Understanding the Spark stack function for pivoting data

Hello! If you’re into big data processing, you’ve probably heard of Spark, right? It’s a popular distributed computing framework used for processing massive datasets. We might have noted the function called stack and wondered, What is the importance and the way to use it?

The stack function in Apache Spark is used for pivoting data by converting multiple columns into key-value pairs.

Syntax: stack(n: int, col1: Column, col2: Column, …, coln: Column)

Understanding the Spark stack function for pivoting data

Introduction to the Spark stack() function

The stack function in Spark is used to pivot multiple columns of data into key-value pairs. It takes in a list of columns and converts the columns into a key-value(k,v) pair, Where the key is the position of the column in the list and the value is the value in the column for each row.


Below is the syntax for the stack function in Spark:

stack(n: int, col1: Column, col2: Column, ..., coln: Column) -> DataFrame


  • n => the number of columns to stack
  • col1, col2, …, coln => The columns to stack

How the Spark stack() function works

The Spark stack() function works by taking multiple columns as input and “stacking” them on top of each other to create a new DataFrame. The Output DataFrame has two columns: a key column and a value column.

Key        |   Value
Column1    |   Value1

Where the key column contains the position of the original column in the list of columns passed to the stack function. The value column contains the value from the original column for each row.

Example usage of the Spark stack() function

Let’s think about an example of how the stack function can be used to pivot data. Let’s say we have a DataFrame that contains the following data:

|id |subject1|subject2|subject3|
|101|90      |80      |70      |
|102|85      |75      |65      |
|103|95      |85      |75      |

We want to pivot the data so that it looks like this:

|id |subject  |marks |
|101|subject1 |90    |
|101|subject2 |80    |
|101|subject3 |70    |
|102|subject1 |85    |
|102|subject2 |75    |
|102|subject3 |65    |
|103|subject1 |95    |
|103|subject2 |85    |
|103|subject3 |75    |

To achieve this, we can use the stack function as follows: Final Pyspark code follows

from pyspark.sql.functions import col, stack

df = spark.createDataFrame(
    [(101, 90, 80, 70),
     (102, 85, 75, 65),
     (103, 95, 85, 75)],
    ["id", "subject1", "subject2", "subject3"])

stacked_df = df.selectExpr(
    "stack(4, 'subject1', subject1, 'subject2', subject2, 'subject3', subject3) as (subject, marks)")


In the above code, we have a dataframe df with columns id, subject1, subject2, and subject3. Then, We use the stack function to pivot the data, by converting the columns subject1, subject2, and subject3 into key-value pairs. At Last, we select the columns student_id, subject, and marks from the pivoted dataframe and display the result.

The resulting stacked_df DataFrame looks like this:

| id| subject|marks|
|101|subject1|   90|
|101|subject2|   80|
|101|subject3|   70|
|102|subject1|   85|
|102|subject2|   75|
|102|subject3|   65|
|103|subject1|   95|
|103|subject2|   85|
|103|subject3|   75|


From the above output, we can see, the `stack` function has converted the three columns (`subject1`, `subject2`, and `subject3`) into key-value pairs (`subject` and `marks`).


In conclusion, We can say that the stack function in Spark is used for pivoting data. It takes in multiple columns and converts them into key-value pairs, making it easier to perform operations on the data. In this article, we have explained what the stack function does, demonstrated how it works, and presented an example of its usage for pivoting data. With this knowledge, you can now use the `stack` function in your Spark applications to manipulate and analyze large datasets.

Good Luck with your Learning !!

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