Online aggregation

Author:

Hellerstein Joseph M.1,Haas Peter J.2,Wang Helen J.1

Affiliation:

1. Computer Science Division, University of California, Berkeley

2. Almaden Research Center, IBM Research Division

Abstract

Aggregation in traditional database systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements. These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Reference31 articles.

1. Query processing and optimization in Oracle Rdb

2. P. Billingsley. Probability and Measure. Wiley New York second edition 1986. P. Billingsley. Probability and Measure. Wiley New York second edition 1986.

3. Processing queries for first-few answers

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