AdaptDB : adaptive partitioning for distributed joins
Name
1005706396-MIT.pdf
Description
Full printable version
Size
588.04 KB
Format
Adobe PDF
Checksum (MD5)
2bdc51d1c1e52c2ca355724e3ad17f67
Author(s)
Lu, Yi, (Ph. D. in Computer Science) Massachusetts Institute of Technology
Advisor(s)
Samuel R. Madden.
Alternative Title
Adaptive partitioning for distributed joins
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Big data analytics often involves complex join queries over two or more tables. Such join processing is expensive in a distributed setting both because large amounts of data must be read from disk, and because of data shuffling across the network. Many techniques based on data partitioning have been proposed to reduce the amount of data that must be accessed, often focusing on finding the best partitioning scheme for a particular workload, rather than adapting to changes in the workload over time. In this thesis, we present AdaptDB, an adaptive storage manager for analytical database workloads in a distributed setting. It works by partitioning datasets across a cluster and incrementally refining data partitioning as queries are run. AdaptDB introduces a novel hyper-join that avoids expensive data shuffling by identifying storage blocks of the joining tables that overlap on the join attribute, and only joining those blocks. Hyper-join performs well when each block in one table overlaps with few blocks in the other table, since that will minimize the number of blocks that have to be accessed. To minimize the number of overlapping blocks for common join queries, AdaptDB users smooth repartitioning to repartition small portions of the tables on join attributes as queries run. A prototype of AdaptDB running on top of Spark improves query performance by 2-3x on TPC-H as well as real-world dataset, versus a system that employs scans and shuffle-joins.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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 (pages 57-59).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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