Distributed dynamic partially stateful dataflow

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 43-49). === This thesis present a distributed implementation of Noria, a new streaming datafl...

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Main Author: Behrens, Jonathan (Jonathan Kyle)
Other Authors: M. Frans Kaashoek and Malte Schwarzkopf.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2018
Subjects:
Online Access:http://hdl.handle.net/1721.1/118054
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-1180542019-05-02T16:17:00Z Distributed dynamic partially stateful dataflow Behrens, Jonathan (Jonathan Kyle) M. Frans Kaashoek and Malte Schwarzkopf. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-49). This thesis present a distributed implementation of Noria, a new streaming dataflow system that simplifies the infrastructure of read-heavy web applications by unifying the database, caching layer, and parts of application logic in a single system. Noria's partially-stateful dataflow allows it to evict and reconstruct state on demand, and avoid prior dataflow systems' restriction to windowed state. Unlike existing dataflow systems, Noria adapts on-line to schema and query changes, and shares state and computation across related queries to eliminate duplicate effort. Noria's distributed design enables it to leverage the compute power of an entire cluster while providing high availability thanks to its fault tolerant design. On a single machine, Noria already outperforms MySQL by up to 7 x, but when running across a cluster of machines, it can scale to tens of millions of reads and millions of writes per second. by Jonathan Behrens. S.M. 2018-09-17T15:55:29Z 2018-09-17T15:55:29Z 2018 2018 Thesis http://hdl.handle.net/1721.1/118054 1051460356 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 49 pages application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Electrical Engineering and Computer Science.
spellingShingle Electrical Engineering and Computer Science.
Behrens, Jonathan (Jonathan Kyle)
Distributed dynamic partially stateful dataflow
description Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 43-49). === This thesis present a distributed implementation of Noria, a new streaming dataflow system that simplifies the infrastructure of read-heavy web applications by unifying the database, caching layer, and parts of application logic in a single system. Noria's partially-stateful dataflow allows it to evict and reconstruct state on demand, and avoid prior dataflow systems' restriction to windowed state. Unlike existing dataflow systems, Noria adapts on-line to schema and query changes, and shares state and computation across related queries to eliminate duplicate effort. Noria's distributed design enables it to leverage the compute power of an entire cluster while providing high availability thanks to its fault tolerant design. On a single machine, Noria already outperforms MySQL by up to 7 x, but when running across a cluster of machines, it can scale to tens of millions of reads and millions of writes per second. === by Jonathan Behrens. === S.M.
author2 M. Frans Kaashoek and Malte Schwarzkopf.
author_facet M. Frans Kaashoek and Malte Schwarzkopf.
Behrens, Jonathan (Jonathan Kyle)
author Behrens, Jonathan (Jonathan Kyle)
author_sort Behrens, Jonathan (Jonathan Kyle)
title Distributed dynamic partially stateful dataflow
title_short Distributed dynamic partially stateful dataflow
title_full Distributed dynamic partially stateful dataflow
title_fullStr Distributed dynamic partially stateful dataflow
title_full_unstemmed Distributed dynamic partially stateful dataflow
title_sort distributed dynamic partially stateful dataflow
publisher Massachusetts Institute of Technology
publishDate 2018
url http://hdl.handle.net/1721.1/118054
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