PySpark Join is used to combine two DataFrames and by chaining these you can join multiple DataFrames; it supports all basic join type operations available in traditional SQL like INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, SELF JOIN. PySpark Joins are wider transformations that involve data shuffling across the network.
PySpark SQL Joins comes with more optimization by default (thanks to DataFrames) however still there would be some performance issues to consider while using.
In this PySpark SQL Join tutorial, you will learn different Join syntaxes and using different Join types on two or more DataFrames and Datasets using examples.
- PySpark Join Syntax
- PySpark Join Types
- Inner Join DataFrame
- Full Outer Join DataFrame
- Left Outer Join DataFrame
- Right Outer Join DataFrame
- Left Anti Join DataFrame
- Left Semi Join DataFrame
- Self Join DataFrame
- Using SQL Expression
1. PySpark Join Syntax
PySpark SQL join has a below syntax and it can be accessed directly from DataFrame.
join(self, other, on=None, how=None)
join() operation takes parameters as below and returns DataFrame.
- param other: Right side of the join
- param on: a string for the join column name
- param how: default
inner. Must be one ofinner,cross,outer,full,full_outer,left,left_outer,right,right_outer,left_semi, andleft_anti.
You can also write Join expression by adding where() and filter() methods on DataFrame and can have Join on multiple columns.
2. PySpark Join Types
Below are the different Join Types PySpark supports.
| Join String | Equivalent SQL Join |
| inner | INNER JOIN |
| outer, full, fullouter, full_outer | FULL OUTER JOIN |
| left, leftouter, left_outer | LEFT JOIN |
| right, rightouter, right_outer | RIGHT JOIN |
| cross | |
| anti, leftanti, left_anti | |
| semi, leftsemi, left_semi |
Before we jump into PySpark SQL Join examples, first, let’s create an "emp" and "dept" DataFrames. 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.
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 the console. Refer complete example below on how to create spark object.
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 |
+---------+-------+
3. PySpark Inner Join DataFrame
Inner join is the default join in PySpark and it’s mostly used. This joins two datasets on key columns, where keys don’t match the rows get dropped from both datasets (emp & dept).
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"inner") \
.show(truncate=False)
When we apply Inner join on our datasets, It drops “emp_dept_id” 50 from “emp” and “dept_id” 30 from “dept” datasets. 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 |
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
4. PySpark Full Outer Join
Outer a.k.a full, fullouter join returns all rows from both datasets, where join expression doesn’t match it returns null on respective record columns.
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"outer") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"full") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"fullouter") \
.show(truncate=False)
From our “emp” dataset’s “emp_dept_id” with value 50 doesn’t have a record on “dept” hence dept columns have null and “dept_id” 30 doesn’t have a record in “emp” hence you see null’s on emp columns. 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|
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|2 |Rose |1 |2010 |20 |M |4000 |Marketing|20 |
|5 |Brown |2 |2010 |40 | |-1 |IT |40 |
|1 |Smith |-1 |2018 |10 |M |3000 |Finance |10 |
|3 |Williams|1 |2010 |10 |M |1000 |Finance |10 |
|4 |Jones |2 |2005 |10 |F |2000 |Finance |10 |
|6 |Brown |2 |2010 |50 | |-1 |null |null |
|null |null |null |null |null |null |null |Sales |30 |
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
5. PySpark Left Outer Join
Left a.k.a Leftouter join returns all rows from the left dataset regardless of match found on the right dataset when join expression doesn’t match, it assigns null for that record and drops records from right where match not found.
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"left")
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftouter")
.show(truncate=False)
From our dataset, “emp_dept_id” 5o 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 |
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
6. Right Outer Join
Right a.k.a Rightouter join is opposite of left join, here it returns all rows from the right dataset regardless of math found on the left dataset, when join expression doesn’t match, it assigns null for that record and drops records from left where match not found.
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"right") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"rightouter") \
.show(truncate=False)
From our example, the right dataset “dept_id” 30 doesn’t have it on the left dataset “emp” hence, this record contains null on “emp” columns. and “emp_dept_id” 50 dropped as a match not found on left. 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|
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|4 |Jones |2 |2005 |10 |F |2000 |Finance |10 |
|3 |Williams|1 |2010 |10 |M |1000 |Finance |10 |
|1 |Smith |-1 |2018 |10 |M |3000 |Finance |10 |
|2 |Rose |1 |2010 |20 |M |4000 |Marketing|20 |
|null |null |null |null |null |null |null |Sales |30 |
|5 |Brown |2 |2010 |40 | |-1 |IT |40 |
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
7. Left Semi Join
leftsemi join is similar to inner join difference being leftsemi join returns all columns from the left 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.
The same result can be achieved using select on the result of the inner join however, using this join would be efficient.
