Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations

TL;DR

This paper presents a general approach for maintaining query results efficiently under database updates, using heavy-light partitioning, applicable to arbitrary join queries with constant delay enumeration.

Contribution

It introduces a novel, generalized maintenance method combining delta queries, view trees, and heavy-light partitioning, optimizing update time via the maintenance width measure.

Findings

Update time matches or improves prior bests.

Method generalizes to arbitrary join queries.

Optimal heavy-light threshold minimizes maintenance width.

Abstract

We study the classical incremental view maintenance problem: Given a query and a database, maintain the query output under single-tuple updates (inserts or deletes) to the database such that the tuples in the query output can be enumerated with constant delay after any update. We introduce a maintenance approach whose update time matches or improves the best update time reported in prior work. Whereas prior approaches are manually tailored to each of a handful of queries, our approach generalizes to arbitrary join queries. It combines three techniques: delta queries, trees of materialized views, and heavy-light data partitioning. The overall update time incurred by our approach for a given join query is characterized by the maintenance width, a new measure that is parameterized by the heavy-light threshold for data partitioning. We show how to find the threshold that minimizes the maintenance width.