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.
There is another consequence of not using the
NOEXPANDhint, which I mentioned in passing a couple of years ago in my article, Optimizer Limitations with Filtered Indexes:
NOEXPANDhints are needed even in Enterprise Edition to ensure the uniqueness guarantee provided by the view indexes is used by the optimizer.
If you use indexed views in your environment, read this article.
I try to parameterize as many stored procedures as possible. This not only minimizes the amount of procedures I need to maintain, it in my opinion is a much cleaner way to code. It disturbs me when I see multiple stored procedures that pull the exact same data, but may have slight differences between them. Whether it be a sort, a where clause, or even just an extra field or two that makes it different, some developers think you need a different procedure for each one . Why not consolidate and parameterize?
The next step might be using dynamic SQL to build a query if there’s as much overlap as we see in Monica’s example.
I actually use them this way on a somewhat regular basis when I don’t want to have to tie back into sys.databases to just get the database name. And also in the WHERE clause when I want to restrict based on a database name.
It’s important to know where you are. Also, where your keys are.
The point is that there is an awful lot more going on inside SQL Server than is exposed in execution plans. Hopefully some of the details discussed in this rather long article will be interesting or even useful to some people.
It is good to have expectations of performance, and to know what plan shapes and properties are generally beneficial. That sort of experience and knowledge will serve you well for 99% or more of the queries you will ever be asked to tune. Sometimes, though, it is good try something a little weird or unusual just to see what happens, and to validate those expectations.
Optimizing update queries seems trivial at first, but as Paul shows, we have a few more tools at our disposal than is apparent at first glance.
The random sample that TABLESAMPLE provides is based on the number of data pages, not the number of records. If you want the number of rows to be specifically limited you will need to use Top(n) as well. I’ve written all my samples based upon AdventureWorksDW so you can run them for yourself later. I’ve listed the variety of ways to call TABLESAMPLE and shown the number of records returned.
TABLESAMPLE is useful for spelunking, but is somewhat limited otherwise.
Say you have records from your monitor that happens to Track EventLogs.
We’ll call it EventLog_Tracking for argument’s sake (hint hint http://thebakingdba.blogspot.com/2015/05/powershell-eventlogtracking-capturing.html) and want to look at trends over time.
Dynamic pivoting is possible (I have an example en passant in a tally table presentation I created a couple years back), but it’s not the easiest thing in the world.
You sometimes want to do things like split a table into two or move a column into another table and when you use SSDT or the compare / merge type of deployments it can be hard to migrate the data in a single deploy as you can’t insert the data into a table that doesn’t exist and you can’t drop the data before it has bee migrated. To fix this we can use pre/post deploy scripts in SSDT. The overall process is:
Pre-Deploy Script, check for column to be migrated
Save data in new table not in SSDT (you could have it in SSDT if you use it for multiple releases etc)
Let SSDT drop the column and create the new one – you will need to have the option set allow data loss on incremental deployments
In the post-deploy copyw the data to the new table
Using separate migration tables is an interesting solution to an old problem.