InfoQ Homepage Performance Content on InfoQ
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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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Web Framework Performance - Examples from Django and Rails
Gareth Rushgrove overviews Ruby on Rails and Django: object caches, fragment and HTTP caching, asset compilation, profiling, log file measurement and framework hooks for instrumentation.
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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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The HipHop Compiler for PHP
Guilherme Ottoni presents the design, implementation, and an evaluation of the HipHop compiler for PHP.
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Using Node.js to Improve the Performance of Mobile Apps and Mobile Web
Tom Hughes-Croucher discusses increasing the performance of web applications and websites by using Node.js’ event-driven approach.
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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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JavaScript Performance Patterns
Stoyan Stefanov explains how to reason about and to address performance issues in JavaScript applications.
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Actionable Metrics - Enabling Decision-Making in Netflix’s Decentralized Environment
Roy Rapoport discusses how Netflix uses metrics to monitor and manage their operating environment along with some notes about their event management system.
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Managing and Monitoring Spring Integration Applications
Gary P Russell shows an application used for managing and monitoring apps built with Spring Integration, and overviews the JMX support provided by Spring Integration.
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Visual Interfaces in ClojureScript
Kevin Lynagh provides the rationale behind visual interfaces, and presents a sample example written in ClojureScript.
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Caching Hypermedia APIs
Tim Stokes discusses various URI caching strategies providing real life examples relying on some of the natural behaviors that are built into the HTTP 1.1 protocol.
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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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