Instance-Optimized Data Structures for Membership Queries

We are near the end of Moore’s law and hardware growth has hit a stagnation. Modern data processing systems need to continuously improve their performance to match the humongous growth of data. Data structures and algorithms such as sorting, indexes, filters, hash tables, query optimization, etc are...

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Bibliographic Details
Main Author: Vaidya, Kapil Eknath
Other Authors: Kraska, Tim
Format: Thesis
Published: Massachusetts Institute of Technology 2023
Online Access:https://hdl.handle.net/1721.1/150074
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author Vaidya, Kapil Eknath
author2 Kraska, Tim
author_facet Kraska, Tim
Vaidya, Kapil Eknath
author_sort Vaidya, Kapil Eknath
collection MIT
description We are near the end of Moore’s law and hardware growth has hit a stagnation. Modern data processing systems need to continuously improve their performance to match the humongous growth of data. Data structures and algorithms such as sorting, indexes, filters, hash tables, query optimization, etc are the fundamental building blocks of these systems and dictate their performance. Traditional data structures and algorithms provide worst-case guarantees by making no assumptions about the data or workload. Thus, the resulting data processing system gives an adequate performance in the average case but may not be optimal for a particular use case. In this thesis, we will look at how to redesign membership query data structures so they can automatically adapt to an individual use case. These instance-optimized data structures act as drop in replacements for their counterparts in systems and improve their performance without any significant overhaul of the system or labor-intensive manual tuning.
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spelling mit-1721.1/1500742023-04-01T03:38:53Z Instance-Optimized Data Structures for Membership Queries Vaidya, Kapil Eknath Kraska, Tim Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science We are near the end of Moore’s law and hardware growth has hit a stagnation. Modern data processing systems need to continuously improve their performance to match the humongous growth of data. Data structures and algorithms such as sorting, indexes, filters, hash tables, query optimization, etc are the fundamental building blocks of these systems and dictate their performance. Traditional data structures and algorithms provide worst-case guarantees by making no assumptions about the data or workload. Thus, the resulting data processing system gives an adequate performance in the average case but may not be optimal for a particular use case. In this thesis, we will look at how to redesign membership query data structures so they can automatically adapt to an individual use case. These instance-optimized data structures act as drop in replacements for their counterparts in systems and improve their performance without any significant overhaul of the system or labor-intensive manual tuning. Ph.D. 2023-03-31T14:30:02Z 2023-03-31T14:30:02Z 2023-02 2023-02-28T14:39:50.935Z Thesis https://hdl.handle.net/1721.1/150074 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Vaidya, Kapil Eknath
Instance-Optimized Data Structures for Membership Queries
title Instance-Optimized Data Structures for Membership Queries
title_full Instance-Optimized Data Structures for Membership Queries
title_fullStr Instance-Optimized Data Structures for Membership Queries
title_full_unstemmed Instance-Optimized Data Structures for Membership Queries
title_short Instance-Optimized Data Structures for Membership Queries
title_sort instance optimized data structures for membership queries
url https://hdl.handle.net/1721.1/150074
work_keys_str_mv AT vaidyakapileknath instanceoptimizeddatastructuresformembershipqueries