PySpark partitionBy() Function with Examples

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This article will explore the partitionBy() function in depth, providing practical examples to illustrate its usage.

What is partitionBy()?

In PySpark, the partitionBy() function is used when saving a DataFrame to a file system, such as HDFS (Hadoop Distributed File System) or S3. The function partitions the output data by the specified columns, generating a separate directory for each partition. This results in each partition being saved into its separate file or directory, which allows for more efficient distributed reads in subsequent data processing tasks.

The basic syntax of the partitionBy() function is as follows:



  • *cols: Specifies the column(s) to partition by.
  • file_format: Specifies the format of the file to be saved, such as ‘csv’, ‘parquet’, ‘json’, etc.
  • file_path: Specifies the directory to save the file(s) to.

Example of partitionBy()

Let’s explore the partitionBy() function using a practical example.

Consider the following DataFrame that includes information about a company’s sales in different cities and countries:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("partitionByExample").getOrCreate()

data = [("USA", "NYC", 2500), 
        ("USA", "LA", 3000), 
        ("UK", "London", 2000), 
        ("UK", "Liverpool", 1500), 
        ("France", "Paris", 3500)]
df = spark.createDataFrame(data, ["Country", "City", "Sales"])

This will display:

|Country|     City|Sales|
|    USA|      NYC| 2500|
|    USA|       LA| 3000|
|     UK|   London| 2000|
|     UK|Liverpool| 1500|
| France|    Paris| 3500|

Now, let’s partition this DataFrame by ‘Country’ and save it as parquet files:


This code will create a new directory named ‘sales_data’ under the ‘/tmp’ directory. Inside ‘sales_data’, there will be a separate subdirectory for each unique country (i.e., ‘Country=USA’, ‘Country=UK’, ‘Country=France’). Each of these subdirectories will contain a parquet file with the corresponding data.

Benefits of Using partitionBy()

There are several benefits to using the partitionBy() function in PySpark:

  • Improved Read Performance: When reading data, Spark can just read the specific partition files it needs, rather than scanning through an entire data file.
  • Distributed Processing: By partitioning the data, Spark can distribute processing tasks across multiple nodes, each working on a separate partition.
  • Data Organization: The partitionBy() function helps to organize large datasets by splitting them into logical, manageable chunks.


Understanding PySpark’s partitionBy() function is crucial for efficient big data processing. This function allows you to partition your data into logical, manageable chunks that can be processed independently, leading to improved read performance and better data organization. The examples provided in this article should serve as a practical guide to effectively using partitionBy() in your PySpark applications.

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