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

andreyvit

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

mrflip

== Overview of Datasets ==

The examples in this book use the "Chimpmark" datasets: a set of freely-redistributable datasets, converted to simple standard formats, with traceable provenance and documented schema. They are the same datasets as used in the upcoming Chimpmark Challenge big-data benchmark. The datasets are:

• Wikipedia English-language Article Corpus (wikipedia_corpus; 38 GB, 619 million records, 4 billion tokens): the full text of every English-language wikipedia article, in

• Wikipedia Pagelink Graph (wikipedia_pagelinks; ) --

• Wikipedia Pageview Stats (wikipedia_pageviews; 2.3 TB, about 250 billion records (FIXME: verify num records)) -- hour-by-hour pageview

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

fabrizioc1

sudo tcpdump -w /tmp/memcached.pcap -i any -A -vv 'port 11211'

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

alghanmi

#include <iostream>
#include <string>
#include <curl/curl.h>


static size_t WriteCallback(void *contents, size_t size, size_t nmemb, void *userp)
{
    ((std::string*)userp)->append((char*)contents, size * nmemb);
    return size * nmemb;
}

zxhfighter

AWK 简明教程

提炼自CoolShell：http://coolshell.cn/articles/9070.html

有一些网友看了前两天的《Linux下应该知道的技巧》希望我能教教他们用awk和sed，所以，出现了这篇文章。我估计这些80后的年轻朋友可能对awk/sed这类上古神器有点陌生了，所以需要我这个老家伙来炒炒冷饭。况且，AWK是贝尔实验室1977年搞出来的文本出现神器，今年是蛇年，是AWK的本命年，而且年纪和我相仿，所以非常有必要为他写篇文章。

之所以叫AWK是因为其取了三位创始人 Alfred Aho，Peter Weinberger, 和 Brian Kernighan 的Family Name的首字符。要学AWK，就得提一提AWK的一本相当经典的书《The AWK Programming Language》，它在豆瓣上的评分是9.4分！在亚马逊上居然卖1022.30元。

我在这里的教程并不想面面俱到，本文和我之前的Go语言简介一样，全是示例，基本无废话。

gubatron

Things to remember when compiling/linking C/C++ software

by Angel Leon. March 17, 2015;

Last update on December 14, 2023

Updated on February 27, 2023

Updated August 29, 2019.

jaxgeller

# in terminal
defaults write ch.sudo.cyberduck rendezvous.enable false