Abstract
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. Keywords: Distributed Database, Query Processing, Query Optimization, Search Space, Search Strategy, Cost Model, Centralized Database, Cache
Highlights
Databases allow users to efficiently store, retrieve and analyze data
It is explored that how the response time is improved by using query optimization
To improve the optimization efficiency of the query these components areanalyzed with the help of tables, diagrams and examples
Summary
Databases allow users to efficiently store, retrieve and analyze data. Databases are very vital for business, research organizations and other fields where data has important role to play. In Principles for Distributed Databases in Telecom Environment it is suggested that there is an attractive option for such organization to switch on the concept of distributing the data over multiple sites, because it has benefits over centralized databases such as availability, reliability, reduced communication overhead, data localization, improved performance and an easier system expansion[2,5]. The role of optimizer is to indicate the alternatives plans and costs associated with them by using cost model, and select that plan which is cost effective[12] Joins is another important factor that affects the determination of suitable query execution plans, is to fetch the data from multiple sites[1]. Distributed database is physically dispersed on multiple sites by fragmentation and replication of data[9,10]. Designer can only overcome these issues if they consider the important aspects of query e.g. query processing and optimization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
