Michael J. Swart

October 28, 2020

Detect Excessive Spinlock Contention on SQL Server

Scaling SQL Server High
The beginning of the school year is behind us and what a semester start! 2020 has been tough on many of us and I’m fortunate to work for a company whose services are in such high demand. In fact we’ve seen some scaling challenges like we’ve never seen before. I want to talk about some of them.

Context

As we prepared to face unprecedented demand this year, we began to think about whether bigger is better. Worried about CPU limits, we looked to what AWS had to offer in terms of their instance sizes.

We were already running our largest SQL Servers on r5 instances with 96 logical CPUs. But we decided to evaluate the pricy u instances which have 448 logical CPUs and a huge amount of memory.

Painful Symptoms

Well, bigger is not always better. We discovered that as we increased the load on the u-series servers, there would come a point where all processors would jump to 100% and stayed there. You could say it plateaued (based on the graph, would that be a plateau? A mesa? Or a butte?)

Graph showing cpu plataued at 100%

When that occurred, the number of batch requests that the server could handle dropped significantly. So we saw more CPU use, but less work was getting done.

The high demand kept the CPU at 100% with no relief until the demand decreased. When that happened, the database seemed to recover. Throughput was restored and the database’s metrics became healthy again. During this trouble we looked at everything including the number of spins reported in the sys.dm_os_spinlock_stats dmv.

The spins and backoffs reported seemed extremely high, especially for the category “XVB_LIST”, but we didn’t really have a baseline to tell whether those numbers were problematic. Even after capturing the numbers and visualizing them we saw larger than linear increases as demand increased, but were those increases excessive?

How To Tell For Sure

Chris Adkin has a post Diagnosing Spinlock Problems By Doing The Math. He explains why spinlocks are useful. It doesn’t seem like a while loop that chews up CPU could improve performance, but it actually does when it helps avoid context switches. He gives a formula to help find how much of the total CPU is spent spinning. That percentage can then help decide whether the spinning is excessive.

But I made a tiny tweak to his formula and I wrote a script to have SQL Server do the math:

  • You still have to give the number of CPUs on your server. If you don’t have those numbers handy, you can get them from SQL Server’s log. I include one of Glenn Berry’s diagnostic queries for that.
  • There’s an assumption in Chris’s calculation that one spin consumes one CPU clock cycle. A spin is really cheap (because it can use the test-and-set instruction), but it probably consumes more than one clock cycle. I assume four, but I have no idea what the actual value is.
EXEC sys.xp_readerrorlog 0, 1, N'detected', N'socket';
-- SQL Server detected 2 sockets with 24 cores per socket ...
 
declare @Sockets int = 2;
declare @PhysicalCoresPerSocket int = 24;
declare @TicksPerSpin int = 4;
 
declare @SpinlockSnapshot TABLE ( 
    SpinLockName VARCHAR(100), 
    SpinTotal BIGINT
);
 
INSERT @SpinlockSnapshot ( SpinLockName, SpinTotal )
SELECT name, spins
FROM   sys.dm_os_spinlock_stats
WHERE  spins > 0;
 
DECLARE @Ticks bigint
SELECT @Ticks = cpu_ticks 
FROM sys.dm_os_sys_info
 
WAITFOR DELAY '00:00:10'
 
DECLARE @TotalTicksInInterval BIGINT
DECLARE @CPU_GHz NUMERIC(20, 2);
 
SELECT @TotalTicksInInterval = (cpu_ticks - @Ticks) * @Sockets * @PhysicalCoresPerSocket,
       @CPU_GHz = ( cpu_ticks - @Ticks ) / 10000000000.0
FROM sys.dm_os_sys_info;
 
