Michael J. Swart

Recently I tackled an issue where a DateTime field was getting updated too often. The query looked something like this:

UPDATE dbo.SOMETABLE
SET MyDateTime = GETUTCDATE()
WHERE SomeTableId = @SomeTableId;

So I decided to give up accuracy for concurrency. Specifically, I decided to only update MyDateTime if the existing value was more than a second ago.

First Attempt: Use DATEDIFF With SECOND

My first attempt looked like this:

UPDATE dbo.SOMETABLE
SET MyDateTime = GETUTCDATE()
WHERE SomeTableId = @SomeTableId
AND DATEDIFF(SECOND, GETUTCDATE(), MyDateTime) > 1;

But I came across some problems. I assumed that the DATEDIFF function I wrote worked this way: Subtract the two dates to get a timespan value and then return the number of seconds (rounded somehow) in that timespan.

But that’s not how it works. The docs for DATEDIFF say:

There’s no rounding involved. It just counts the ticks on the clock that are heard during a given timespan.

Check out this timeline. It shows three timespans and the DATEDIFF values that get reported:

But that’s not the behavior I want.

How About DATEDIFF with MILLISECOND?

Using milliseconds gets a little more accurate:

UPDATE dbo.SOMETABLE
SET MyDateTime = GETUTCDATE()
WHERE SomeTableId = @SomeTableId
AND DATEDIFF(MILLISECOND, GETUTCDATE(), MyDateTime) > 1000;

And it would be good for what I need except that DATEDIFF using MILLISECOND will overflow for any timespan over a month. For example,

SELECT DATEDIFF (millisecond, '2017-11-01', '2017-12-01')

gives this error:

Msg 535, Level 16, State 0, Line 1
The datediff function resulted in an overflow. The number of dateparts separating two date/time instances is too large. Try to use datediff with a less precise datepart.

SQL Server 2016 introduced DATEDIFF_BIG to get around this specific problem. But I’m not there yet.

Use DATEADD

I eventually realized that I don’t actually need to measure a timespan. I really just need to answer the question “Does a particular DateTime occur before one second ago?” And I can do that with DATEADD

UPDATE dbo.SOMETABLE
SET MyDateTime = GETUTCDATE()
WHERE SomeTableId = @SomeTableId
AND MyDateTime < DATEADD(SECOND, -1, GETUTCDATE()) ;

Update: Adam Machanic points out another benefit to this syntax. The predicate AND MyDateTime < DATEADD(SECOND, -1, GETUTCDATE()) syntax is SARGable (unlike the DATEDIFF examples). Even though there might not be a supporting index or SQL Server might not choose to use such an index it in this specific case, I prefer this syntax even more.

So How About You?

Do you use DATEDIFF at all? Why? I'd like to hear about what you use it for. Especially if you rely on the datepart boundary crossing behavior.

Devops, Agile and Continuous Delivery

Sometimes I get asked about work and our software development practices. Often these questions use words like agile, devops, or continuous delivery as in “Are you agile?” or “Do you do continuous delivery?”. But these questions rarely have yes or no answers. I always want to answer “It’s a work in progress”.

One of the things I like best about the PASS Summit is the opportunity to talk to people to find out how they do development. After speaking with others, it seems like everyone’s processes are always going to be works-in-progress. But I’ve come to realize that at D2L, we’re pretty far along. Here are just some of the things we do:

Things We Do

• Deployments to production are scheduled and almost completely automatic. This lets us deploy way more often than we used to.
• All code – including procedures and table definitions – are checked in.
• Automatic tests are run on every pull request and merge.
• Safety is a huge concern. We use feature flags and other techniques, but it remains difficult to maintain large complicated systems safely.
• We use blue-green deployments for zero downtime.
• The database layer is an exception to this blue-green technique. So it’s very important to be able to rollback any changes or configuration.

Sardines and Whales

This means we must also support thousands of copies of our main database. They’re used for client sites, test sites, qa sites, or whatever. So that leads to a variety of server configurations that I refer to as sardines and whales:

Look at those sardines. They’re quite happy where they are. The server can handle up to a thousand databases when there’s almost no activity.

