32 GB of parquet cannot fit in 32GB of RAM
They used a c5.4xlarge which has a 10Gbps, which at constant 100% saturation would take in the ballpark of 9 minutes to pull all of that data from S3, so that is your best case scenario for pulling the data (without even considering writing it back!)
Minute differences in how these query engines schedule IO would have drastic effects in the benchmark outcomes, and I doubt the query engine itself was constantly fed during this workload, especially when evaluating DuckDB and Polars.
The irony of workloads like this is that it might be cheaper to pay for a gigantic instance to run the query and finish it quicker, than to pay for a cheaper instance taking several times longer.
esafak•49m ago
It seems like these single-node libraries can process a terabyte on a typical machine, and you'd have have over 10TB before moving to Spark.
mynameisash•5m ago
I'm surprised by how often people jump to Spark because "it's (highly) parallelizable!" and "you can throw more nodes at it easy-peasy!" And yet, there are so many cases where you can just do things with better tools.
Like the time a junior engineer asked for help processing 100s of ~5GB files of JSON data which turned out to be doing crazy amounts of string concatenation in Python (don't ask). It was taking something like 18 hours to run, IIRC, and writing a simple console tool to do the heavy lifting and letting Python's multiprocessing tackle it dropped the time to like 35 minutes.
Right cool for the right job, people.