Graphite

The purpose today

• What is Graphite
• Why it's so great
• How to graph

What is Graphite?

graphite.ch.meteogroup.net

Definition

ALERTING

METRICS

BOTH

Nagios

Icinga

Graphite

Cacti

Munin

Hyperic

Zenoss

Zabbix

What is Graphite?

Django web application

• Carbon - relays (receiving data), aggregations, persisting on disk, caches
• Whisper - fixed-size database, similar in design to rrd (round-robin-database)
• WebUI - simple dashboard, visualization of metrics

Why graphing?

• Discover trends and patterns
  • What time of the day do we get the most users?
  • When x happened, what was the effect on y?
  • How many hits am I getting per hour?
  • How does this compare to last week? last month?
• Predict future events
  • ​When will we need to add more servers? Databases?
• Negative feedback
  • Did the release into production fix the problem x?

Graphite --

• No poller
• No all in one solution
• No easy backups

Graphite ++

• Simple
• Powerful
• Functions
  • ​sum, derivatives, integrals, timeshift, scale, averages, etc.
• Api
  • ​Nagios integration, 3rd party custom dashboards
• Scalable
• Easy to feed data
• Ecosystem 
  • ​http://graphite.readthedocs.org/en/latest/tools.html

A Common Graphite Stack

Collectd

Poller(s)

Scripts

Applications

Carbon

Whisper

Graphite web

Dashboards

Collectd

Agent for system/hardware level metrics

Plugins:

• disk io
• disk space
• cpu
• memory
• mysql
• JMX
• java,
• load

How to graphite data

Bash

#!/bin/bash

timestamp = `date +%s`

value = 10

echo "identifier.metric.name $value $timestamp" | nc -w l graphite.host.name 2003

 

 

schema.application_name.namespace.metric_name.metric_type

What to collect?

• Hardware/OS, Load, Disk space, Disk IO, Network activity
• Application metrics
  • ​How often function x is called
  • Average value of function x
  • Average running time of function x
• Database/Datastore
  • ​performance metrics
  • number of slow queries
• Events
  • ​Deployments
• Log files
  • ​Application logs (exception counts, hits, ...)

Final thoughts

• You don't know what metrics you need until you need it
• Identify your KPIs
• Don't graph everything
  • More graphs === less overview
  • Too many metrics slows down graphing
  • Too many metrics can kill a host
• Correlate
  • Include other metrics (deployments, failover)
  • Application metrics with database metrics
  • Other influences (marketing campaigns, ...)

 

 

Questions?