Icinga provides an out-of-the box integration for InfluxDB. It allows you to leverage the data that is collected for monitoring purposes and store the metrics in your time series database. While collecting data that way is pretty easy, proper visualization requires more attention. Misinterpretation of data is one of the most common causes of wrong conclusions. It make us hunt ghosts during debugging sessions. There are many common pitfalls which we can avoid if we follow some rules. In this webinar, we will show some of the most common mistakes in visualizations and how to avoid them.

1. How not to Fail at Data
Visualization by Using Icinga and
InfluxDB
August 4th, 2020

2. Responsibilities Contact
Product
Partnerships
@bobapple
blerim.sheqa@icinga.com
Blerim Sheqa
CPO

3. What is Icinga?

4. Icinga Overview
Icinga Stack
Monitoring
Availability, Reliability,
Observability
Log
Management
ElasticStack, Graylog
Automation
Director, CfgMgmt-Support,
API
Metrics and
Analytics
InfluxDB, Grafana, Graphite

6. Icinga Overview
Key Aspects
Dynamic
Rule based configuration
Automation
Scalable
Built-in cluster
High available and distributed
Extendable
Your infrastructure, your monitoring
Integrated with your tools

7. Icinga with InfluxDB

8. Icinga with InfluxDB

9. • Write all metrics directly to InfluxDB
• leveraging the HTTP API
• Names and tags are fully configurable
• add data such as location, environment, OS, …
• Add thresholds optionally
• identify exceeded thresholds
• Add metadata optionally
• e.g. state, execution time, latency, …
Icinga with InfluxDB
Key Aspects

10. • High availability mode
• automated failover
• Secure
• with authentication and SSL
• Buffering
• custom buffering intervals and thresholds
Icinga with InfluxDB
Key Aspects

11. Icinga with InfluxDB

12. • By default data is kept forever
• Retention policy
• for housekeeping
• Continuous query
• for downsampling
Icinga with InfluxDB
Data Retention

13. Icinga with InfluxDB
High Availability
enable_ha = true enable_ha = true

14. Common Pitfalls in Data
Visualization

15. Misinterpretation

16. Conventions

17. Follow Conventions

18. Follow Conventions

19. Comparability

20. Comparability

21. Comparability

22. Comparability

23. Comparability

24. Comparability

25. Comparability

26. Comparability

27. • Proper stacking of values
• Add grid, y-axis and x-axis
• Even if it looks boring!
• Split into multiple graphs
• where applicable
Comparability
Summary

28. Readability

29. Readability

30. Readability

31. • Which questions do you want to answer?
• Single purpose dashboards
• improves the performance
• decrease complexity
• Add related information
• state changes, threshold exceedance, alerts, …
Readability
How to improve

32. Know your Data

33. • Quality over quantity
• Trust the data
• Know the details
• what do you collect
• at what pace
• how long is it stored
• who needs the data
Know your Data

34. Q&A

35. Call for Papers is now open!
We’re looking for great speakers – submit
your speaker application today.
November 10 – 11, 2020
North America Virtual
Experience
www.influxdays.com/virtual-experience-2020/