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Production Profiling: What, Why
and How
Richard Warburton (@richardwarburto)
https://www.opsian.com
Why Performance Matters
Development isn’t Production
Profiling vs Monitoring
Production Profiling
Conclusion
Customer Experience
Amazon: 100ms of latency costs 1% of sales
Google: 500ms seconds in search page generation time drops traffic by 20%
Responsive Applications make more Money
Reduce Costs
Why Performance Matters
Development isn’t Production
Profiling vs Monitoring
Production Profiling
Conclusion
Development isn’t Production
Testing in development can be easier
Tooling often desktop-based
Not representative of production
Unrepresentative Hardware
vs
Unrepresentative Software
Unrepresentative Workloads
vs
The JVM may have very different behaviour in production
Hotspot does adaptive optimisation
Production may optimise differently
Production profiling  what, why and how (JBCN Edition)
Why Performance Matters
Development isn’t Production
Profiling vs Monitoring
Production Profiling
Conclusion
Ambient/Passive/System Metrics
Preconfigured numerical measure about the
system
CPU Time Usage / Page-load Times
Cheap and sometimes effective
Coarse Grained Instrumentation
Measures time within some instrumented section of the code
Time spent inside the controller layer of your web-app or performing SQL queries
More detailed and actionable though expensive
Production Profiling
What methods use up CPU time?
What lines of code allocate the most objects?
Where are your CPU Cache misses coming from?
Automatic, can be cheap but often isn’t
Production profiling  what, why and how (JBCN Edition)
Understand don’t Overwhelm
Too Little
You can’t understand production
problems
Too Much
Needle in a Haystack
You are the problem (overhead)
Unactionable Metrics
Many metrics provide pretty graphs but don’t progress treatment
Normalisation of Deviance
“Some of the tests always fail, so we just ignore them.”
“Some of the alerts get triggered regularly, so we just ignore them.”
Alert false positives have a cost
Surely a better way?
Not just Metrics - Actionable Insights
What about Profiling?
Why Performance Matters
Development isn’t Production
Profiling vs Monitoring
Production Profiling
Conclusion
How to use Production Profilers
1) Extract relevant time period and apps/machines
2) Choose a type of profile: CPU Time/Wallclock Time/Memory
3) View results to tell you what the dominant consumer of a resource is
4) Fix biggest bottleneck
5) Deploy / Iterate
CPU Time vs Wallclock Time
Profiling Hotspots
Profiling Treeviews
Profiling Flamegraphs
Instrumenting Profilers
Add instructions to collect timings (Eg: JVisualVM Profiler)
Inaccurate - modifies the behaviour of the program
High Overhead - > 2x slower
Sampling/Statistical Profilers
WebServerThread.run()
Controller.doSomething() Controller.next()
Repo.readPerson()
new Person()
View.printHtml() ??? ???
Safepoint Bias + Latency Impact
WebServerThread.run()
Controller.doSomething() Controller.next()
Repo.readPerson()
new Person()
View.printHtml() ???
Advanced Statistical Profiling in Java
OS Signals to interrupt threads on resource consumption threshold
JVM’s signal handler-safe AsyncGetCallTrace to walk the stack
Open Source Java Profilers
High Overhead
VisualVM
hprof
Twitter’s CPUProfile
Anything GetAllStackTraces based
Low Overhead
Async Profiler
Honest Profiler
Java Mission Control
Profiling Support in the Linux Kernel
perf and eBPF
perf-map-agent for the JVM
Hardware events (L1/L2/L3 cache misses, branch mispredictions, etc.)
People are put off by practical as
well as technical issues
Barriers to Ad-Hoc Production Profiling
Low overhead unbiased tools are
relatively new
Generally requires access to
production
Process involves manual work - hard
to automate
What if we profiled all the time?
Historical Data
Allows for post-hoc incident analysis
Enables correlation with other data/metrics
Performance regression analysis
Aggregation
Aggregation and
Indexing
Samples
Reports
Putting Samples in Context
Application version
Environment parameters (machine type, CPU, location, etc.)
Desktop profilers don’t do this
Summary
Production Profiling is possible - all the time with low overhead
Production Profiling is desirable - can give deep insights
Problems can be overcome
Why Performance Matters
Development isn’t Production
Profiling vs Monitoring
Prod Profiling Problems
Conclusion
Performance Matters
Development isn’t Production
Metrics can be unactionable
Instrumentation has high overhead
Production Profiling provides insight
We need an attitude shift on profiling
+ monitoring
Always
On
Proactive
not Reactive
Systematic
not Ad Hoc
Please do Production Profiling.
All the time.
Any Questions?
https://www.opsian.com/
The End

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Production profiling what, why and how (JBCN Edition)