An overview on StatsD and Graphite

1. Measure Anything,
Measure Everything
StatsD & Graphite
By Avi Revivo / 2015

2. New development checklist
 Code standards
 Comments
 Logic
 Error handling
 Testable code
 Logging
 What is missing?

3. What about?
 Feature usage
 Performance
 Business KPIs

4. So why is it not there?
2 main reasons

5. #1 Complexity
It’s just not simple enough

6. #2 Understanding the Developer’s Role
“Dear Engineers,
Your job is not to write code.
I know. You think you were hired to write code. In fact, your entire interview process
centered around how well you could write code. And I’m sure you do it really well.
But it’s not your job.
Your job is to improve our product for our users.”
Blog post Link

7. Let’s focus on complexity
How simple should it be? Dead simple!
As simple as writing a log line…

8. Meet StatsD
A network service that runs on the Node.js that listens
for statistics, like counters and timers, sent over UDP or
TCP and sends aggregates to one or more pluggable
backend services.

9. Example: Sending a Metric
echo "foo:1|c" | nc -u -w0 127.0.0.1 8125
<metricname>:<value>|<type>

10. Key Concepts
 Buckets - Each stat is in its own "bucket". They are not
predefined anywhere.
 Values - Each stat will have a value.
 Flush - After the flush interval timeout, stats are aggregated
and sent to an upstream backend service.

11. Available Metric Types
Counting
gorets:1|c
Timing
glork:320|ms|@0.1
Gauges
gaugor:333|g
Sets
uniques:765|s
Sampling supported

12. Meet Graphite
An all-in-one solution for storing and visualizing realtime time-
series data in an efficient manner.

14. Graphite Architecture
Graphite Web
A powerful
dashboard for our
metrics and a
powerful plotting API.
Carbon
The core of Graphite.
It consists of set of
three processes that
take care of receiving
incoming data,
aggregating it and
persisting it to disk.
Whisper
An efficient time-
series based
database.

15. Understanding Carbon
Carbon's job is to make that data available for real-time graphing
immediately and try to get it stored on disk as fast as possible.
It support configurable data persistency rules and full control
over the way data is aggregated.

16. Carbon Retention Rules
 For a timespan of 6 hours I want to store my data in intervals of 10 seconds.
 For a timespan of 7 days I want to store my data in intervals of 1 minute.
 For a timespan of 1 year I want to store my data in intervals of 1 hour.
 For a timespan of 5 year I want to store my data in intervals of 24 hours.
[counts]
Pattern = .count$
retentions = 10s:6h;1m:7d;1h:1y,1d:5y

17. Carbon Aggregation Rules
[sum]
Pattern= .count$
aggregationMethod = sum
stats.app.users.logins.count
stats.app.emails.sent.count
Avergage, Min, Max etc.

19. DEMO