SELECT ISNULL(Snap.SpinLockName, 'Total') as [Spinlock Name], 
       SUM(Stat.spins - Snap.SpinTotal) as [Spins In Interval],
       @TotalTicksInInterval as [Ticks In Interval],
       @CPU_Ghz as [Measured CPU GHz],
       100.0 * SUM(Stat.spins - Snap.SpinTotal) * @TicksPerSpin / @TotalTicksInInterval as [%]
FROM @SpinlockSnapshot Snap
JOIN sys.dm_os_spinlock_stats Stat
     ON Snap.SpinLockName = Stat.name
GROUP BY ROLLUP (Snap.SpinLockName)
HAVING SUM(Stat.spins - Snap.SpinTotal) > 0
ORDER BY [Spins In Interval] DESC;

This is what I see on a very healthy server (r5.24xlarge). The server was using 14% cpu. And .03% of that is spent spinning (or somewhere in that ballpark).

A screen shot showing an example of results

More Troubleshooting Steps

So what’s going on? What is that XVB_LIST category? Microsoft says “internal use only” But I can guess. Paul Randal talks about the related latch class Versioning Transaction List. It’s an instance-wide list that is used in the implementation of features like Read Committed Snapshot Isolation (RCSI) which we do use.

Microsoft also has a whitepaper on troubleshooting this stuff Diagnose and resolve spinlock contention on SQL Server. They actually give a technique to collect call stacks during spinlock contention in order to try and maybe glean some information about what else is going on. We did that, but we didn’t learn too much. We learned that we use RCSI with lots of concurrent queries. Something we really can’t give up on.

So Then What?

What We Did

Well, we moved away from the u instance with its hundreds of CPUs and we went back to our r5 instance with only (only!) 96 logical CPUs. We’re dealing with the limits imposed by that hardware and accepting that we can’t scale higher using that box. We’re continuing to do our darnedest to move data and activity out of SQL Server and into other solutions like DynamoDb. We’re also trying to partition our databases into different deployments which spreads the load out, but introduces a lot of other challenges.

Basically, we gave up trying to scale higher. If we did want to pursue this further (which we don’t), we’d probably contact Microsoft support to try and address this spinlock contention. We know that these conditions are sufficient (if not necessary) to see the contention we saw:

  • SQL Server 2016 SP2
  • U-series instance from Amazon
  • Highly concurrent and frequent queries (>200K batch requests per second with a good mix of writes and reads on the same tables)
  • RCSI enabled.

Thank you Erin Stellato

We reached out to Erin Stellato to help us through this issue. We did this sometime around the “Painful Symptoms” section above. We had a stressful time troubleshooting all this stuff and I really appreciate Erin guiding us through it. We learned so much.

October 23, 2020

In Memory OLTP Defeated Our Tempdb Problems

Filed under: Miscelleaneous SQL,Technical Articles — Michael J. Swart @ 10:29 am
Scaling SQL Server High
The beginning of the school year is behind us and what a semester start! 2020 has been tough on many of us and I’m fortunate to work for a company whose services are in such high demand. In fact we’ve seen some scaling challenges like we’ve never seen before. I want to talk about some of them.

At D2L, we’re the perfect candidate customer for In Memory OLTP features, but we’ve held off adopting those features for years. Our servers handle tons of super quick but super frequent queries and so we find ourselves trying to address the same scaling challenges we read about in Microsoft’s customer case studies.

But there’s only one In Memory feature in particular that I care about. It’s the Memory Optimized Table Types. Specifically, I’ve always wanted to use that feature to avoid tempdb object allocation contention. Recently I finally got my chance with a lot of success. So even though I could say I’m happy with In Memory features, I think it’s more accurate to say that I feel relieved at having finally squashed my tempdb issues.

Summary of Article

The Trouble With Tempdb

We use table valued parameters with our procedures a lot (like thousands a second). We’re lucky that the table variables are not created on each execution, they’re cached. We rely heavily on the reduced overhead that this gives us. It’s for that reason we much prefer table variables over temp tables.

