Apache Spark has become a full-proof standard framework for data processing. It is enabling organisations to process huge amounts of data in a relatively short time and use a small number of resources. Spark offers several easy-to-use APIs for machine learning (ML), ELT (extract, transform, load) and graph processing over large data sets from a wide range of sources.
Spark 3.0 is the major release that was made available in 2020 by Apache. Built on version 2.x, it comes with numerous amazing features, performance improvements, bug fixes, and much more.
In this blog, we will discuss the top Spark 3.0 best practices for data science. It will allow data scientists to leverage the power of Spark 3.0 for better results.
Top 3 Spark 3.0 Best Practices for Data Science
Following are the top Spark 3.0 best practices that can help you reduce your runtime and scale up your data science projects significantly:
1. Begin with Sampling the Data
If you want to make big data work, then first you need to check whether or not you are in the right direction. In other words, use a small chunk of data to check if it works for you or not. A good idea is to sample around 10% of data while making sure that the pipelines are working properly. In this way, you can use the SQL section in Spark 3.0 UI and check the numbers growing in the entire flow, while not having to wait for the process to run.
Using this approach, if you achieve your desired runtime with this small sample, then you can safely scale up in a smooth way.
2. Have a Basic Understanding of Cores, Tasks and Partitions
This is perhaps the most important thing to remember when working with Spark 3.0 — one partition is there for one task that runs on only one core.
You should always know how many total partitions you have. Further, follow each task in every stage and match them with the exact number of cores in your Spark 3.0 connection. Below are a few tips to do this:
- The task to core ratio should be 2 to 4 tasks per core.
- Each partition should be around 200 to 400 MB in size.
3. Debugging Spark 3.0
Debugging a lengthy code is always a cumbersome task for a data scientist. Isn't it?
Sparks works in a relaxed manner when it comes to debugging. In other words, it waits till the time when an action is called before running the graph of computation codes. For example count(), show(), etc.
This makes it hard to spot bugs and places where corrections need to be made. Here, the best practice is to split the code using functions like df.cache() and then going forward with df.count() to allow Spark to calculate the df at each section of your split code.
After this, you can check the computation of each section and identify the bugs using Spark 3.0 UI.
Moreover, use this practice only after the sampling process we have discussed above.
Spark 3.0 is undoubtedly the best framework for processing huge amounts of data. Its speed, flexibility and efficiency make it a state-of-the-art framework for big data. It can do magic in increasing efficiency and performance provided that you know how to leverage it to its full capacity. The best practices we have discussed above will help you use Spark 3.0 in an efficient way. We hope it helps!