-
The first two weeks with the Apple M1
·Apple recently published new computers that contain their new M1 processors. I was quite excited about them because of the promises made by various benchmarks regarding performance and energy consumption but also because it is also a new platform. Most things won’t work there and some assumption on how we work today have to change if you want to use...
-
Fast JDBC access in Python using pyarrow.jvm (2020 edition)
·About a year ago, I have benchmarked access databases through JDBC in Python. Recently, the maintainer of
jpypegave me a heads-up that they significantly improved performance on their side. While this is actually the library I’m comparing mypyarrow.jvm-based approach to, I have a high appreciation for any performance tuning that is... -
Calculating levenshtein distances with fletcher
·Levenshtein distance is a typical measure to compare two different strings. It gives you the minimal number of add, remove and replace operations to transition from one string to another.
-
Trimming down pyarrow’s conda footprint (Part 2 of X)
·We have again reduced the footprint of creating a conda environment with
pyarrow. This time we have done some detective work on the package contents and removed contents fromthrift-cppandpyarrowthat are definitely not needed at runtime. -
Removing Python as a dependency of R
·Surprisingly Python was a runtime dependency of R on conda-forge. As R doesn’t need Python to run, this was a bit weird. We got rid of this by splitting up the GLib package.