5 Apache Spark Best Practices For Data Science – Starweaver
5-apache-spark-best-practices-for-data-science

5-apache-spark-best-practices-for-data-science

5 Apache Spark Best Practices For Data Science

Data science and Spark often go hand in hand. Spark has been used by many data scientists to help them sort their data with the help of Python, and see some great results in the process. This will ensure that you can find the patterns in your data, no matter what may show up. Some of the Apache Spark best practices you can follow include:

What is Apache Spark?

To help us learn more about how to use Apache Spark, we need first to understand why it is so important. There are many options out there when you are ready to handle Big Data and make this data work for your needs. However, there are a few benefits to bringing Spark into the organization and using all its features compared to some of the other choices out there. These benefits include:

  • Many companies already have the basic infrastructure necessary for Spark in place and ready to go. It makes it easy to implement and use as needed.
  • This is a widely used program to use with Big Data. It is simple to work with, and many other companies like to use it as well.
  • It works well with the Pandas library. For those who love to use Python and all the neat things that come with this programming language, Apache Spark is an excellent tool to work with.

The Best Practices to Use with Apache Spark

Now that we have some of the benefits down, we need to look at the best practices to utilize with this service. There is much that you can do with this service; it is more about learning how to use it to see the best results properly. Some best practices to consider with spark data science includes:

Start with Something Small

It is tempting to jump right in and want to do as much work as possible with Apache Spark. It seems like the perfect tool to get the job done and see results. However, if you want to see how it works, and catch any major issues that may show up, then you need to start small.

If we would like to make this significant data work, we need to check with a smaller sample to see if we are heading in the right direction. An excellent place to start is with a fraction of your data, maybe ten percent. This helps you to check the pipelines of the system and could make it easier to catch mistakes or other issues that could show up. You can also work with the SQL section and get everything in place, while not waiting for all that data to load up.

If you can reach the desired runtime when working with a small bit of data, it is easier to do some scaling and add in more data. Maybe add in another ten percent, and then another, before adding in the rest. This gives you time to test the system and your algorithms, see what patterns are emerging, and make changes when necessary.

Understand All the Basics

If you don’t first understand how data science and Apache work together, or how Apache is supposed to work at all, then you are wasting a ton of time. You need to have the basics down to use this system and to get the most out of it.

The tasks, partitions, and cores are items you need to consider. For example, one barrier will make for one job that will run on one core. You should always keep track of how many partitions you have. You can do this by following how many tasks you have in each stage, before matching these same tasks up to the number of cores you have in a Spark connection.

This process does take a little time to get down, but some ethical rules that you should follow, and test out, as you include:

  • The ratio between your tasks and your cores needs to fall somewhere between two to four jobs for every base.
  • The size of your partitions should fall between 200MB and 400MB depending on how much memory you have for each worker. You can tune this to work best for your needs.

Learning How to Debug Spark

Spark is going to work with something known as lazy evaluation. It means that it will wait around until the action is called up, and then it will execute the graph with all the instructions. It is hard sometimes because it makes things harder and you may struggle to find where the bugs are, or the best places to optimize your code.

The right way that you can handle this is to use the Spark UI. It will give you an inside look at the computation found in each section and will help you spot the problems. Do this regularly to ensure you find the bugs and can get them fixed quickly.

Finding and Solving Any Skewness

Skewness happens when we try to divide the data into partitions, and when we do transformations, the partition size is likely to change. It may create a significant variation in how big the barriers are, which leads to skewness in the data. You can find this skewness by looking through all the stage details through the Spark UI and then look for the difference that happens between the max and the median.

The reason that skewness is terrible is that it could cause some of the later stages to wait for these tasks, and the cores will wait around without doing anything. If you know where the skewness occurs, you can go through and change the partitioning to avoid these issues.

Handling the Iterative Code

This one is more advanced to work with, but important if you want to get your code to work. Because Spark works with lazy evaluation, it is going only to build up some computational graphs. It is a problematic issue when you try to use the iterative process because DAG will spend a lot of time reopening the previous iteration, causing the whole program to get huge.

In some cases, this gets so big that the driver won’t be able to keep the information in the memory. And since the application is often stuck, it is hard to locate this. The Spark UI, since it can’t find the problem, will act like no job is running until the problem gets so bad the driver crashes.

It is an inherent issue with Spark for data science, and you may need to add some coding to take care of it. Df.checkpoint() or df.localCheckpoint() added in every 5 to 6 iterations will be an excellent way to stop this problem and make the program work. This code can break up the lineage and the DAG that shows up and will save the results from a brand new checkpoint for you.

There are several Apache Spark best practices that you can work with, and data science and Spark often go hand in hand when it is time to do your Big Data. Learning how to handle Apache Spark, and why it is such a valuable tool can make a difference in how much you can get done with your work as well.

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