limit parallelism of ParquetSource to reduce memory footprint

Hi!

I set up a ParquetSource with a JDBCSink and ran into memory issues.
The parquet files are stored in an s3 bucket and have been written by spark (snappy-compressed ~500 MByte).

Spark writes one file per partition (default=200). This causes eel to use lots of memory since many subscriptions are submitted to the executor, although I set up the stream like this `source.toDataStream().to(sink, parallelism = 1)`

In the code I found that you initialize the executor like this `val executor = Executors.newCachedThreadPool()` which creates an unbounded ThreadPool.

I did some experiments and repartitioned the spark dataframe to 1 and stored it again. Here's the comparison (see screenshots below):
memory usage of the 200 files parquet source: >1.2 Gbyte*
memory usage of the 1 file parquet source: 83 MByte constantly.

(*) I let it run on my local machine with normal DSL internet connection. On the server it ran oom pretty quickly - meaning it used more than 2GByte (my XmX setting for the app).



Can you think of a way to limit the amount of parallelism? Happy to provide a merge request if you point me in the right direction.

Look at the ParquetSource implementation to find where the Executors.newCachedThreadPool() is used. Understand how the source handles multiple files and subscriptions. The goal is to modify the thread pool initialization to allow limiting parallelism, perhaps by adding a configurable parameter. Check existing tests for ParquetSource to see how to validate changes.

Written by the indexing model from the issue text.