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![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
[...]
[...]](https://image.slidesharecdn.com/prometheusdesignandphilosophy-whyitisthewayitis-161010171121/75/Prometheus-design-and-philosophy-11-2048.jpg)
![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”](https://image.slidesharecdn.com/prometheusdesignandphilosophy-whyitisthewayitis-161010171121/75/Prometheus-design-and-philosophy-13-2048.jpg)
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Prometheus design and philosophy
Prometheus is an opinionated monitoring system designed for dynamic cloud environments, initiated by SoundCloud in 2012, motivated by the need for efficient monitoring tools. Key features include a dimensional data model, powerful query language, and operational simplicity, while it does not support SQL-like queries and instead uses its own PromQL. The document discusses Prometheus's architecture, functionality, and alternative monitoring solutions, emphasizing best practices in monitoring.
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Prometheus design and philosophy
- 1. Prometheus Prometheus Design andPhilosophy 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 in2012 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 andTSDB: ● instrumentation ● metrics collection and storage ● querying ● alerting ● dashboarding / graphing / trending What is Prometheus? Focus on: ● operational systems monitoring ● dynamic cloud environments
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- 6. Prometheus Four main sellingpoints 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?
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- 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 formetrics 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.
- 17. Prometheus Alternatives and Workarounds ●run Prometheus on same network segment ● open port(s) in firewall / router ● open tunnel / VPN
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- 20. Prometheus Drawbacks ● operational bottleneck ●SPOF, no isolation ● can’t scrape selectively ● harder up-ness monitoring ● harder to associate metadata
- 21. Prometheus One Exporter perProcess
- 22. Prometheus No Clustering? ● reallyhard 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 notabout Prometheus ● it’s about monitoring best practices ● it’s our opinion
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- 26. Prometheus Why not JSON? Weoptimized for two extremes: Text format ● easy to construct ● relatively efficient ● readable ● streamable Protobuf format ● very efficient ● robust ● streamable JSON? Worse in all categories.
- 27.
- 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 afloat64... This is crazy! I only need integers! What about precision?
- 31. Prometheus Everything is afloat64... ● 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