jbreiding

#
# Enable CEPH repos as described at http://ceph.com/docs/master/install/get-packages/#rpm
# Install ceph-deploy package
#

export DATA_DEV=sdb
export JRNL_DEV=sdc
export FS_TYPE=xfs
export CEPH_S_NODE=ceph-s
export CEPH_RELEASE=giant

jbreiding

// Sample file using the Google C++ coding standard.
//
// http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml
//
// General rules:
//   - Indents are two spaces. No tabs should be used anywhere.
//   - Each line must be at most 80 characters long.
//   - Comments can be // or /* but // is most commonly used.
//   - File names should be lower_case.c or lower-case.c
//

jbreiding

#!/usr/bin/env python
#

# import modules used here -- sys is a very standard one
import sys, argparse, logging

# Gather our code in a main() function
def main(args, loglevel):
  logging.basicConfig(format="%(levelname)s: %(message)s", level=loglevel)
  

jbreiding

/**
 * More info?
 * a.dotreppe@aspyct.org
 * http://aspyct.org
 * @aspyct (twitter)
 *
 * Hope it helps :)
 */

#include <stdio.h>

jbreiding

Keybase proof

I hereby claim:

• I am jbreiding on github.
• I am jbreiding (https://keybase.io/jbreiding) on keybase.
• I have a public key whose fingerprint is B84F 6509 7D19 E283 93DB 3A92 DE90 BF23 7D85 5971

To claim this, I am signing this object:

jbreiding

using System;
using System.IO;
using System.Messaging;
using System.Text;
using Newtonsoft.Json;

public class JsonMessageFormatter : IMessageFormatter
{
    private static readonly JsonSerializerSettings DefaultSerializerSettings = 
        new JsonSerializerSettings {

jbreiding

namespace BloomFilter
{
	using System;
	using System.Collections;

	/// <summary>
	/// Bloom filter.
	/// </summary>
	/// <typeparam name="T">Item type </typeparam>
	public class Filter<T>

jbreiding

"""
Simple Linear Probabilistic Counters

Credit for idea goes to:
http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html
http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/

Installation:
    pip install smhasher
    pip install bitarray

jbreiding

    /// <summary>
    /// A hash combiner that is implemented with the Fowler/Noll/Vo algorithm (FNV-1a). This is a mutable struct for performance reasons.
    /// </summary>
    public struct FnvHash
    {
        /// <summary>
        /// The starting point of the FNV hash.
        /// </summary>
        public const int Offset = unchecked((int)2166136261);

jbreiding

// Test/check FNV-1(a) hash implementations
var testvectors = new[] { "", "a", "b", "c", "d", "e", "f", "fo", "foo", "foob", "fooba", "foobar", "\0", "a\0", "b\0", "c\0", "d\0", "e\0", "f\0", "fo\0", "foo\0", "foob\0", "fooba\0", "foobar\0", "ch", "cho", "chon", "chong", "chongo", "chongo ", "chongo w", "chongo wa", "chongo was", "chongo was ", "chongo was h", "chongo was he", "chongo was her", "chongo was here", "chongo was here!", "chongo was here!\n", "ch\0", "cho\0", "chon\0", "chong\0", "chongo\0", "chongo \0", "chongo w\0", "chongo wa\0", "chongo was\0", "chongo was \0", "chongo was h\0", "chongo was he\0", "chongo was her\0", "chongo was here\0", "chongo was here!\0", "chongo was here!\n\0", "cu", "cur", "curd", "curds", "curds ", "curds a", "curds an", "curds and", "curds and ", "curds and w", "curds and wh", "curds and whe", "curds and whey", "curds and whey\n", "cu\0", "cur\0", "curd\0", "curds\0", "curds \0", "curds a\0", "curds an\0", "curds and\0", "curds and \0", "curds and w\0", "curds and wh\0