Posts

gRPC makes the specification and implementation of networked APIs a snap. But what is the simplest way to document a gRPC API? There seem to be some hosted providers by Google, e.g. SmartDocs, but I have yet to find a gRPC-specific tool. For REST API frameworks, documentation is commonly generated along with live examples using OpenAPI (formerly swagger). By using grpc-gateway it appears to be pretty straight forward to generate a REST/gRPC API combo from protocol buffers and then hook into the OpenAPI specification. ...

I went on a brief adventure looking into creating a lightweight certificate authority (CA) in Go to issue certificates for mTLS connections between peers in a network. The CA was a simple command line program and the idea was that the certificate would initialize its own self-generated certs whose public key would be included in the code base of the peer-to-peer servers, then it could generate TLS x.509 key pairs signed by the CA. Of course you could do this with openssl, but I wanted to keep a self-coded Go version around for posterity. ...

Developer computers often get a lot of cruft built up in non-standard places because of compiled binaries, assets, packages, and other tools that we install over time then forget about as we move onto other projects. In general, I like to reinstall my OS and wipe my disk every year or so to prevent crud from accumulating. As an interemediate step, this post compiles several maintenance caommands that I run fairly routinely. ...

This post is a response to Go: Multiple Errors Management. I’ve dealt with a multiple error contexts in a few places in my Go code but never created a subpackage for it in github.com/bbengfort/x and so I thought this post was a good motivation to explore it in slightly more detail. I’d also like to make error contexts for routine cancellation a part of my standard programming practice, so this post also investigates multiple error handling in a single routine or multiple routines like the original post. ...

For scientific reproducibility, it has become common for me to output experimental results as zip files that contain both configurations and inputs as well as one or more output results files. This is similar to .epub or .docx formats which are just specialized zip files - and allows me to easily rerun experiments for comparison purposes. Recently I tried to dump some json data into a zip file using Python 3.8 and was surprised when the code errored as it seemed pretty standard. This is the story of the crazy loophole that I had to go into as a result. ...

When benchmarking Python programs, it is very common for me to use memory_profiler from the command line - e.g. mprof run python myscript.py. This creates a .dat file in the current working directory which you can view with mprof show. More often than not, though I want to compare two different runs for their memory profiles or do things like annotate the graphs with different timing benchmarks. This requires generating my own figures, which requires loading the memory profiler data myself. ...

I’m getting started on some projects that will make use of extensive Python performance profiling, unfortunately Python doesn’t focus on performance and so doesn’t have benchmark tools like I might find in Go. I’ve noticed that the two most important usages I’m looking at when profiling are speed and memory usage. For the latter, I simply use memory_profiler from the command line - which is pretty straight forward. However for speed usage, I did find a snippet that I thought would be useful to include and update depending on how my usage changes. ...