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 |
+------+--------+---------------+-----------+-----------+------+------+
8. Left Anti Join
leftanti join does the exact opposite of the leftsemi, leftanti join returns only columns from the left dataset for non-matched records.
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftanti") \
.show(truncate=False)
Yields below output
+------+-----+---------------+-----------+-----------+------+------+
|emp_id|name |superior_emp_id|year_joined|emp_dept_id|gender|salary|
+------+-----+---------------+-----------+-----------+------+------+
|6 |Brown|2 |2010 |50 | |-1 |
+------+-----+---------------+-----------+-----------+------+------+
9. PySpark Self Join
Joins are not complete without a self join, Though there is no self-join type available, we can use any of the above-explained join types to join DataFrame to itself. below example use inner self join.
empDF.alias("emp1").join(empDF.alias("emp2"), \
col("emp1.superior_emp_id") == col("emp2.emp_id"),"inner") \
.select(col("emp1.emp_id"),col("emp1.name"), \
col("emp2.emp_id").alias("superior_emp_id"), \
col("emp2.name").alias("superior_emp_name")) \
.show(truncate=False)
Here, we are joining emp dataset with itself to find out superior emp_id and name for all employees.
+------+--------+---------------+-----------------+
|emp_id|name |superior_emp_id|superior_emp_name|
+------+--------+---------------+-----------------+
|2 |Rose |1 |Smith |
|3 |Williams|1 |Smith |
|4 |Jones |2 |Rose |
|5 |Brown |2 |Rose |
|6 |Brown |2 |Rose |
+------+--------+---------------+-----------------+
4. Using SQL Expression
Since PySpark SQL support native SQL syntax, we can also write join operations after creating temporary tables on DataFrames and use these tables on spark.sql().
empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")
joinDF = spark.sql("select * from EMP e, DEPT d where e.emp_dept_id == d.dept_id") \
.show(truncate=False)
joinDF2 = spark.sql("select * from EMP e INNER JOIN DEPT d ON e.emp_dept_id == d.dept_id") \
.show(truncate=False)
5. PySpark SQL Join on multiple DataFrames
When you need to join more than two tables, you either use SQL expression after creating a temporary view on the DataFrame or use the result of join operation to join with another DataFrame like chaining them. for example
df1.join(df2,df1.id1 == df2.id2,"inner") \
.join(df3,df1.id1 == df3.id3,"inner")
6. PySpark SQL Join Complete Example
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
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)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"inner") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"outer") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"full") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"fullouter") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"left") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftouter") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"right") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"rightouter") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftsemi") \
.show(truncate=False)
empDF.join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftanti") \
.show(truncate=False)
empDF.alias("emp1").join(empDF.alias("emp2"), \
col("emp1.superior_emp_id") == col("emp2.emp_id"),"inner") \
.select(col("emp1.emp_id"),col("emp1.name"), \
col("emp2.emp_id").alias("superior_emp_id"), \
col("emp2.name").alias("superior_emp_name")) \
.show(truncate=False)
empDF.createOrReplaceTempView("EMP")
deptDF.createOrReplaceTempView("DEPT")
joinDF = spark.sql("select * from EMP e, DEPT d where e.emp_dept_id == d.dept_id") \
.show(truncate=False)
joinDF2 = spark.sql("select * from EMP e INNER JOIN DEPT d ON e.emp_dept_id == d.dept_id") \
.show(truncate=False)
Examples explained here are available at the GitHub project for reference.
Conclusion
In this PySpark SQL tutorial, you have learned two or more DataFrames can be joined using the join() function of the DataFrame, Join types syntax, usage, and examples with PySpark (Spark with Python), I would also recommend reading through Optimizing SQL Joins to know performance impact on joins.
Happy Learning !!
Related Articles
- PySpark Join Two or Multiple DataFrames
- PySpark Replace Empty Value With None/null on DataFrame
- PySpark show() – Display DataFrame Contents in Table
- PySpark – Loop/Iterate Through Rows in DataFrame
- PySpark Count Distinct from DataFrame
- PySpark – Drop One or Multiple Columns From DataFrame
- PySpark SQL Types (DataType) with Examples
- PySpark SparkContext Explained
Very good job!!
there is no any 60 value. I think you meant to write 50 🙂
Thanks for pointing it out. I have corrected it now.
Surper content, really helped a lot !!!
This page is really nice to learn spark! really great job. appreciate your efforts.
I have small suggestion, these is a one method in join something like below. if possible please add it in the page.
df = df1.join(df2, [‘id’], ‘inner’).
this will work if id column is there in both df1 and df2.
Hi Kalai, Sure will add this. Thanks for sharing.
Your method really helps!
Really great job!
well explained
Love the way of explanation
well explained
Very happy with contents thank you
The best site for learning pyspark
The best site for learning pyspark
Very nice neet and complete information thank you so much