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Category: Indexing

Optimizing Read Performance of Heaps

Uwe Ricken continues a series on heaps in SQL Server:

Heaps are not necessarily the developer’s favourite child, as they are not very performant, especially when it comes to selecting data (most people think so!). Certainly, there is something true about this opinion, but in the end, it is always the workload that decides it. In this article, I describe how a Heap works when data are selected. If you understand the process in SQL Server when reading data from a Heap, you can easily decide if a Heap is the best solution for your workload.

Uwe hits on a couple of the (few) use cases where heap performance can match and sometimes surpass clustered index performance.

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Finding Index Usage Stats in Query Store

Grant Fritchey gives us another option for determining whether an index is in use:

One of the most frequent questions you’ll hear online is how to determine if a particular index is in use. There is no perfect answer to this question. You can look at the sys.dm_db_index_usage_stats to get a pretty good picture of whether or not an index is in use. However, this DMV has a few holes through which you could be mislead.

I thought of another way to get an idea of how and where an index is being used. This is also a flawed solution, but, still, an interesting one.

What if we queried the information in Query Store?

Be sure to read Grant’s warning before jumping into this, but at least it gives us another option, as well as a better understanding of which queries are using particular indexes.

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Using Computed Columns to Avoid Scans without Changing Queries

Andy Mallon shares a trick you don’t want to use too often, but can get you out of a pinch:

We’ve all been there. You’ve got a query where the JOIN or WHERE predicate is not SARGable. You’ve read about how this can be a problem, and how bad it is for performance.

Alas, you cannot change the query. Sometimes this reason is political, sometimes it’s because you’ve got a third-party app and simply don’t have access to the code. But you do have access to the database…

This is the type of thing you learn about and use maybe twice in your career, and then you get frustrated with the third-party vendor which won’t fix their code.

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Removing Redundant Indexes

Guy Glantser helps find and remove redundant indexes in SQL Server:

For some reason, which I have never understood, SQL Server allows you to create duplicate indexes on the same object (table or view). You can create as many non-clustered indexes as you like with the exact index keys and included columns as well as the exact index properties. The only difference between the indexes would be the index ID and the index name. This is a very undesirable situation, because there is clearly no benefit from having the same index more than once, but on the other hand there is quite a lot of overhead that each index incurs. The overhead includes the storage consumed by the index, the on-going maintenance during DML statements, the periodical rebuild and/or reorganize operations, and the additional complexity that the optimizer has to deal with when evaluating possible access methods to the relevant table or view.

I don’t fully agree with Guy’s definition of redundancy, but it’s more a quibble than anything else—if I have an index on (A,B,C) and an index on (A,B), it might seem redundant, but there are cases when it isn’t. For example, suppose C is a large NVARCHAR column such that we barely fit (A,B,C) into the window for an index (1700 bytes in SQL Server 2016, 900 in prior versions), but A and B are INT types. If we have a lot of cases where we need (A,B) but not C, that second index is definitely not redundant.

Regardless, click through for Guy’s argument and a script to help you find potentially redundant indexes.

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Filtered Indexes and the Optimizer

Paul White covers a couple of issues around filtered indexes:

This is a many-to-many merge join, where the execution engine must keep track of duplicates from the outer input in a worktable, and rewind as necessary. Duplicates? We are scanning a unique index!

It turns out the optimizer does not know that a filtered unique index produces unique values. This is a one-to-one join, but the optimizer costs it as if it were many-to-many. The higher estimated cost of a many-to-many merge join explains why a hash join plan is chosen.

Read the whole thing.

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When a Non-Clustered Index on Clustered Columns Makes Sense

Allen White gives us a scenario where adding a non-clustered index which is the same column as the clustered index can make sense:

Recently I was asked about adding a non-clustered index to a table (let’s call it Images) with just one column. It had been added in the development database and it improved performance dramatically. I looked at it and it had the same key as the clustered index on that table.

In reviewing the query I saw that Images was joined to the other tables in the query, but none of the columns were used, so Images was joined to ensure that values from the other tables had rows in Images. The query plan shows a significantly higher number of reads against Images without the new NCI (non-clustered index) than when it’s present.

I do agree that this can help—as we obviously see. The backseat query tuner in me wonders if maybe there’s another way to write the query to prevent the scan by using CROSS APPLY, but that’d only help if they were getting a small percentage of rows from the parent table expression built from the combination of the clustered index scan and index seek in the second example.

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Tuning a Query Searching for a Substring in Text

Eddy Djaja gives us two methods for improving performance of a search for a fixed substring:

The reason substring function is used because the column ACCOUNTDISPLYVALUE has multiple values combined in one column. In this case, the query is searching for the Account Number which is the first six characters. The long running query is listed below:

set statistics io on
go
select sum(ACCOUNTINGCURRENCYAMOUNT)from [d365].[GeneralJournalAccountMultiCompanyEntries]where substring([ACCOUNTDISPLAYVALUE], 1, 6)  = '877601'

Eddy gives us two solutions. As a quick note, these solutions work because the query is looking for a specific stretch of characters after a specific starting point. For arbitrary text, things get a little trickier.

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The Iterative Nature of Index Tuning

Erik Darling explains why index tuning is typically not a one-shot process:

For many people, index tuning means occasionally adding an index when there’s a report about a slow query. Those indexes might come from a query plan, or from the missing index DMVs, where SQL Server stores every complaint the optimizer files when it thinks an index might make a query better.

Sure, there are some people who think index tuning means rebuilding indexes or running DTA and checking all the boxes, but I ban those IP addresses.

The prudent choice, of course. Read on for Erik’s thoughts on the subject, which are quite good.

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Altering an Indexed View Drops All of Its Indexes

Kendra Little has some thoughts on indexed views:

When I first heard about this behavior, it sounded like a bug to me: why should an alteration like adding a column to a view remove all the indexes?

A colleague of mine at Redgate wondered: is the behavior the same with the new CREATE OR ALTER syntax as it is with just plain ALTER? (Spoiler: I tested and it is the same: CREATE OR ALTER also drops all indexes on the view.)

Click through for the answer.

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Rebuilding Rowstore Indexes Online on Tables with Columnstore Indexes

Niko Neugebauer explains something about rebuilding rowstore indexes:

This blogpost will be about older SQL Server versions (2016, 2017) and some implications that I have found that people rather do not understand, until they hit the problems in productions – the ONLINE creation & rebuild operations for the Rowstore indexes when having Columnstore Indexes on their tables.
As you should know by now – SQL Server 2017 & SQL Server 2019 respectively brought the ONLINE features support for the Columnstore Indexes (Nonclustered(2017) & Clustered(2019)) and I have blogged about those news in Columnstore Indexes – part 96 (“Nonclustered Columnstore Index Online Rebuild”) in 2017 and in Columnstore Indexes – part 123 (“Clustered Columnstore Index Online Rebuild”) in 2018.

What I honestly did not expect is the amount of the same question I have faced as in the last 3 months regarding the rebuild operations for the the ONLINE creation & rebuild operations for the Rowstore indexes when having Columnstore Indexes.

Read on to see the demonstration for when you have a clustered columnstore index and a nonclustered columnstore index.

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