Tag: Redgate

At the recent Redgate Summit in Chicago, I demo’d (lightly) the ML based Alert thresholds in Redgate Monitor and decided to write a little about this.

Video of this post below.

This is part of a series of posts on Redgate Monitor. Click to see the other posts.

Noisy Alerts

When people used to setup Redgate Monitor in the 2015 timeframe (formerly SQL Monitor) they sometimes complained about the noisiness of the alerts. Just too many alerts were sent out.

I felt this way about other products I’d used in the past, and our dev teams worked hard with support to enhance the produce and tune our defaults to make them less noisy. Many customers appreciate this, though a new install can take a little tuning to customize to what is helpful and actionable vs what is noise for each customer.

A Better Way

As the AI-LLM rise started in 2024, we started to work on different ways to use this tech in Monitor. One of our first ideas was an ML based alert that didn’t work on a set level to trigger, but rather would look at historical data and adjust the threshold for alerts. In this way you would

We released this in v 14.0.37, so you need to be on that version or higher to use this. This is in a documentation page that describes how this work. Basically we take 14 days worth of history (the min required) and run that through a machine learning algorithm to decide what a predicted level should be. There is a pad added, and you can still set a min threshold and a duration.

If the value exceeds the predicted value + pad, an alert is sent out.

This is intended to reduce the amount of alerting from a system that might have a variable workload, but one that repeats and is predictable.

Enabling Alerts

This is available for the following alerts so far:

• Processor (CPU) utilization
• Server waits
• DTU utilization
• Query throughput

If you go into the configuration for any of these alerts, you wil see a “dynamic alert” toggle that can be enabled. You can see this below where is says “Use dynamic alert thresholds”.

When you do that, you can still set the levels, and as shown below (from the doc page), you get an idea of how the threshold works. The predicted values are shown as the line. If the line gets into the red areas, an alert is raised.

The time limit works as shown for sensitivity. The value would have to get into the blue area, so you can see a pad around the predicted value alerts are not raised.

That’s it. Set the alert and if there is 14 days worth of data, each machine gets its own custom alert levels.

Seeing the Expected Values

When an alert fires, the alert includes the predicted values as well as the values recorded. You can see below in this alert that CPU was expected to decay, but hadn’t, so an alert is fired where the green line is shown.

At the top of the alert, you can see that this was generated by an ML process.

Summary

This dynamic levels should reduce the amount of alerts you get from variable workloads, since the predictions are made based on each machine’s history. You can set some threshold and sensitivity over time, but the actual values used for alerts are predicted.

There is also a feedback place in the alert so that you can let us know if this is helpful or not. We use feedback from you to help better tune our the ML works.

If you have feedback in general, please let us know as we value your opinions and comments on how we shape the future of Redgate Monitor.

Redgate Monitor is a world class monitoring solution for your database estate. Download a trial today and see how it can help you manage your estate more efficiently.

Video Walk-through

You can see me check this out in the video below.

AI is everywhere. It’s in the news, it’s being added to every product, management wants everyone to be more productive with AI.

Redgate Monitor isn’t immune from this wave. We have spent a lot of time over the last year trying to learn about AI tech, experiment with it, and find ways that it provides values to customers, not just becomes a marketing label.

The big area is query analysis, though this wasn’t the first area. I’ll discuss another area in a different post, but this one looks at AI assisted query analysis.

This feature is a part of Redgate Monitor Standard, which hopefully gives many of you confidence that we haven’t abandoned this edition for the Enterprise one. We still haves teams working on features for everyone in Redgate Monitor.

This is part of a series of posts on Redgate Monitor. Click to see the other posts.

Getting Help with Queries

If you find a problem area on your server and scroll down to look at queries, you can expend the query details with the arrow on the left. When you do this, you see something like this:

Lots of these details are familiar, but the AI sparkle and purple button is new. It says “Analyze Query” and what this does is send some query details and context to an AI LLM that Redgate runs in AWS for analysis.

When you click this, it takes a few seconds and then a response from the AI LLM comes back. You get a summary, which in this case tries to look at the query, infer some intent and then use details from the query to give a quick view of what this query is doing. You can see this in the image below.

Below this, we give a performance analysis. In this case, we get the state and some observations about how this query plan looks. In this case, we see estimates of cost and rows, parallelism, etc. and then that this is a system function, there are implicit conversions, and large rows.

The section below this has recommendations. These are guesses at things that might help improve performance. In this case, there aren’t a lot of items, but some of these can help, like the implicit conversions.

Here is another plan, in this case, there are three items noted from the query. There is some detail and ordering. The implicit conversions here are critical as these can dramatically slow the query. The second item, noting over 1 million rows, is another thing to look at and lastly, SELECT *.

Below this are positives and action items. You shouldn’t blindly follow these items, but rather evaluate and test if they make a difference. In this case, these would help.

We try to intelligently pass through context to the AI LLM, but we are not custom training LLMs. We are hosting the models, so no data is being saved. We send context, get a response, and then that session closes with none of your data kept. You can read more in our AI FAQ.

Note the upper right corner of the first image, where we tell you that this content might have mistakes and you should check. You can also click thumbs up/down and provide feedback if you wish.

Summary

This post looks at the AI query analysis, which is in preview as of Feb 2026. This is intended to help you more quickly analyze what’s happening on your servers. This should help your team have a consistent view of what is going on and help share knowledge among team members.

If you find issues, or value, in this feature, please send us feedback as we are looking to improve this feature over time.

If you have feedback, please let us know as we value your opinions and comments on how we shape the future of Redgate Monitor.

Redgate Monitor is a world class monitoring solution for your database estate. Download a trial today and see how it can help you manage your estate more efficiently.