Vijay Panwar

Hello Petros,

I appreciate your insightful questions and genuine interest in AI-driven database optimization and predictive maintenance. It is encouraging to hear that you found the article engaging and are already engaging in practical experimentation, as this is indeed the most effective way to enhance your understanding and refine your methods.

In response to your initial inquiry regarding AI for Query Optimization, you are correct in your assessment of `sys.query_store_runtime_stats`. The available columns provide statistical aggregates such as minimum, maximum, average, and standard deviation for runtime metrics. Although direct metrics like `qs.total_duration` or `qs.cpu_time` may not be presented as individual fields, utilizing `avg_duration` and `stdev_duration` can effectively facilitate anomaly detection when analyzed alongside workload patterns. We have discovered that training AI models, including unsupervised clustering or classification models, on these runtime statistics over time can help identify queries' "normal" behavior and subsequently flag any deviations. Furthermore, after detecting anomalies, AI can play a crucial role in the follow-up process by identifying patterns across stored procedures. For instance, it can reveal if multiple stored procedures exhibit similar query structures or indexing issues. By leveraging natural language processing (NLP) and AI pattern recognition, we can partially automate the identification of missing indexes or suboptimal query patterns by examining execution plans and correlating them with runtime anomalies.

Regarding Predictive Maintenance, your comprehension aligns well with the concept. The goal is to gather `sys.dm_io_virtual_file_stats` metrics at regular intervals—whether daily, hourly, or per workload cycle. While the values may fluctuate from day to day based on usage, capturing these variations is precisely what we aim to achieve. By constructing a time-series dataset or a performance matrix over time, we can input this data into regression models or more sophisticated machine learning techniques, such as Long Short-Term Memory (LSTM) networks, to predict future I/O stalls. AI not only aids in forecasting but also in generating actionable insights.