But that whale is on a huge server and is extremely busy. Because of the high volume of transactions, we sometimes encounter tempdb contention due to our frequent use of table valued parameters. One technique I’ve been looking forward to evaluating is using memory optimized table types.

Maybe We Can Use In Memory OLTP?

I’m actually not very interested in memory optimized tables. I’m much more interested in the memory optimized table types. Those types can be used for table valued parameters. I can’t tell you how excited I was that it might solve my tempdb pet peeve.

But our dreams for the feature died

We’re leaving the feature behind for a few reasons. There’s an assumption we relied on for the sardine servers: Databases that contain no data and serve no activity should not require significant resources like disk space or memory. However, when we turned on In Memory OLTP by adding the filegroup for the memory-optimized data, we found that the database began consuming memory and disk (about 2 gigabytes of disk per database). This required extra resources for the sardine servers. So for example, 1000 databases * 2Gb = 2Tb for a server that should be empty.

Another reason is that checkpoints began to take longer. Checkpoints are not guaranteed to be quick, but on small systems they take a while which impacts some of our Continuous Integration workflows.

At the PASS Summit, I talked to a Hekaton expert panel. I also talked to a couple people in the Microsoft SQL Server clinic about some of my issues. They all recommended that we upgrade to SQL Server 2016 (which we can’t yet). Maybe I didn’t phrase my questions well, but I didn’t come away with any useful strategy to pursue.

I later talked to a Speaker Idol contestant Brian Carrig (@briancarrig) after hearing him talk briefly about his experiences with In Memory OLTP. He mentioned his own hard-fought lessons with In Memory OLTP including some uncomfortable outages.

The final nail in the coffin, as it were, is that once you turn on In Memory OLTP, you can’t turn it off. Once the In Memory OLTP filegroup is added, it’s there for good. Like I said, safety is a huge concern for us so we’re giving up on the feature for now.

Resurrecting the Feature?

The feature was designed for whales, not sardines. Maybe someday we will try to get those sardine servers to not fail with In Memory OLTP. Until then, the feature goes back on the shelf.

We encountered a CPU issue which took over a month to understand and fix. Since then, it’s been another couple months and so I think it may be time for a debrief.

The cause was identified as a growing number of ghost records that SQL Server would not clean up no matter what. Our fix was ultimately to restart SQL Server.

Symptoms and Data Collection

Here’s what we found.

• At time marked ‘A’ on the graph, we noticed that CPU increased dramatically. It was hard not to notice.
• We used sp_whoisactive to identify a frequent query that was taking a large amount of CPU. That query had never been a problem before. It was a select against a queue table – a table whose purpose was to store data for an implementation of a work queue. This table had a lot of churn: many INSERTS and DELETES. But it was small, never more than 100 rows.
• So next, we ran the query manually in Management Studio. SET STATISTICS IO, TIME ON gave us a surprise. A simple COUNT(*) of the table told us there were 30 rows in the table, but reading it took 800K logical page reads!
• What pages could it be reading? It’s impossible for a table to be that fragmented, it would mean less than one row per page. To look at the physical stats we ran:

  select sum(record_count) as records, sum(ghost_record_count) as ghost_records, sum(version_ghost_record_count) as version_ghost_records from sys.dm_db_index_physical_stats(db_id(), object_id('<table_name>'), default, default, 'detailed') where index_id = 1 and index_level = 0

  select sum(record_count) as records, sum(ghost_record_count) as ghost_records, sum(version_ghost_record_count) as version_ghost_records from sys.dm_db_index_physical_stats(db_id(), object_id('<table_name>'), default, default, 'detailed') where index_id = 1 and index_level = 0

  And that gave us these results:

  
  Interesting. The ghost records that remain are version_ghost_records, not ghost_records. Which sounds like we’re using some sort of snapshot isolation (which we’re not), or online reindexing (which we are), or something else that uses row versions.

• Over time, those version_ghost_records would constantly accumulate. This ghost record accumulation was also present in all other tables, but it didn’t hurt as much as the queue table which had the most frequent deletes.

