- High level overview https://yogthos.github.io/ClojureDistilled.html
- An Animated Introduction to Clojure https://markm208.github.io/cljbook/
- Interactive tutorial in a browser https://tryclojure.org/
- Interactive exercises http://clojurescriptkoans.com/
- Clerk notebooks with introductory examples https://github.clerk.garden/anthonygalea/notes-on-clojure
- More interactive exercises https://4clojure.oxal.org/
- Lambda Island tutorials https://lambdaisland.com/
- Functional Programming with Clojure resources https://practicalli.github.io/
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't really have SREs and they make engineers pretty much do everything,
Projeto: https://github.com/newtonbeck/palestra-clojure
Slides: https://www.slideshare.net/analuisabavati/clojure-do-zero-ao-deploy-de-uma-api-rest-157352485
- Instalar o Leiningen - https://leiningen.org/
UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.
Amazon changed the install in Linux 2. One no-longer using 'yum' See: https://aws.amazon.com/amazon-linux-2/release-notes/
sudo amazon-linux-extras install docker
sudo service docker start
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.
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.
| import React, { Component } from 'react'; | |
| import TextField from 'components/base/TextField'; | |
| const WAIT_INTERVAL = 1000; | |
| const ENTER_KEY = 13; | |
| export default class TextSearch extends Component { | |
| constructor(props) { | |
| super(); |
| using System; | |
| using System.Diagnostics; | |
| namespace BinarySearchTree | |
| { | |
| class Node | |
| { | |
| public int value; | |
| public Node left; | |
| public Node right; |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs