Spark master memory requirements related to data size

Question

Are Spark master memory requirements related to the size of the processed data?

The Spark driver and Spark workers/executors deal with processed data directly (and execute application code), so their memory needs can be linked to the size of the processed data. But is the Spark master in any way affected by the data size? It seems to me that it isn't, because it just manages the Spark workers and doesn't work with the data itself directly.

Answer 1

Spark main data entities like DataFrames or DataSets are based on RDD, or Resilient Distributed Datasets. They are distributed meaning the processing generally takes place in the executors.

Some RDD actions will end with data on the driver process though. Most notably collect and other actions that use it (like show, take or toPandas if you are using python). collect, as the name implies, will collect some or all of the rows of the distributed datasets and materialize them in the driver process. At this point, yes, you will need to take into account the memory footprint of your data.

This is why you will generally want to reduce as much as possible the data you collect. You can groupBy, filter, and many other transformations so that if you need to process the data in the driver, it's the most refined possible.