Optimizing T-SQL COUNT

This is a continuation of my previous post about paging in SQL Server. When it comes to paging, you would naturally want to know the total number of rows satisfying, so you can display some nice, useful information to your end-users.

You would think, well, it’s just a count, and a simple query like this would be enough:

SELECT COUNT(Id) FROM MySecretTable

There should be nothing to worry about, right? Actually, there is.

Let’s get back to the example in previous post – we have to count the total number of orders in that big table.

SELECT COUNT(ObjectId) FROM OrderGroup_PurchaseOrder

Because ObjectId is the clustered index of OrderGroup_PurchaseOrder, I did expect it to be use that index and be pretty fast. But does it? To my surprises, no.

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The art of paging

No this is not really “art” – I’m just trying to have a more clickbait title. It’s more about understanding what you have at your disposal and use them for your benefits – in this case – how new SQL statement can drastically improve your performance.

In this blogpost we will look into paging feature of SQL Server. in Commerce we usually work with large set of data – millions of rows are fairly common, and it’s natural to load data by page. There is no point loading thousands, or even millions of rows in one go. First it’s not practical to display all of them. Second you’ll likely end up with an timeout exception and/or an out of memory exception. Even if you are lucky enough to get through, it’s still able to take your SQL Server instance to a knee, and transferring that much data over network will be another bottleneck for your system. So my friends, the best practice for loading data is to do it by batches, and to not load everything at once.

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Fixing a stored procedure

At Episerver development team, we understand the importance of good performance. Who would not like a lightning fast website? We work hard to ensure the framework is fast, and we seize (almost) every opportunity to make it faster.

You know in Commerce 10.2 we introduced a new cart mode – serializable cart, and it’s proven to bring great performance compared to the “old/traditional” approach. Our own tests showed an improvement of 3-5x times faster. But can it be even faster? Probably yes.

And actually we did some improvements in later versions. In the scope of this blog post, we will just focus into a specific aspect – and to learn a little more about SQL Server performance optimization.

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Please, rebuild your database indexes, now

I will make it quick and to the point: if you are expecting a lot of customers visiting your site tomorrow (and you should) for Black Friday, you should rebuild your database indexes, now.

On average, it will help you to serve more customers and they will be happier with a more responsive, faster website. On best cases it will help prevent catastrophes.

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Super charged Episerver Commerce performance

Well, that was the title of my talk at the last week Meetup in Lund – hosted by Avensia. My first time in Lund – it is a very beautiful (small) city. It was a very nice event with about thirty developers from inside and outside Avensia, and I hope I did give some good information to make your Commerce solutions faster!

Then it comes to my attention that some of you might be interested in the talk. Unfortunately I don’t think the talk was recorded (I’d have been famous on Youtube!), but here’s the slides from the talk:

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A curious case of SQL execution plan, part 2

Recently I wrote about how to look into, identify and solve the problem with a SQL Server execution plan – as you can read here: http://vimvq1987.com/curious-case-sql-execution-plan/

I have some more time to revisit the query now, and I realized I made a “small” mistake. The “optimized” query is using a Clustered Index Scan

So it’s not as fast as it should be, and it will perform quite poorly in no cache scenario (when the buffer is empty, for example) – it takes about 40s to complete. Yes it’s still better than the original one, both in non cached and cached cases. But it’s not good enough. An index scan, even cached, is not only slower, but also more prone to deadlocks. It’s also worse in best case scenario, when the original one can use the proper index seek.

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A curious case of SQL execution plan

I said this already, and I will say it again: SQL Server optimizer is smart. I can even go further and say, it’s smarter than you and me (I have no doubt that you are smart, even very, very smart 🙂 ). So most of the cases, you leave it to do whatever it thinks is the best.

But there are cases SQL Server optimizer is fooled by the engine – it gets confused and chooses an sub-optimal plan, because it was given wrong, outdated, or incorrect information. That’s when you need to step in.

Today I face one case like that, as reported here: http://world.episerver.com/forum/developer-forum/Episerver-Commerce/Thread-Container/2017/10/database-timeout-on-productvariant-update/

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Episerver Commerce catalog performance optimization – part 4

Recently I worked on a support case where a customer reported deadlocks and timeout exceptions on queries to a specific table – NodeEntryRelation. Yes, it was mentioned in this post. However, there is more to it.

Keeping the indexes healthy definitely help to improve performance and avoid deadlocks and timeout exceptions. However it can only work to a limit, because even if the indexes are in their perfect state (the fragmentation level is 0%), the query will still take time.

Looking in the query we talked about – ecf_Catalog_GetChildrenEntries – what does it do. It lists the entries which are direct children of a catalog. So normally entries belong to categories (nodes), but it’s possible (Although not recommended) to have entries that belong directly to a catalog.

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Maintaining your indexes

Indexes are crucial to SQL Server performance. Having the right indexes might make the difference of day and night with your application performance – as I once talked here.

However, even having the right indexes is not everything. You have to keep them healthy. Indexes, as any other kinds of storage, is subjected to fragmentation. SQL Server works best if the index structure is compact and continuous, but with all of the inserts/updates/deletes, it’s inevitable to get fragmented. When the fragmentation grows, it starts affecting the performance of SQL Server: Instead of having to read just one page, it now have to read two, which increases both time and resource needed, and so on and so forth.

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Episerver Commerce commandTimeout configuration

We at Episerver take performance seriously – as one of the feature that constantly monitored and fine-tuned. This is especially true for database accesses, as they are usually the bottlenecks of the system (accessing databases are I/O operations and in most of the cases it’s much more expensive than reading/writing to memory, or even some complex computation in promotions)

However, we can’t always make our queries blazing fast. In cases when the data set is simply too big, it will take time for SQL Server to complete it, no matter how smart the query was written, or how efficient the indexes were added. In some extreme cases when the data set is big enough, it will result in the infamous exception System.Data.SqlClient.SqlException: Execution Timeout Expired. The timeout period elapsed prior to completion of the operation or the server is not responding.

Of course, in such cases, the best solution is to take another approach. Is it possible to restructure your data (for example, catalog), to make it smaller chunks that SQL Server can swallow? Or instead of loading all at once, you can try to load by small batches?

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