Moving 1/3 of a heap

Published on: 2020-07-22

Watch this week’s video on YouTube.

A Giant Heap

Recently I had to filter out 1.2 billion records from a 3.5 billion row heap. Don’t ask me why this 3.5 billion row table is stored as a heap.

If the lack of a clustered index wasn’t bad enough, I also had some other restrictions:

  • I couldn’t add a clustered index (or any index for that matter) sorted on the key I needed to filter on. It wasn’t my system, and I needed to access the 1.2 billion records sooner than it would take to get a clustered index approved and added. Even then, maybe the lack of a clustered index on this table is a feature and my request would be denied. Who knows.
  • I didn’t have a server that could store all of the data. At first I thought of copying all 3.5 billion rows to my own server and indexing it how I needed, but I didn’t have enough storage space anywhere to do that.
  • My connection to the server had a relatively short timeout set on it. This also couldn’t be changed. If I couldn’t copy all 3.5 billion rows because of storage, I also couldn’t copy all 1.2 billion records in one fell swoop because the connection would timeout.

Iterative Process

Scanning the table hundreds of times…

I struggled with this problem for a little bit. My deadline clock was ticking and I was stuck as to how I could copy and subsequently query the 1.2 billion rows of data I needed. My focus transitioned from “what is the best way to do this” to “how do I do this”.

The solution that ended up working for me was to query the table hundreds of times, each time filtering out and copying only 1 week of data by running a query similar to this:

FROM dbo.MyBigHeap
	CreateDate >= @StartDate
	AND CreateDate < @StopDate

Yes, this did cause me to scan the entire table hundreds of times, but in the end it was the right amount of data that I could copy at a time before the connection timed out.

Eventually I had the 1.2 billion rows I needed copied to my own server. I had a clustered column store index on the table (primarily for the compression savings) and some nonclustered indexes to support the queries I would need to run on it. Was this the best solution? I don’t know. But it worked for me given the constraints and deadline I had to meet.

Moral of the Story

Always put a clustered index on your tables. Even if you don’t have a use case to sort/filter them immediately, you will be creating a world of pain when someone comes along who does need to query that data.

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SQL Server 2019 Feature Power Rankings

Published on: 2019-10-29

Watch me comment on all of the features in this week’s video.

With the release of SQL Server 2019 imminent, I thought it’d be fun to rank which features I am most looking forward to in the new release.

(Also, I needed a lighter blogging week since I’m busy finishing preparing for my two sessions at PASS Summit next week – hope to see you there!).

SQL Server 2019 Feature rankings

I decided to rank these features on two axes: Excitement and Priority

Excitement is easy to describe: how excited I am about using these features. In my case, excitement directly correlates with performance and developer usability improvements. That doesn’t mean “Low Excitement” features aren’t beneficial; on the contrary, many are great, they just don’t top my list (it wouldn’t be fun to have a quadrant with everything in the top right).

Priority is how quickly I’ll work on implementing or tuning these features. The truth is that some of these features will work automatically once a SQL Server instance is upgraded, while some will require extra work (ie. query rewriting, hardware config). Once again, “Low Priority” features aren’t bad, they just won’t be the features that I focus on first.

Finally, these rankings are based on Microsoft’s descriptions of these features and what little tinkering I’ve done with pre-releases of SQL Server 2019. As far as I know, this chart will totally change once I start using these features regularly in production environments.

And here are my rankings in list form in case that’s more your style:

High Excitement, High Priority

  • Scalar function inlining
  • Memory grant feedback
  • sys.dm_exec_query_plan_stats
  • Accelerated Database Recovery
  • Table Variable deferred compilation

High Excitement, Low Priority

  • Big Data Clusters
  • Polybase all the things
  • Enhancements to running on Windows, Linux, and containers

Low Excitement, High Priority

  • Batch mode on rowstore indexes
  • Index encrypted columns
  • Optimize for sequential key
  • Useful truncation error messages

Low Excitement, Low Priority

  • New graph functions
  • Java language extension

What are you most excited for in 2019? What features did I miss? Disagree with where something should be ranked? Let me know in the comments below.

