Here’s Another One

In the first post, I looked at a relatively large table. 50 million rows is a decent size.

But 50 million row tables might not be the target audience for this wait.

So, we’re gonna go with a >2 billion row table. Yes, dear reader, this table would break your PUNY INTEGER limits.

Slightly different

The full setup scripts are pretty long, but I’ll show the basic idea here.

Because this table is going to be fairly large, I’m gonna use clustered column store for maximum compressions.

The first test is just with a single statistics object.

Fork In The Road

Since every sane person in the world knows that updating column store indexes is a donkey, I’m switching to an insert to tick the modification counter up.

Query Time

To test the timing out, I can use a pretty simple query that hits the UserId column:

The query runs for ~3 seconds, and…


We spent most of that three seconds waiting on the stats refresh.

I know, you’re looking at those parallelism waits.

But what if the stats update went parallel? I’ll come back to this in another post.

Query Times Two

If you’re thinking that I could test this further by adding more stats objects to the UserId column you’d be dreadfully wrong.

SQL Server will only update one stats object per column. What’s the sense in updating a bunch of identical stats objects? I’ll talk about this more in another post, too.

If I reload the table, and create more stats objects on different columns, though…

And then write a bigger query after inserting more data to tick up modification counters…


This query runs for 14 seconds, and all of it is spent in the stats update.

Bigger, Badder

Alright, prepare to be blown away: things that are fast against 50 million rows are slower against 2 billion rows.

That include automatic stats updates.

So yeah, if you’re up in the billion row range, automatic stats creation and updates might just start to hurt.

If you move to SQL Server 2019, you’ll have some evidence for when refreshes take a long time, but still nothing for when the initial creation takes a long time.

Thanks for reading!

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