PySpark leftsemi join is similar to inner join difference being left semi-join returns all columns from the left DataFrame/Dataset and ignores all columns from the right dataset. In other words, this join returns columns from the only left dataset for the records match in the right dataset on join expression, records not matched on join expression are ignored from both left and right datasets.
In this PySpark article, I will explain how to do Left Semi Join(semi, left semi, left_semi ) on two DataFrames with PySpark Example.
Alternatively, you can be achieved the same output as Left Smi Join using select on the result of the inner join however, using this join would be efficient.
Before we jump into PySpark Left Semi 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 DataFrame 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 Semi Join Example
In order to use Left Semi Join, you can use either Semi, Leftsemi, left_semi as a join type.
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftsemi") \
.show(truncate=False)
Below is the result of the above join expression.
leftsemi join
+------+--------+---------------+-----------+-----------+------+------+
|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 |
+------+--------+---------------+-----------+-----------+------+------+PySpark SQL Left Semi Join
Let’s see how use Left Semi Join on PySpark SQL expression, In order to do so first let’s create a temporary view for EMP and DEPT tables.
<pre><code class="language-Scala">
empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")
joinDF2 = spark.sql("SELECT e.* FROM EMP e LEFT SEMI JOIN DEPT d ON e.emp_dept_id == d.dept_id") \
.show(truncate=False)
</code></pre>This also returns same output as above.
Conclusion
In this PySpark article, Left Semi Join (left semi) join is similar to inner join difference being left semi join returns all columns from the left dataset and ignores all columns from the right dataset.
Hope you Like it !!
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