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2019, Journal of Institute of Science and Technology
Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.
1982
Efficient ways to process unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches for improving the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating and choosing among alternative access plans. This paper surveys these approaches in the framework of a general query evaluation procedure using the relational calculus representation of queries. The focus is on centralized database systems; some relationships to other system types are studied. Acknowledgment This work was supported i n part by the Deutsche Forschungsgemeinschaft (DFG) under grant no. SCHM 450/2-1.
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.
Query Processing is the systematic method of accessing the require information from a database system in an expected and reliable trends. Database management systems must be agile to respond to requests for information from the user i.e. process queries. In huge database systems that may be running on unreliable and elusive domain it is no easy to outcome to dynamic database query plans based on information available exclusively at compile time. Obtaining and finding the database results in a prompt manner deals with the method of Query Optimization. Adequate processing of queries is a major requirement in various interactive environments that associates huge amounts of data. Dynamic query processing in environments such as the multimedia search, Web, and distributed systems has shown a main impact on performance and optimization. This paper will suggest and propose the main concepts of query processing and query optimization in the relational database systems. It is also describing and differentiating query-processing method in relational database systems.
2011
In today's computational world,cost of computation is the most significant factor for any database management system.Searching a query from a database incurs various computational costs like processor time and communication time.Then, there are costs because of operations like projection, selection, join etc.DBMS strives to process the query in the most efficient way (in terms of 'Time') to produce the answer.In this paper we proposed a novel method for query optimization using heuristic based approach. In the proposed algorithm,a query is searched using the storage file which shows an improvement with respect to the earlier query optimization techniques. Also, the improvement increases once the query goes more complicated and for nesting query.
2013
Query optimizers are critical to the efficiency of modern relational database systems. If a query optimizer chooses a poor query execution plan, the performance of the database system in answering the query can be very poor. This study describes that there are numerous alternative ways to execute a query. These are so called execution plans. A component in the database management system called the Query Optimizer decides how to pick an efficient execution plan. For this the optimizer deploys cost-based optimization. Approximate execution costs are calculated for various plans, and one with low cost is chosen. The execution cost is a weighted function of the system resources needed to execute the query. Examples of such system resources are the CPU time or the number of I/O operations. In order to come up with reasonable cost estimates, the optimizer needs to estimate the size of sub-queries. This is important, for instance, when choosing the join order of the relations. To estimate the sizes of sub-queries, the optimizer needs to know the selectivity of the query predicates.
ACM SIGMOD Record, 2009
Traditionally, database systems were optimized in the following way: "Given a set of machines, try to minimize the response time of each request." This paper argues that today, users would like a database system to optimize the opposite question: "Given a re- ...
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.
Object-oriented database systems began developing in the mid-80's out of a necessity to meet the requirements of applications beyond the data processing applications which were [are] served by relational database systems. We propose in this paper a new approach that permits to enrich technique of query optimization existing in the object-oriented databases and the comparative analysis of query optimization for relational databases and object oriented database based on cost, cardinality and no of bytes. Seen the success of query optimization in the relational model, our approach inspires itself of these optimization techniques and enriched it so that they can support the new concepts introduced by the object databases.
International Journal of Database Theory and Application
Now days in the field of service oriented technologies cloud computing plays an important role. The main aim of cloud computing is to make people compute and store the resources easily and efficiently. Recent focus is deal with data expressing and searching. To improve the performance in the cloud requires the optimization of data processing time. Our study gives a comprehensive survey on numerous models and approaches used for query optimization to minimize execution time and to improve resource utilization. We have reviewed various research work done on query optimization for conventional SQL and MapReduce platforms.
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.
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.
Query Processing is the systematic method of accessing the require information from a database system in an expected and reliable trend. Database systems must be agile to respond to requests for information from the user i.e. process queries. In huge database systems that may be running on unreliable and elusive domain it is no easy to outcome to dynamic database query plans based on information available exclusively at compile time. Obtaining and finding the database results in a prompt manner deals with the method of Query Optimization. Adequate processing of queries is a major requirement in various interactive environments that associates huge amounts of data. Dynamic query processing in environments such as the multimedia search, Web, and distributed systems has shown a main impact on performance and optimization. This paper will suggest and propose the main concepts of query processing and query optimization in the relational database systems. It is also describing and differentiating query-processing method in relational database systems.
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.
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.
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.
2015
The query processer and optimizer is an important component in today’s relational database management system. This component is responsible for translating a user query, usually written in a non-procedural language like SQL – into an efficient query evaluation program that can be executed against database. In this paper, we identify many of the common issues, themes, and approaches that extend this work and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a “value-add” over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understating the field of query processing and optimization in general.
2013
Abstract: Query optimizers are critical to the efficiency of modern relational database systems. If a query optimizer chooses a poor query execution plan, the performance of the database system in answering the query can be very poor. This study describes that there are numerous alternative ways to execute a query. These are so called execution plans. A component in the database management system called the Query Optimizer decides how to pick an efficient execution plan. For this the optimizer deploys cost-based optimization. Approximate execution costs are calculated for various plans, and one with low cost is chosen. The execution cost is a weighted function of the system resources needed to execute the query. Examples of such system resources are the CPU time or the number of I/O operations. In order to come up with reasonable cost estimates, the optimizer needs to estimate the size of sub-queries. This is important, for instance, when choosing the join order of the relations. To...
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.
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.
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