A relational architecture for graph, linear algebra, and business intelligence querying
Abstract/Contents
- Abstract
- Modern analytics workloads extend far beyond the SQL-style business intelligence queries that relational database management systems (RDBMS) were designed to process efficiently. As a result, RDBMSs often incur orders of magnitude performance gaps with the best known implementations on modern analytics workloads such as graph analysis and linear algebra queries. The relational model is therefore largely forsaken on such workloads, resulting in a flurry of activity around designing specialized (low-level) graph and linear algebra packages. In this dissertation we present a new type of relational query processing architecture that overcomes these shortcomings of traditional relational architectures. To do this we present a new in-memory query processing engine called EmptyHeaded. EmptyHeaded uses a new, worst-case optimal (multiway) join algorithm as its core execution mechanism, making it fundamentally different from nearly every other relational architecture. With EmptyHeaded, we show how the crucial optimizations for graph analysis, linear algebra, and business intelligence workloads can be captured in such a novel relational architecture. The work presented in this dissertation shows that, unlike traditional RDBMSs, this new type of relational query processing architecture is capable of delivering efficient performance in multiple application domains.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Aberger, Christopher R |
|---|---|
| Degree supervisor | Olukotun, Oyekunle Ayinde |
| Degree supervisor | Ré, Christopher |
| Thesis advisor | Olukotun, Oyekunle Ayinde |
| Thesis advisor | Ré, Christopher |
| Thesis advisor | Zaharia, Matei |
| Degree committee member | Zaharia, Matei |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Christopher R. Aberger. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Christopher Richard Aberger
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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