PySpark SQL Left Outer Join (left, left outer, left_outer) returns all rows from the left DataFrame regardless of match found on the right Dataframe when join expression doesn’t match, it assigns null for that record and drops records from right where match not found.
In this PySpark article, I will explain how to do Left Outer Join (left, leftouter, left_outer) on two DataFrames with Python Example.
Before we jump into PySpark Left Outer Join examples, first, let’s create an emp and dept DataFrame’s. here, column emp_id is unique on emp and dept_id is unique on the dept dataset’s and emp_dept_id from emp has a reference to dept_id on dept dataset.
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("sparkbyexamples.com").getOrCreate()
emp = [(1,"Smith",-1,"2018","10","M",3000), \
(2,"Rose",1,"2010","20","M",4000), \
(3,"Williams",1,"2010","10","M",1000), \
(4,"Jones",2,"2005","10","F",2000), \
(5,"Brown",2,"2010","40","",-1), \
(6,"Brown",2,"2010","50","",-1) \
]
empColumns = ["emp_id","name","superior_emp_id","year_joined", \
"emp_dept_id","gender","salary"]
empDF = spark.createDataFrame(data=emp, schema = empColumns)
empDF.printSchema()
empDF.show(truncate=False)
dept = [("Finance",10), \
("Marketing",20), \
("Sales",30), \
("IT",40) \
]
deptColumns = ["dept_name","dept_id"]
deptDF = spark.createDataFrame(data=dept, schema = deptColumns)
deptDF.printSchema()
deptDF.show(truncate=False)
This prints emp and dept DataFrame to console.
Emp Dataset
+------+--------+---------------+-----------+-----------+------+------+
|emp_id|name |superior_emp_id|year_joined|emp_dept_id|gender|salary|
+------+--------+---------------+-----------+-----------+------+------+
|1 |Smith |-1 |2018 |10 |M |3000 |
|2 |Rose |1 |2010 |20 |M |4000 |
|3 |Williams|1 |2010 |10 |M |1000 |
|4 |Jones |2 |2005 |10 |F |2000 |
|5 |Brown |2 |2010 |40 | |-1 |
|6 |Brown |2 |2010 |50 | |-1 |
+------+--------+---------------+-----------+-----------+------+------+
Dept Dataset
+---------+-------+
|dept_name|dept_id|
+---------+-------+
|Finance |10 |
|Marketing|20 |
|Sales |30 |
|IT |40 |
+---------+-------+PySpark DataFrame Left Outer Join Example
Below is an example of how to use Left Outer Join (left, leftouter, left_outer) on PySpark DataFrame.
empDF.join(deptDF,empDF.emp_dept_id === deptDF.dept_id,"left") \
.show(false)
empDF.join(deptDF,empDF.emp_dept_id === deptDF.dept_id,"leftouter") \
.show(false)
From our dataset, emp_dept_id 6o doesn’t have a record on dept dataset hence, this record contains null on dept columns (dept_name & dept_id). and dept_id 30 from dept dataset dropped from the results. Below is the result of the above Join expression.
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|emp_id|name |superior_emp_id|year_joined|emp_dept_id|gender|salary|dept_name|dept_id|
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|1 |Smith |-1 |2018 |10 |M |3000 |Finance |10 |
|2 |Rose |1 |2010 |20 |M |4000 |Marketing|20 |
|3 |Williams|1 |2010 |10 |M |1000 |Finance |10 |
|4 |Jones |2 |2005 |10 |F |2000 |Finance |10 |
|5 |Brown |2 |2010 |40 | |-1 |IT |40 |
|6 |Brown |2 |2010 |50 | |-1 |null |null |
+------+--------+---------------+-----------+-----------+------+Using PySpark SQL Left Outer Join
Let’s see how to use Left Outer Join on PySpark SQL expression, In order to do so first let’s create a temporary view for EMP and DEPT tables.
empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")
joinDF2 = spark.sql("SELECT e.* FROM EMP e LEFT OUTER JOIN DEPT d ON e.emp_dept_id == d.dept_id") \
.show(truncate=False)
This also returns same output as above.
Conclusion
In this PySpark article, you have learned Left Outer Join is used to get all rows from the left dataset regardless of match found on the right dataset when join expression doesn’t match.
Hope you Like it !!
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