1. 2002 Called; They want their rrdtool shell scripts back
Dave Josephsen

2. A Brief history of time-series data visualization architectectures
2002 Called; They want their rrdtool shell scripts back
A Brief history of time-series data visualization
architectectures
Dave Josephsen

3. A Tale of 3 Sysadmin A Brief history of time-series data visualization
2002 Called; They want their rrdtool shell scripts back
A Brief history of time-series data visualization
Architectures
A Tale of 3 Sysadmin
Dave Josephsen

4. Jer, Per, and quitter (aka Dave)
2012

5. Jer, traditional needs for Fortune-500
Suitcorp
>5000 hosts
>20,000 services
1, 9-story office building
Plenty of Budget
Beefy Hardware
1.5m/1000 hosts
2012

6. Nagios + NG + Drraw (ho-hum)
2012

7. Per, near real-time data from Lots of hosts
Singularity.gov
80,000 hosts in 80 clusters
No budget
Mad Scientists
No measurable impact allowed
15 second polling interval (max)
CPU, Mem, Disk, Net
Needs to alert on performance thresholds
2012

8. Enter Ganglia
2012

9. That's all fine but what about Nagios?
Awesome Nagios Integration
Easily send data from Nagios to Ganglia with gmetric
Monitor server metrics stored in Ganglia with Nagios with a series of included Nagios plug-ins
Check host heartbeat
Check single metric on a specific host
Check multiple metrics on a specific host
Check multiple metrics on a set of hosts
Verify a single metric is the same on a set of hosts
Display Ganglia graphs in Nagios via the Gweb URL interface
Monitor Ganglia with Nagios (duh)
2012

10. Not just for mad scientists with supercomputers
Ganglia is a great fit if
You want to offload Performance data processing.
You're worried about scale
You want a super-lightweight metric gathering agent
You need near-real time data
You want a really great rrdtool FE
Drag scaling, trend-lines, holt-winters forecasting, time-shifts
Lots more
2012

11. Quitter.. er.. Dave: Graph everything always
Massive Ginormic
DevOps “paradise” (nightmare)
Visualize datapoints on irregular intervals
Code promotions
Function calls
LOTS of metrics (millions)
Centralized time-series visualization for LOTS of very different data sources
Nagios
Application instrumentation
Sales... thingies
2012

12. Enter Graphite Life after RRDTool Carbon Whisper
Trivial, remote, updates
Smart buffering/cacheing
Horizontal scalability
Whisper
Automatic provisioning
Interval-agnosticism
Type agnosticism
Graphite
Functions!
Typeglobs!
Graphic Stolen from:
2012

13. Not just for billion dollar mega-giants
Graphite works great if
You want to combine data from multiple monitoring systems
Nagios, Ganglia, Collectd etc..
You want to assimilate data from other groups or business units
Dev, Sales, etc..
You want really flexible centralized visualization that scales
You want to empower non-ops groups to explore their own data
2012

14. Functions! Rate is the derivative of the counter:
Say you have counter data:
&target=router1.bytes&target=router2.bytes
&target=derive(router1.bytes)
OR: &target=router[12].bytes
But actually, the raw counter data is kind of interesting if
We visualize it correctly:
&target=router1.bytes&target=secondYAxis(router2.bytes)
2012

15. Moar functions! &target=user.registrations
&target=summarize(user.registrations,”1h”)
&target=summarize(user.registrations,”1h”)
&target=summarize(user.registrations,”1h”)&target=threshold(400,”goal)
&target=summarize(user.registrations,”1h”)&target=timeShift(summarize(user.registrations,”1h”),”30d”)&target=threshold(400,”goal)
2012

16. Nagios World Conference
OK BYE!
(and speaking of “buy”...)
2011
Nagios World Conference