nvartolomei

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

• http://mvdirona.com/jrh/talksandpapers/jamesrh_lisa.pdf

Basic tenets

• Does the design expect failures to happen regularly and handle them gracefully?
• Have we kept things as simple as possible?

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Title

<Additional information about your API call. Try to use verbs that match both request type (fetching vs modifying) and plurality (one vs multiple).>

• URL

  <The URL Structure (path only, no root url)>

• Method:

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The Case For Go

• Iron.io: How We Went from 30 Servers to 2 with Go
• Go at Google: Language Design in the Service of Software Engineering
• Behind the Scenes: Big Data at Torbit
• pool.ntp.org DNS server in Go
• Juju at Canonical
• Go at bitly
• NSQ: realtime distributed message processing at scale

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#System Design Interview Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

##Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

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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

• Scaling Twitter: Making Twitter 10000 Percent Faster
• Scaling Twitter
• Blaine Cook on Scaling Twitter - YouTube

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Frontend Development

Attention: if you attempt to fork this gist, github will think that you are a spammer and you will have to confirm that you are human with them. Apparantly there are too many links in this list. Also I update it rather frequently (see revisions on the left), so it's probably wise to not fork it anyway.

• Guides
  • Hack Design
  • Designer School
  • TheExpressiveWeb
  • Talks To Help You Become A Better Front-End Engineer In 2013
• Web Development Teaching Materials

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/**
 * A "Left-leaning Red-Black Tree"
 * 
 * http://www.cs.princeton.edu/~rs/talks/LLRB/LLRB.pdf
 */
public class LLRB<K extends Comparable<K>, V> {

    public static void main(String[] args) {
        LLRB<String, String> llrb = new LLRB<String, String>();
        

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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
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD               150,000   ns    0.15 ms
Read 1 MB sequentially from memory      250,000   ns    0.25 ms
Round trip within same datacenter       500,000   ns    0.5  ms