Design And Implementation Of Cost Estimation Model In Da Meng DBMS

Relational query languages provide a high level"declarative"interface to access data stored in relational databases. For each input query, the optimizer has a number of alternative plans. To choose the best one from a set of candidate plans has been the primary research ever since the RDBMS.A cost estimation model is to estimate the cost of any given plan without executing it. The cost here may refer to CPU time, I/O cost, memory usage, etc. Cost estimation should be efficient since it is repeatedly revoked during the optimization process. For a number of reasons, cost estimation may give inaccurate result and query optimizer may produce sub-optimal query plans, leading to a significant degradation of performance. Actions should be taken to detect sub-optimal plans and recover from such errors. Furthermore, similar mistakes should be avoided in future. Cost estimation model also need a convenient and low-cost approach to maintain statistical summaries.To get the exact cost for a given plan, statistics on accessed tables should be collected. There are many ways to describe this information, and equal-depth histogram is the most used in commercial DBMS. We can estimate the intermediate cardinality then I/O cost with these information.In this paper, We proposed compute formulas for logical operators in DM DBMS, an approach to get and maintain the statistics, as well as an improvement method to detect and tackle sub-optimal plan selection.In order to implement this cost estimation model, we improved the query engine of DM. Experiments showed that with this cost estimation model, DM can choose execution plan more reasonable, which brings better performance.