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Are Stored Procedures Faster Than Stand-Alone Queries?

Published on: 2019-10-15

Watch this week’s episode on YouTube.

A few months ago I was presenting for a user group when someone asked the following question:

Does a query embedded in a stored procedure execute faster than that same query submitted to SQL Server as a stand alone statement?

The room was pretty evenly split on the answer: some thought the stored procedures will always perform faster while others thought it wouldn’t really matter.

In short, the answer is that the query optimizer will treat a query defined in a stored procedure exactly the same as a query submitted on its own.

Let’s talk about why.

Start with a Plan

While submitting an “EXEC <stored procedure>” statement to SQL Server may require fewer packets of network traffic than submitting the several hundred (thousands?) lines that make up the query embedded in the procedure itself, that is where the efficiencies of a stored procedure end*.

*NOTE: There are certain SQL Server performance features, like temporary object cachingnatively compiled stored procedures for optimized tables, etc… that will improve the performance of a stored procedure over an ad hoc query. However in my experience, most people aren’t utilizing these types of features so it’s a moot point.

After receiving the query, SQL Server’s query optimizer looks at these two submitted queries exactly the same. It will check to see if a cached plan already exists for either query (and if one does, it will use that), otherwise it will send both queries through the optimization process to find a suitable execution plan. If the standalone query and the query defined in the stored procedure are exactly the same, and all other conditions on the server are exactly the same at the time of execution, SQL Server will generate the same plans for both queries.

To prove this point, let’s look at the following query’s plan as well as the plan for a stored procedure containing that same query:

	@UserId int
    COUNT(*) AS UpVotes 
	dbo.Posts p
    INNER JOIN Votes v
		ON v.PostId = p.Id 
    p.OwnerUserId = @UserId
	and VoteTypeId = 2

EXEC dbo.USP_GetUpVotes 23
DECLARE @UserId int = 23

    COUNT(*) AS UpVotes 
	dbo.Posts p
    INNER JOIN Votes v
		ON v.PostId = p.Id 
    p.OwnerUserId = @UserId
	and VoteTypeId = 2
Execution Plan for both stored procedure and ad hoc query
I didn’t include a screenshot of the second plan because it is identical.

As you can see, the optimizer generates identical plans for both the standalone query and the stored procedure. In the eyes of SQL Server, both of these queries will be executed in exactly the same way.

But I Swear My Stored Procedures Run Faster!

I think that a lot of the confusion for thinking that stored procedures execute faster comes from caching.

As I wrote about a little while back, SQL Server is very particular about needing every little detail about a query to be exactly the same in order for it to reuse its cached plan. This includes things like white space and case sensitivity.

It is much less likely that a query inside of a stored procedure will change compared to a query that is embedded in code. Because of this, it’s probably more likely that your stored procedure plans are being ran from cached plans while your individually submitted query texts may not be utilizing the cache. Because of this, the stored procedure may in fact be executing faster because it was able to reuse a cached plan. But this is not a fair comparison – if both plans would pull from the cache, or if both plans had to generate new execution plans, they would both have the same execution performance.

So does it matter if I use stored procedures or not?

So while in the majority of cases a standalone query will perform just as quickly as that same query embedded in a store procedure I still think it’s better to use stored procedures when possible.

First, embedding your query inside of a stored procedure increases the likelihood that SQL Server will reuse that query’s cached execution plan as explained above.

Secondly, using stored procedures is cleaner for organization, storing all of your database logic in one location: the database itself.

Finally, and most importantly, using stored procedures gives your DBA better insight into your queries. Storing a query inside of a stored procedure means your DBA can easily access and analyze it, offering suggestions and advice on how to fix it in case it is performing poorly. If your queries are all embedded in your apps instead, it makes it harder for the DBA to see those queries, reducing the likelihood that they will be able to help you fix your performance issues in a timely manner.

Thanks for reading. You might also enjoy following me on Twitter.

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