mm53bar

gem 'dogstatsd-ruby'

alq666

require 'rubygems'
require 'dogapi'

# Create a simple client
# You typically want to do:
#   Dogapi::Client.new(your_actual_api_key_as_a_string, ...)
dog = Dogapi::Client.new(YOUR_API_KEY_HERE)

# Emit a list of points in one go as a list of (timestamp, value)
# here we pretend to send a point a minute for the past hour

alq666

alerts_by_day <- read.csv(‘by_day.csv’)
ggplot(alerts_by_day, aes(day_of_year, daily, color=factor(notifying))) + geom_line()
+ xlab("Day of year")
+ ylab("Service Alerts")
+ ggtitle("Notifying v. silent alerts per day")
+ geom_smooth()

alq666

alq666

#!/bin/bash -e
# Check python 2.6 or greater
python -V 2>&1 | awk '$2 !~ /^2.[67]|^3/ {exit 1}'
# Download virtualenv
curl https://raw.github.com/pypa/virtualenv/master/virtualenv.py > virtualenv.py
python virtualenv.py $HOME/datadog
source $HOME/datadog/bin/activate
# Install dogapi
pip install dogapi
# Set up the dog alias

alq666

A short introduction to Datadog functions

You can apply functions to metric queries in the graph editor, as long as you use the JSON editor.

The general format is:

function(metric{scope} [by {filter}])

In case of binary operators (+, -, /, *), the format is:

mlambie

root@arcee:/var/log# curl http://localhost/server-status?auto
BusyWorkers: 1
IdleWorkers: 9
Scoreboard: _W.________.....................................................................................................................................................................................................................................................

  VS.

mlambie@blitzwing:~$ curl http://localhost/server-status?auto
Total Accesses: 557
Total kBytes: 17924

bcambel

2012-05-31 15:17:40,719 - root - INFO - Logging to /tmp/dd-agent.log
2012-05-31 15:17:40,719 - root - WARNING - Pid file: /tmp/dd-agent.pid
2012-05-31 15:17:40,719 - root - INFO - Running in foreground
2012-05-31 15:17:40,719 - agent - DEBUG - Collecting basic system stats
2012-05-31 15:17:40,738 - agent - DEBUG - System: {'nixV': ('Ubuntu', '10.04', 'lucid'), 'cpuCores': 2, 'machine': 'x86_64', 'platform': 'linux2', 'pythonV': '2.6.5', 'processor': ''}
2012-05-31 15:17:40,738 - agent - DEBUG - Creating checks instance
2012-05-31 15:17:40,785 - agent - INFO - Running on EC2, instanceId: i-fdf2929e
2012-05-31 15:17:40,786 - checks - INFO - Dogstream parsers: []
2012-05-31 15:17:40,788 - checks - INFO - Starting checks
2012-05-31 15:17:40,788 - checks - DEBUG - SIZE: <function getApacheStatus at 0x2ca5a28> wrote 5 bytes uncompressed

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

alq666

BEGIN {
    skipped = 0;
    processed = 0;
}

/Skipped/ {
    skipped++;
}

/Saving/ {