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Enhance Your Nagios Experience with Data Visualization and Performance Analytics

Discover how to elevate your Nagios monitoring with enhanced data visualization techniques and performance analytics. Alexis Lê-Quôc shares insights on leveraging comprehensive logging, real-time graphs, and R with ggplot2 for better data interpretation. Learn how to extract and analyze your Nagios logs to gain valuable insights into service alerts. This guide will help you understand trends, improve alerting, and optimize your monitoring process using PostgreSQL and other powerful tools.

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Enhance Your Nagios Experience with Data Visualization and Performance Analytics

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  1. Pretty Nagios Charts • Alexis Lê-Quôc (@alq) • http://dtdg.co/nagios2012

  2. Pretty Nagios Charts • Alexis Lê-Quôc (@alq) • http://dtdg.co/nagios2012

  3. @alq Dev & Ops Nagios user since 2008 Datadog co-founder

  4. Survey

  5. Compared to Nagios World 2011...

  6. Is your Nagios doing... (a) Better (b) About the same (c) Worse

  7. Why does it matter?

  8. How do you find out?

  9. MemoryAnalytics

  10. First, the pipeline

  11. Your Nagios logsare a treasure

  12. Comprehensive Accurate Written for you

  13. Hard to comprehend

  14. Query Extract Analyze Present IngredientsRaw logsSimple parserSQL databaseR ggplot2 or d3.js

  15. nagios.log PostgreSQL R ggplot2 Nice graphs

  16. Python nagios.log nagios.csv nagios table Postgres R Data Frame ggplot2 PDF RPostgresql

  17. Let’s play!

  18. 5 lines of R

  19. Data Geometry Chrome ggplot(dd_by_day_raw, aes(occurrence_doy, daily, color=factor(notifying)))+ geom_line()+ xlab("Day of year")+ ylab("Service Alerts")+ ggtitle("Notifying v. silent alerts per day")

  20. Linear regression ggplot(dd_by_day_raw, aes(occurrence_doy, daily))+ geom_line()+ geom_smooth()+ xlab("Day of year")+ ylab("Service Alerts")+ ggtitle("Daily trends")

  21. ggplot(dd_by_day_raw, aes(occurrence_doy, daily, color=factor(notifying)))+ geom_line()+ geom_smooth()+ xlab("Day of year")+ ylab("Service Alerts")+ ggtitle("Notifying v. silent alerts per day")

  22. Tuesdays???

  23. ggplot(dd_by_hod, aes(occurrence_dow, daily, group=occurrence_dow))+ geom_boxplot()+ scale_x_discrete(breaks=seq(0, 6), labels=c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"))+ xlab("Day of the week")+ ylab("Number of alerts")+ ggtitle("Daily distribution")

  24. As a Service Fun with Nagios stats...

  25. Performance Metrics Nagios Traffic Other Sources Real-time graphs + analytics

  26. Play Stack: • parser • postgres • R • ggplot2 • Service Stack: • parser • postgres • Hadoop • d3.js

  27. Quick demo

  28. Real-Time Graphing And more... “Share what you see” Metric-based Alerts Correlation

  29. Curious? http://datadoghq.com

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