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Day: March 1, 2023

Generating Nested Time Series Models

Steven Sanderson can’t stop at just one time series:

There are many approaches to modeling time series data in R. One of the types of data that we might come across is a nested time series. This means the data is grouped simply by one or more keys. There are many methods in which to accomplish this task. This will be a quick post, but if you want a longer more detailed and quite frankly well written out one, then this is a really good article

The quick post doesn’t include a lot of commentary but does show the code you’d use for the operation.

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Brief Code File Analysis with Python

Matt Eland reviews the code:

Last year I devised some ways of analyzing the history and structure of code in a visual way, but I didn’t document much of that to share with the community. This article explores the process I used to build a CSV file containing the path, extension, project, and line of code count in all source files in a project.

Click through for the Python code and an explanation of what it’s doing.

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The Most Common SQL Server Engine Errors Generating Support Tickets

Joseph Pilov collects a list:

About 6 months ago we decided to look at what SQL Server engine error messages are most commonly generating support cases to Microsoft. The end goal was to update the documentation for those error messages to allow our customers to find answers for themselves before they have to call Microsoft for technical assistance.

The task, as we suspected from previous experience, was not easy because we had to mine cases for error numbers and a relatively small number of support cases get reported with error messages when they are opened. Please report full error messages when you open support cases with Microsoft – it would help us get you answers faster. Still, we were able to find the trends even from the small percentages because were looking for the top 20 or so most common errors, based on case count, and we needed relative information – which error is reported more than another.

Click through for the list. A benefit from going through this exercise is that Microsoft has provided more information on each of those error IDs, hopefully making it easier for people to diagnose and resolve problems without needing to reach out to support.

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Peeking into Azure SQL DB via Extended Events

Grant Fritchey observes the observers:

Last week I posted the results from using Extended Events to snoop on what happens inside an AWS RDS database. This week, I’m taking a look at what happens on Azure SQL Database. I’m using the same toolset again, if for no other reason that I’m consistent in my approach. So it’s basically just rpc_completed & sql_batch_completed on the database in question. Let’s check out the results.

Here’s the prior post, in case you missed it like I did.

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Options to Export Power BI to Tables

Gilbert Quevauvilliers counts the ways:

I was recently helping out a customer and they contacted me asking why was the export option not in the format that they expected.

I had a look and now because there are so many options to export data, each one exports the data differently.

My goal for this blog post is to show you what each export type looks like, so when a user is exporting data, they can export in the format they expect.

It turns out that there are several such ways and Gilbert describes each.

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Speeding Up Queries via IF EXISTS

Chad Callihan doesn’t need to wait for the query’s end credits sequence:

When checking for the existence of a value or values in a table, typically, the whole table does not need to be read. The goal is to obtain more of a true or false answer, whether a criteria is met or not. If the criteria is met with the first few records then there’s no need to read on. Nonetheless, you may come across scripts written to unnecessarily retrieve the complete count of a value in a table. Let’s compare the performance difference between using COUNT(*) and using “IF EXISTS” when checking a table for values.

One’s going to give you a full scan and the other will give you a semi-join. Read on to see what the practical effect of this is.

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Fun with Implicit Conversions to DateTime

Andrea Allred gets tested:

I have been teaching a T-SQL 101 class and for the homework, we asked the students to get all the records where our heroes had a birthdate between 1995 through 1999. I expected something like this:


Imagine my surprise when one of the students turned in this:

SELECT FirstName, LastName, Birthdate
FROM Heroes
WHERE Birthdate BETWEEN '1995' AND '1999'

When I first saw the query I thought, “There is no way they ran that and it worked.” So I wrote it up and ran it on my data. Guess what? IT RUNS AND RETURNS DATA! I was shocked.

Click through to see what it returns and how that’s not quite right.

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