mauryasankalp

References from my talk 'Without Resilience, Nothing Else Matters'

These are the Kickstarter Engineering and Data role definitions for both teams.

Technical	Data	People
Junior Software Engineer
Software Engineer	Data Analyst
Senior Software Engineer	Data Scientist	Engineering Manager
Staff Engineer	VP of Data	Engineering Director
Principal Engineer		CTO

Architecture in Elm

This document is a collection of concepts and strategies to make large Elm projects modular and extensible.

We will start by thinking about the structure of signals in our program. Broadly speaking, your application state should live in one big foldp. You will probably merge a bunch of input signals into a single stream of updates. This sounds a bit crazy at first, but it is in the same ballpark as Om or Facebook's Flux. There are a couple major benefits to having a centralized home for your application state:

There is a single source of truth. Traditional approaches force you to write a decent amount of custom and error prone code to synchronize state between many different stateful components. (The state of this widget needs to be synced with the application state, which needs to be synced with some other widget, etc.) By placing all of your state in one location, you eliminate an entire class of bugs in which two components get into inconsistent states. We also think yo

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

-E: will describe the underlaying queries of the \ commands (cool for learning!)
-l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

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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The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

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

If you have two days to learn the very basics of modelling, Domain-Driven Design, CQRS and Event Sourcing, here's what you should do:

In the evenings read the [Domain-Driven Design Quickly Minibook]{http://www.infoq.com/minibooks/domain-driven-design-quickly}. During the day watch following great videos (in this order):

Eric Evans' [What I've learned about DDD since the book]{http://www.infoq.com/presentations/ddd-eric-evans}
Eric Evans' [Strategic Design - Responsibility Traps]{http://www.infoq.com/presentations/design-strategic-eric-evans}
Udi Dahan's [Avoid a Failed SOA: Business & Autonomous Components to the Rescue]{http://www.infoq.com/presentations/SOA-Business-Autonomous-Components}
Udi Dahan's [Command-Query Responsibility Segregation]{http://www.infoq.com/presentations/Command-Query-Responsibility-Segregation}
Greg Young's [Unshackle Your Domain]{http://www.infoq.com/presentations/greg-young-unshackle-qcon08}
Eric Evans' [Acknowledging CAP at the Root -- in the Domain Model]{ht

Stevey's Google Platforms Rant

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 real

	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