TL;DR notes from articles I read today.

Making Python programs blazingly fast

• Find what parts of your code are slowing down the program. A simple & lazy solution is to use a Unix time command. You can also use cProfile for detailed profiling.
• Once bottlenecks are identified, time the slow function without measuring the rest of the code. 
• The most obvious way of making it faster is to use built-in data types.
• Caching or memoization with lru_cache will help improve functions that perform expensive I/O operations or some fairly slow recursive functions.
• You can improve performance, by using seemingly unnecessary assignments like local variables.
• You can also speed up your code just by wrapping the whole code in the main function and calling it once.
• Avoid or limit using dot operators (.) as they trigger dictionary lookup using getattribute, which creates extra overhead in your code.
• Operations on strings like modulus (%s) or .format() can get quite slow when running in a loop. Go for f-string instead which is the most readable, concise and fastest method.

Full post here, 5 mins read

Go’s tooling is an undervalued technology

• As it has no external dependencies, you can build the latest version of Go using just the compiler in seconds and cross-compiling only needs setting a couple of environmental variables (GOOS and GOARCH).
• Go uses decentralized package management, or rather, module management. There is no central module manager or module registration, no external repository to trust and no need for an internal one.
• Since a module only needs to be hosted on a reachable network with valid HTTPS certification, with the network path becoming the name, there is no worry over duplicating popular module names.
• Dependencies are cryptographically locked to versions. So, an upstream source cannot change a published module for those depending on it.
• As each dependency is a single point of failure, Go checks with a module proxy before fetching the dependency. If you prefer, there is a GOINSECURE option for experimentation to avoid HTTPS certification.

Full post here, 6 mins read

Design principles for your HTTP APIs

• Impose consistency so that similar endpoints behave in similar ways, even in edge scenarios, with consistent vocabulary, URL structure, request/response formats and error handling. This will help ensure that users don’t need to read extensive documentation and handling updates becomes easier for developers.
• Achieve performance by avoiding early optimization, waiting until you have the right metric in place to optimize based on hard data - and collect that data from day one, with an APM tool.
• Use metrics to inform evolution, so that you update and add features based on actual user usage of endpoints to avoid or minimize disruptions to existing implementations.
• For complex APIs, avoid a 1:1 mapping between database tables and API resources. Build in usability by simplifying business transactions to require a single API call rather than multiple. If it isn’t possible, be as flexible as you can.
• Adopt simplicity by building on top of universally accepted standards and tools. This will mean less overheads and less room for mistakes.


Full post here, 7 mins read

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