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Growing Distributed Systems

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Hello Bryce Kerley @bonzoesc basho

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Distributed Systems What even are they?

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Distributed Systems

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Distributed Systems Always Broken

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Distributed Systems Light Cones

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Mutual visibility Soldier Fires Sniper Fires

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Distributed Systems A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable. Lamport, 1987

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Distributed Systems •Rules and Theory •Inconsistency and Unavailability •Distributed Rails

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Byzantine Generals No Right Answer

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Criticality •Amazon S3, July 2008 •GitHub, Dec. 2012 •Northeast US, 2003

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Murphy’s Law at Scale •Backblaze •Gears of War 2

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CAP Theorem Consistency Availability Partition tolerance

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Rules, Theory, and Practice Compromise

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Distributed Systems •Rules and Theory •Inconsistency and Unavailability •Distributed Rails

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CAP Theorem lose Consistency or Availability during Partitions

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CP Systems Zookeeper

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Unavailability Don’t fill requests

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Unavailability

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AP Systems Riak

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Inconsistency Accept writes Perform reads

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Inconsistency

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Hybrid Multiple Stores

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Hybrid Where It Counts

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Hybrid Riak 2 Strong Consistency

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Distributed Systems •Rules and Theory •Inconsistency and Unavailability •Distributed Rails

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Distributed Rails You’re Already Distributed

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Distributed Rails Rails

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Distributed Rails Rails Web DB

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Distributed Rails SQL Rules

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Distributed Rails NoSQL No Rules

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Distributed Rails Temptation

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Distributed Rails Service Oriented Architecture

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Service Oriented Extract Parts

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Service Oriented Documented Protocols

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Service Oriented Smaller Deployments

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Testing Unit Tests

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Testing Service Tests

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Testing Acceptance Tests

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Testing Refactoring [GitHub] with Science Wynn Netherland, Big Ruby, Feb. 2014

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Offline Apps Latency Garbage Collection Packet loss Airplane rides Local internet

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Offline Apps Smarter clients

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Offline Apps You’re Already Doing It

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Offline Strategies Conflicts

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Offline Strategies Operational Transform

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Offline Strategies Git

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Offline Strategies Vesper

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Distributed Systems •Rules and Theory •Inconsistency and Unavailability •Distributed Rails

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Ruby Works

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Make Something Awesome

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Grow When You Need To

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Thanks Bryce Kerley @bonzoesc [email protected] http://bitly.com/roa-dist

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Bonus Slides

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Harvest & Yield Harvest: fraction of data Yield: probability of success

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NoSQL and Rails SQL ACID

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NoSQL and Rails Atomicity Consistency Isolation Durability

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NoSQL and Rails NoSQL BASE

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NoSQL and Rails Basically Available Soft-state Eventually consistent

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Why Extract? Break coupling Easier deploys Simpler responsibility Forced abstraction Prevent introspection Standardization Flexible backends

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Offline Libraries Breeze PouchDB Lawnchair

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Must Watch Jeff Hodges “Practicalities of Productionizing Distributed Systems” youtu.be/BKqgGpAOv1w