Note that provided environments will not be available outside the workshop - you can follow instructions from https://github.com/PierreVincent/prometheus-workshop to run the environment yourself. In the world of cloud native and distributed applications, Prometheus has quickly risen to be one of the leading open-source monitoring tools. In this workshop, you will get to learn as much as possible to get you started with Prometheus for monitoring a service-oriented architecture. We will cover: - The core concepts of Prometheus - Instrumenting your code to expose metrics - Querying Prometheus to gain insights on how your applications behave - Defining rules to trigger alerts based on metrics and thresholds - Building Grafana dashboards combining multiple metrics

1. Cloud Native Monitoring
with
Prometheus & Grafana
April 26th, 2019 – Dublin
@PierreVincent pvincent.io

2. @PierreVincent
Reaching production is
only the beginning

3. Pierre Vincent
Infrastructure & Reliability Manager
@PierreVincent
pvincent.io

4. @PierreVincent
Workshop Overview
Slides - Metrics & Prometheus basics
Part 1 - Intro to Prometheus UI and Queries
Part 2 - Building Grafana Dashboards
Part 3 - Creating Prometheus Alerts
Part 4 - Instrumenting Code (Golang)

5. @PierreVincent
System
metrics
Application
metrics
Business
metrics
CPU usage Error rates Customer conversions
Metrics

6. @PierreVincent
“Cloud Native” changes the game
Monolithic architectures
Long-running instances
Long-running servers
Loosely-coupled architectures
Short-lived instances
Short/Medium-lived servers
Microservices
Auto-scaling
deployments
Multiple
deploys/day
Cloud VMsAuto-scaling
clusters
SOA

7. @PierreVincent
Servers / VMs
Appliances/Infra
Services
/metrics
/metrics
/metrics
Prometheus
Overview

8. @PierreVincent
Scraping for samples
User
Service
/metrics
# HELP http_requests_total Total number of http requests
by response status code
# TYPE http_requests_total counter
http_requests_total{endpoint="/login",status="200"} 1584
http_requests_total{endpoint="/login",status="500"} 9
...
metric
http_requests_total
labels
endpoint=/login
status=200
timestamp
1519205931
value
1584
tsdb
Each value
results in a
sample
Every scrape
interval
Persist

9. @PierreVincent
Our example
http-simulator
/metrics
http_requests_total
http_request_duration_milliseconds
+ standard go metrics
Option 1: Deploy on your own cluster
See instructions in kubernetes/install
Option 2: Use pre-deployed setup
prometheus.prom-workshop.pvincent.io
grafana.prom-workshop.pvincent.io
OR

10. http://grafana.prom-workshop.pvincent.io
PierreVincent/prometheus-workshop
http://prometheus.prom-workshop.pvincent.io

11. @PierreVincent
Exercises 1 - Counters & Rates
● What's the overall request rate (with a 1 minute rolling-window) for the http-
simulator service?
● How many requests per minute are errors?
● What's the error rate (in %) of requests to the /users endpoint?
sum(rate(http_requests_total{app="http-simulator"}[1m]))
60*sum(rate(http_requests_total{app="http-simulator",
status="500"}[1m]))
100 * sum(rate(http_requests_total{app="http-simulator",
endpoint="/users", status="500"}[1m])) /
sum(rate(http_requests_total{app="http-simulator",
endpoint="/users"}[1m]))

12. @PierreVincent
Exercises 2 - Latency distribution
● What is the median latency of all requests to the http-simulator service?
● Does the /users endpoint fulfill the SLO of 3 Nines requests responding within
400ms?
histogram_quantile(0.5,rate(http_request_duration_milliseconds_
bucket{app="http-simulator"}[5m]))
sum(http_request_duration_milliseconds_bucket{app="http-
simulator", status="200", endpoint="/users", le="400"}) /
sum(http_request_duration_milliseconds_count{app="http-
simulator", status="200", endpoint="/users"})

13. @PierreVincent
Exercises 3 - Grafana widgets
Some examples of widgets (or come up with your own ones):
● Graph of latency distribution
● Cumulative % graph of endpoint request rate
● Memory usage over time
● CPU usage over time
● Graph % of requests fulfilling the SLO of 400ms for /login endpoint
● ...