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Ruby statsd

branch: master
README.md

StatsD

A network daemon for aggregating statistics (counters and timers), rolling them up, then sending them to graphite or mongodb.

Installation

gem install statsd

Configuration

Create config.yml to your liking. There are 2 flush protocols: graphite and mongo. The former simply sends to carbon every flush interval. The latter flushes to MongoDB capped collections for 10s and 1min intervals.

Example config.yml

---
bind: 127.0.0.1
port: 8125

# Flush interval should be your finest retention in seconds
flush_interval: 10        

# Graphite
graphite_host: localhost
graphite_port: 2003

# Mongo
mongo_host: localhost
mongo_database: statsdb

# If you change these, you need to delete the capped collections yourself!
# Average mongo record size is 152 bytes
# 10s and 1min data is transient so we'll use MongoDB's capped collections. These collections are fixed in size.
# 5min and 1d data is interesting to preserve long-term. These collections are not capped.
retentions: 
    - name: stats_per_10s
      seconds: 10
      capped: true
      cap_bytes: 268_435_456 # 2**28
    - name: stats_per_1min
      seconds: 60
      capped: true
      cap_bytes: 1_073_741_824 # 2**30
    - name: stats_per_5min
      seconds: 600
      cap_bytes: 0 
      capped: false
    - name: stats_per_day
      seconds: 86400
      cap_bytes: 0 
      capped: false

Server

Run the server:

Flush to Graphite (default): statsd -c config.yml

Flush and aggregate to MongoDB: statsd -c config.yml -m

Client

In your client code:

require 'rubygems'
require 'statsd'
STATSD = Statsd::Client.new('localhost',8125)

STATSD.increment('some_counter') # basic incrementing
STATSD.increment('system.nested_counter', 0.1) # incrementing with sampling (10%)

STATSD.decrement(:some_other_counter) # basic decrememting using a symbol
STATSD.decrement('system.nested_counter', 0.1) # decrementing with sampling (10%)

STATSD.timing('some_job_time', 20) # reporting job that took 20ms
STATSD.timing('some_job_time', 20, 0.05) # reporting job that took 20ms with sampling (5% sampling)

Concepts

  • buckets Each stat is in it's own "bucket". They are not predefined anywhere. Buckets can be named anything that will translate to Graphite (periods make folders, etc)

  • values Each stat will have a value. How it is interpreted depends on modifiers

  • flush After the flush interval timeout (default 10 seconds), stats are munged and sent over to Graphite.

Counting

gorets:1|c

This is a simple counter. Add 1 to the "gorets" bucket. It stays in memory until the flush interval.

Timing

glork:320|ms

The glork took 320ms to complete this time. StatsD figures out 90th percentile, average (mean), lower and upper bounds for the flush interval.

Sampling

gorets:1|c|@0.1

Tells StatsD that this counter is being sent sampled ever 1/10th of the time.

Gauges

gaugor:333|g

StatsD now also supports gauges, arbitrary values, which can be recorded.

Guts

Graphite

Graphite uses "schemas" to define the different round robin datasets it houses (analogous to RRAs in rrdtool):

[stats]
priority = 110 
pattern = ^stats\..*
retentions = 10:2160,60:10080,600:262974

That translates to:

  • 6 hours of 10 second data (what we consider "near-realtime")
  • 1 week of 1 minute data
  • 5 years of 10 minute data

This has been a good tradeoff so far between size-of-file (round robin databases are fixed size) and data we care about. Each "stats" database is about 3.2 megs with these retentions.

MongoDB

Statd::Mongo will flush and aggregate data to a MongoDB. The average record size is 152 bytes. We use capped collections for the transient data and regular collections for long-term storage.

Inspiration

Etsy's blog post.

StatsD was inspired (heavily) by the project (of the same name) at Flickr. Here's a post where Cal Henderson described it in depth: Counting and timing. Cal re-released the code recently: Perl StatsD

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