Result Sets

Kevin Feasel

2016-01-19

Syntax

Kenneth Fisher learns and teaches us about RESULT SETS:

Quick definition. A result set is the output of a query. It could result in a one row, one column output or a 100+ column, million+ row output. Either way that’s a result set. Note: you can have multiple result sets from a single object (stored procedure, function etc) call.

This was introduced in SQL Server 2012 and there are a couple of security-related scenarios in which RESULT SETS is helpful.  It also lets you rename columns in stored procedure calls, if you’re into that sort of thing.

COUNT(*) Versus COUNT(1)

Manoj Pandey asks what the difference between COUNT(*) and COUNT(1) is:

So, we can clearly and without any doubt say that both COUNT(*) & COUNT(1) are same and equivalent.

Both of these are different from COUNT(SomeColumnName), though.

Case Statement Short-Circuiting

Richie Lee talks about using the CASE statement to short-circuit a logical expression:

The issue here is that SQL is a declarative language: unlike procedural languages, there is no guarantee on the ordering of the operations, because optimizers. And SQL Server decides to do something other than what we’d expect: it tries to evaluate the value “Apu” as a date. But by using a CASE expression we can force the optimizer to take the input and match it to the expression (in this case, when a value is a date then convert it to a date) before checking if the value is older than 7 days.

This does work most of the time, but there are exceptions, so as always, test your code.

UPDATE FROM Clause Usage

Rob Farley shows us UPDATE FROM:

1. It can let you access data in the columns of those tables, to use in predicates or expressions.

2. It can let you filter the data in the base table, by only allowing rows which match, such as when using an inner join or right outer join.

3. It can cause rows in the base table to be returned multiple times, if multiple rows in the joined table match a single row in the base table.

4. It can introduce NULL rows, if a full or right outer join is being done (or a left outer join with the base table second) and there are rows in the joined table that don’t match any rows in the base table.

This is a useful bit of T-SQL-specific syntax, but it’s a sharper edge than most UPDATE statements.  For a look back in history, Hugo Kornelis wanted to deprecate this syntax with the release of SQL Server 2008 (though MERGE has its own bugs and “Won’t Fix” problems, so in retrospect, perhaps it’s best that we still have UPDATE FROM).

Use Parentheses Wisely

Jen McCown plays around with the AND and OR operators:

Specifically, how is it evaluated when your where clause says “WHERE This AND That OR Something AND that”, without any clarifying parenthesis?

Let’s play around with this. The simplest test scenario is a SELECT 1. If I get a 1 back, that means my WHERE clause evaluated to true, right? Right.

Parentheses should clarify statements.  If I see an “AND” and an “OR” in a WHERE clause, I want to see parentheses, even if you’ve gotten it right.  It’s too easy to misinterpret precedence.

Object Level Auto-Stats

Andy Mallon on auto-statistics:

I’m not going to say whether I think Auto Update Statistics should be on or off. Instead, I’m going to argue that there are definitely scenarios when you want to have this on AND there are scenarios where you want it turned off. Can you really have this both ways? Absolutely.

I’m going to say yes to auto-update unless you know the answer is no for an object.  But it’s nice to know that the fine-grained option is available.

Error Handling

Continuing my “classics” series, Erland Sommarskog has a three-part series (with three appendices) on error handling that will take up your entire weekend:

Why do we have error handling in our code? There are many reasons. In a forms application we validate the user input and inform the users of their mistakes. These user mistakes are anticipated errors. But we also need to handle unanticipated errors. That is, errors that occur because we overlooked something when we wrote our code. A simple strategy is to abort execution or at least revert to a point where we know that we have full control. Whatever we do, simply ignoring an unanticipated error is something we should never permit us. This can have grave consequences and cause the application to present incorrect information to the user or even worse to persist incorrect data in the database. It is also important to communicate that an error has occurred, lest that the user thinks that the operation went fine, when your code in fact performed nothing at all.

Error handling is a crucial part of development.  And given that SQL Server has…peculiarities…when it comes to error handling, I highly recommend reading this series.

Checking Object Compression

Richie Lee has a quick script to check which objects are compressed.  Given that I ended up needing to use this script within a day of his posting it (hey, I’m as lazy as anybody else…), I figured it was worth linking.

Drop IF EXISTS

Kevin Feasel

2015-11-09

Syntax

It’s syntactic sugar, but oh, is it tasty.  From Manoj Pandey:

To make sure I checked the MSDN BOL and found that this is a new feature added to the SQL Server 2016 version. And as per this msdn article this enhancement has been add with theCTP 3 release.

For IF EXISTS option/syntax the MSDN BoL mentions: Conditionally drops the [object] only if it already exists.
This is a very helpful enhancement added to these DDL statements and would reduce a lot of effort and coding lines.

Create IF NOT EXISTS is also a thing.  A glorious thing.

Grouping Clauses

William Brewer goes into nice detail on the grouping clauses ROLLUP, CUBE, and GROUPING SETS.

ROLLUP and CUBE had their heyday before SSAS. They were useful for providing the same sort of facilities offered by the cube in OLAP. It still has its uses though. In AdventureWorks, it is overkill, but if you are handling large volumes of data you need to pass over your data only once, and do as much as possible on data that has been aggregated. Events that happened in the past can’t be changed, so it is seldom necessary to retain historic data on an active OLTP system. Instead, you only need to retain the aggregated data at the level of detail (‘granularity’) required for all foreseeable reports.

Imagine you are responsible for reporting on a telephone switch that has two million or so calls a day. If you retain all these calls on your OLTP server, you are soon going to find the SQL Server labouring over usage reports. You have to retain the original call information for a statutory time period, but you determine from the business that they are, at most, only interested in the number of calls in a minute. Then you have reduced your storage requirement on the OLTP server to 1.4% of what it was, and the call records can be archived off to another SQL Server for ad-hoc queries and customer statements. That’s likely to be a saving worth making. The CUBE and ROLLUP clauses allow you to even store the row totals, column totals and grand totals without having to do a table, or clustered index, scan of the summary table.

As long as changes aren’t made retrospectively to this data, and all time periods are complete, you never have to repeat or alter the aggregations based on past time-periods, though grand totals will need to be over-written!

I’ve used ROLLUP and GROUPING SETS fairly regularly but not so much CUBE.  Read the whole thing and figure out that the aggregation & summarization monster you have to maintain can maybe be re-written in a much simpler way.

Via Database Weekly.

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