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+---
+layout: slides
+title: Techmeetup
+description: Techmeetup todo
+transition: slide
+permalink: /slides/techmeetup/
+---
+
+
+Application Observability: A Developer’s Perspective
+
+Odin (Ondřej Popelka)
+
+{% comment %}
+Application Observability from the developers' point of view
+{% endcomment %}
+
+
+
+### What I Do
+- Senior Backend Engineer at Keboola
+- Architecture, Service Design, API Design, Resources setup via Terraform, CI pipelines, Coding, Monitoring, Operations, 24/7 Support, Vacuuming, Washing the dishes, ...
+
+
+
+
+## What We do
+
+- Data Operating System, Data Stack, Data processing platform.
+- If you have a (big)data problem, we're likely to have it solved.
+- If you have 2+ information systems in your company that do not talk to each other, we make them talk.
+
+
+
+
+{% comment %}
+ If you have this kind of problem, you know immediately that we can solve it for you. If you don't have
+ this kind of problem, it is very difficult to explain to you what we do.
+{% endcomment %}
+
+
+
+## What does DevOps do?
+
+- SRE gives us the Kubernetes cluster.
+- SRE gives us the networking (private clusters).
+- SRE gives us the monitoring tools.
+- SRE watches us that we do not do anything *obviously stupid*.
+- The UI consumes the API blueprint.
+
+---
+
+**For everything else, there is Devops**
+
+
+
+
+## Random Numbers
+
+- 20 domain services (PHP8, NodeJs, GO + lots of Python & PHP7 + bits of Java, PHP5, R)
+- 1 monolith service (~7 more domains)
+- 1000+ integrations
+- 120+ kubernetes nodes, 9 production stacks, 3 clouds (AWS, Azure, GCP)
+- 280+ requests per second, 24+ million / day
+- 260.000+ asynchronous jobs a day -- ranging from 1 second to 24 hours
+- 1.500.000+ LoC code, 13 developers, 4 SRE
+
+
+
+## Environment
+
+- High heterogeneity,
+- High load variability,
+- High request length variability,
+- Uneven distribution of requests,
+
+---
+
+### Must have
+- High automation, High reliability, High observability
+
+
+
+
+## Easy part
+
+### Latency is the king
+- Latency is what the user feels.
+- Measure XXth percentile (p90, p75, p50).
+- Big difference means that the service is unstable.
+
+
+
+
+{% comment %}
+that the data is still heavily aggregated - the obvious way how to aggregate is to take the average,
+which doesn't work very well, the percentile is better,
+p90 means 90% of requests are faster than this, 10% is slower.
+{% endcomment %}
+
+
+## Still Easy part
+- Graph of obviously bad latency
+
+
+
+
+{% comment %}
+APDEX - measure user satisfaction for a metric for which the target performance has been set
+{% endcomment %}
+
+
+
+## Still Easy part ?
+- Graph of obviously bad latency:
+
+
+- APDEX Monitoring -- Application Performance Index 
+
+
+
+
+## Error rate is the Queen
+
+- The very first metric is Error rate
+- First to look at when something goes wrong
+- First to monitor with APDEX
+
+
+
+
+
+
+## "Weird" API endpoints
+
+- If the request fails, it's actually a valid situation.
+- Error rate can be very high, but **never 100%**.
+- Always monitor individual endpoints **not services**!
+- "Negative" metric -- there must be at least some requests succeeding.
+- I do appreciate tips on how to monitor these.
+
+
+
+## Diagnosing
+
+
+
+- Latency breakdown;
+- Breakdown of time spent in "3rd party" services:
+
+
+
+
+
+
+## FlameGraph is God
+
+- It is absolutely crucial that they are cross-service.
+- One request:
+
+
+
+
+
+
+## FlameGraph cont.
+
+- Break down of time spent by the business logic:
+
+
+
+
+
+
+## FlameGraph cont.
+
+- Includes time in DB by 3rd party services:
+
+
+
+
+
+
+## What are good metrics ?
+
+
+- Incident proven:
+ - 250+ incidents per month,
+ - Fail, fail, fail, succeed...
+
+- After an incident:
+ - Find what metric/alarm should've triggered;
+ - Find metrics that shouldn't have triggered;
+
+---
+Metrics give suspicion
+× Flamegraphs and traces **give insight**
+
+
+{% comment %}
+metric != alarm != escalation policy
+{% endcomment %}
+
+
+
+## How to get a good metric?
+
+- Must be representative of the end-user experience.
+- At the same time it can be totally Meaningless™.
+- ex. "Iteration time":
+ - When divided by the number of jobs it represents the upper bound of the time between a job is received on internal queue and forwarded to the worker to be switched to the processing state and picked up by the processing engine.
+ - It should be between 0.1 and 5
+ - Why not 7 ?
+
+- Beware of changes in code that affect the metric!
+
+
+
+
+## When watch the metrics?
+
+- When incidents are triggered;
+- Ideally every second morning;
+- After deploy and During database migrations;
+
+
+
+
+
+## What are the best dashboards?
+
+Eventually all end up like this:
+
+
+
+**The best ones are those that do not eat your battery when you're on 24/7**
+
+
+
+
+
+
+## Who's watching the costs?
+
+---
+
+### Budget alerts
+- Everything else is wrong.
+- Applies to personal pet projects too.
+- Budget alerts also apply to the **cost of the monitoring**.
+
+
+
+
+
+## Hard Part
+
+- Asynchronous jobs
+ - Containers that run from seconds to up to days
+
+- Endless loop
+ - Non-interactive daemons that run for days to months
+ - Queue workers, stream processors, ...
+
+---
+Some other time...
+
+
+
+
+
+## Thanks
+
+Questions & Comments ?
+
+[linkedin.com/in/odinuv](https://www.linkedin.com/in/odinuv)
+
+---
+
+
+
+
+[keboola.com/about/jobs](https://www.keboola.com/about/jobs)
+
+
+
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+
+- APDEX Monitoring -- Application Performance Index 
+
+
+- Latency breakdown;
+- Breakdown of time spent in "3rd party" services:
+
+
+
+- Incident proven:
+ - 250+ incidents per month,
+ - Fail, fail, fail, succeed...
+
+- After an incident:
+ - Find what metric/alarm should've triggered;
+ - Find metrics that shouldn't have triggered;
+
+---
+Metrics give suspicion
+× Flamegraphs and traces **give insight**
+
+
+
+
+[keboola.com/about/jobs](https://www.keboola.com/about/jobs)
+
+