- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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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 |
rsync (Everyone seems to like -z, but it is much slower for me)
- a: archive mode - rescursive, preserves owner, preserves permissions, preserves modification times, preserves group, copies symlinks as symlinks, preserves device files.
- H: preserves hard-links
- A: preserves ACLs
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@binkmail.com | |
@bobmail.info | |
@chammy.info | |
@devnullmail.com | |
@letthemeatspam.com | |
@mailinater.com | |
@mailinator.net | |
@mailinator2.com | |
@notmailinator.com | |
@reallymymail.com |
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#! /usr/bin/env python | |
import redis | |
import random | |
import pylibmc | |
import sys | |
r = redis.Redis(host = 'localhost', port = 6389) | |
mc = pylibmc.Client(['localhost:11222']) |
... or Why Pipelining Is Not That Easy
Golang Concurrency Patterns for brave and smart.
By @kachayev
(extracted from the now defunct SGI project at http://lavarand.sgi.com/cgi-bin/how.cgi via the magical Internet Archive Wayback Machine)
Lava Lamps can be used as a source of randomness, which can be used to establish a random number generator. The output of the RNG can then be consumed by various computer applications.
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// A small SSH daemon providing bash sessions | |
// | |
// Server: | |
// cd my/new/dir/ | |
// #generate server keypair | |
// ssh-keygen -t rsa | |
// go get -v . | |
// go run sshd.go | |
// | |
// Client: |
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package main | |
import ( | |
"fmt" | |
"reflect" | |
) | |
type Foo struct { | |
FirstName string `tag_name:"tag 1"` | |
LastName string `tag_name:"tag 2"` |
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