I have recently watched Larry Hastings talk about Removing Python's GIL: The Gilectomy that has been recorded at PyCon 2016. Larry very graphically outlines the problems from not having proper concurrency features. He also tells a nice story on how the Python community is struggling with this situation for the last 24 years.

The TestTalks podcast is a great resource for anybody who is looking for inspiration in the field of QA, test automation and continuous integration. I recently have been given the honor of being interviewed by Joe Colantonio.

A few years ago I personally attended a talk delivered by Tony Hoare. Amongst other things I very much enjoyed his remarks on performance. I hope I will never forget and never fail to act on his famous quote "premature optimization is the root of all evil". I have seen the horrible results of hand optimizing Go code by default and I want to avoid that my codebase looks anything like that. Today I like to get started with some performance checks of the Go codebase I have been working on. Of cause I start with some profiling...

With better technologies, infrastructure and tools the database gets more and more attention. This is especially relevant with todays NoSQL movement in mind. Within this context scalability is a central subject of discussion and consequently performance is getting more important, too.

If I am about to implement something it usually turns out to be a good idea to first start with some prototypes & benchmarks. This post summarizes the outcome of such an experiment to read data from compressed tar archives effectively. I did some similar benchmarks about 30 month ago so I repeated the measurements to see if there have been improvements and added results for my new Go reader.