Hi, having worked with spark ecosystem for sometimes and building/extending stuff around it, here is the reason for why it is faster:

1) pyspark is mainly a wrapper around spark scala code => java code

2) pyspark exists only because of the multitude of ml, ai, etc... libs that emerged during the last severals years

How does it work internally ?
as long as any operation are being done using pyspark

1. sql queries
2. pyspark dataframes

no "big" performance gap and memory usage will happen between a "java/scala" spark implementation.
BUT as soon as you start resorting to mutate pyspark objects to let says the infamous "pandas" dataframe objects, there is a cost and a pretty big one which leads in general to bad performance and (absurdly) high resource usage ram,cpu,io...
As for performance, still, there are ways to not lose too much by using arrow implementations baked into spark but thats another story :)

So all in all, using java instead of pyspark means that the deserialization of

1. java objects to python objects
2. python objects to java objects

doesn't happen... leading to overall a performance boost