Improving Query Performance in Distributed Database

IEEE Transactions on Computers, 2000

A model is developed for determining the optimal policy for processing a given relational model query. The model is based on operating cost (processing cost and communication cost), which is a function of selection of sites for processing query operations, sequence of operations, file size, and data reduction functions. The optimal policy specifies the site selection and sequence of operations that yield minimum operating cost. The query is first decomposed into a set of relational algebra operations whose precedence relationships are expressed as a query tree. Additional query trees may be generated by permuting these operations. A set of query processing graphs is then generated for a given query tree. Each node of a query processing graph represents the execution of a set of operations at a single site. Since the neighboring nodes represent distinct processing sites, the arcs between nodes represent the communication cost among sites. Theorems based on the cost model and the query processing graphs are developed for determining the optimal sites for processing the operations and for selecting the local optimal graphs from the set of query processing graphs. Use of these theorems greatly reduces the computation requirements in determining the optimal query processing policy. An example is given to illustrate the model. Index Terms-Distributed database, local operation group, optimal query processing, query operating cost, query processing graph, query tree, relational algebra, relational database.

Computing, Information Systems, Development Informatics & Allied Research Journal, 2016

Optimizing query processing in distributed database system is an important research area considering the volume of data and information being processed these days. Many techniques have been proposed for optimizing query processing in distributed databases. In this paper, we proposed a combination of two of the most commonly used techniques for optimizing query: datashipping and query-shipping techniques. This hybridized technique provides solution for storing and processing data and information for quick retrieval of information in a distributed database when the data to be retrieved are not located within a single computer system. Using this technique, each employee in an organization that is geographically located in different regions can decide to hide information about any of the employee whose data is in the organization's database by preserving their individual query intent. In the client machine, the data-shipping technique is used to help in the local processing and communication cost while the query-shipping technique is used in the server machine to optimize the given data selection. In some cases data management (update) can also be carried out by the database administrator thus the technique combines the features of both the data-shipping and query-shipping techniques. We compared the results obtained with previous results when the techniques were separately used and we discovered that our technique performs better in terms of the time and complexities of the algorithm used.

This paper addresses the processing of a query in distributed database systems using a sequence of semijoins. The objective is to minimize the intersite data traffic incurred by a distributed query. A method is developed which accurately and efficiently estimates the size of an intermediate result of a query. This method provides the basis of the query optimization algorithm. Since the distributed query optimization problem is known to be intractable, a heuristic algorithm is developed to determine a low-cost sequence of semijoins. The cost comparison with an existing algorithm is provided. The complexity of the main features of the algorithm is analytically derived. The scheduling time for sequences of semijoins is measured for example queries using the PASCAL program which implements the algorithm.

Query optimization is one of the essential problems in centralized and distributed database. The data allocation to different sites is proposed in a distributed DMS(Database Management System) before a query in order to decrease, the next communicative costs namely an optimized bed production which is of ‘NP’ issues. In this article, it was attempted to examine both the methods to allocate data and produce optimized design in a distributed system and the space to query for query optimization in the distributed environment and show the need concerning optimization method in view of different aspects of optimization process. We install a new method for optimization in distributed database environment which indicates somehow our simple optimization design is executed relatively well until the database design is physical

This paper strongly emphasizes the approach for query optimization which is a frame work model for distributed computing environment systems. We have two popular methods for query optimization. One which is traditional which follows the stages like query planning, deployment and adaptation. The second one which is our main experimental approach which follows the stages like query planning and deployment together as a single stage followed by adaptation [1]. The approach of integration of planning and deployment while writing for distributed queries which involve many sub-queries in distributed data stream systems and applications. This method makes use of hierarchical network partitions which provides operator level-reuse which utilizing network characteristics to maintain an appropriate search space during query planning and deployment. The approach has been practically experimented and proved its efficiency over the traditional methods.

The query optimization problem in large-scale distributed databases is NP nature and difficult to solve. The complexity of the optimizer increases as the number of relations and number of joins in a query increases. being carried out to find an appropriate algorithm to seek an optimal solution especially when the size of the database increases. Various Optimization Strategies have been reviewed in this paper and the studies show that the performance of distributed query optimization is improved when Ant Colony Optimization Algorithm is integrated with other optimization algorithms.

2012

Query optimization in distributed databases is explicitly needed in many aspects of the optimization process, often making it imperative for the optimizer to consult underlying data sources while doing cost based optimization. This not only increases the cost of optimization, but also affects the trade-offs involved in the optimization process significantly. The leading cost in this optimization process is the cost of costing that traditionally has been considered insignificant. The optimizer can only afford a few rounds of messages to the under-lying data sources and hence the optimization techniques in this environment must be geared toward gathering all the required cost information with minimal communication. In this paper, a cache based query optimization model has been proposed which shows better hit ratio even for the initial queries made since local cache has been used instead of global cache. A cache is implanted between the local optimizer and local database. Whenever a qu...

Journal of Heuristics, 1997

The query optimizer is the DBMS (data base management system) component whose task is to find an optimal execution plan for a given input query. Typically, optimization is performed using dynamic programming. However, in distributed execution environments, this approach becomes intractable, due to the increase in the search space incurred by distribution. We propose the use of the tabu search metaheuristic for distributed query optimization. A hashing-based data structure is used to keep track of the search memory, simplifying significantly the implementation of tabu search. To validate this proposal, we implemented the tabu search strategy in the scope of an existing optimizer, which runs several search strategies. We focus our attention on the more difficult problems in terms of the query execution space, in which the solution space includes bushy execution plans and Cartesian products, which are not dealt with very often in the literature. Using a real-life application, we show the effectiveness of tabu search when compared to other strategies.

I would like to thank my supervisor Dr Dan Olteanu for his incredible level of enthusiasm and encouragement throughout the project. I am also very grateful for the continuous level of feedback and organisation as well as the amount of time he has devoted to answering my queries. I feel that I now approach complex and unknown problems with enthusiasm instead of apprehension as I used to. I couldn't have had a better supervisor.

2009

Query processing is an important concern in the field of distributed databases. The main problem is: if a query can be decomposed into subqueries that require operations at geographically separated databases, determine the sequence and the sites for performing this set of operations such that the operating cost (communication cost and processing cost) for processing this query is minimized. The problem is complicated by the fact that query processing not only depends on the operations of the query, but also on the parameter values associated with the query. Distributed query processing is an important factor in the overall performance of a distributed database system.