Mitigation – Rebuild Indexes

Does an index rebuild clean these up? In this case, yes. An index rebuild reduced the number of version ghost records for the table. At the time marked ‘B’ in the timeline, we saw that an index rebuild cleaned up these records and restored performance. But only temporarily. The version_ghost_records continued to accumulate gradually.

At time ‘C’ in the timeline, we created a job that ran every 15 minutes to rebuild the index. This restored performance to acceptable levels.

More investigation online

Kendra Little – Why Is My Transaction Log Growing In My Availability Group?
This is a great video. Kendra describes a similar problem. Long running queries on secondary replicas can impact the primary server in an Availability Group (AG). But we found no long running queries on any replica. We looked using sp_whoisactive and DBCC OPENTRAN. We didn’t see any open transactions anywhere.

Amit Banerjee – Chasing the Ghost Cleanup in an Availability Group
Amit also mentions that log truncation would be prevented in the case of a long-running query on a replica. However, in our case, log truncation was occurring.

Uwe Ricken – Read Committed Snapshot Isolation and high number of version_ghost_record_count
Uwe Ricken also blogged recently about a growing number of version_ghost_records. He talked about looking for open transactions that use one of the snapshot isolation levels. Unfortunately it didn’t apply to our case.

Bitemo Erik Gergely – A very slow SQL Server
Another example of a long running query keeping version_ghost_records around.

dba.stackexchange – GHOST_CLEANUP Lots of IO and CPU Usage
This stackexchange question also describes a problem with lots of CPU and an inefficient, ineffective ghost cleanup task for databases in an AG. There’s an accepted answer there, but it’s not really a solution.

Calling Microsoft Support

So we called Microsoft support. We didn’t really get anywhere. We spoke with many people over a month. We generated memory dumps, PSSDiag sessions and we conducted a couple screen sharing sessions. Everyone was equally stumped.

After much time and many diagnostic experiments. Here’s what we did find.

• Availability Groups with readable secondaries are necessary (but not sufficient) to see the problem. This is where the version_ghost_records come from in the first place. Readable secondaries make use of the version store.
• We ran an extended event histogram on the ghost_cleanup event. There was a ridiculous amount of events. Like millions per minute, but they weren’t actually cleaning up anything:

  CREATE EVENT SESSION ghostbusters ON SERVER ADD EVENT sqlserver.ghost_cleanup( ACTION( sqlserver.database_id ) ) ADD TARGET package0.histogram( SET filtering_event_name=N'sqlserver.ghost_cleanup', source=N'sqlserver.database_id' )

  CREATE EVENT SESSION ghostbusters ON SERVER ADD EVENT sqlserver.ghost_cleanup( ACTION( sqlserver.database_id ) ) ADD TARGET package0.histogram( SET filtering_event_name=N'sqlserver.ghost_cleanup', source=N'sqlserver.database_id' )

• Microsoft let me down. They couldn’t figure out what was wrong. Each new person on the case had to be convinced that there were no open transactions. They couldn’t reproduce our problem. And on our server, we couldn’t avoid the problem.

Resolution

Ultimately the time came for an unrelated maintenance task. We had to do a rolling upgrade for some hardware driver update. We manually failed over the Availability Group and after that, no more problem!

It’s satisfying and not satisfying. Satisfying because the problem went away for us and we haven’t seen it since. After the amount of time I spent on this, I’m happy to leave this problem in the past.

But it’s not satisfying because we didn’t crack the mystery. And restarting SQL Server is an extreme solution for a problem associated with an “Always On” feature.

If you’re in the same boat as I was, go through the links in this post. Understand your environment. Look for long running queries on all replicas. And when you’ve exhausted other solutions, mitigate with frequent index rebuilds and go ahead and restart SQL Server when you can.

In 2015, Postgres added support for ON CONFLICT DO UPDATE to their INSERT statements. Their docs say that using this syntax guarantees an atomic INSERT or UPDATE outcome; one of those two outcomes is guaranteed, even under high concurrency.

