Welcome to the Metrics

Welcome to the Metrics
September 2–3, 2020
springone.io
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#springone@springone

@didierDIB @Alexandre_Roman
Welcome to this session!
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Didier Burkhalter
Solution Engineer, VMware
@didierDIB
Alexandre Roman
Solution Engineer, VMware
@Alexandre_Roman

Agenda
Observability 101
What, why, how?
Failure is the new normal
Brace yourself, failure is coming
Less is more
Build better metrics
3
Questions? Use SpringOne Slack channel #session-welcome-to-the-metrics

Observability 101
What, why, how ?

Observability, one (academic) deﬁnition, upon others
A measurable characteristic of a system that enables it to be “seen”
(i.e., observed) such that its internal state and behavior can be inferred from
external interrogation.
The ability to query a system to discover insights and understanding.
5
Observability lays the critical foundation:
➔ for Reliability Engineering practices,
➔ providing a human-centered, data-informed approach
➔ to making informed decisions and strategic investments as learning
organizations.
“How well do I understand the state of this system right now?”

Observability goals
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Understand, meet &
adapt to
business intents &
needs (graal)
(make) Eﬀective &
happier System
Reliability Engineers &
all operation folks
Provide Stable,
performant, adaptive
systems, cost eﬀective
systems
& operating models
Customer
centricity

Observability - the WHAT
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Relationship based on
“System Value domains” &
three pillars
Metrics
Observables
produce information
Traces
Observers
consume “data”
Logs
Observability is based on shared “contracts” / “instruments” / “semantics” (or supposed to)
The big (mis) Stakes :
- Semantics misalignment between producers and consumers
- “Asymmetric“ / “asynchronous” relationships
This leads to wrong data interpretation and project latency

Observability - the HOW
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Metrics
Observable’s
Systems produce
information
Traces
Observer’s
consume “data”
Logs
Observability tools
Collect
Process, interpret &
transform, filter
Display
Alert
Analytics, get Trends, Machine Learning
Store
Fix
Scale
Observer’s
consume “data”
Observers
consume “data”
Observable’s
Systems produce
information
Observables
Systems produce
information
Tension on Observability
“cardinality”
“Act” &
“ReAct”

Observability - HOW & WHO
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Observables
Systems produce
information
Observers
consume “data”
and act
Analytics >> put better & better perspectives on metrics, in
dynamic situations
SLI’s
Svc level
Indicators
Bridge & bring shared sense to “metrics + dashboard”
Based on SRE practices
Collaborate, modelize, experiment, iterate, adapt, analyse
Human designed,
no knowledge on
consumers
To meet business intents, in efficient, secure and cost effective ways
on Service Level’s contracts
SLO’s
Svc level
Objectives
Human designed,
need help from product team
(ideally) Shared contracts

You build it, you run it
You measure it !

How do you drive your business ?
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OR+
Scrape metrics from your apps and your platforms
★ Make sure the meaning of each metric is well understood by everyone
Use meaningful metrics
★ Ask yourself: what are the key metrics you need to monitor your business?

Observability instantiation, for every body, at any time
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Beneﬁts for Dev’s and Operation ( wealth & multiplicity )
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CI/CD Run
Dev / DevOps practices
Design / Dev / Build Manage
SRE practicesBridges with shared Semantic /
communication / collaboration
Machine Learning, Analytics, Trends
Observe

@didierDIB @Alexandre_Roman 13
See Everything, Move Fast
“Software Developer”
Platform Services | Applications
Developer Abstractions
IaaS | CaaS | PaaS
DATACENTER
Virtualized Infrastructure
Physical Infrastructure
Platform /
DevOps
Infrastructure
Ops
AppDev
“The Business”
Customer Experience
External Influences / Data Sources
Speed / Effectiveness

Demo
Monitor your app with a 360° dashboard

DATACENTER
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Developer Abstractions
IaaS | CaaS | PaaS
“Software Developer”
Platform Services | Applications
Virtualized Infrastructure
Physical Infrastructure
“The Business”
Customer Purchases
Collect
Visualize
Normalize
Correlate
Find Platform /
DevOps
Infrastructure
Ops
AppDev
See Everything, Move Fast

Failure is the new normal
Brace yourself, failure is coming

We live in a distributed world
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My app
Business
domain B
Business
domain A
Cross-cutting
domain
We used to have a monolithic app:
a single process where all business code is
running as a single unit
but
We all know that
scaling monolithic apps is hard
Adding new code is simple when using this
architecture model

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My app
Business
domain B
Business
domain A
Cross-cutting
domain

Business app BBusiness app B
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My app
Business app A
Business app B
Gateway
(cross-cutting code)

Scalability is not a problem anymore
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App instances can be
killed at any time
What happens when
you add a new node?
How to scrap metrics from
all these instances?
Synchronize conﬁgurationacross all instances
Now you have new problems to tackle...

Oops, my app is broken!
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Simply Restart Everything
S R E
twitter.com/mrb_bk/status/1276548776015138816

Decentralized metrics and practices
Each microservice owns its metrics
★ A microservice is built by a team who knows best how to observe the code
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No more centralized, static metrics conﬁguration
★ You don’t know how many instances you’ll run: discover app instances at runtime
Trace network calls across app instances... and clouds
★ Leveraging open standard such as OpenTracing

Use Prometheus with Kubernetes
apiVersion: apps/v1
kind: Deployment
metadata:
name: visits
spec:
selector:
matchLabels:
app: petclinic
tier: visits
template:
metadata:
labels:
app: petclinic
tier: visits
annotations:
prometheus.io/scrape: "true"
prometheus.io/path: "/actuator/prometheus"
prometheus.io/port: "8080"
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Add OpenTracing support to your Spring Boot app

<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>

<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>
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Demo
Petclinic with microservices deployed to Kubernetes

Less is more
Build better metrics

Expose metrics from a Spring Boot app is so easy
Spring Boot relies on Micrometer for metrics
★ Stick to this API for metrics
★ Micrometer is an abstraction layer for metrics: Prometheus, Wavefront, etc.
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Many metrics exposed by default: memory, requests, etc.
Create your own metrics
to bring “business” perspective to your app
Too ?
★ Prefer quality over quantity

Demo
Adding custom metrics to Petclinic

Create custom dashboards
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Create custom metrics
final List<PetType> petTypes = repo.findPetTypes();
for (final PetType petType : petTypes) {
final String petTypeName = repo.findPetTypeById(petType.getId()).get().getName();
Gauge.builder("petclinic.pets", () -> {
return repo.countPetsByType(petType);
}).baseUnit("animals")
.description("Number of pets")
.tag("type", petTypeName)
.register(reg);
}
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Rely on MicrometerAPI only

Create meaningful metrics
Gauge.builder("petclinic.pets", () -> {
return repo.countPetsByType(petType);
}).baseUnit("animals")
.description("Number of pets")
.tag("type", petTypeName)
.register(reg);
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Naming
Meaning
+
=
󾠮
Implicit contract
between code and
monitoring tools
󾠯
󾠮
󾠯

Better observability leads to better apps to better business
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More PROACTIVE & efficient
Communicate - Collaborate - Act
Agility & Trust thru software development lifecycle
Learn and Anticipate

Try Wavefront for free!
wavefront.com/sign-up
Get demo source code
github.com/alexandreroman/spring-petclinic-k8s
Don’t miss this session
Make Your Kubernetes Clusters Production-Ready with Tanzu - Tiﬀany Jernigan
#springone@springone
Stay Connected.
Questions? Use SpringOne Slack channel #session-welcome-to-the-metrics

Welcome to the Metrics

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