1. Prometheus
From technical monitoring to business
obervability
Julien Pivotto (@roidelapluie)
Sysadmin Days Paris
October 18th, 2018

2. user{name="roidelapluie"} 1
I like Open Source
I like monitoring
I like automation
... and all of that is my daily job at inuits

3. inuits

4. Sysadmin
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5. Sysadmin's view
Access to a lot of components
Range from the frontends to the databases
With 24x7 oncall shifts

6. DevOps
In a DevOps world, more data, more awareness
More changes, different scale
Evolution
How can we keep up??

7. The DevOps principles: CAMS
(a definition of DevOps)
Culture
Automation
Measurement
Sharing
(Damon Edwards and John Willis, 2010 http://devopsdictionary.com/wiki/CAMS)
This talk is about all of it..

8. Monitoring
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12. Traditional Monitoring
It works - OK
It does not work - CRITICAL
It kinda works - WARNING
I don't know - UNKNOWN

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16. Creative Commons Attribution-Share Alike 3.0 Unported
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17. Real world
It works ; it does not work ; it kinda works ; it
maybe works ; no one uses it ; it is broken ; some
things are broken ; it should work but it does not ;
where are my users? help me...

18. The Technical bias
By looking at technical service, we miss the
actual point
Are we serving our users correctly?
Just looking at the traffic light will not tell you
about the traffic jams.

19. Observability
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20. Metrics
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21. Metric
Name
Labels (Key-Value Pairs)
Value (Number)
Timestamp
Fetched at a high frequency

22. Name: Number of HTTP requests
Labels:
status: 200
vhost: inuits.eu
method: post
Value: 1823
Timestamp: Thu Oct 18 10:18:06 CEST 2018

23. Name: Number of HTTP requests
Labels:
status: 200
vhost: inuits.eu
method: post
Value: 2123
Timestamp: Thu Oct 18 10:18:36 CEST 2018

24. 300 Requests in 30 s = 10 requests per seconds
(POST for inuits.eu with response code 200)

25. http_request_total{job="fe",instance="fe1",code="200"}

26. Types of metrics
Counters
Gauges
Histograms
Summaries

27. Counters
Always go up
start from zero
rate, increase
e.g. number of http requests

28. Gauges
Go up and down
Average, Sum, Max, ...
^ over time
e.g. concurrent users

29. Histograms and summaries
Sets of requests
Using "buckets"
Useful to get duration, percentiles, SLA

30. Metrics and monitoring
Metrics do not represent problems
Metrics represent a state, give insights
Metrics can be graphed
You can alert based on them

31. Exposed metrics are "raw"
In general you can just expose counters, and let
the monitoring server do the real maths.
That keeps the overhead very low of apps.

32. Tooling
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33. What are the needs ?
Ingest metrics at high frequency
React to changes
Empower people
Alert on metrics

34. Use one toolchain
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35. Stop with:
Having 1 "monitoring" + 1 "graphing" stacks
Big all in one tools: think decentralize, scale
Auto Discovery (use service discovery instead)
Manual config
Fragile monitoring (think HA)

36. Prometheus
https://prometheus.io/

37. Prometheus
Open Source monitoring tool
Complete Ecosystem
For cloud and on prem
Built around metrics

38. Cloud Native
Easy to configure, deploy, maintain
Designed in multiple services
Container ready
Orchestration ready (dynamic config)
Fuzziness

39. Efficient
"Scrapes" millions of metrics
Scales
Manages its own optimized db
(prometheus/tsdb)

40. How does it work?

41. How does it work?

42. How does it work?

43. How does it work?

44. How does it work?

45. Pull vs Push
Prometheus pulls metrics
But does not know what it will get!
The target decides what to expose
(short term batches can still push to a
"pushgateway")

46. Exporters
Expose metrics with an HTTP API
Bindings available for many languages (for
"native" metrics)
Exporters do not save data ; they are not
"proxies" and don't "cache" anything

47. Common exporters
Node Exporter: Linux System Metrics
Grok Exporter: Metrics from log files
SNMP Exporter: Network devices
Blackbox exporter: TCP, DNS, Http requests

49. Grafana
Open Source (Apache 2.0)
Web app
Specialized in visualization
Pluggable
Multiple datasources: prometheus, graphite,
influxdb...
Has an API!

50. Grafana and Prometheus
Prometheus shipped its own consoles
Now it recommends Grafana and deprecated
its own consoles

51. Business Metrics
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52. What are business metrics?
Metrics that effectively tell you how you fullfil
your customers' requests
Provide quality and level of service to
customers

53. CPU usage is no money
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54. Where to get them?
Frontends
Databases
Caching systems (sessions, ...)
...
Each one of them requires a cross-team
understanding of the business.

55. Where to start?
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56. USE
Brendan Gregg's USE method
U = Utilisation S = Saturation E = Errors
For resources like network, CPU, memory,...
Also asynchrone processes, ...

57. RED
Tom Wilkie's RED method
R = Requests E = Errors D = Duration
HTTP Requests, synchrone processes,...

58. What to get?
Request Rate
Saturation
Error Rate
Duration

59. Before we dig in ..
What we will see now is monitoring data. It should
not be used for precise usages, like invoicing.

