3 ms·
As a fellow HPC user, I tried a couple of years ago to do a tricky data format conversion using these newfangled tools. I was essentially just taking a huge (mu
by semi-extrinsic 2y ago
As a fellow HPC user, I tried a couple of years ago to do a tricky data format conversion using these newfangled tools. I was essentially just taking a huge (multi-terabyte) 3D dataset, transposing it and changing the endianness.
The solutions I was able to put together using Dask and Spark and such were all insanely slow, they just got killed by Slurm without getting anywhere. In the end I went back to good ole' shell scripting with xxd to handle most of the heavy lifting. Finished in under an hour.
The appeal of these newfangled tools is that you can work with data sizes that are infeasible to people who only know Excel, yet you don't need to understand a single thing about how your data is actually stored.
If you can be bothered to read the file format specification, open up some files in a hex editor to understand the layout, and write low-level code to parse the data - then you can achieve several orders of magnitude higher performance.
- fifilura 2y agoI think command line tools is going to be fine if all you do is process one row at a time. Or if your data has a known order. But if you want to do some kind of grouping or for example pivoting rows to columns, I think you will still benefit from a distributed tool like Spark or Trino. That can do the map/reduce job for you in a distributed way.