james-huston

# run this every minute from cron as a quick and dirty solution to get some
# haproxy stats on each of your listeners over to stackdriver

instance=$(curl -s "http://169.254.169.254/latest/meta-data/instance-id")

# while [	1 ]; do
  cp /dev/null /tmp/hastats.json
  echo 'show stat' | socat - UNIX-CLIENT:/tmp/haproxy.sock \
    |while IFS=',' read pxname svname qcur qmax scur smax slim stot bin bout dreq \
        dresp ereq econ eresp wretr wredis status weight act bck chkfail chdown lastchg \

XVilka

THIS GIST WAS MOVED TO TERMSTANDARD/COLORS REPOSITORY.

PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!

Earlz

rem this is a generic media player
rem this prog was made by jordan earls
starty:
print "type 1 to choose where media is"
print "type 2 to pause music"
print "type 3 to resume music"
print "type 4 to reload music"
print "type 5 to change volume"
print "type 6 to change speed"
start:

inactivist

"""
Taken from my suggestion at:
https://groups.google.com/forum/#!msg/tweepy/OSGRxOmkzL4/yaNT9fL9FAIJ

Note that this is an incomplete snippet; you'll need to create the API auth object.
"""

import simplejson as json
import tweepy

schmurfy

require 'celluloid'

WORKERS_COUNT = (ARGV[0] || 1).to_i

class Output
  include Celluloid
  
  def puts(msg)
    Kernel.puts(msg)
  end

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

RedBeard0531

// derived from http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm

function map() {
    emit(1, // Or put a GROUP BY key here
         {sum: this.value, // the field you want stats for
          min: this.value,
          max: this.value,
          count:1,
          diff: 0, // M2,n:  sum((val-mean)^2)
    });