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[ subject:"Computer engineering." ]
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Transparent in-memory cache for Hado...
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Nandakumar, Venkatesh.
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Transparent in-memory cache for Hadoop-MapReduce.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Transparent in-memory cache for Hadoop-MapReduce./
作者:
Nandakumar, Venkatesh.
面頁冊數:
109 p.
附註:
Source: Masters Abstracts International, Volume: 54-02.
Contained By:
Masters Abstracts International54-02(E).
標題:
Computer engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1572259
ISBN:
9781321462449
Transparent in-memory cache for Hadoop-MapReduce.
Nandakumar, Venkatesh.
Transparent in-memory cache for Hadoop-MapReduce.
- 109 p.
Source: Masters Abstracts International, Volume: 54-02.
Thesis (M.A.S.)--University of Toronto (Canada), 2014.
This item must not be sold to any third party vendors.
Many analytic applications built on Hadoop ecosystem have a propensity to iteratively perform repetitive operations on same input data. To remove the burden of these repetitive operations, new frameworks for MapReduce have been introduced, which make users follow its programming model. We propose a solution to the problem of application rewriting that newer frameworks impose. We re-architected Hadoop core to add in-memory caching and cache-aware task-scheduling.
ISBN: 9781321462449Subjects--Topical Terms:
621879
Computer engineering.
Transparent in-memory cache for Hadoop-MapReduce.
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Many analytic applications built on Hadoop ecosystem have a propensity to iteratively perform repetitive operations on same input data. To remove the burden of these repetitive operations, new frameworks for MapReduce have been introduced, which make users follow its programming model. We propose a solution to the problem of application rewriting that newer frameworks impose. We re-architected Hadoop core to add in-memory caching and cache-aware task-scheduling.
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We set out to match the performance of a state-of-the-art high speed, in-memory MapReduce architecture with caching (Spark). While Spark reimplements the MapReduce paradigm, it comes with a new set of new API's and abstractions. We maintain the familiar Hadoop framework and API's, thus complete backward compatibility for any existing Hadoop-based software. This ensures no changes to existing applications code whatsoever. It guarantees no-pain installation over existing deployments while providing 4.5-12X performance improvement. We perform comparable to, and in some cases outperform Spark.
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