InfoQ Homepage Performance & Scalability Content on InfoQ
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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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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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Petabyte Scale Data at Facebook
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
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Facebook News Feed: Social Data at Scale
Serkan Piantino discusses news feeds at Facebook: the basics, infrastructure used, how feed data is stored, and Centrifuge – a storage solution.
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Not Your Father’s Transaction Processing
Michael Stonebraker compares how RDBMS, NoSQL and NewSQL support today’s big data transaction processing needs. He also introduces VoltDB, an in-memory NewSQL database.
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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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The Startup Hangover: Supporting 15M Users
Phil Calçado presents SoundCloud’s approach to dealing with scalability issues when their user number grew beyond what they initially could support by creating services in various languages.
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MongoDB - Born in the Cloud
Ross Lawley introduces MongoDB, explaining why it is a good solution for cloud deployment.
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Event Processing at Massive Scale
Uri Cohen discusses several types of queues with their pros and cons used in financial and trading industries for highly parallelized data processing.