InfoQ Homepage Performance & Scalability Content on InfoQ
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Scaling Stack Overflow: Keeping it Vertical by Obsessing Over Performance
David Fullerton shares some of the things the Stack Exchange tech team have learned along the way while scaling one of the top sites in the world primarily through vertical scaling.
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How 30 Years of Ticket Transaction Data Helps you Discover New Shows!
Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
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Performance Testing Crash Course
Dustin Whittle explains how to evaluate performance and scalability on the server-side and the client-side with tools like Siege, Bees with Machine Guns, Google PageSpeed, WBench, and more.
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0 – 100 MPH - Launching a New Product at Scale
Dan Macklin explains why bet365 has adopted Erlang as a core development platform and goes through the highs and lows of managing change in one of the world's biggest on-line bookmakers.
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Spring Batch Performance Tuning
Gunnar Hillert and Chris Schaefer examine various scalability options in order to improve the robustness and performance of the Spring Batch applications.
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Not Exactly! Fast Queries via Approximation Algorithms
Fangjin Yang, creator of Druid, shows how approximation algorithms can help system scale out linearly and process huge amount of data quickly with small memory footprint.
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Scaling Gilt: from Monolithic Ruby Application to Distributed Scala Micro-Services Architecture
Yoni Goldberg describes some of the technological innovations that have helped Gilt to reach its current size, and highlight some of the core challenges that the company's engineering team faces.
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Scaling Foursquare: From Check-ins to Recommendations
Jon Hoffman discusses the general architecture, storage systems and development practices created to handle the ever increasing volume and complexity at Foursquare.
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Scaling Chartbeat from 8 Million Open Browsers to Realtime Analytics and Optimization
Wesley Chow presents Chartbeat's real-time analytics platform and how able to handle the requests in a cost efficient manner using a custom written analytics engine in C and Lua.
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Machine Learning at Netflix Scale
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
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Scaling Continuous Deployment
Avleen Vig discusses the changes Etsy has implemented to scale continuous deployments over the last 12 months, in both software and infrastructure.
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Planning for Overload
Fred Hebert introduces two strategies for handling overload -load-shedding and back-pressure- along with different ways to make them work in Erlang focusing on the importance of planning for overload.
CONTENT IN THIS BOX PROVIDED BY OUR SPONSOR
Application Performance Management in the Microservices Age
Manual Monitoring – Bad! Automatic Monitoring – Good!
Application Performance Management in a Containerized World
Open Source Microservices Monitoring is a Terrible Idea
Yes Virginia, Automatic Monitoring DOES Exist
Finding the Needle in the Containerized Haystack
The Ultimate Application Monitoring Guide for Kubernetes
