Query optimization in distributed relational databases

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.

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.

2007

Distributed database system technology is one of the major developments in information technology area. It will continue to have a very significant impact on data processing in the upcoming years because distributed database systems have many potential advantages over centralized systems for geographically distributed organizations. The continuing interest in distributed database systems in the research community and the marketplace and the introduction of many commercial products indicate that distributed database systems will play a more important role in data processing and eventually will replace centralized systems as the major database technology in the future. The availability of high speed communication networks and, especially, the phenomenal popularity of the Internet and the intranets will undoubtedly speed up the transition process. Some challenging problems must be solved before the full potential benefits of distributed database technology can be realized. Among them is query processing (including query optimization), one of the most important issues in distributed database system design. The query optimization problem in large-scale distributed databases is NP-hard in nature and difficult to solve. In this study, the query optimization problem is reduced to a join ordering problem similar to a variant of traveling salesman problem. We explored several heuristics and a genetic algorithm for solving the join ordering problem. Some computational experiments on these algorithms were conducted and solution qualities compared. The computation experiments show that heuristics and genetic algorithms are viable methods for solving query optimization problem in large scale distributed database systems. 262 issues related to the problem, to model the problem, taking into consideration the most important factors, to propose some solution methods for these models, and, finally, to conduct computational experiments and compare the results to determine the effectiveness and efficiency of the solution techniques (algorithms). We believe that the development of the comprehensive models for the query optimization in large-scale systems, as well as finding effective and/or efficient solution techniques to solve the problems that have been identified are important and will contribute to the use of and research on distributed database technology.

2014

The query optimizer is a significant element in today’s relational database management system. This element is responsible for translating a user-submitted query commonly written in a non-procedural language-into an efficient query evaluation program that can be executed against the database. This research paper describes architecture steps of query process and optimization time and memory usage. Key goal of this paper is to understand the basic query optimization process and its architecture.

Indian Journal of Science and Technology, 2018

Objectives: This paper brings to light different query optimization components and their optimizing functionalities which are helpful to improve the response time of query and the efficiency of distributed database. A cache based optimization is also analyzed to highlight the query optimization process. Methods: As data is the most valuable asset for any organization due to this they want to get access and use it efficiently and in a timely manner. To evaluate the efficiency of query optimization its different components e.g. search space, search strategy and cost model are evaluated with the help of examples, tables and diagrams. By comparing the different results, a cache based optimization technique is also evaluated. Findings: It is observed that in search space generated plans are equivalent in the sense they provide same results but their operation, implementation and performance is different. Different algorithms of search strategy are also examined to get the quicker and accurate results and notice that movement of search strategy is greatly depend upon join ordering and cost model. It is also observed that the cost model is helpful to select the best query execution plan but it depends upon the different parameters for example queue length, sever distance, server capacity and load. The latest cache based query optimization technique is also examined and noted that it is key to improve the response time of query as its computational cost is very low. It will be more helpful if it is placed at each site. Applications and Future Improvements: Currently cache based query optimization is applicable only for homogeneous distributed databases. In future this technique can also be implemented for heterogeneous type of databases.

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.

International Journal of Computer Applications, 2013

Query optimization is the most critical phase in query processing. Query optimization in distributed databases explicitly needed in many aspects of the optimization process, this is not only increases the cost of optimization, but also changes the trade-offs involved in the optimization process significantly .This paper describes the synthetically evolution of query optimization methods from uniprocessor relational database systems to parallel database systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) type of algorithm (static or dynamic), (ii) working environments (re-optimization or rescheduling) and (iii) level of modification. The major contributions of this paper are: (I) Understanding the mechanisms of query optimization methods with respect to the considered environments and their constraints (e.g. parallelism, distribution, heterogeneity, large scale, dynamicity of nodes). (ii) Study the problem of query optimization particular in term of heterogeneously environment and pointing out their main characteristics, which allow comparing them and help to Implement new query optimization algorithm and model. These contributions is led to performance enhancement of query optimization in distributed database system through classify by different QEPs and minimize the response time.

Distributed database is emerging as a boon for large organizations as it provides better flexibility and ease compared to centralized database. As the data is growing over the distributed environment day by day, a better distributed management system is required to manage this large data. Query optimization is a process of finding out better query execution plan from multiple available options. As there a multiple sites in distributed database having parts of the data, query optimization is one of the challenging tasks in distributed database. In this review paper query optimization challenges in distributed database and its basic steps have been studied. And a review of some proposed systems has been done.

Execution of Structured Query Language (SQL) queries in optimized way in the distributed database is a hitch that most of the database programmer faces since the inception of database technology. Query optimization in network is one of the hardest problems in the database area. The commercialization and success of database systems is primarily due to the development of complicated query optimization techniques. Database users post their queries in a declarative mode by by means of SQL or Object Query Langua ge (OQL) and the Query Optimizer of the related database system find a best plan to execute the same. The optimizer determines the best indices to be used to execute a query and the order in which the operations of a query should be executed. To achieve t his, the optimizer estimate alternative plans, and also estimate the cost of query plan by means of a cost model, and then selects the plan with lowest cost. There has been much research into this field. In this paper, we will review the difficulty of dist ributed query optimization; and will emphasis on the various components of the query optimizer required in distributed environment, i.e. cost model, search space and search strategy. A review of the existing work in this field is shown and future work is h ighlighted based on recent work that utilizes mobile agent technologies.

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