Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity

TL;DR

This paper compares three dynamic clustering techniques for Object-Oriented Databases, demonstrating that a simpler method, DRO, achieves better performance with lower overhead than more complex algorithms.

Contribution

Introduces a simple, effective clustering technique (DRO) for OODBs and compares it with more complex methods using empirical benchmarks.

Findings

DRO has lower overhead than DSTC and StatClust.

DRO achieves better overall performance in benchmarks.

Complex clustering algorithms are less practical due to overhead.

Abstract

We present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance.