- 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
There are a lot of ways to serve a Go HTTP application. The best choices depend on each use case. Currently nginx looks to be the standard web server for every new project even though there are other great web servers as well. However, how much is the overhead of serving a Go application behind an nginx server? Do we need some nginx features (vhosts, load balancing, cache, etc) or can you serve directly from Go? If you need nginx, what is the fastest connection mechanism? This are the kind of questions I'm intended to answer here. The purpose of this benchmark is not to tell that Go is faster or slower than nginx. That would be stupid.
So, these are the different settings we are going to compare:
- Go HTTP standalone (as the control group)
- Nginx proxy to Go HTTP
- Nginx fastcgi to Go TCP FastCGI
- Nginx fastcgi to Go Unix Socket FastCGI
// Just before switching jobs: | |
// Add one of these. | |
// Preferably into the same commit where you do a large merge. | |
// | |
// This started as a tweet with a joke of "C++ pro-tip: #define private public", | |
// and then it quickly escalated into more and more evil suggestions. | |
// I've tried to capture interesting suggestions here. | |
// | |
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_, | |
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant, |
Ideas are cheap. Make a prototype, sketch a CLI session, draw a wireframe. Discuss around concrete examples, not hand-waving abstractions. Don't say you did something, provide a URL that proves it.
Nothing is real until it's being used by a real user. This doesn't mean you make a prototype in the morning and blog about it in the evening. It means you find one person you believe your product will help and try to get them to use it.
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 |
package main | |
import ( | |
"crypto/tls" | |
"crypto/x509" | |
"fmt" | |
"io" | |
"log" | |
) |
#! /usr/bin/env python | |
""" Convert values between RGB hex codes and xterm-256 color codes. | |
Nice long listing of all 256 colors and their codes. Useful for | |
developing console color themes, or even script output schemes. | |
Resources: | |
* http://en.wikipedia.org/wiki/8-bit_color | |
* http://en.wikipedia.org/wiki/ANSI_escape_code |