Query processing in a distributed data base

Information Processing Letters, 1980

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.

IJRASET, 2021

The fundamental goal of this postulation is to introduce various models for single also as numerous inquiry handling in the Distributed data set framework which brings about less question handling cost. One of the significant issues in the plan and execution of Distributed Information Base Management Systems (DDBMS) is productive inquiry handling. The objective of dispersed inquiry improvement decreases to minimization of measure of information to be communicated among destinations for handling a given inquiry. The issue of question handling in DDBS (1 1) has been concentrated broadly in writing. In the greater part of calculations, the capability of the question will contain a grouping of tasks. In such cases, while executing tasks from right to left, as per the request for tasks in arrangement, the aftereffect of an activity might be an operand to the next activity. Since the tasks are subject to each other, at a moment in particular one activity at one site will be executed despite the fact that the climate is dispersed. Then frameworks at any remaining locales will be inactive for this inquiry. Another model, Totally Reducible Relation Model (CRK Medel), which permits parallelism and processes numerous tasks all the while at all important locales is introduced. It is expected that the tasks are in the type of conjunctions. So every activity can be handled freely. In this model at some moment, relations at every single significant site will be totally diminished by relating sets of every appropriate activity (Determinations, Semijoins and Joins) all the while. Thus, every connection will be checked just a single time to deal with all appropriate tasks by decreasing VO cost.

Performance Evaluation, 1984

In this paper we briefly present the design of a distributed relational data base system. Then, we discuss experimental observations of the performance of that system executing both short and long commands. Conclusions are also drawn concerning metrics that distributed query processing heuristics should attempt to minimize. Lastly, we comment on architectures which appear viable for distributed data base applications.

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.

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.

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.

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.