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Category: Syntax

Fundamentals of Inline TVFs

Itzik Ben-Gan explains Inline Table-Valued Functions:

Compared to the previously covered named table expressions, iTVFs resemble mostly views. Like views, iTVFs are created as a permanent object in the database, and therefore are reusable by users who have permissions to interact with them. The main advantage iTVFs have compared to views is the fact that they support input parameters. So, the easiest way to describe an iTVF is as a parameterized view, although technically you create it with a CREATE FUNCTION statement and not with a CREATE VIEW statement.

It’s important not to confuse iTVFs with multi-statement table-valued functions (MSTVFs). The former is an inlinable named table expression based on a single query similar to a view and is the focus of this article. The latter is a programmatic module that returns a table variable as its output, with multi-statement flow in its body whose purpose is to fill the returned table variable with data.

Now that we have that sorted, click through to see examples and dive into performance ramifications.

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Top with Percent

Kevin Wilkie is on the top shelf:

In the last blog post, we went over the extreme basics of using the TOP operator in SQL. We showed how to grab things like the TOP 10 of a certain item.

That ability will get you through a number of criteria that you will be asked to perform. But what if you’re asked to grab the top five percent of performers in your company? Or in a region? It’s kinda hard to do that if you only have what we know so far, right?

Read on for the answer.

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UDFs and STRING_AGG

Erik Darling has a bone to pick with STRING_AGG():

If you’re like me and you got excited by the induction of STRING_AGG into the T-SQL Lexicon because of all the code odd-balling it would replace, you were likely also promptly disappointed for a few reasons.

Read on for one post which covers all of those reasons. Even with that disappointment, I’m still happy with STRING_AGG() on the whole, myself. There are some extra steps it’d be nice to eliminate in certain circumstances, but 60% of the time, it works every time.

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Unique Constraints vs Unique Indexes

Erik Darling calls out unique key constraints:

I do love appropriately applied uniqueness. It can be helpful not just for keeping bad data out, but also help the optimizer reason about how many rows might qualify when you join or filter on that data.

The thing is, I disagree a little bit with how most people set them up, which is by creating a unique constraint.

Data modeling Kevin wants to use unique key constraints because that’s the correct thing to do. Implementation Kevin uses unique nonclustered indexes for the reasons Erik describes. Not mentioned in Erik’s post but potentially relevant is that operations on unique nonclustered indexes can be done online, whereas unique key constraint operations (creation and alteration via drop+create) are offline.

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Partitioning vs Bucketing in Hive

The Hadoop in Real World team explains the difference between partitioning and bucketing in Apache Hive tables:

Now let’s say you also filter the sales record by sku (stock-keeping unit aka. barcode)  in addition to sale_date and country. Creating a partition on sku will result in many partitions which is not ideal as it might result in uneven and smaller partitions.

Hadoop is not efficient in processing small volumes of data. There is a better way.

Read on to understand when each technique makes sense.

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Concatenating in SQL Server

Lee Markup takes us through a pair of very useful functions in SQL Server:

SQL Server concatenation methods have been enhanced in modern versions of SQL Server. SQL Server 2012 introduced the CONCAT() function. In SQL Server 2017 we get CONCAT_WS().

A common usage of concatenation, or joining column values together in a string, is combining a FirstName and LastName column into a FullName column.  Another common usage might be for creating an address column that pulls together building number, street, city and zip code.

Read on to learn more. CONCAT() and CONCAT_WS() are also extremely helpful for change detection in ETL processes. For example, you might have a queue table to process and only want to update records in which relevant source fields changed, ignoring the ones which don’t exist in your destination. A combination of HASHBYTES() and CONCAT_WS() will do the trick quite nicely.

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Order, Sort, Cluster, and Distribute in Hive

The Hadoop in Real World team give us three methods (and one synonym) to organize results in Hive:

Hive provides 3 options to order or sort the result of records – order by, sort by, cluster by and distribute by. Which option you choose has performance implications. So it is important to understand the difference between the options and choose the right one for the use case at hand.

Click through for a high-level overview of the techniques.

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Avoiding WHILE 1=1 Loops

Aaron Bertrand does not believe in the power of the infinite loop:

A short time ago a colleague had an issue with a Microsoft SQL Server stored procedure. They were using our recommended approach for batching updates, but there was a small problem with their code that led to the procedure “running forever.” I think we’ve all made a mistake like this at one point or another; here’s how I try to avoid the situation altogether.

The argument isn’t “don’t use WHILE loops” or “don’t use batching logic,” but instead to ensure that you have a break condition somewhere. It’s reasonable to ask for an end state before you begin processing something, after all.

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