210|789 12 6118|
+-----------+----------+---------+------+-----+-------------+----------------+-----------+

Mastering Pyspark

Using LIKE Operator or like Function¶