parthdesai93

/* requires AWS creds to be updated.
 * if they aren't, update using AWS.config.update() method before instatiing the client. 
 * 
 * import this module where you instantiate the client, and simply pass this module as the connection class. 
 *
 * eg:
 * const client = new Client({
 *   node,
 *   Connection: AwsConnector
 * });

drmalex07

README

Create a template service file at /etc/systemd/system/[email protected]. The template parameter will correspond to the name of target host:

[Unit]
Description=Setup a secure tunnel to %I
After=network.target

djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

levi

LOL eSports API

Unofficial documentation to internal Riot Games APIs for LOL eSports.

• Tournament Week Schedule
• Week Schedule
• Program Blocks
• League
• Tournament
• Match

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

eed3si9n

import scala.reflect.macros.Context
import scala.util.matching.Regex
import java.util.regex.PatternSyntaxException
 
object Macros {
  implicit class RegexContext(val c: String) {
    def regex(): Regex = macro regexImpl
  }
 
  def regexImpl(c: Context)(): c.Expr[Regex] = {