What are the best practices for Spark DataFrame caching?

Question

Hi,

When caching a DataFrame, I always use "df.cache().count()".

However, in this reference, it is suggested to save the cached DataFrame into a new variable:

• When you cache a DataFrame create a new variable for it cachedDF = df.cache(). This will allow you to bypass the problems that we were solving in our example, that sometimes it is not clear what is the analyzed plan and what was actually cached. Here whenever you call cachedDF.select(…) it will leverage the cached data.

I didn't understand well the logic behind it. And I couldn't find similar suggestions in other articles. 

My question is what the best practice is when using caching? 

Answer 1

@Mohammad Saber - Thanks for the question and using MS Q&A platform.

When it comes to caching a DataFrame in Spark, there are a few best practices that you can follow to ensure optimal performance.

Firstly, it is recommended to cache only the DataFrames that are used frequently in your application. Caching all DataFrames can lead to excessive memory usage and slow down your application.

Secondly, it is recommended to persist the DataFrame in memory and disk by using the persist() method. This method allows you to specify the storage level, which can be MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, MEMORY_AND_DISK_SER, DISK_ONLY, etc. Depending on the size of your DataFrame and the available memory, you can choose the appropriate storage level.

Regarding your question about creating a new variable for the cached DataFrame, it is a good practice to do so. When you cache a DataFrame, it is stored in memory and can be accessed by multiple operations. However, if you perform any transformations on the DataFrame after caching, Spark will need to recompute the entire DataFrame. By creating a new variable for the cached DataFrame, you can ensure that the cached data is not lost due to any transformations.

So, the best practice is to cache only the frequently used DataFrames, persist them in memory and disk using the appropriate storage level, and create a new variable for the cached DataFrame to avoid recomputing the entire DataFrame due to any transformations.

For more details, refer to Spark DataFrame Cache and Persist Explained.

Hope this helps. Do let us know if you any further queries.

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