Prometheus Design and Philosophy by Julius Volz at Docker Distributed System Summit Prometheus - https://github.com/Prometheus Liveblogging: http://canopy.mirage.io/Liveblog/MonitoringDDS2016

1. Prometheus
Prometheus Design and Philosophy
Why It Is the Way It Is
Julius Volz, October 2016

2. Prometheus
Opinionated Monitoring System
● Prometheus is different
● potentially surprising decisions
● disclaimer: our opinion

3. Prometheus
● Started in 2012 at SoundCloud by Matt and Julius
● Inspired by Google’s monitoring tools
● Motivation
○ needed to monitor dynamic cloud environment
○ unsatisfying data models, querying, and
efficiency in existing approaches
● Now in CNCF, many other companies and people
Origin

4. Prometheus
Monitoring system and TSDB:
● instrumentation
● metrics collection and storage
● querying
● alerting
● dashboarding / graphing / trending
What is Prometheus?
Focus on:
● operational systems monitoring
● dynamic cloud environments

5. Prometheus
Architecture

6. Prometheus
Four main selling points
1. Dimensional data model
2. Powerful query language
3. Efficiency
4. Operational simplicity

7. Prometheus
● raw log / event collection
● request tracing
● “magic” anomaly detection
● durable long-term storage
● automatic horizontal scaling
● user / auth management
What does Prometheus NOT do?

8. Prometheus
Expression browser

9. Prometheus
Built-in graphing

10. Prometheus
Grafana Support

11. Prometheus
Data Model
api_http_requests_total
method=POST
method=GET
method=...
path=/tracks
path=/users
path=...
status=200
status=404
status=...
job=api-server
job=node
job=...
instance=1.2.3.4:80
instance=1.2.3.4:81
instance=...
Labels > Hierarchy
api-server
1.2.3.4:80
/tracks
GET
200
404
[…]
POST
[…]
/users
[…]
1.2.3.4:81
/tracks
GET
200
[…]
[...]
/users
[...]
[...]

12. Prometheus
Labels > Hierarchy
● more flexible
● more efficient
● explicit dimensions
api_http_requests_total{method=”post”}
vs.
api-server.*.*.post.*

13. Prometheus
Non-SQL Query Language
PromQL: rate(api_http_requests_total[5m])
SQL: SELECT job, instance, method, status, path, rate(value, 5m) FROM api_http_requests_total
PromQL: avg by(city) (temperature_celsius{country=”germany”})
SQL: SELECT city, AVG(value) FROM temperature_celsius WHERE country=”germany” GROUP BY city
PromQL: rate(errors{job=”foo”}[5m]) / rate(total{job=”foo”}[5m])
SQL:
SELECT errors.job, errors.instance, […more labels…], rate(errors.value, 5m) /
rate(total.value, 5m) FROM errors JOIN total ON […all the label equalities…] WHERE
errors.job=”foo” AND total.job=”foo”

14. Prometheus
● better for metrics computation
● only does reads
PromQL

15. Prometheus
Pull vs. Push
● automatic upness monitoring
● horizontal monitoring
● more flexible
● simpler HA
● less configuration
● yes, it scales!

16. Prometheus
But but...

17. Prometheus
Alternatives and Workarounds
● run Prometheus on same
network segment
● open port(s) in firewall / router
● open tunnel / VPN

18. Prometheus
Uber-Exporters
or...
Per-Process Exporters?

19. Prometheus
Per-Machine Uber-Exporters

20. Prometheus
Drawbacks
● operational bottleneck
● SPOF, no isolation
● can’t scrape selectively
● harder up-ness monitoring
● harder to associate metadata

21. Prometheus
One Exporter per Process

22. Prometheus
No Clustering?
● really hard to get right
● first thing to fail when you
need it (e.g. during network
outage)
● keep it simple, focus on
operational monitoring
● HA for alerting still easy

23. Prometheus
Conclusion
● it’s not about Prometheus
● it’s about monitoring best
practices
● it’s our opinion

24. Prometheus
Demo Time!

25. Prometheus
Thanks!

26. Prometheus
Why not JSON?
We optimized for two extremes:
Text format
● easy to construct
● relatively efficient
● readable
● streamable
Protobuf format
● very efficient
● robust
● streamable
JSON? Worse in all categories.

27. Prometheus
Relabeling - WTF?

28. Prometheus
Relabeling - OK...
● a new DSL
● steep learning curve
● ...but very flexible
The alternative:
many special config options.

29. Prometheus
Stateful Client Libraries
● client libs keep state
● but not much
● manage metrics for you
● pre-aggregation is more
efficient

30. Prometheus
Everything is a float64...
This is crazy! I only need integers!
What about precision?

31. Prometheus
Everything is a float64...
● it’s simpler
● we compress it incredibly well
● float64 integer precision until 2^53
To run into trouble:
Increment counter 1 million times per
second for over 285 years

32. Prometheus
Auth or Multi-User?
● focus on great monitoring
● too many different ways
● solve auth externally
● multitenancy is more difficult