Solving the Enigmatic Error: “requests module is not found in pySpark” – A Jupyter on Kubernetes Conundrum

Are you tired of encountering the frustrating error “requests module is not found in pySpark” while running your sparkling Jupyter notebook on Kubernetes? Well, worry no more! This article will delve into the heart of the issue, providing you with a comprehensive guide to troubleshoot and resolve this pesky problem once and for all.

Understanding the Culprits: pySpark and requests Module

Before we dive into the solution, let’s take a step back and understand the two main actors in this drama: pySpark and the requests module.

pySpark: The Sparkling Python API

pySpark is the Python API for Apache Spark, a powerful big data processing engine. It allows Python developers to leverage the scalability and flexibility of Spark, making it an ideal choice for data-intensive applications. pySpark provides high-level APIs for manipulating data structures, performing data processing, and creating machine learning models.

The Humble requests Module: A HTTP Request Library

The requests module is a lightweight, user-friendly library for making HTTP requests in Python. It provides a simple, intuitive way to send HTTP requests and interact with web servers, making it a popular choice for web scraping, API interactions, and other web-related tasks.

The Problem: “requests module is not found in pySpark”

So, what happens when you try to use the requests module within a pySpark context, say, in a Jupyter notebook running on Kubernetes? You’re met with an infuriating error message:

from pyspark.sql import SparkSession
import requests

spark = SparkSession.builder.appName('My Spark App').getOrCreate()
requests.get('https://www.example.com')

# Output:
# ModuleNotFoundError: No module named 'requests'

This error is particularly perplexing because you’ve likely installed the requests module in your Python environment. So, what’s going on?

The Culprit: Spark’s Isolated Execution Environment

The root of the issue lies in Spark’s execution model. When you run a Spark application, including a pySpark script, Spark creates an isolated execution environment for the application. This environment is separate from the Python environment where you installed the requests module.

In this isolated environment, Spark only includes a limited set of Python modules, which don’t include the requests module by default. This means that when you try to import the requests module within a pySpark context, it’s not found, resulting in the error.

Solution 1: Install requests Module in Spark’s Execution Environment

One way to resolve this issue is to install the requests module in Spark’s execution environment. You can do this by adding the following code to your pySpark script:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('My Spark App').getOrCreate()

spark.sparkContext.addPyFile('https://files.pythonhosted.org/packages/c7/56/19b536e9e9ab542e02bd85bb5a9a07a2e0e4d5e21c4c6d6fb36c5c41ce1_requests-2.25.1-py2.py3-none-any.whl')

from pyspark.sql.functions import spark_udf
spark_udf('import requests; print("requests module installed")')

print("requests module should be available now")
requests.get('https://www.example.com')

This code adds the requests module wheel file to Spark’s execution environment using the `addPyFile` method. Then, it creates a Spark UDF (User-Defined Function) to import the requests module and print a success message. Finally, it attempts to use the requests module to send an HTTP request.

Solution 2: Use Spark’s broadcastVariable to Share requests Module

Another approach is to use Spark’s `broadcastVariable` to share the requests module with the Spark execution environment. Here’s an example:

from pyspark.sql import SparkSession
import requests

spark = SparkSession.builder.appName('My Spark App').getOrCreate()

# Create a broadcast variable for the requests module
requests_bc = spark.sparkContext.broadcast(requests)

# Use the broadcast variable to access the requests module
def send_request(url):
    return requests_bc.value.get(url)

result = send_request('https://www.example.com')
print(result.text)

In this example, we create a broadcast variable `requests_bc` that contains the requests module. We then define a function `send_request` that uses the broadcast variable to access the requests module and send an HTTP request.

Solution 3: Use a Custom Python Package with requests Module

A more elegant solution is to create a custom Python package that includes the requests module and distribute it to the Spark execution environment. Here’s a step-by-step guide:

1. Create a new Python package, say, `my_requests_package`, with the following structure:

         
         my_requests_package/
         __init__.py
         requests/
         __init__.py
         requests.py
         setup.py
         
       

2. In the `setup.py` file, specify the requests module as a dependency:

         
         from setuptools import setup
   
         setup(
           name='my_requests_package',
           version='1.0',
           packages=['my_requests_package', 'my_requests_package.requests'],
           install_requires=['requests'],
         )
         
       

3. Build and install the package using `pip`:

         
         pip install .
         
       

4. In your pySpark script, add the custom package to Spark’s execution environment:

         
         from pyspark.sql import SparkSession
   
         spark = SparkSession.builder.appName('My Spark App').getOrCreate()
   
         spark.sparkContext.addPyFile('my_requests_package-1.0.tar.gz')
   
         from my_requests_package.requests import requests
   
         result = requests.get('https://www.example.com')
         print(result.text)
         
       

Jupyter on Kubernetes: Additional Considerations

When running Jupyter notebooks on Kubernetes, you’ll need to ensure that the requests module is installed in the Jupyter container and made available to the Spark execution environment. You can achieve this by:

• Adding the requests module to the Jupyter container’s ` requirements.txt` file
• Installing the requests module using `pip` in the Jupyter container
• Using a Kubernetes deployment strategy, such as a Helm chart, to manage the Jupyter container and its dependencies

Conclusion

In this article, we’ve explored the “requests module is not found in pySpark” error and provided three solutions to tackle this issue. By understanding the isolated execution environment of Spark and using one of the proposed solutions, you can successfully use the requests module within a pySpark context, even when running on Jupyter on Kubernetes.

Remember, when working with Spark and Jupyter on Kubernetes, it’s essential to consider the nuances of each technology stack to ensure seamless integration and optimal performance.

Solution	Description
Install requests module in Spark’s execution environment	Add the requests module wheel file to Spark’s execution environment using `addPyFile`.
Use Spark’s broadcastVariable to share requests module	Create a broadcast variable for the requests module and use it to access the module within the Spark execution environment.
Use a custom Python package with requests module	Create a custom Python package that includes the requests module and distribute it to the Spark execution environment.

We hope this comprehensive guide has helped you resolve the “requests module is not found in pySpark” error and empowered you to build more robust and efficient data pipelines with pySpark and Jupyter on Kubernetes.

Frequently Asked Question

Got stuck with PySpark and Kubernetes in Jupyter? Don’t worry, we’ve got you covered! Here are some FAQs to help you troubleshoot those pesky “requests module not found” errors.