jorinvo

You got your hands on some data that was leaked from a social network and you want to help the poor people.

Luckily you know a government service to automatically block a list of credit cards.

The service is a little old school though and you have to upload a CSV file in the exact format. The upload fails if the CSV file contains invalid data.

The CSV files should have two columns, Name and Credit Card. Also, it must be named after the following pattern:

YYYYMMDD.csv.

squito

## this should get loaded by your ~/.Rprofile
## either just stick all these definitions right in ~/.Rprofile, or
## have ~/.Rprofile source this file, etc.

source.dirs <- c(
  ## add any "base" locations here.  this is like a "classpath" in java
  ## my default is c("~/myRUtils/src","~/companyRUtils/src","~/publicRUtils"),
  ## but you can use whatever you like
  paste(Sys.getenv("HOME"),"myRUtils","src", sep="/"),
  paste(Sys.getenv("HOME"),"companyRUtils","src", sep="/"),

aviflax

Introduction

This gist started with a collection of resources I was maintaining on stream data processing — also known as distributed logs, data pipelines, event sourcing, CQRS, and other names.

Over time the set of resources grew quite large and I received some interest in a more guided, opinionated path for learning about stream data processing. So I added the reading list.

Please send me feedback!

ragingwind

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

MLnick

import spark.streaming.{Seconds, StreamingContext}
import spark.storage.StorageLevel
import spark.streaming.examples.twitter.TwitterInputDStream
import com.twitter.algebird._
import spark.streaming.StreamingContext._
import spark.SparkContext._

/**
 * Example of using CountMinSketch monoid from Twitter's Algebird together with Spark Streaming's
 * TwitterInputDStream

MLnick

import spark.streaming.StreamingContext._
import spark.streaming.{Seconds, StreamingContext}
import spark.SparkContext._
import spark.storage.StorageLevel
import spark.streaming.examples.twitter.TwitterInputDStream
import com.twitter.algebird.HyperLogLog._
import com.twitter.algebird._

/**
 * Example of using HyperLogLog monoid from Twitter's Algebird together with Spark Streaming's

taf2

<script>
  _invoke = []; // name your api global array
  _invoke.push(['call1', 'arg1', 'arg2']); // make an api call.
(function() {
  var _s=document.createElement('script');
  _s.src=window.location.protocol + '//your-script-here.js'; // yes we include protocol for all the browsers
  var _fs = document.getElementsByTagName('script')[0];
  _fs.parentNode.insertBefore(_s, _fs);
})();
</script>

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:

necolas

NOTE I now use the conventions detailed in the SUIT framework

Template Components

Used to provide structural templates.

Pattern

t-template-name

eed3si9n

trait CanReadXML[A] {
  def reads(seq: scala.xml.NodeSeq): Either[String, A]
}

trait Foo { def foo: Int }
trait Bar { def bar: Int }

object Def {
  def fromXML[A: CanReadXML](seq: scala.xml.NodeSeq) =
    implicitly[CanReadXML[A]].reads(seq)