Hari haran hariharan-uno

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

Around 2006-2007, it was a bit of a fashion to hook lava lamps up to the build server. Normally, the green lava lamp would be on, but if the build failed, it would turn off and the red lava lamp would turn on.

By coincidence, I've actually met, about that time, (probably) the first person to hook up a lava lamp to a build server. It was Alberto Savoia, who'd founded a testing tools company (that did some very interesting things around generative testing that have basically never been noticed). Alberto had noticed that people did not react with any urgency when the build broke. They'd check in broken code and go off to something else, only reacting to the breakage they'd caused when some other programmer pulled the change and had problems.

What forces layout / reflow

All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.

Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.

Element APIs

Getting box metrics

elem.offsetLeft, elem.offsetTop, elem.offsetWidth, elem.offsetHeight, elem.offsetParent

There are three easy to make mistakes in go. I present them here in the way they are often found in the wild, not in the way that is easiest to understand.

All three of these mistakes have been made in Kubernetes code, getting past code review at least once each that I know of.

Loop variables are scoped outside the loop.

What do these lines do? Make predictions and then scroll down.

func print(pi *int) { fmt.Println(*pi) }

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!

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Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
Scaling Twitter: Making Twitter 10000 Percent Faster

	package main

	import (
	"fmt"
	"os"
	"os/exec"
	"syscall"
	)

	func main() {

	package main

	import (
	"fmt"
	"strconv"
	"strings"
	)

	type Maze struct {
	x [5][5]int

	[unix_http_server]
	file=/tmp/supervisor.sock ; path to your socket file

	[supervisord]
	logfile=/var/log/supervisord/supervisord.log ; supervisord log file
	logfile_maxbytes=50MB ; maximum size of logfile before rotation
	logfile_backups=10 ; number of backed up logfiles
	loglevel=error ; info, debug, warn, trace
	pidfile=/var/run/supervisord.pid ; pidfile location
	nodaemon=false ; run supervisord as a daemon