How do you perform a self-join in Spark SQL and Spark Dataframe?

Performing a self-join in Apache Spark SQL and Spark DataFrames can be a challenging task, especially if you are new to these technologies. However, with the right understanding and guidance, it can be a simple and effective way to analyze data.

A self-join is a join operation that is performed on a single table, where the table is joined with itself based on a common column. It is used to combine data from two or more rows in the same table. There are different scenarios where a self-join can be useful, such as finding hierarchical relationships between rows or identifying patterns in the data.

In this blog, we will explore how to perform a self-join in Apache Spark SQL and Spark DataFrames with detailed examples and explanations.

Left self-join in Spark SQL

In a left self-join, all the rows from the left table are returned along with any matching rows from the same table. This type of join is used when we want to find all the employees who have the same manager.

Let's assume we have a table 'employees' with the following data:


To perform a left self-join on this table, we can use the following Spark SQL query:

SELECT e1.emp_name, e2.emp_name AS manager_name
FROM employees e1
LEFT JOIN employees e2 ON e1.manager_id = e2.emp_id

This query will return the following result:


Here, we are joining the 'employees' table with itself based on the 'manager_id' and 'emp_id' columns. The alias 'e1' and 'e2' are used to differentiate between the two instances of the 'employees' table. The SELECT statement is used to select the 'emp_name' column from the left table and the 'emp_name' column from the right table, which is aliased as 'manager_name'.

In the resulting table, we can see that each employee is listed along with their respective manager's name.

Left self-join in Spark Dataframe

In Spark Dataframe, we can perform a left self-join using the 'join' method.

Using the same 'employees' table, let's see how we can perform a left self-join:

val df = Seq((1, "John", 2), (2, "Alex", 2), (3, "Jane", 1), (4, "Sarah", 3))
    .toDF("emp_id", "emp_name", "manager_id")

val result ="e1")
    .join("e2"), col("e1.manager_id") === col("e2.emp_id"), "left")
    .select(col("e1.emp_name"), col("e2.emp_name").alias("manager_name"))

Here, we first create a Spark Dataframe 'df' with the same data as the previous example. We then use the 'as' method to alias the dataframe as 'e1' and 'e2'. We then perform a left self-join on the 'df' dataframe using the 'join' method and passing the join condition as an argument. The third argument 'left' specifies that we want to perform a left self-join.

Finally, we use the 'select' method to select the 'emp_name' column from the left dataframe and the 'emp_name' column from the right dataframe, which is aliased as 'manager_name'.


Self-joins are useful when we need to compare data within the same table. Spark SQL and Spark Dataframe provide different ways to perform self-joins depending on the requirements of the use case. Understanding these join types is important when working with large datasets in Spark.