Work-sharing framework for Apache Spark

Apache Spark is a popular framework for distributed data processing that generalizes the MapReduce model and significantly improves the performance of many use cases. People can use Spark to query enormous data sets faster than before to gain insights for a competitive edge in industry. Often these ad-hoc queries perform similar work, and there is an opportunity to share the work of different queries. This can reduce the total computation time even more. We have developed a Wrapper class which performs such optimizations. In particular, its strategy of lazy evaluation allows duplicate computation to be avoided and multiple related Spark jobs to be executed at the same time, reducing the scheduling overhead. Overall, the system demonstrates significant efficiency gains when compared to default Spark.

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

• Yu, Lucy, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Matei Zaharia.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

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/1721.1/113441

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113441