Manhattan, our real-time, multi-tenant distributed database for Twitter scale
Manhattan, our real-time, multi-tenant distributed database for Twitter scale As Twitter has grown into a global platform for public self-expression and conversation, our storage requirements have grown too. Over the last few years, we found ourselves in need of a storage system that could serve millions of queries per second, with extremely low latency in a real-time environment. Availability and
Facebook’s advanced erasure codes | StorageMojo
Facebook’s advanced erasure codes by Robin Harris | Friday, June 21, 2013 | Architecture, Clusters, Information Management | 4 comments We want our data protected from device failures. When there is a failure we want to get our data back quickly. And we want to pay as little as possible for the protection and the restore. How? Recent research by hyper-scale system managers – mostly Microsoft and F
LinkedIn: Creating A Low Latency Change Data Capture System With Databus
This is a guest post by Siddharth Anand, a senior member of LinkedIn's Distributed Data Systems team. Over the past 3 years, I've had the good fortune to work with many emerging NoSQL products in the context of supporting the needs of a high-traffic, customer facing web site. In 2010, I helped Netflix to successfully transition its web scale use-cases from Oracle to SimpleDB, AWS' hosted database
AWS、ニフティ、IIJ GIOのストレージ速度を検証、比較してみた
今回は、IaaS（Infrastructure as a Service）について解説していきたいと思う。 「今さら聞けない！ 各種クラウドサービスの違い」にも書いたように、IaaSはPaaSよりさらに柔軟な運用が可能な形態、平たく言えば仮想サーバを借りてインターネット越しに利用するサービスだ。ホスティングサービス（専用サーバレンタルなど）と大きく異なる点としては、料金の支払い体系とサーバ構築の速さ、自由度が挙げられる。 AWS、ニフティクラウド、IIJ GIOのストレージを比較 今回筆者は、IaaSストレージサービスの1つである「ニフティクラウドストレージ」に触れる機会があった。ニフティクラウドストレージはこの分野のパイオニア的存在である「Amazon Simple Storage Service（Amazon S3）」との互換性の高さをうたう。ストレージに対してHTTPを用いたシンプル
Storage Infrastructure Behind Facebook Messages – Perspectives
Disclaimer: The opinions expressed here are my own and do not necessarily represent those of current or past employers. Recent CommentsRaffaele Santopaolo on David Patterson Retires After 40 YearsJames Hamilton on Seagate HAMRTom Davies on Seagate HAMRMatt on Seagate HAMRJames Hamilton on Seagate HAMRMatt on Seagate HAMRJames Hamilton on Cost of Power in Large-Scale Data CentersChris K. on Cost of
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XORing Elephants: Novel Erasure Codes for Big Data ∗ Maheswaran Sathiamoorthy University of Southern California msathiam@usc.edu Megasthenis Asteris University of Southern California asteris@usc.edu Dimitris Papailiopoulos University of Texas at Austin
SSSSLIDE
Google Code Archive - Long-term storage for Google Code Project Hosting.
Large-scale Incremental Processing Using Distributed Transactions and Notifications
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DRBD:What is DRBD