Serverless Days Amsterdam - Choosing a Serverless Monitoring Platform
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Considerations for choosing a Serverless Monitoring Platform.
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Serverless Days Amsterdam - Choosing a Serverless Monitoring Platform
- 1. Choosing a Serverless Monitoring Platform Presented by Josh Carlisle Microsoft MVP | AppDynamics @ Cisco
- 2. Choices.. Choices Serverless Enterprise Application Insights AWS X-Ray Cloud Native Google StackDriver Open Source Jaeger (Not Exhaustive List)
- 3. Look in the logs Any Exceptions? Was there a new release
- 7. Why is this so hard? No Status-Quo Ephemeral Compute Highly Distributed Platform Limitations
- 8. So how do we choose?
- 9. 3 Pillars of Observability Logs Metrics Traces
- 10. Logs – The Double-Edged Sword Often Answer “Why” Deep Diagnostics Haystack Developer Effort
- 12. Logging Recommendation Consider Profilers (Stack Traces) Avoid Logging Code 1 Not First Source
- 13. Provide Insights Understanding Metrics Correlating Metrics Metrics – Insights & Complexity Real-Time
- 14. Metrics Recommendation Metric Baslines Machine Learning (AI Ops)
- 15. Tracing (Distributed) – Giving the Complete Picture Issue Source X End to End Visibility Gaps in Visibility ?
- 16. End To End Visibility DownstreamUpstream
- 17. End To End Visibility – In Practice
- 20. Why do we want to avoid coding? { } Technical Debt (The Sneaky Kind!) Time to Visibility Decreased Visibility
- 21. The Magic of Byte Code Instrumentation Source Code Intermediate Code (MSIL, ByteCode) Compile Native Code RuntimeMonitoring Agent Injects Instrumentation
- 22. No Free Lunch Limited to Dynamic Languages and languages with intermediate code Can Add Overhead (if not done smartly) Can Cause Crashes (rare for mature products)
Editor's Notes
- You can have success or failure with them all We all agree on the important of monitoring being a first class citizen and one of the major factors that makes\breaks your application Give it more then passing thoughts Serverless monitoring is hard, the rules are all changed and because of that visibility is impacted https://devops.com/metrics-logs-and-traces-the-golden-triangle-of-observability-in-monitoring/ https://www.oreilly.com/library/view/distributed-systems-observability/9781492033431/ch04.html
- Creating a new haystack to find your need on Often times logging itself requires time from developers solving business problems. Serverless leads to agility but you lose agility coding in observability into your solutions. Temping when we’re missing lots of traditional forms of monitoring with Serverless most of the Serverless platforms became heavily reliant on logging Traditional Agents don’t work so many times they require SDK Logs requires context understanding which makes logs not really appropriate for operations requiring more development effort and taking away from developer time
- Often the first place developers go is logs
- Avoid coding logging. It adds to maintenance and complexity of your applications, potentially adds direct dependencies, and reduces productivity gains of Serverless When available consider using profilers. Most major languages have Code is debt
- we’ve all been there. Looking at metrics and wondering is that a bad thing? Or maybe it’s only bad when you see X = Y but not X+Y+Z And that goes into the challenges of Correlating metrics. We have hundreds and sometimes thousands of metrics streaming in. How we do know which metric is the cause and which is the Metrics are often real time or near real time to allow you to get ahead of an issue potentially Much easier to operationalize metrics Potentially few metrics then we may be used because we are reliant on Cloud Watch and Azure Monitor for many metrics
- - Metrics are going to be the workhorse of your monitoring solution. You’re going to be driving alerts from them, potentially proactive action or remediation, building dashboards, and deciding on diagnostic paths
- Complexity in how it stiches the traces together. Either has heavy dependencies on logs or runtime needs to pass “tokens” around which requires knowledge of how applications communicate on various platforms Tracing only gives you part of the picture and not visibility into underlying health of platform or infrastructure so it’s not as useful by itself (Trace+Metrics)
- Consider the value in upstream and downstream Show Spotlight in a dark alley with bad guy right out of view Downstream is big with Serverless End User experience is king What happens when you’re part of a larger ecosystem. Does your monitoring platform support distributed tracing. What’s the experience like? Is it visual or is it all in analytics? Do you have to write code? Does it actually support tracing through your platform and frameworks (especially older ones) Your serverless solution is often times a part of a larger ecosystem (unless you have monolythic server solution)
- Support the platforms that are important to you (help also in platform selection at times) Metrics of Distributed Trace