A Fine Slice Of SQL Server
Martin Schoombee needs a calendar:
It’s pretty sad that we don’t have a built-in calendar visualization in Power BI, and the custom visuals in the marketplace don’t have everything I need/want for my own internal reporting…so I decided to experiment a little and see how close I could get with the standard graphs that are available.
Read on to see how close Martin could get. It’s actually more calendar-looking than I would have expected, though also frustratingly limited.
Comments closedVitaly Bruk won’t be late to meetings anymore:
Today, I have late for the team meeting … 🙁
When I joined a Teams meeting, the team discussed ANSI_PADDING. One of the teammates fires at me with the teacher’s voice: “What are we talking about? What is ANSI_PADDING? Shoot!”.
That was a joke, but … just in case, let’s see what is this.
Read on to learn what the ANSI_PADDING attribute is and why it’s relevant.
Chad Callihan provides solid advice:
Everyone has an opinion on naming stored procedures. One opinion that can be agreed upon is that starting a stored procedure with “sp_” is not the way to go. Using “sp_” can only do harm. Let’s take a look at why that is.
Read on for Chad’s reasoning.
I don’t like prefixes at all for stored procedures (or tables). It’s a common misunderstanding of Hungarian notation, as you don’t provide any new and relevant information in the object name: of course it’s a stored procedure; I’m using “EXEC” to execute it, so what else could it be?
The exception to a very good rule is something which you intend to put in the master database and execute from any database context. The best example of this is sp_whoisactive.
Comments closedThe Big Data in Real World team talks on-the-fly execution plan changes:
Apache Spark, the popular distributed computing framework, has been widely adopted for processing large-scale data. With the release of Apache Spark 3.0, a groundbreaking feature called Adaptive Query Execution (AQE) was introduced. AQE addresses the limitations of traditional static execution plans by dynamically optimizing query execution based on runtime statistics. In this blog post, we will explore how AQE works and how it significantly improves the performance of Spark applications.
This sounds pretty similar to adaptive query processing in SQL Server, though a look at the Spark documentation shows that there are some practical differences in implementation versus what SQL Server does.
Comments closedRobert Cain continues a series on KQL:
The Kusto Windowing Function
row_rank_denseis an interesting function. It lets you get a unique count of a specific column in a dataset. Unlike other methods of getting counts,row_rank_denseallows you to see each individual row of data.First though, if you’ve not read the introductory post on Windowing Functions, Fun With KQL Windowing Functions – Serialize and Row_Number, you should do so now as it introduced several important concepts needed to understand how these Windowing Functions work.
Read on to see how this window function works.
Comments closedJared Poche shares some thoughts:
First, I wanted to point out that there are fixes to issues specific to Parameter Sensitive Plan Optimization in SQL Server 2022. The issue itself is documented at the link below.
When I heard about the issue, I heard that it could cause SQL Server 2022 to cause stack dumps every 15 minutes. The time interval made me think it was related to the default interval for Query Store, and that seems to be the case.
Jared has several thoughts on the topic as he puts together a talk on PSPO.
Comments closedThis is a question that’s come up twice this morning, firstly where can we find a history of database snapshots and secondly where can we find a history of restores from snapshot?
Frustratingly, SQL doesn’t make this at all easy for us and if this is something that you want to record, you’re going to have a do a little extra work.
Let’s take a look at each part in turn.
Read on for a messy solution.
Comments closedAaron Bertrand writes a query, or maybe two:
At Stack Overflow, our environment has multiple implementations of a largely – but not 100% – identical schema. (By “schema,” I mostly mean the set of tables within a database.) I sometimes need to retrieve or update data across a large number of sites. Let’s say, pulling or removing information from the
Userstable, and related tables, across all of our databases.
Most of the time, we think of “schema drift” as “different from a Platonic ideal of our schema” and try to correct it. In this case, Aaron shows how we can still write queries while embracing schema drift as a basic fact.
Comments closedIn this case, my notebook threw an error at me but the command seemed to finish without any issue. Sounds vague? It did to me. The notebookcell I tried to run had a lot of stuff happening at the same time.
As you can see in the above screenshot, the status shows green checkmarks but there’s an error as well. The error message was not really clear to me, but that can really be me lack of deep level experience. So, I logged a call with Microsoft Support and see what they could come up with.
I’ve had enough experience with Spark to see the issue and figure the response, but click through for the screenshot and what Reitse did to resolve the issue.
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Comments closed