InfoQ Homepage Twitter Content on InfoQ
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Decomposing Twitter: Adventures in Service-Oriented Architecture
Jeremy Cloud discusses SOA at Twitter, approaches taken for maintaining high levels of concurrency, and briefly touches on some functional design patterns used to manage code complexity.
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Timelines at Scale
Raffi Krikorian explains the architecture used by Twitter to deal with thousands of events per sec - tweets, social graph mutations, and direct messages-.
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Scaling Scalability: Evolving Twitter Analytics
Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
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Storm: Distributed and Fault-Tolerant Real-time Computation
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
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Real-Time Delivery Architecture at Twitter
Raffi Krikorian details Twitter’s timeline architecture, its “write path” and “read path”, making it possible to deliver 300k tweets/sec.
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Storm: Distributed and Fault-tolerant Real-time Computation
Nathan Marz discusses Storm concepts –streams, spouts, bolts, topologies-, explaining how to use Storms’ Clojure DSL for real-time stream processing, distributed RPS and continuous computations.
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Timelines @ Twitter
Arya Asemanfar presents Twitter’s timeline architecture, the entire sequence of steps a tweet goes through until it reaches the timeline of each user following the person who tweeted.
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Everything I Ever Learned about JVM Performance Tuning @twitter
Attila Szegedi shares lessons learned tuning the JVM at Twitter, spending most of his talk discussing memory tuning, CPU usage tuning, and lock contention tuning.
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Storm: Distributed and Fault-tolerant Real-time Computation
Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.
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Big Data in Real Time at Twitter
Nick Kallen discusses how Twitter handles large amounts of data in real time by creating 4 data types and query patterns -tweets, timelines, social graphs, search indices-, and the DBs storing them.
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NoSQL at Twitter
Ryan King presents how Twitter uses NoSQL technologies - Gizzard, Cassandra, Hadoop, Redis - to deal with increasing data amounts forcing them to scale out beyond what the traditional SQL has to offer
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NoSQL at Twitter
Kevin Weil presents how Twitter does data analysis using Scribe for logging, base analysis with Pig/Hadoop, and specialized data analysis with HBase, Cassandra, and FlockDB.