This blog post discusses the options and benefits of parallelizing Python code on Spark when working with Pandas. It compares Pandas UDFs and the ‘concurrent.futures’ module as two approaches to concurrent processing in order to determine their use cases. The post also covers the challenges of working with large datasets and the performance results of different methods.
Parallelising Python on Spark: Options for Concurrency with Pandas
Leverage the benefits of Spark when working with Pandas
In today’s data-driven world, it is crucial for businesses to effectively process and analyze large datasets. However, the single-threaded nature of Python can limit scalability and performance when working with big data. This is where Spark comes in.
Spark is a powerful distributed computing framework that allows us to process large and complex data structures. By parallelizing the workload, we can take advantage of Spark’s distributed processing capabilities and significantly improve performance.
In this blog post, we will explore two approaches to achieve concurrency when working with Pandas, a popular Python package for data analysis. These approaches are Pandas UDFs and the ‘concurrent.futures’ module. We will compare these options and discuss their use cases.
The Challenge
Pandas is a great tool for working with datasets in the analytics space. However, its single-threaded nature can limit scalability, especially when working with larger datasets or when performing the same analysis across multiple subsets of data.
To overcome this challenge, we need to consider more sophisticated approaches that allow us to take advantage of distributed processing. Spark provides the infrastructure and tools to achieve this.
Use Cases for Concurrency
There are several use cases where concurrency is essential for efficient data processing:
- Applying uniform transformations to multiple data files
- Forecasting future values for several subsets of data
- Tuning hyperparameters for machine learning models and selecting the most efficient configuration
By parallelizing the workload, we can process these tasks more efficiently and achieve better performance.
The Data
Let’s consider an example where we have historical data for thousands of disks, and we want to predict future free space values for each disk. We have a CSV file containing 1,000 disks, each with one month of historical data for free space measured in GB.
To perform this prediction, we’ll compare two machine learning algorithms: linear regression and fbprophet. We’ll use the Root Mean Squared Error (RMSE) as a validation metric to determine the most appropriate model for each disk.
Introducing Concurrency
Python is single-threaded by default, which means it does not fully utilize all available compute resources. To overcome this limitation, we have three options:
- Implement a for loop to calculate predictions sequentially
- Use Python’s ‘concurrent.futures’ module to run multiple processes concurrently
- Use Pandas UDFs (user-defined functions) to leverage distributed computing in PySpark while maintaining Pandas syntax and compatibility
In this blog post, we will focus on the third option, as it provides the most efficient and scalable solution for our use case.
Interpreting the Results
After comparing the performance of the three approaches (sequential, concurrent.futures, and Pandas UDFs) under different environment conditions, we have the following findings:
- Predicting 1,000 disks is generally more time-consuming than predicting 100 disks
- The sequential approach is the slowest, as it cannot take advantage of underlying resources efficiently
- Pandas UDFs are inefficient for smaller tasks but can compensate for this with parallelization
- Both sequential and concurrent.futures approaches do not fully utilize the clustering available in Databricks
Based on these findings, it is clear that leveraging Pandas UDFs with Spark provides the most efficient solution for processing larger and more complex datasets. However, for smaller datasets, the concurrent.futures module can also be a cost-effective option.
Closing Thoughts
When it comes to implementing AI solutions and leveraging the power of Spark, it is important to consider the specific context and requirements of your business. By selecting the right approach and tools, you can optimize performance and achieve your desired outcomes.
If you’re looking to evolve your company with AI, it’s important to identify automation opportunities, define measurable KPIs, select the right AI solution, and implement gradually. By doing so, you can stay competitive and redefine your way of work.
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For the full details and results of the comparison, please refer to the original blog post on our website.
