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Reading Up on Observability and Monitoring – Adron Hall
“key in understanding the difference in monitoring — the combing of data to determine the state or well-being of a system — versus observability — the view into and understanding of the state of events within a system.”
monitoring  itmanagement  observability 
15 days ago by cote
Monitoring in the time of Cloud Native – Cindy Sridharan – Medium
Observability being about getting required information so that we can reactively. Section on tracing is good.

Logs - Large storage, potentially affect performance, not necessarily reliable storage.
Metrics - Less storage, easily processible to judge system health, alert. Usually system not request level.
Trace - As request flows through add meta data. Usually sampled.

"Application developers now have one job. We’re at a time when it has never been easier for application developers to focus on just making their service more robust and trust that if they do so, then the open source software they are building on top of will pay the concomitant dividends."

"We “monitored” something because we expected something to behave a certain way. What’s worse, we expected something to fail in a very specific manner and wanted to keep tabs on this specific failure. An “explicit, predictable failure” centric approach to monitoring becomes a problem when the number of failure modes both increases and failure itself becomes more implicit."

"Opting in to the model of embracing failure entails designing our services to behave gracefully in the face of failure. In other words, this means turning hard, explicit failure modes into partial, implicit and soft failure modes. Failure modes that could be papered over with graceful degradation mechanisms like retries, timeouts, circuit breaking and rate limiting. Failure modes that can be tolerated owing to relaxed consistency guarantees with mechanisms like eventual consistency or aggressive multi-tiered caching. Failure modes that can be even triggered deliberately with load shedding in the event of increased load that has the potential to take down our service entirely, thereby operating in a degraded state.

But all of this comes at the cost of increased overall complexity and the buyer’s remorse often acutely felt is the loss of ability to easily reason about systems."

"Now I’m not someone who believes that automating everything is a panacea, but the advent of platforms like Kubernetes means that several of the problems that human and failure centric monitoring tools of yore helped “monitor” are already solved. Health-checking, load balancing and taking failed services out of rotation and so forth are features these platforms provide for free. That’s their primary value prop."

" An observable system is one that exposes enough data about itself so that generating information (finding answers to questions yet to be formulated) and easily accessing this information becomes simple."

"I see both traces and metrics as an abstraction built on top of logs that pre-process and encode information along two orthogonal axes, one being request centric, the other being system centric."

"Most importantly, having an understanding of the entire request lifecycle makes it possible to debug requests spanning multiple services to pinpoint the source of increased response time or resource utilization. As such, traces largely help one understand the which and sometimes even the why — like which component of a system is even touched during the lifecycle of a request and is slowing the response?"

"The second problem with tracing instrumentation is that it’s not sufficient for developers to instrument their code. A large number of applications in the wild are built using open source frameworks or libraries which might require additional instrumentation. This becomes all the more challenging at places with polyglot architectures, since every language, framework and wire protocol with widely disparate concurrency patterns and guarantees need to cooperate. Indeed, tracing is most successfully deployed in organizations where there are a core set of languages and frameworks used uniformly across the company."
cloud  Monitoring  Observability  Microservice 
20 days ago by colin.jack
OpenCensus
A single distribution of libraries that automatically collects traces and metrics from your app, displays them locally, and sends them to any analysis tool.
microservices  monitoring  observability  tracing 
4 weeks ago by webframp
OpenCensus
A single distribution of libraries that automatically collects traces and metrics from your app, displays them locally, and sends them to any analysis tool.
monitoring  observability 
4 weeks ago by mpm

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