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

harlow

Golang Workers / Job Queue

A running example of the code from:

• http://marcio.io/2015/07/handling-1-million-requests-per-minute-with-golang
• http://nesv.github.io/golang/2014/02/25/worker-queues-in-go.html

This gist creates a working example from blog post, and a alternate example using simple worker pool.

TLDR: if you want simple and controlled concurrency use a worker pool.

lttlrck

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






\

yojimbo87

Markdown cheatsheet

Create

• Class
• Cluster
• Edge
• Vertex

Insert

• Insert

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

ashrithr

Introduction to Kafka

Kafka acts as a kind of write-ahead log (WAL) that records messages to a persistent store (disk) and allows subscribers to read and apply these changes to their own stores in a system appropriate time-frame.

Terminology:

• Producers send messages to brokers
• Consumers read messages from brokers
• Messages are sent to a topic

acolyer

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

• http://mvdirona.com/jrh/talksandpapers/jamesrh_lisa.pdf

Basic tenets

• Does the design expect failures to happen regularly and handle them gracefully?
• Have we kept things as simple as possible?

stephenll

#  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases.
#                Much of this was originally copied from:
#                http://natelandau.com/my-mac-osx-bash_profile/
#
#  Sections:
#  1.   Environment Configuration
#  2.   Make Terminal Better (remapping defaults and adding functionality)
#  3.   File and Folder Management

MLnick

import spark.SparkContext
import SparkContext._

/**
 * A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
 * to Spark.
 * Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
 */
object MovieSimilarities {