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Comprehensions in Python the Jedi way

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

All of the tasks presented in the examples can be accomplished with the extensive standard library available in Python. These solutions would arguably be more terse and efficient in some cases. I don't have anything against the standard library. To me there is a certain

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On why stateful code is bad
===========================

STUDENT: Sir, can I ask a question?
TEACHER: Yes!
STUDENT: How do you put an elephant inside a fridge?
TEACHER: I don't know.
STUDENT: It's easy, you just open the fridge and put it in. I have another question!
TEACHER: Ok, ask.
STUDENT: How to put a donkey inside the fridge?

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#!/bin/bash

# Update and upgrade packages
sudo apt-get update && sudo apt-get upgrade

# Install postgres 9.3
sudo apt-get install postgresql-9.3 postgresql-server-dev-9.3 postgresql-contrib-9.3

# Having Postgres 9.2 already, this will create database instance configured to run on port 5433

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Latency Comparison Numbers
--------------------------
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

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#
# Wide-open CORS config for nginx
#
location / {

     if ($request_method = 'OPTIONS') {

        add_header 'Access-Control-Allow-Origin' '*';
        
        #

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# Download latest archlinux bootstrap package, see https://www.archlinux.org/download/
wget http://ftp.nluug.nl/os/Linux/distr/archlinux/iso/2016.01.01/archlinux-bootstrap-2016.01.01-x86_64.tar.gz

# Make sure you'll have enough entropy for pacman-key later.
apt-get install haveged

# Install the arch bootstrap image in a tmpfs.
mount -t tmpfs none /mnt
cd /mnt
tar xvf ~/archlinux-bootstrap-2016.01.01-x86_64.tar.gz --strip-components=1

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package main

import (
	"fmt"
	"runtime"
	"time"
)

var (
	INTERVAL_SEC = 10

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package main

import (
    "fmt"
    "http"
    "io/ioutil"
    "os"
    )

func main() {

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Direct links to free Springer books (pdf versions)

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

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Nginx Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400,000 to 500,000 requests per second (clustered), most what i saw is 50,000 to 80,000 (non-clustered) requests per second and 30% CPU load, course, this was 2xIntel Xeon with HT enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

First, you will need to install nginx, my way to install nginx is compiling it from source, but for now we will use apt-get