Wood Hwang sgc109

System Design Cheatsheet

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

Basic Steps

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?

Moved to git repository: https://github.com/denji/nginx-tuning

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 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading 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.

Git

Global Settings

Related Setup: https://gist.github.com/hofmannsven/6814278
Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/
Interactive Beginners Tutorial: http://try.github.io/
Git Cheatsheet by GitHub: https://services.github.com/on-demand/downloads/github-git-cheat-sheet/

Reminder

Press minus + shift + s and return to chop/fold long lines!

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

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 io.github.sandornemeth.spring;

	import java.util.Arrays;
	import java.util.stream.StreamSupport;

	import org.slf4j.Logger;
	import org.slf4j.LoggerFactory;
	import org.springframework.context.event.ContextRefreshedEvent;
	import org.springframework.context.event.EventListener;
	import org.springframework.core.env.AbstractEnvironment;

	# Built application files
	/*/build/

	# Crashlytics configuations
	com_crashlytics_export_strings.xml

	# Local configuration file (sdk path, etc)
	local.properties

	# Gradle generated files