Remember that this is an application problem and is not a SQL problem. We only get into trouble when applications (or people) expect results to be sorted when they’re not. So unless you have a tiny application, or a huge amount of discipline, it’s likely that there is some part of your application that assumes sorted results when it shouldn’t.
Here’s a method I used that attempts to identify such areas, exposing those assumptions. It involves reversing indexes.
It’s an interesting idea to try out in a dev environment.
We have had an index job that has been failing for a while. This is one of those things you really don’t want to clean up because no one is complaining, but you know you should. I had heard that I could rebuild one partition at a time, but where to start? Today, I worked my way through it, so here it is so that you can do it too.
First you need to find the biggest indexes, there is a good chance those are the ones that live on partitions. I am removing Primary Keys.
This can be a real time-saver if a majority of your partitions either are read-only or at least rarely update.
Whenever you set up a script to create or drop an index, you want a safety check in there. Drop the index if it exists. Create the index if it doesn’t.
Or do something else programmatically. Checking if an index exists is a pretty frequent task. But there’s no simple function to test if an index exists in SQL Server.
Good article, and in the comments, Kendra talks about the next logical step: consolidating indexes.
A scan of the entire index is over 1600 pages. This query read three. It’s a scan, but it’s a scan which stopped after reading one page of the leaf (the other two are likely the root and intermediate pages, used to locate the first page in the leaf).
The scan read one row and then stopped, because that’s all that was needed. It did that, because there was a 1 row row-goal added to the query. For more details on row goals, see Paul White’s article on the subject.
Read the whole thing.
Corollary: sometimes a seek isn’t really a seek; sometimes it’s a scan even when the icon says “seek” because that’s more efficient.
What about adding a clustered index and dropping it? Nooooooo, and again, I learned something new. This causes two rebuilds of the non-clustered indexes as they are rebuilt with the cluster addition and then rebuilt when the table changes back to a heap (to get the heap locations). That’s crazy, and certainly not what we want.
Also read Matthew Darwin’s comment, as “Don’t do X” usually has an “Except when Y” corollary.
This warning is telling me that Parameterization is to blame for the filtered index not being used. From here, I see 3 options.
Remove the parameters and use literals. (not practical)
Use Dynamic SQL
Use OPTION(RECOMPILE) at the bottom of the query.
This is the classic issue with filtered indexes: you expect them to be used, but when you check the plan, they aren’t.
It’s not just queries that reading extra pages can slow down. DBCC CHECKDB, backups, and index and statistics maintenance all have to deal with all those pages. Lowering fill factor without good reason puts you in the same boat as index fragmentation does, except regular maintenance won’t “fix” the problem.
Like everything else, the appropriate fill factor depends upon your context.
But why oh why didn’t SQL use my filtered indexes for even smaller subsets of the filter condition? It seemed insane to me that SQL would know the filter for the index is on (x > y), but wouldn’t use them even if (z > x).
The solution was to put the filtered column in the include list. This lets SQL generate statistics on the column, and much like getting rid of the predicate key lookup, allows you to search within the filtered index subset for even more specific information.
Filtered indexes are as useful as they are mercurial.
This new tool for the SSDT Dev Pack adds a menu item (Tools–>SSDT Dev Pack –> Find Duplicate Indexes) what it does is scan all the projects in the solution (it doesn’t follow “this database” references to examine them, maybe a future version) and then print to the output window a list of all the duplicate indexes based on the table, the columns and included columns – I don’t check anything else so you might actually want a duplicate in some circumstances but these should be very few and far between.
If you double click on the index it will take you to the location in the code where it actually is so you can delete it 🙂
A very useful tool gets even more useful.
This result was observed right after the finish of the loading script, where we can clearly see 4 Delta-Stores for 10 Million Rows. 3 of the Delta-Stores are Closed and 1 Delta-Store is Open, which is an absolutely impossible combination if we think about Clustered Columnstore Indexes, where one would expect to have 10 Compressed Row Groups or 10 Delta-Stores (9 Closed & 1 Open).
If you take a more detailed look at the associated sizes of the closed Delta-Stores, you will see that they increase each time a new Delta-Store is being used. For example, the first one is capped at 1.048.567 Rows, the second one is capped at 2.097.152 and the last closed Delta-Store is set to 4.193.904 Rows – meaning that the size is being constantly doubled.
I’d like to see this as the first step toward expanded sizes for compressed rowgroups.