Adaptive scheduling in Spark
Name
965643791-MIT.pdf
Description
Full printable version
Size
414.5 KB
Format
Adobe PDF
Checksum (MD5)
5fe4dcf55b2387a0d63fc7430cad814e
Author(s)
Mahajan, Rohan
Advisor(s)
Matei Zaharia.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Because most data processing systems are distributed in nature, data must be transferred between machines. Currently, Spark, a prominent such system, predetermines the strategies for shuffling this data, but in certain situations, different shuffle strategies would improve performance. We add functionality to track metrics about the data during the job and appropriately adapt the shuffle strategy. We show improvements in ShuffledRDD performance, joins using Spark's RDD interface, and joins in Spark SQL.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 33).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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