This document discusses using metrics to monitor Quarkus applications. It recommends metrics like throughput, memory usage, queue time, average response time, and error rates. It explains how Quarkus supports Micrometer for instrumenting applications with metrics and integrating with monitoring systems. The document includes a demo of adding metrics to code. It provides tips for using annotations and tags to gain more insights from metrics. Source code examples are linked.

1. dn.dev/extra-quarkus-metrics
Extra Micrometer practices
with Quarkus
Ana-Maria Mihalceanu
Developer Advocate
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2. Hello! I am Ana
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Java Champion, Certified Architect
Developer Advocate @ Red Hat
Passionate about solving complex scenarios involving Java-based
frameworks and multiple cloud providers.
ammbra1508

3. 3

4. Using metrics that matter(1)
Metrics that matter
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Throughput
Being able to observe throughput variation can
help engineers gauge how many containers and
their respective resources are needed to
support the traffic.
Memory
Knowing how and when the language (used to
develop applications) allocates and cleans up
memory is essential to scaling.
Queue time
Observing time of requests flowing from load
balancers to web apps and background job
worker queues.

5. Using metrics that matter(2)
Metrics that matter
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Average response time
This metric is influenced by how the
transactions are handled across different parts
of your system, and has a high impact on
end-user satisfaction.
Error rates
A few applications error to track: percentage of
HTTP errors, logged exceptions , thrown
exceptions.

6. Why Quarkus?
Metrics that matter
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https://developers.redhat.com/blog/2021/02/11/enhancing-the-development-loop-with-quarkus-remote-development
Developer Experience
Live Reload
Continuous Testing
DevServices
Remote Development
Unifies imperative and reactive
Supports both reactive and
imperative programming models.
Micrometer extensions
Instrument your application using
Micrometer and integrate with the
registry of your choice (Datadog,
Prometheus, JMX, Stackdriver,
Azure Monitor etc).
OpenTelemetry extensions
Use OpenTelemetry extension to
trace services or the various
exporters correlation of traces with
metrics.

7. Seeing is
believing
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DEMO

8. Start to code
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code.quarkus.io
quarkus ext add quarkus-micrometer

9. TAKEAWAYS
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Global tags
Create your own global tags to help further you inspecting performance statistics per
region.
Use annotations
Check the number of invocations with @Counted annotation on top of the methods.
Add @Timed annotation on top of the expected methods to run long-running
operations and deserve a closer inspection.
Follow distribution of events
Obtain the summary of events via DistributionSummary.
Dynamic Tags
Get more insights by dynamically adding more tags.

10. The code
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https://github.com/ammbra/micrometering-with-quarkus

11. Additional
resources
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More to learn from

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https://quarkus.io
@quarkusio
https://quarkusio.zulipchat.com

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