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Category: T-SQL

Efficient Polling of Remote Data Sources

Ed Pollack looks at ways of optimizing linked server connections to remote SQL Server and Postgres instances:

Data rarely resides in one place. Oftentimes, there is a need to collect data from many different data sources and combine them locally into meaningful reports, analytics, or tables. The process for accessing, collecting, validating, and using data from remote data sources requires the same level of design and architectural considerations as building data structures for a software application.

In this article, methods of accessing remote data will be introduced with the goal of presenting best practices and ways to optimize load processes. This will lead into a discussion of performance and how to avoid the latency often associated with loading data from remote locations.

Click through for the article.

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The Dynamic SQL Hammer

Erik Darling talks about dynamic SQL:

I think it was Doug Lane who coined the stages of dynamic SQL. One of them dealt with the fact that once you start using it, you just wanna use it everywhere, even places where you think you don’t need it.

Most of the time, you don’t. A parameter is good enough. But like we saw last week, sometimes parameters can backfire, and you can use dynamic SQL to save query performance.

That’s one great use, but it’s one you’re gonna have to use constantly. Most of the time when you’re using dynamic SQL, it’s not going to be to correct performance.

Click through for Erik’s thoughts.

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Recursive Common Table Expressions

Itzik Ben-Gan continues a series on table expressions:

Recursive CTEs have many use cases. A classic category of uses has to do with handling of graph structures, like our employee hierarchy. Tasks involving graphs often require traversing arbitrary length paths. For example, given the employee ID of some manager, return the input employee, as well as all subordinates of the input employee in all levels; i.e., direct and indirect subordinates. If you had a known small limit on the number of levels that you might need to follow (the degree of the graph), you could use a query with a join per level of subordinates. However, if the degree of the graph is potentially high, at some point it becomes impractical to write a query with many joins. Another option is to use imperative iterative code, but such solutions tend to be lengthy. This is where recursive CTEs really shine—they enable you to use declarative, concise and intuitive solutions.

I’ll start with the basics of recursive CTEs before I get to the more interesting stuff where I cover searching and cycling capabilities.

Read the whole thing. There are some good uses of recursive common table expressions (I used them to figure out nested foreign key relationships, for example), so it’s worth your time.

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Automating Database Restorations

Hugo Kornelis walks us through automated restoration of database backups:

Now I’ve been to quite a few conferences. And I’ve heard a lot of DBAs talk about best practices. One of the constants in those talks is: automate your restores. So I felt confident that a quick internet search would surely be enough to find me an existing script for restoring a database. Sure, I’d need to modify it to restore to a test database, but that should be minimal effort.

To my surprise, I was unable to find a script. Is scripting this so easy that every DBA can do it with their eyes closed, and nobody feels a need to share it? Is it so hard that they all decided they’re sitting on gold and will only share it for big money? Or were my internet search skills simply severely lacking?

Anyway, bottom line is that I had to do it myself. And I’ll share the result in this post.

Click through for the script and a detailed explanation of how it works.

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An UPSERT Pattern to Avoid

Aaron Bertrand doesn’t like a common insert/update pattern:

I think everyone already knows my opinions about MERGE and why I stay away from it. But here’s another (anti-)pattern I see all over the place when people want to perform an upsert (update a row if it exists and insert it if it doesn’t):

IF EXISTS (SELECT 1 FROM dbo.t WHERE [key] = @key) BEGIN
UPDATE dbo.t SET val = @val WHERE [key] = @key;
END
ELSE
BEGIN
INSERT dbo.t([key], val) VALUES(@key, @val);
END

This looks like a pretty logical flow that reflects how we think about this in real life:

Does a row already exist for this key?
YES: OK, update that row.
NO: OK, then add it.

Click through to learn why this is a bad idea.

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Generating Predictions with SQL Server ML Services

Jeffin Mathew walks us through SQL Server Machine Learning Services:

The purpose of this blog is to explore the process of running ML predictions on SQL server using Python. We are going to train and test the data to predict information about bike sharing for a specific year. We are going to be using the provided 2011 data and predict what 2012 will result in. The 2012 data already exists inside the dataset, so we will be able to compare the predicted to the actual amount.

For certain use cases—especially when the data already exists in SQL Server, and especially especially when you can use native scoring—Machine Learning Services does a great job.

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Weighted Randomization using T-SQL

Louis Davidson won’t be stuck with uniform distributions:

The thing is, while I have 77 pictures of my favorite roller coaster, Expedition Everest at Disney’s Animal Kingdom, and 70 of The Tower of Terror at Hollywood Studios, I only have 2 of the Flame Tree Restaurant at Animal Kingdom and many other things that aren’t as exciting to post about. If I randomly choose attractions using a non-weighted random number generator, it would be just as likely to get the lesser items as the same frequency as the greater items. Hence, I want my popular items to come up most frequently, but every once in a while, I want to be surprised by something different.

This is where I needed to build a weighted randomized value.

Read on to see how Louis implements this.

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Building a Time Duration String

Ajay Dwivedi has a function to build out time duration as a string:

As DBA, there are various scenarios where I display duration in results. More often, for visual effects, I like to convert the same duration into [DD hh:mm:ss] format.

For example, for representing the duration of total waits, resource waits & signal waits for wait types from [sys].[dm_os_wait_stats].[wait_time_ms] on Grafana dashboard by converting to [DD hh:mm:ss] format:-

Click through for the function.

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Recursion in T-SQL

Bert Wagner takes us through writing recursive statements in T-SQL:

Recursive queries are fun to plan and write. They can be frustrating too depending on the complexity of the problem you are trying to solve.

This post shows one solution for finding all records that are related, either directly or via intermediate records, using recursive queries in SQL Server.

When you know the data size will be fairly small and performance isn’t critical, recursion can be an elegant solution to a data access problem.

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The Row Count Spool Operator

Hugo Kornelis dives into another operator:

The Row Count Spool operator is one of the four spool operators that SQL Server supports. It counts the number of rows in its input data, and can then later return that same amount of rows, without having to call its child operators to produce the input again.

The Row Count Spool can be viewed as similar to Table Spool, but optimized for cases where the amount of rows is relevant but their content is not. Because the content of the rows is not relevant, the operator does not need to use tempdb to store its input in a worktable; it only has to keep a running count as it reads the input. The other two spool operators have different use cases: Index Spool is used to enable the spool operator to return specific subsets of the input multiple times, and the Window Spool operator is used to support the ROWS and RANGE specifications of windowing functions.

Read on to see where this might be useful and when it may appear.

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