Презентация на тему Humans By The Hundred

Презентация на тему Humans By The Hundred, предмет презентации: Аналитика. Этот материал содержит 76 слайдов. Красочные слайды и илюстрации помогут Вам заинтересовать свою аудиторию. Для просмотра воспользуйтесь проигрывателем, если материал оказался полезным для Вас - поделитесь им с друзьями с помощью социальных кнопок и добавьте наш сайт презентаций ThePresentation.ru в закладки!

Слайды и текст этой презентации

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Humans By The Hundred

Scaling Big Data for Big Team Growth


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$ whoami

SRE Manager at Yelp
CWRU Alum
Pittsburgh native
<3 Web Operations
Just a dude


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Yelp’s Mission:

Connecting people with great
local businesses.


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Yelp Stats:

As of Q2 2015


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What is Yelp?

Many sites: www, m, biz, api
Mobile apps
Partner platform
Hundreds of developers
Thousands of servers


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Why Am I Here?


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DATA


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This talk is about people


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The Goal


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Iterate as fast as possible


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Regardless of how many people are participating


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Deployment


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How It Starts


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Deployment: the early days

Get a few people together in slack/irc/etc.
Merge up the code
Run the tests
Manually test it in stage
Cross your fingers


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Things get slower...

Tests take longer to run
More hosts = longer downloads
More developers = more eyeballs
More features = more code


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The Problem: Humans Are Fallible


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The Problem: Humans Are Fallible

“…oh @$#&”


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The Problem, With Math

Assume:
Every change has a chance of success: 98%
That means no test failures, no reverts, etc.
Every deploy has a number of changes: n
Any failure in the pipeline invalidates the deploy
Let’s figure out the probability of a successful deployment: p


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The Problem, With Math

Only you
p = .98 (98%)
You and a friend
p = .98 * .98 = .96 (96%)
You and nine co-workers
p = .98 * .98 * .98 * … * .98 = .82 (82%)


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The Problem, With Math

p = (.98)n


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The Problem, With Math

p = (.98)n

exponential decay!


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This doesn’t scale!

More developers = more changes
More changes = longer deploys
Longer deploys = less time to develop
Less time to develop = slower to iterate
Slower to iterate != the goal


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Mitigating Exponential Decay

p = (.98)n


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Mitigating Exponential Decay

p = (.98)n



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Making it harder to screw up

Write more tests
Write better tests
Get better code reviews
Get better infrastructure
Switch programming languages
Use better tools


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Just write better software and stop making mistakes!


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PROBLEM SOLVED


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The Real World

Testing builds confidence in our changes
Testing does not protect you from failure
Better tools, tests, and infrastructure can raise our success rates


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Mitigating Exponential Decay

p = (.98)n


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Mitigating Exponential Decay

p = (.98)n



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Service-Oriented Architecture

Large monolith → smaller services
Services communicate over network
Usually HTTP, but you can do RPC, SOAP, etc.
Service = independent code base
Independent deployments


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Service-Oriented Architecture

Benefits
Smaller code bases = upper bound to n
Failure domains become isolated
Technology independence
Federated responsibility


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Service-Oriented Architecture

Drawbacks
everything becomes decoupled
function calls start looking like HTTP requests
versioning can be a nightmare
tracking dependencies is hard
data consistency becomes challenging
end-to-end testing becomes hard(er), if not impossible


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SOA scales people, not code.


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Conquering SOA

With the monolith, it’s easy to focus on mean time between failures (MTBF)


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Conquering SOA

In a SOA, focus on mean time to recovery (MTTR)


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Conquering SOA

Fail fast
Anticipate failure
Leverage iteration speed to recover fast


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Conquering SOA

Treat everything as distributed
That means everything will fail
Use timeouts, retries
Find ways to degrade gracefully
Fail fast & isolated
Don’t rely on synchronous processes
Prepare for eventual consistency


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Reaping the Benefits

Smaller failure domains
Fewer people & changes to manage
Deploys get smaller
Deploys get faster
Deploys become continuous


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Reaping the Benefits

Smaller changes
means smaller code reviews
means faster validation
means smaller blast radius
means faster iteration


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Continuous Delivery

Everyone works against master branch
Master is deployed when commits added
Deployment gated by tests
Monitoring knows something is wrong before you do!


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PROBLEM SOLVED


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Testing


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Tests are hard to get right.


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How can we do better?


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“Not Recommended” Tests


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“Not Recommended” Tests

If a test fails on master:
a feature is broken on the live website, or
your test sucks and you should ditch it
In either case, we disable it
Ticket is created
Developers can fix it later or just bin it and start fresh


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Reliable tests >> test coverage.


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Don’t always run all the tests!


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Tests of external services should be monitoring


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Define your boundaries.


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yelp.com / dataset_challenge

61K businesses
61K checkin-sets
481K business attributes

1.6M reviews
366K users
2.8M edge social-graph
495K tips

Your academic project, research or visualizations, submitted by Dec 31, 2015
=
$5,000 prize + $1,000 for publication + $500 for presenting*

*See full terms on website

Academic dataset from 10 cities in 4 countries!


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@YelpEngineering

YelpEngineers




engineeringblog.yelp.com

github.com/yelp


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yelp.com/careers


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Questions?


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