djangofan

 import java.io.IOException;
 import java.net.Inet4Address;
 import java.net.InetAddress;
 import java.net.MalformedURLException;
 import java.net.URL;
 import java.net.UnknownHostException;
 import java.util.Arrays;
 
 import jpcap.JpcapCaptor;
 import jpcap.JpcapSender;

procrastinatio

Introduction

So HAProxy is primalery a load balancer an proxy for TCP and HTTP. But it may act as a traffic regulator. It may also be used as a protection against DDoS and service abuse, by maintening a wide variety of statistics (IP, URL, cookie) and when abuse is happening, action as denying, redirecting to other backend may undertaken ([haproxy ddos config], [haproxy ddos])

jhansche

#!/bin/sh
# Usage: ./watch-fds.sh <application_id> [delay_secs = 5]

APP_ID=${1:?missing application id}
DELAY=$(( ${2:-5} ))

DEVICE_LIMIT=$(( $(adb shell ulimit -n) ))
WARN_THRESHOLD=$(( ${DEVICE_LIMIT} / 3 ))
echo "Will warn at ${WARN_THRESHOLD}"

gtallen1187

"A little bit of slope makes up for a lot of y-intercept"

01/13/2012. From a lecture by Professor John Ousterhout at Stanford, class CS140

Here's today's thought for the weekend. A little bit of slope makes up for a lot of Y-intercept.

[Laughter]

varemenos

Get Git log in JSON format

git log --pretty=format:'{%n  "commit": "%H",%n  "abbreviated_commit": "%h",%n  "tree": "%T",%n  "abbreviated_tree": "%t",%n  "parent": "%P",%n  "abbreviated_parent": "%p",%n  "refs": "%D",%n  "encoding": "%e",%n  "subject": "%s",%n  "sanitized_subject_line": "%f",%n  "body": "%b",%n  "commit_notes": "%N",%n  "verification_flag": "%G?",%n  "signer": "%GS",%n  "signer_key": "%GK",%n  "author": {%n    "name": "%aN",%n    "email": "%aE",%n    "date": "%aD"%n  },%n  "commiter": {%n    "name": "%cN",%n    "email": "%cE",%n    "date": "%cD"%n  }%n},'

The only information that aren't fetched are:

• %B: raw body (unwrapped subject and body)
• %GG: raw verification message from GPG for a signed commit

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

72lions

/**
 * Creates a new Uint8Array based on two different ArrayBuffers
 *
 * @private
 * @param {ArrayBuffers} buffer1 The first buffer.
 * @param {ArrayBuffers} buffer2 The second buffer.
 * @return {ArrayBuffers} The new ArrayBuffer created out of the two.
 */
var _appendBuffer = function(buffer1, buffer2) {
  var tmp = new Uint8Array(buffer1.byteLength + buffer2.byteLength);