Tip | Takeaways for performance troubleshooting#

Sometimes slow performance is simply a reality.

Let’s say that you’ve reviewed your code for improvements and checked to see if your visual recipes are using the optimal dataset types and execution engines. Consider how much data you are processing. Is it a very wide dataset? Does it have a huge number of rows? If so, it might be difficult to reduce runtime further. Think about ways you can reduce the amount of data used in your recipe.

If you are processing a large number of rows, you can investigate partitioning your data, which builds in a structure to allow you to process specific slices of your data instead of your entire dataset at once. This can be a good option to help improve performance if this will work for your use case.

Think creatively about if you can alter your dataset, change the order in which you process your data, or sync your data to another dataset type that can improve performance.