If you’re starting with PySpark or working on multiple projects which may require different versions of PySpark, it’s essential to keep track of the version you’re using. The reason is that different versions of PySpark accommodate different features and API changes. This guide will show you various ways to find out your PySpark version easily using Python. So let’s dive in and give you the tools you need to ascertain your PySpark environment.
Why Knowing Your PySpark Version Is Important
Before we proceed to find out the version of PySpark, let’s take a moment to discuss why this is important. Software projects often rely on specific libraries and their versions to function correctly, and this is very true for PySpark as well. PySpark versions may have different compatibility with certain versions of Python and Apache Spark itself. Additionally, various versions can have different sets of features, optimizations, and bug fixes. Knowing your PySpark version is crucial for debugging, replication of environments, and ensuring compatible functionality across different development environments and production.
Checking PySpark Version in a Python Script
In a Python script, finding the PySpark version can be accomplished in just a single line of code after you have set up a Spark session.
Setting Up SparkSession
To interact with Spark through PySpark, you first need to create a SparkSession. This is an entry point for programming Spark with the Dataset and DataFrame API. Here’s a simple way to setup a SparkSession:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Find PySpark Version") \
.getOrCreate()
Sample Output:
If you run the above code, you won’t see an output, as this simply initializes the SparkSession. You might see logs that Spark prints out, which can vary depending on your Spark configuration.
Actual Version Check
Now that you have SparkSession initialized, determining the PySpark version is as simple as querying the version attribute:
print(spark.version)
Sample Output:
3.0.1
This output indicates that the version of PySpark being used is 3.0.1. Note that the exact version number might vary based on your specific install.
Using PySpark Shell or pyspark Command
If you’re using a terminal, you can check the PySpark version quickly by using the PySpark shell. The PySpark version is displayed when you start the PySpark shell.
Starting the PySpark Shell
To open the PySpark shell, simply type pyspark into your terminal and press Enter:
$ pyspark
Sample Output:
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.0.1
/_/
Using Python version 3.8.5 (default, Jul 28 2020 12:59:40)
SparkSession available as 'spark'.
As seen in the welcome message, the Spark and PySpark version here is 3.0.1. Along with the version, the above output also indicates the Python version that is being used by Spark.
Querying PySpark Version from PyPI (Python Package Index)
When you install PySpark using pip, the Python package installer, you can also check the version of the package before you even install it. Here’s how you can use pip to list the information for PySpark, which includes the version:
pip show pyspark
Sample Output:
Name: pyspark
Version: 3.0.1
Summary: Apache Spark Python API
Home-page: https://spark.apache.org/
Author: Apache Software Foundation
Author-email: dev@spark.apache.org
License: Apache License, Version 2.0
Location: /usr/local/lib/python3.8/dist-packages
Requires: py4j
Required-by:
The “Version” field in the output tells you the version of PySpark that is installed in your system. In this case, it is version 3.0.1.
Checking PySpark Version Programmatically
If you need to check the PySpark version within a larger Python program, you can capture the version as a string like this:
import pyspark
pyspark_version = pyspark.__version__
print(f"The version of PySpark is: {pyspark_version}")
Sample Output:
The version of PySpark is: 3.0.1
Using pyspark.__version__, you can securely obtain the version of PySpark, and use it anywhere in your code programmatically.
Conclusion
Knowing your PySpark version is crucial for a multitude of reasons, including compatibility checks, debugging, and development consistency. With the methods described above, you should easily find the PySpark version in various environments such as within a Python script, using a terminal and the PySpark shell, as well as programmatically within the Python environment. Understanding your working environment in depth helps in reducing the potential issues that can arise from version mismatches and allows for smoother transitions between development and production systems.
Regardless of whether you’re working on a personal project or in an enterprise setting, managing your PySpark version should now be a straightforward task. Next time you or a member of your team needs to perform this check, you’ll have all the knowledge at hand to help them proceed with confidence.