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Parallel computing / Distributed computing architecture / MapReduce / Programming paradigms / Cloud computing / Cloud infrastructure / Apache Hadoop / Pipeline / Sawzall / Computing / Concurrent computing / Computer programming


FlumeJava: Easy, Efficient Data-Parallel Pipelines Craig Chambers, Ashish Raniwala, Frances Perry, Stephen Adams, Robert R. Henry, Robert Bradshaw, Nathan Weizenbaum Google, Inc. {chambers,raniwala,fjp,sra,rrh,robertwb,n
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Document Date: 2011-10-20 20:35:17

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City

Toronto / /

Company

parallelDo()s A / Google / MySql / MapReduce / implemented using a single MapReduce / using raw Java MapReduce / /

Country

Canada / /

Currency

USD / /

/

Facility

Java Introduction Building / SiteData pipeline / One pipeline / Derived Operations The FlumeJava library / FlumeJava library / /

IndustryTerm

execution services / hand-optimized chain / parallel processing / internal build tools / similar systems / lower-level services / related systems / producer-consumer chain / domain-specific logs-processing language / /

Organization

US Federal Reserve / /

Person

Stephen Adams / Ashish Raniwala / Frances Perry / Robert R. Henry / Robert Bradshaw / /

Position

driver / Map worker / Parallel Programming General / ith Reduce worker / i+1st Reduce worker / Reduce worker / programmer / /

Product

PCollection / M-16 / /

ProgrammingLanguage

Java / C++ / /

ProvinceOrState

Ontario / /

Technology

API / JTag / load balancing / Java / Caching / parallel processing / /

SocialTag