Looking at serverless on Azure at the moment. Trying to tune Logging and metrics. Most of the cloud providers have a free quota of logging limits and you run a couple.of perf runs most likely we may exceed that. Trying to find the sweet spot of sampling there.
Next is trying to control the scaling of functions so that I dont introduce back pressure on downstream systems
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Hey Hari! Ah yeah — I guess if you've got a dynamic or unpredictable workload you'd want to sample based on invocations, rather than a static percentage, etc... 🤔
Oh, you're throttling your own service to accommodate downstream services? Is it something you could do with a queue? Or the downstream have a specific integration pattern?
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Looking at serverless on Azure at the moment. Trying to tune Logging and metrics. Most of the cloud providers have a free quota of logging limits and you run a couple.of perf runs most likely we may exceed that. Trying to find the sweet spot of sampling there.
Next is trying to control the scaling of functions so that I dont introduce back pressure on downstream systems
Hey Hari! Ah yeah — I guess if you've got a dynamic or unpredictable workload you'd want to sample based on invocations, rather than a static percentage, etc... 🤔
Oh, you're throttling your own service to accommodate downstream services? Is it something you could do with a queue? Or the downstream have a specific integration pattern?