InfoQ Homepage Scalability Content on InfoQ
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Scale @Reddit Triple Team Size w/o Losing Control
Nick Caldwell discusses his engineering team's approach to Agile development as they scaled from 40 to 120 engineers.
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The Anatomy of a Distributed System
Tyler McMullen talks through the components and design of a real system, built to perform very high volumes of health checks, done across a cluster of machines for reliability and scalability.
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Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli Kannan describes the algorithmic, scaling, deployment considerations involved in a an application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox
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Loquat: A Design for Large-scale Distributed Applications
Christopher Meiklejohn introduces Loqaut, a design for large-scale actor programming on the Erlang virtual machine.
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React+Redux at Scale
Daniel Cousineau looks at how React and Redux scale, not just in terms of quantitative performance, but in terms of architecture and team participation.
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Serverless Platform: Scientific Computation @Scale
Diptanu Choudhury talks about the platform they are developing at NASA for running computations as functions which would make it easier for researchers to program their applications & algorithms.
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Scaling with Apache Spark
Holden Karau looks at Apache Spark from a performance/scaling point of view and what’s needed to handle large datasets.
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Deep Learning for Image Understanding at Scale
Stacey Svetlichnaya discusses strategies and challenges building deep learning systems for object recognition at scale, using automatic labels in Flickr image search as a case study.
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Scaling Your Swagger-Based Web API with Google Cloud Endpoints
Guillaume Laforge presents some of the options and technical solutions to build a scalable API solution using Google Cloud.
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Moving Past the Scaling Myth
Michael Feathers examines the notion of scale variant structuring and what systems design could look like without the assumption that structural reorganization at different scales is not necessary.
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Scaling Instagram Infrastructure
Lisa Guo overviews Instagram's infrastructure, its history, multi-data center support, tuning uwsgi parameters for scaling, performance monitoring and diagnosis, and Django/Python upgrade.
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Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
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