Adaptive scheduling in Spark

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Mahajan, Rohan

Advisor dc:contributor.advisor

• Matei Zaharia.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/105977

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/105977