InfoQ Homepage Scalability Content on InfoQ
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RabbitMQ and .NET with EasyNetQ
Mike Hadlow explains why RabbitMQ makes a compelling solution for building scalable systems, overviewing its exchange-binding-queue routing topology and showing how to build messaging patterns with it
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Racing Thru the Last Mile: Cloud Delivery & Web-Scale Deployment
Alex Papadimoulis conducts a tutorial on delivery and deployment at scale.
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Startup Architecture: How to Lean on Others to Get Stuff Done
Robbie Clutton takes a look at the tools assisting a startup in making technical decisions needed for scaling and growing.
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Lessons from Building and Scaling LinkedIn
Jay Kreps discusses the evolution of LinkedIn's architecture and lessons learned scaling from a monolithic application to a distributed set of services, from one database to distributed data stores.
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Facebook Messages: Backup & Replication Systems on HBase
Nicolas Spiegelberg discusses Facebook Messages built on top of HBase, the systems involved and the scaling challenges for handling 500TB of new data per month.
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Racing Thru the Last Mile: Cloud Delivery Web-Scale Deployment
Alex Papadimoulis discusses various deployment strategies, scalable delivery, with examples from real-world organizations such as AllRecipes.com, Twitter, and Google.
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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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(un)Common Sense
Mike Solomon shares some of the experiences and lessons learned scaling YouTube over the years.
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Scaling Pinterest
Yashwanth Nelapati and Marty Weiner share lessons learned growing Pinterest: sharding MySQL, caching, server management, all on Amazon EC2.
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Running the Largest Hadoop DFS Cluster
Hairong Kuang explains how Facebook uses HDFS to store and analyze over 100PB of user log data.
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Executing Queries on a Sharded Database
Neha Narula provides advice on choosing a data store for a web applications and executing distributed queries.
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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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