60. Caching System Monitoring
(USE)

61. Caching System Monitoring

62. What do we learn?
Users can connect to the platform: The
authentication works
The platform is currently used

63. Benefits
Connected users = they can use the platform
Know when you can do maintenances
Know about your user's general habits (trends)

64. Database

65. Database

66. Database
Using SQL exporters to query the data from
your database
Requires a cross team approach
Gets you fine grained, quality data

67. Database trap
Do not try to replace BI/Reporting
Do not take too many labels -- stay in the
monitoring area

68. Frontends
RED

69. sum by (instance, env) (
rate(http_requests_duration_count[5m])
)

70. Frontends
RED

71. sum by (code, env) (
rate(http_requests_duration_count{code!="200"}[5m])
) / ignoring (code) group_left
sum by (env) (
rate(http_requests_duration_count[5m])
)

72. Frontends
RED

73. sum by (env) (
rate(http_requests_duration_sum[5m])
) /
sum by (env) (
rate(http_requests_duration_count[5m])
)

74. What can we learn?
We have traffic from outside
How much traffic
Quality of the trafic
How long it really takes (end to end)

75. Adding Time
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76. Timeseries
How we use time: We take the metrics for the
last 7 weeks
We take the median value (exclude 3 top and 3
low)
Excludes anomalies due to
incidents/holidays...

77. http_requests:rate5m offset 1w
offset queries data in the past

78. ­ record: past_request_rate
expr: http_requests:rate5m offset 1w
labels:
when: 1w

79. ­ record: past_request_rate
expr: http_requests:rate5m offset 2w
labels:
when: 2w

80. max without(when) (
bottomk(1,
topk(4,
past_request_rate
)
)
)

81. Result
RED

82. Oops...
RED

83. What do we learn?
Predict users habits
Deviation from the norm are not normal
It means that users can not reach us/use our
services

84. Why business metrics matter?
Good service depends on: linux health, dns,
network, ntp, disk space, cpu, open files, database,
cache systems, load balancers, partners,
electricity, virtualization stack, nfs, ... and it moves
over time
Customers won't call you because your disk is full!

85. Partners
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86. Given that the End User matters
We have decided to standadize metrics
exchange between partners
Prometheus format used (soon to be
OpenMetrics)
Everyone knows HTTP!

87. What do we exchange?
We are not interested in partner's internal (and
don't want to expose us)
We are exchanging precomputed metrics (rate
over 5 minutes, duration over 5 minutes),
excluding servers, instances, ...
Identify, in the chain, the bottlenecks and the
issues

88. Dashboards
We define our dashboards in two parts:
10 graphes on top about the business: RED,
USE, Alerts, data from partners, monitoring
robots, state of the monitoring
hidden by default: Technical Health - ntp, disk,
db, network, jvm, ...

89. Limited number of graphes
Errors in RED
Attention points in Yellow/Orange

90. technical view; more graphes; empty when OK

91. Dashboards
Duplicate the dashboard to have an historical
view

94. Dashboards
Easy drill down between dashboards / with pre
defined variables

95. Dashboard
Provide relevant help where needed
(from the haproxy documentation)

96. Dashboards
On product launch / change / ... extract
relevant data from the service and build a
"temporary dashboard"
Share with the teams and managers, show on
big screen

97. Conventions
Color conventions, general:
RED = Bad
Yellow = Attention
Blue/Green = OK
Also:
RED = problem at our side
Yellow/orange = problem at partners side

98. HTTP Codes
2xx: Greens
3xx: Yellows
4xx: Blues (404: grey)
5xx: Orange/Red
! Same accross all dashboards to enable easy
reading

99. Side note: github.com/grafana/grafonnet-lib/
A Jsonnet Library to write grafana dashboards.

100. Conclusion
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101. Quick Answers
Business monitoring allows yo to know early
when things are wrong
Provides clear asnwers to your customers in
minutes (no more "I will check")
// to make between technical and business
metrics (to find causes)

102. What happened?
Is it REALLY fixed?
When?
Until when (technical and business)?
What did I miss? What is the impact?

103. Metrics benefits
Because you run queries and alerts from a
central location
You can run queries accross targets/jobs
Detect faulty instances, alert for server X
based on metrics of server Y

104. Metrics benefits
Trends
Dynamic thresholds
Predictions

105. Do not underestimate the monitoring of the
development / staging environments.

106. Business metrics are good candidates
to wake up someone at night.

107. Prometheus benefits
Pull Based , metrics centrincs
The targets (e.g. developers) choose the
metrics they expose => Empowering people
HTTP permits TLS, Client Auth, ... and cross
org sharing of metrics
Becoming a standard in the industry

108. Grafana
Central point for all teams
Show current and pas status
Should give you the opportunity to answer
questions

109. Focusing on Business Metrics is hard work that
will show benefits accross teams and provide
visibility towards hierarchy, enabling you to gain
trust and move on more quickly towards a DevOps
model.

110. Julien Pivotto
roidelapluie
roidelapluie@inuits.eu
Inuits
https://inuits.eu
info@inuits.eu
Contact