Press "Enter" to skip to content

Category: T-SQL Tuesday

SQL Server Baselines with the TIG Stack

Mark Wilkinson combines Telegraf, InfluxDB, and Grafana:

Lots of folks wonder why I would go through the trouble of building out a system when so many vendors have already solved the problem of collecting baseline metrics. The answer at the time was simple: cost. With my setup I could monitor close to 600 instances (including dev) for $3,000 USD per year. That includes data retention of ~2 years! Are there some administration costs as far as my time is concerned? Of course. In the begining things were a little rough as I learned more about InfluxDB, but once things were configured correctly the most work I’ve had to do is to expand the size of the data drive as we started collecting more metrics.

Click through for more info and check out the GitHub repo.

Leave a Comment

T-SQL Tuesday 137 Round-Up

Steve Jones wraps up the latest T-SQL Tuesday:

I hosted the blog party this month, with the invite to write about notebooks. These are a neat technology, and I’ve written about them at SQLServerCentral.

This post is a wrap-up of the various responses to my invitation. First, quite a few people give credit to either Aaron Nelson or Rob Sewell for their writings and work with notebooks, so check out their blogs.

Click through for the list of respondents.

Comments closed

Lessons from using Notebooks

Glenn Berry takes us through some of the past (and sometimes present) challenges of running notebooks in Azure Data Studio:

I have to admit that I do not use Jupyter notebooks or Azure Data Studio (ADS) everyday. Last August, I made separate Jupyter notebook versions of my SQL Server Diagnostic Information Queries. There was a separate version for SQL Server 2012 through SQL Server 2019, along with one for Azure SQL Database. This was after a number of requests from people in the community.

Creating these notebooks was a pretty decent amount of work. Luckily, this was right around the time that Azure Data Studio was making it much easier to edit and format markdown for the text blocks. Since then, Azure Data Studio is even easier to use for editing and formatting. Even more fortuitous was the fact that Julie Koesmarno (@MsSQLGirl) volunteered to greatly improve my formatting!

Unfortunately, there has not been as much interest in my Jupyter notebooks as I hoped for. There are probably a number of reasons for this.

Read on for Glenn’s notes.

Comments closed

Running Jupyter Notebooks from Powershell

Rob Farley has a change of heart:

The concept is that if I have a notebook with a bunch of queries in it, I can easily call that using Invoke-SqlNotebook, and get the results of the queries to be stored in an easily-viewable file. But I can also just call Invoke-SqlCmd and get the results stored. Or I can create an RDL to create something that will email me based on a subscription. And I wasn’t sure I needed another method for running something.

Read on to see what changed Rob’s mind.

Comments closed

T-SQL Tuesday 136 Wrap-Up

Brent Ozar rounds up the usual suspects, plus several more:

For this month’s T-SQL Tuesday, I asked you to blog about your most-loved and least-loved data types.

Crazy, right? How could people possibly love or hate data types? Well, if you’ve been working with them for a while, I figured you’d have built up an array of tips or pain points, and y’all delivered with 29 interesting blog posts.

Click through for a lengthy list of interesting posts.

Comments closed

Disliking User-Defined Data Types

Andy Levy has a bone to pick:

Here’s the thing – these types are really just aliases for native types in SQL Server, but more constrained. Constrain yourself to UDTs and you’ll have trouble right-sizing your fields. Let’s say you’ve got three data types for text data:

– myShortString (varchar(10))
– myString (varchar(256))
– myBigString (varchar(8000))

These lengths are not helping anyone. You can’t store email addresses or names in myShortString. But myString is probably way too much for that data. You’re going to waste memory because of how SQL Server estimates memory grants and your indexes will be bloated. Maybe you just need to create more UDTs to cover these situations. But that just compounds the other problems, doesn’t it?

Pushes glasses up the bridge of his nose. Teeeeechnically, an e-mail address may be up to 256 characters long, including a username of up to 64 characters (and maybe two angle brackets, depending on the host). So myString would actually be perfect. Steve Jones has a comment about 300, but that was probably the original standard of 320. Regardless, I realize how far beside the point that is, and Andy’s point is a good one, as well as the several others he makes in the post.

One quick note on defined types: they really do make a lot of sense in a domain-driven design, especially when working with functional programming languages. Defining a CustomerID as an int is fine, but if I know my customer IDs are natural numbers (1, 2, 3, …), 9 digits long, and do not contain the sequence 2345 (because my company considers this an unlucky number sequence), creating a CustomerID type which provides this sort of type checking is great because you keep the rules as close to the data as possible and ensure consistency. It’s also more restrictive than int, so you can cast back down to an int when you’re ready to interact with some remote system. So short answer, do this all day in F#, but not in the database.

Comments closed

In Defense of Float

Hugo Kornelis levies a defense of floating point data types:

Let’s return to the database. Let’s figure out a way to store these numbers appropriately.

Could we use decimal (or its synonym numeric)? Well, yes. We can. We need 25 digits after the decimal place for 0.0000000000000000000004052, and 9 digits before the decimal place for 299900000, so that would fit in a decimal(34,25). But if you try to compute c2 so you can then multiply that to the m, you’ll run into an overflow error.

Hugo does a good job of defending the float data type.

Comments closed