Category Archives: tempdb

Hekatonized Tempdb

At PASS Summit 2018, I attended a session led by Pam Lahoud (t) of the SQL Tiger Team , entitled “TempDB: The Good, The Bad, and The Ugly”. If you have access to the PASS recordings from 2018, I highly recommend watching this session.

It was a really fantastic presentation, detailing the full history of how the SQL Server engineering team has attempted to optimize TempDB in various ways. The two problems that busy servers can have with regard to TempDB are allocation page contention, and metadata contention, and the engineering team should be applauded for its clever approaches to solving these types of contention throughout the years. To be clear, all of the optimizations were related to temp table usage in stored procedures, not scripts.

However, none of those solutions for contention scaled – some only relocated the issue. As part of Pam’s presentation, she did a demo with a single TempDB metadata table that was “Hekatonized”  – actually using the In-Memory OLTP engine – and the difference in throughput was significant. She said that Microsoft intends to convert the remaining system tables in TempDB to be memory-optimized (you’ll need SQL 2019 CTP 3.0 or later to test).

So once you’ve got it installed or have started a container running it – how to you automagically convert TempDB system tables to be memory-optimized? With TSQL, of course:

ALTER SERVER CONFIGURATION SET MEMORY_OPTIMIZED TEMPDB_METADATA = ON;

Like other changes to TempDB, in order for the new memory-optimization to take effect a restart of the SQL Server service is required. Once the service is restarted, system tables in TempDB are now memory-optimized (it should be that way for RTM, but in CTP 3.0, it could be the case that not all system tables have been converted to Hekaton). You can reverse this setting with the following command, and again restarting the SQL Server service:

ALTER SERVER CONFIGURATION SET MEMORY_OPTIMIZED TEMPDB_METADATA = OFF;

Unless your workload was truly hammering TempDB, you probably won’t see much difference in TempDB performance.

We need to be careful with this new In-Memory power, because depending on workload characteristics, we might need a whole lot more memory just to handle what’s going on in TempDB. Also, if you have scripts and/or monitoring that interrogate system tables in TempDB, you might be affected by some of the restrictions if TempDB system tables are memory-optimized. As the CTP release notes, state:

“A single transaction may not access memory-optimized tables in more than one database. This means that any transactions that involve a memory-optimized table in a user database will not be able to access TempDB system views in the same transaction.”

Another thing I want to make clear is that this new TempDB optimization only affects system tables in TempDB – not the tables you create; #table and ##table do not become memory-optimized as a result of this new feature.

After all, it’s name is MEMORY_OPTIMIZED_TEMPDB_METADATA

In Pursuit of Batch Mode on Rowstore

In her excellent blog post entitled “Batch Mode Hacks for Rowstore Queries in SQL Server“, Kendra Little b|t pays homage to Itzik Ben-Gan, Niko Neugebauer, and others.

The solutions she details will indeed result in batch mode for rowstore queries. I had already seen the solution proposed by Mr. Ben-Gan, and as is typically the case, a simple example is given to illustrate the concept, and these types of examples are almost always single-threaded.

I have a client that used Itzik Ben-Gan’s solution of creating a filtered nonclustered columnstore index to achieve batch mode on a rowstore (in fact I proposed that the client consider it). They have an OLTP system, and often perform YTD calculations. When they tested, processing time was reduced by 30 to 50 percent, without touching a single line of application code. If that ain’t low hanging fruit, I don’t know what is —

However, during testing, I noticed some intermittent blocking that didn’t make sense to me. But I couldn’t nail it down, and they went live with the “filtered nonclustered columnstore index” solution.

Once they deployed – and there was a lot of concurrency – I could see what had eluded me during my proof of concept: blocking in tempdb.

The repro is super-simple:

  • Create a table, and insert some sample data
  • Create a stored procedure that does the following:
    SELECT from that table into a #temp table
    Create a filtered nonclustered columnstore index on the #temp table, using a filter that cannot possibly be true, i.e. IDcolumn < 0 and IDcolumn > 0
    SELECT from the #temp table (return results)

From the first connection, issue a BEGIN TRAN execute the stored procedure. Note the spid for this connection. Then open a separate connection, issue a BEGIN TRAN and execute the stored procedure. Note the spid for this connection.

You’ll notice that the first connection has no issues, but when you execute the proc in the second connection, it gets blocked.

When you peel back the layers, you can see that the first connection requests and obtains a schema modification lock on the #temp table (Sch-M).

The second connection requests a schema stability lock on the same object_id, and is blocked (Sch-S).

To be clear, what’s happening here is that separate connections are placing incompatible locks on the same temporary object in tempdb, which is supposed to be impossible (but in fact the object_id is the same). My gut tells me that this is perhaps related to metadata when creating the NCCI, but I couldn’t prove that.

It should be noted that if you remove the filter on the NCCI, there is no blocking, and also if you use a regular filtered nonclustered index (not columnstore), this issue persists. Of course, in the real world, removing the filter is not an option because what we’re interested in speed, and if there’s one thing that columnstore indexes are not fast at, it’s being created.

Hopefully if/when Microsoft fixes this, it will be back ported to earlier versions of SQL Server.

I can reproduce this on SQL 2016 and 2017 (and even 2019, but that’s not really fair, cause it’s not RTM yet…)

If you think that Microsoft should fix this, please upvote my Azure User Voice entry here.

Repro code: