高负载下数据库动态性能预测模型的研究与应用

数据库性能预测具有可利用的历史数据较少和受外界不确定性因素影响较大的特点,传统的单一、静态预测模型很难满足生产实际的需要。本文重点介绍在高负载下,通过构建数据库动态预测组合模型,克服传统马尔科夫链方法的不足,可以有效预测在逻辑读剧增的情况下导致的SQL性能问题。建立灰色-马尔科夫链预测模型,预测随机时间序列数据的总体发展趋势,使用这些技术可以在Oracle数据库系统处于高负载状态下,对SQL语句执行情况做出准确的性能预测,迅速定位性能瓶颈,有效预防性能问题。

Research and Application of Database Dynamic Performance Prediction Model Under High Load

Database performance prediction has less historical data available and larger affection by external uncertainty factors.The traditional single,static prediction model is very difficult to meet the needs of actual production.This paper focuses on building a database dynamic prediction combination model under high load to overcome the deficiencies of the traditional Markov chain methods.So it can effectively predict the SQL performance problems caused by sharp increase of logical reads.The overall trends of random time series data can be predicted by building gray-Markov chain prediction model.When the Oracle database system is under high load conditions,these techniques can be used to accurately predict the executive performance of SQL statement,to quickly locate the performance bottlenecks and to effectively prevent the performance problems.