Now, that is one amazing feature isn’t it? Postgres worked really hard to include that feature and kudos to them. I would trade ten features like stretch db for one feature like this. It makes a very common use case simple. And it takes away the awkwardness from the developer.

But if you work with SQL Server, the awkwardness remains and you have to take care of doing UPSERT correctly under high concurrency.

I wrote a post in 2011 called Mythbusting: Concurrent Update/Insert Solutions. But since then, I learned new things, and people have suggested new UPSERT methods. I wanted to bring all those ideas together on one page. Something definitive that I can link to in the future.

The Setup

I’m going consider multiple definitions of a procedure called s_AccountDetails_Upsert. The procedure will attempt to perform an UPDATE or INSERT to this table:

CREATE TABLE dbo.AccountDetails
(
    Email NVARCHAR(400) NOT NULL CONSTRAINT PK_AccountDetails PRIMARY KEY,
    Created DATETIME NOT NULL DEFAULT GETUTCDATE(),
    Etc NVARCHAR(MAX)
);

Then I’m going to test each definition by using a tool like SQL Query Stress to run this code

declare @rand float = rand() + DATEPART( second, getdate() );
declare @Email nvarchar(330) = cast( cast( @rand * 100 as int) as nvarchar(100) ) + '@somewhere.com';
declare @Etc nvarchar(max) = cast( newid() as nvarchar(max) );
exec dbo.s_AccountDetails_Upsert @Email, @Etc;

Antipattern: Vanilla Solution

If you don’t care about concurrency, this is a simple implementation. Nothing fancy here:

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
  IF EXISTS ( SELECT * FROM dbo.AccountDetails WHERE Email = @Email )
 
    UPDATE dbo.AccountDetails
       SET Etc = @Etc
     WHERE Email = @Email;
 
  ELSE 
 
    INSERT dbo.AccountDetails ( Email, Etc )
    VALUES ( @Email, @Etc );

Unfortunately, under high concurrency, this procedure fails with primary key violations.

Antipattern: MERGE Statement

The MERGE statement still suffers from the same concurrency issues as the vanilla solution.

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
    MERGE dbo.AccountDetails AS myTarget
    USING (SELECT @Email Email, @Etc etc) AS mySource
        ON mySource.Email = myTarget.Email
    WHEN MATCHED THEN UPDATE
        SET etc = mySource.etc
    WHEN NOT MATCHED THEN 
        INSERT (Email, Etc) 
        VALUES (@Email, @Etc);

Primary Key violations are still generated under high concurrency. Even though it’s a single statement, it’s not isolated enough.

Antipattern: Inside a Transaction

If you try putting the vanilla solution inside a transaction, it makes the whole thing atomic, but still not isolated. You still get primary key violations:

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
  BEGIN TRAN
    IF EXISTS ( SELECT * FROM dbo.AccountDetails WHERE Email = @Email )
 
      UPDATE dbo.AccountDetails
         SET Etc = @Etc
       WHERE Email = @Email;
 
    ELSE 
 
      INSERT dbo.AccountDetails ( Email, Etc )
      VALUES ( @Email, @Etc );
 
  COMMIT

Antipattern: Inside a Serializable Transaction

So this should be isolated enough, but now it’s vulnerable to deadlocks:

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
  SET TRANSACTION ISOLATION LEVEL SERIALIZABLE
  BEGIN TRAN
    IF EXISTS ( SELECT * FROM dbo.AccountDetails WHERE Email = @Email )
 
      UPDATE dbo.AccountDetails
         SET Etc = @Etc
       WHERE Email = @Email;
 
    ELSE 
 
      INSERT dbo.AccountDetails ( Email, Etc )
      VALUES ( @Email, @Etc );
 
  COMMIT

Antipattern: Using IGNORE_DUP_KEY

I want to mention one more bad solution before I move on to the good solutions. It works and it’s interesting, but it’s a bad idea.

