LH*lh is a new data structure for scalable high-performance hash les on the increasingly popular switched multicomputers, i.e., MIMD multiprocessor machines with distributed RAM memory and without shared memory. An LH*lh le scales up gracefully over available processors and the distributed memory, easily reaching Gbytes. Address calculus does not require any centralized component that could lead to a hot- spot. Access times to the le can be under a millisecond and the le can be used in parallel by several client processors. We showthe LH*lh design, and report on the performance analysis. This includes experiments on the Parsytec GC/PowerPlus multicomputer with up to 128 Power PCs and 32 MB of distributed RAM per node. We prove the e ciency of the method and justify various algorithmic choices that were made. LH*lh opens a new perspective for high-performance applications, especially for the database management of new types of data and in real-time environments.

Spatial data storage stresses the capability of conventional DBMSs. We present a scalable distributed data structure, hQT*, which offers support for efficient spatial point and range queries using order preserving hashing. It is designed to deal with skewed data and extends results obtained with scalable distributed hash files, LH*, and other hashing schemas. Performance analysis shows that an hQT* file is a viable schema for distributed data access, and in contrast to traditional quad-trees it avoids long traversals of hierarchical structures. Furthermore, the novel data structure is a complete design addressing both scalable data storage and local server storage management as well as management clients addressing. We investigate several different client updating schemes, enabling better access load distribution for many "slow" clients. Keywords Scalable Distributed Data Structure, Spatial Point Index, Ordered Files, Multicomputers 1 Introduction Research is increasingly focusing o...

Contents 1 Introduction 11 1.1 The Need for High Performance Databases . . . . . . . . 11 1.2 Conventional Databases . . . . . . . . . . . . . . . . . . 13 1.3 Distributed Databases . . . . . . . . . . . . . . . . . . . 13 1.4 Multidatabases . . . . . . . . . . . . . . . . . . . . . . . 14 1.5 Data Servers . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.6 Parallel Data Servers . . . . . . . . . . . . . . . . . . . . 15 1.7 Database Machines . . . . . . . . . . . . . . . . . . . . . 17 1.8 Overview of Some Data Servers . . . . . . . . . . . . . . 18 1.9 Current Trends . . . . . . . . . . . . . . . . . . . . . . . 21 1.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 21 2 Properties of Structures for Servers 23 2.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3 Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Availabilit