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Category: Query Tuning

Cross-Database Query Folding in Power Query

Chris Webb doesn’t want query folding limits:

A few months ago a new option was added to the Sql.Database and Sql.Databases functions in Power Query in Power BI and Excel which allows Power Query queries that combine data from different SQL Server databases to fold. Here’s a simple example showing how to use it.

Read on for the setup and the demonstration. This is pretty nice for cases where you have user data in one database and reference data in another, or you’re trying to join together data from two separate systems.

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T-SQL Query Transformations and Performance

Erik Darling isn’t satisfied with “good enough”:

Query tuning is hard work, especially when queries run for a long time and you have to test various changes trying to get it down to a reasonable runtime.

There are, of course, things you pick up intuitively over time, and figuring out where problems in actual execution plans has gotten easier with operator runtimes.

Beyond basic query tuning intuition comes the really creative stuff. The stuff you’re amazed anyone ever thought of.

Click through for two really interesting examples.

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Performance Differences on a View with a WHERE Clause

Reitse Eskens looks at an oddity:

I was teaching a class and during an interesting discussing an attendee told me that views with a filter took a long time to produce results, even if the result set itself was quite small. I wanted to test this out for myself to see what was happening. I’ll take you along this short journey in this blog. The outcomes have been validated against a SQL 2017, SQL 2019 and SQL 2022 instance.

Click through for an example.

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Query Hints: Ad Hoc vs Query Store

Grant Fritchey sets up a showdown:

I recently presented a session on the Query Store at Data Saturday Rhineland and the question came up: If there’s already a query hint on a query, what happens when you try to force a similar query hint?

Yeah, OK, that is a weird one. I don’t know the answer, but I’m about to find out.

Click through for a very interesting demo. To be honest, I expected the opposite result, so this was surprising.

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Execution Plans of Graph Tables in SQL Server

Hugo Kornelis looks at the execution plan:

Welcome to part twenty-one of the plansplaining series, where we will continue our look at execution plans for graph queries. In the previous post, we looked at the internal structure of node and edge tables, and discovered that they have a few hidden columns. Now let’s look how those columns are used in graph queries.

Read on for the example and a deeper dive into how graph tables actually work.

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Finding Memory Grant Details in sp_WhoIsActive

Erik Darling has a video for us. There’s no graf that I can include here, so I’m stuck having to come up with my own explanation…

This is an interesting video covering a fairly new feature in sp_WhoIsActive, as well as giving us some good information around the numbers meaning pages rather than (something)bytes, yet the memory_info column gives us results in kilobytes.

Also, be sure to grab version 12 of WhoIsActive.

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Hard-Coding Values in T-SQL Queries

Matthew McGiffen defends a practice:

If someone else comes along to look at this code they don’t know what the value of zero means. My code’s not clear. Worse I might refer to the same value multiple times in my procedure, so if I need to change it later I have to change it in multiple places.

Good practice from other coding languages would say that I replace it with a meaningfully named constant. As mentioned, in T-SQL we don’t have constants so I’ll compromise and use a variable:

And that good idea is where everything goes off the rails. There are a few places where T-SQL offers you “close enough” to other programming languages but where performance suffers as a result: ‘constant variables” like in this example, nested views and user-defined functions for encapsulation, etc.

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Finding All Implicit Warnings via Query Store

Jose Manuel Jurado Diaz has a script for us:

During our last session in SQL Data Saturday, we received a question about if it is possible to know all the conversion implicit captured by Query Data Store. In the following example, I would like to share with you an example how to capture this considering among of SQL Antipatterns. 

Basically, in sys.query_store_plan  we found the column called query_plan that contains the text of the execution plan. With this information plus other Query Data Store DMVs we could see the information required. 

Read on to see how you can shred out implicit conversions from the Query Store plans.

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Showing KQL Queries

Dany Hoter looks at some KQL query plans:

Each visual on the page is going to summarize data from one or more queries and add the summarize part of the query.

If your model contains multiple tables in direct query with relations between them, the connector will generate joins between the tables.

Selecting values in filters will create multiple where conditions.

In order to see the final query and understand the performance implications of each query and the total query load created by a report, you need to use the command “.show queries” in the context of the database.

Click through for Dany’s notes on the topic, including a few tips on what to look for.

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