ALTER TABLE dbo.AccountDetails 
  DROP CONSTRAINT PK_AccountDetails;
 
ALTER TABLE dbo.AccountDetails
  ADD CONSTRAINT PK_AccountDetails 
  PRIMARY KEY ( Email ) WITH ( IGNORE_DUP_KEY = ON )
GO
 
CREATE PROCEDURE dbo.s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
  -- This statement will not insert if there's already a duplicate row,
  -- But it won't raise an error either
  INSERT dbo.AccountDetails ( Email, Etc ) 
  VALUES ( @Email, @Etc );
 
  UPDATE dbo.AccountDetails 
     SET @Etc = Etc
   WHERE @Email = Email;

So what are some better solutions?

Pattern: Inside a Transaction With Lock Hints (Update More Common)

My prefered solution.

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
SET XACT_ABORT ON;
BEGIN TRAN
 
  UPDATE TOP (1) dbo.AccountDetails WITH (UPDLOCK, SERIALIZABLE)
     SET Etc = @Etc
   WHERE Email = @Email;
 
  IF (@@ROWCOUNT = 0)
  BEGIN      
      INSERT dbo.AccountDetails ( Email, Etc )
      VALUES ( @Email, @Etc );
  END 
COMMIT

Using some tips from Aaron Bertrand in Please stop using this UPSERT anti-pattern.
This is a solid solution, but every implementation is different so every time you use this pattern, test for concurrency.

Pattern: Inside a Transaction With Lock Hints (Insert More Common)

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
SET XACT_ABORT ON;
BEGIN TRAN
 
  INSERT dbo.AccountDetails ( Email, Etc )
  SELECT @Email, @Etc
  WHERE NOT EXISTS (
      SELECT *
      FROM dbo.AccountDetails WITH (UPDLOCK, SERIALIZABLE)
      WHERE Email = @Email
  )
 
  IF (@@ROWCOUNT = 0)
  BEGIN      
      UPDATE TOP (1) dbo.AccountDetails
      SET Etc = @Etc
      WHERE Email = @Email;
  END 
COMMIT

Pattern: MERGE Statement With Serializable Isolation

Nice, but be careful with MERGE statements.

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
    SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
 
    MERGE dbo.AccountDetails AS myTarget
    USING (SELECT @Email Email, @Etc etc) AS mySource
        ON mySource.Email = myTarget.Email
    WHEN MATCHED THEN UPDATE
        SET etc = mySource.etc
    WHEN NOT MATCHED THEN 
        INSERT (Email, Etc) 
        VALUES (@Email, @Etc);

For alternative syntax, skip the SET TRANSACTION ISOLATION LEVEL SERIALIZABLE and include a lock hint: MERGE dbo.AccountDetails WITH (HOLDLOCK) AS myTarget.

Pattern: Just Do It

Just try it and catch and swallow any exception

CREATE PROCEDURE s_AccountDetails_Upsert ( @Email nvarchar(4000), @Etc nvarchar(max) )
AS 
 
BEGIN TRY
 
  INSERT INTO dbo.AccountDetails (Email, Etc) VALUES (@Email, @Etc);  
 
END TRY
 
BEGIN CATCH
 
  -- ignore duplicate key errors, throw the rest.
  IF ERROR_NUMBER() IN (2601, 2627) 
    UPDATE dbo.AccountDetails
       SET Etc = @Etc
     WHERE Email = @Email;
 
END CATCH

I wrote a post about it in Ugly Pragmatism For The Win.
And I wrote about a rare problem with it in Case study: Troubleshooting Doomed Transactions.

I have no concurrency concerns here but because of this issue – not to mention performance concerns – it’s no longer my first preference.

Performance and Other Concerns

When describing each pattern so far, I haven’t paid attention to performance, just concurrency. There are many opportunities to improve the performance of these solutions. In particular, pay very close attention to your average use case. If your workload UPDATEs a row 99% of the time and INSERTs a row only 1% of the time, then optimal implementation will look different than if the UPDATE and INSERT frequency is reversed.

Check out these links (thanks Aaron Bertrand)

• The overhead of error handling
• Checking for potential constraint violations before entering SQL Server TRY and CATCH logic
• Trouble with MERGE