I would be interested in seeing some spark v dask benchmarks too. Haven’t seen any yet though.
In my experience, Dask really shines when you implement custom numpy computations that could only be done in spark UDFs. We saw a decent performance difference there, but for common built-in computations I’d imagine that spark has better performance.
Edit: after some googling I found this paper with benchmarks.
> Results show that despite slight differences between Spark and Dask, both engines perform comparably. However, Dask pipelines risk being limited by Python’s GIL depending on task type and cluster configuration. In all cases, the major limiting factor was data transfer.
In my experience, Dask really shines when you implement custom numpy computations that could only be done in spark UDFs. We saw a decent performance difference there, but for common built-in computations I’d imagine that spark has better performance.
Edit: after some googling I found this paper with benchmarks.
https://arxiv.org/pdf/1907.13030.pdf
> Results show that despite slight differences between Spark and Dask, both engines perform comparably. However, Dask pipelines risk being limited by Python’s GIL depending on task type and cluster configuration. In all cases, the major limiting factor was data transfer.