Adriano Di Pasquale

Distributed Searching of k-Dimensional Data with Almost Constant Costs

In this paper we consider the dictionary problem in the scalable distributed data structure paradigm introduced by Litwin, Neimat and Schneider and analyze costs for insert and exact searches in an amortized framework. We show that both for the 1-dimensional and the k- dimensional case insert and exact searches have an amortized almost constant costs, namely O (log (1+A) n) messages, where n is the total number of servers of the structure, b is the capacity of each server, and A = b/2. Considering that A is a large value in real applications, in the order of thousands, we can assume to have a constant cost in real distributed structures. Only worst case analysis has been previously considered and the almost constant cost for the amortized analysis of the general k-dimensional case appears to be very promising in the light of the well known dificulties in proving optimal worst case bounds for k-dimensions.

Chapter 6: Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B + -tree, hashing with chaining, and, in some cases, linear hashing [52]. Object-oriented and object-relational systems have also adopted these structures to a great extend.

ADST: An Order Preserving Scalable Distributed Data Structure with Constant Access Costs

Scalable Distributed Data Structures (SDDS) are access methods specifically designed to satisfy the high performance requirements of a distributed computing environment made up by a collection of computers connected through a high speed network. In this paper we propose an order preserving SDDS with a worst-case constant cost for exact-search queries and a worst-case logarithmic cost for update queries. Since our technique preserves the ordering between keys, it is also able to answer to range search queries with an optimal worst-case cost of O(k) messages, where k is the number of servers covering the query range. Moreover, our structure has an amortized almost constant cost for any single-key query.Hence, our proposal is the first solution combining the advantages of the constant worst-case access cost featured by hashing techniques (e.g. LH*) and of the optimal worst-case cost for range queries featured by order preserving techniques (e.g., RP* and DRT). Furthermore, recent proposals for ensuring high-availability to an SDDS can be easily combined with our basic technique. Therefore our solution is a theoretical achievement potentially attractive for network servers requiring both a fast response time and a high reliability.Finally, our scheme can be easily generalized to manage k-dimensional points, while maintaining the same costs of the 1-dimensional case.

A Very Efficient Order Preserving Scalable Distributed Data Structure

SDDSs (Scalable Distributed Data Structures) are access methods specifically designed to satisfy the high performance requirements of a distributed computing environment made up by a collection of computers connected through a high speed network. In this paper we present and discuss performances of ADST, a new order preserving SDDS with a worst-case constant cost for exact-search queries, a worst-case logarithmic cost for update queries, and an optimal worst-case cost for range search queries of O(k) messages, where k is the number of servers covering the query range. Moreover, our structure has an amortized almost constant cost for any single-key query. Finally, our scheme can be easily generalized to manage k-dimensional points, while maintaining the same costs of the 1-dimensional case. We report experimental comparisons between ADST and its direct competitors (i.e., LH*, DRT, and RP*) where it is shown that ADST behaves clearly better. Furthermore we show how our basic technique can be combined with recent proposals for ensuring high-availability to an SDDS. Therefore our solution is very attractive for network servers requiring both a fast response time and a high reliability.