Ljubljana, Slovenia

Redis

API Backpressure
It's days like this where I love my job. I'm implementing back pressure for API calls that communicate to an external service. Depending on how overloaded the external service is, my API interface adapts to use caching more extensively, or to use a service friendly request retry strategy, minimizing impact on infrastructure and possibly even resolving problems when they occur. This is done by keeping track of a lot of data - timeouts, error responses, request duration, ratios between failed and successful requests,.
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Optimization strategy
Sometimes it's not about just optimizing CPU time away. Looking at the details I could optimize away a badly written SQL query along with some more trivial big-O problem in regard with sorting of video clips which I got out of serialized data. All from a few carefully placed calls. Reducing network bandwidth is just as important as CPU time.
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In process performance statistics with Redis
Every once in a while you need to do a sanity check of your code, how it performs and what you can do to improve it. This is most apparent with a code-base that is developed and refactored over a period of several years. There can be several problems that negatively impact performance due to dependency issues or legacy code that should have been removed but was forgotten during a refactoring sprint.
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