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

ibeex

A warning occurred (42 apples)
An error occurred

piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = git@github.com:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

awidegreen

Introduction

• C-a == Ctrl-a
• M-a == Alt-a

General

:q        close
:w        write/saves
:wa[!]    write/save all windows [force]
:wq       write/save and close

pkmishra

import os
import random
from scrapy.conf import settings
class RandomUserAgentMiddleware(object):
    def process_request(self, request, spider):
        ua  = random.choice(settings.get('USER_AGENT_LIST'))
        if ua:
            request.headers.setdefault('User-Agent', ua)

class ProxyMiddleware(object):

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

everilae

# A simple generator wrapper, not sure if it's good for anything at all.
# With basic python threading
from threading import Thread

try:
    from queue import Queue

except ImportError:
    from Queue import Queue
 

jasongilman

Atom Clojure Setup

This describes how I setup Atom for an ideal Clojure development workflow. This fixes indentation on newlines, handles parentheses, etc. The keybinding settings for enter (in keymap.cson) are important to get proper newlines with indentation at the right level. There are other helpers in init.coffee and keymap.cson that are useful for cutting, copying, pasting, deleting, and indenting Lisp expressions.

Install Atom

Download Atom

The Atom documentation is excellent. It's highly worth reading the flight manual.

ClintLiddick

find_package(Threads REQUIRED)

ExternalProject_Add(
  googletest
  GIT_REPOSITORY https://github.com/google/googletest.git
  UPDATE_COMMAND ""
  INSTALL_COMMAND ""
  LOG_DOWNLOAD ON
  LOG_CONFIGURE ON
  LOG_BUILD ON)

hemebond

A SaltStack AWS Auto Scaling Solution

Overview

The AWS Auto Scaling Goup, configured with a customised Cloud-Init file, sends a notification to an SNS Topic, which in turn passes it onto an SQS queue that the Salt Master is subscribed to. A Reactor watches for the auto scaling events and pre-approves the new minion based on its Auto Scaling group name and instance ID.

Salt Master Configuration