Research On System Performance Tuning And Optimization In Oracle Database

This paper studies the performance adjustment and optimization direction of Oracle database.Through the exploration and practice of multiple optimization methods,through the analysis of the effects of massive data processing operations,an efficient and feasible tuning strategy and implementation plan are summarized.Analyze the storage and use characteristics of massive data and summarize the traditional optimization methods for querying massive data.It is found that the traditional optimization method does not effectively partition the data table,making the data too scattered,and often requires multiple link operations during the query,resulting in the query becoming too complicated.At the same time,the partitioned data space may have conflicts in the hash sequence or the keyword density is too small to waste space.The improvement of the original massive data query optimization method combines the characteristics of the storage and use of massive data,avoids the disadvantages of traditional optimization methods,and forms a large amount of data based on the above-mentioned tactics to divide space according to liveness.Query optimization method.This paper establishes a method for query optimization of massive data based on the space of liveness,and improves the shortcomings of the original method of partitioning space.Based on the theoretical and experimental research,this paper proposes to divide the large data table by the frequency of the use of keywords in the query statement,and introduces the concept of active space at the same time,which effectively avoids possible conflicts in the hash sequence.The above improved method was integrated and experimented.Through simulation and experimentation on massive data in the server,it was found that the improved space partitioning method can effectively improve the query efficiency of massive data under certain conditions,and the space utilization rate after the partition has also been greatly improved.,while reducing the complexity of the query makes the query can quickly locate the target data,reducing the search scope.