But when we crank up the demand, we can still run into catastrophic trouble. When tempdb contention hits us, throughput doesn’t just plateau, it drops hard. This kind of contention we see is like a kind of traffic jam where anyone who needs to use tempdb (i.e. everyone) has to wait for it. These tempdb traffic jams are rough. We even created a lighter version of sp_whoisactive that avoids tempdb issues for times like those.

I won’t go on too long about our troubles (I’ve written about tempdb issues a few times already: 1, 2, 3, 4, 5, 6, 7). The usual advice is to increase the number of tempdb data files. We were using 48 data files and really looking hard for other options.

SQL Server 2019 has some promising options. In TEMPDB – Files and Trace Flags and Updates, Oh My! Pam Lahoud points out how SQL Server can use all the PFS pages in the tempdb data files, not just the first available one. But we couldn’t move to 2019 that quickly. So we looked at Memory Optimized Table Types to help us.

Memory Optimized Table Types Can Help

Improving temp table and table variable performance using memory optimization tells us how. Our main goal is to avoid tempdb contention and memory optimized table variables don’t use tempdb at all. As long as we can be sure that the number of rows stored in these table variables is small, it’s all pros and no cons.

But it wasn’t easy for us to implement. In 2017, I wrote about Postponing Our Use Of In Memory OLTP. There were just some challenges that we couldn’t overcome. We’re not alone in struggling with the limitations of In Memory features. But our challenges weren’t the usual limitations that folks talk about and so they’re worth exploring.

The Challenge of Sardines and Whales

We have one product that we deploy to many clients. Each client gets their own database. The big ones (whales) have their own servers but the small ones (sardines) get grouped together.

Sardines and Whales

So the overhead of enabling In Memory on all the sardines was going to cause issues. The In Memory OLTP filegroup requires up to 4GB of disk space which isn’t easy to handle with hundreds of sardines. So we’re left with this dilemma. We’d like to use In Memory on the biggest whales, but not on the sardines. We tackled that in two ways

  1. Decide that the choice to add the In Memory filegroup is configuration, not product. This still required some changes though. Our backup and restore processes needed to at least handle the new filegroups, but they couldn’t expect it.
  2. Add an exception to our processes that allow schema drift in the definition table types. Our plan was to manually alter the table types to be memory optimized, but only on the largest whales. Introducing schema drift is not ideal, but we made this choice deliberately.

This whole challenge could have been avoided if memory optimized table types didn’t require an In Memory OLTP file group. I get the sense that the memory optimized table types don’t actually use that folder because I noticed something interesting. SQL Server 2019 introduces memory optimized tempdb metadata tables without a memory optimized filegroup! How did they pull that off? I’m a bit jealous. I asked Pam Lahoud and it turns out that the In Memory filegroups are still required for memory optimized table types and will continue to be. It turns out that Microsoft can make certain assumptions about the tempdb metadata tables that they can’t with regular table types. 😟

Some Implementation Surprises

As we implemented our plan, we also encountered some interesting things during testing that might be useful for you if you’re considering In Memory features.

  • The default directory for storing database files should point to a folder that exists on the database server and on any secondary nodes in the same availability group. So if the default location is E:\SQLData then make sure there’s an E drive on every node. SQL Server will need to create an xtp folder in there.
  • When adding the In Memory OLTP file group, the folder that contains it should also exist on all secondary nodes.
  • In SQL Server 2014, I noticed that the addition of the memory optimized file group required up to 4 Gb of space. In SQL Server 2016, I see that that still happens, but the space isn’t taken until the first memory optimized table type I create. That’s also when the xtp folder gets created.
  • Adjusting the table types to be memory optimized was a challenge because we wanted the process to be online. I wrote about how we pulled that off earlier this week in How to Alter User Defined Table Types (Mostly) Online

Success!

Things worked out really really well for us. Our main goal was to avoid tempdb contention and we succeeded there. But there’s an additional performance boost. When you insert into a regular table variable, that data gets written to tempdb’s transaction log. But that’s not the case for memory optimized tables. So even though I really just care about avoiding contention, the boost in performance is significant and measurable and really nice.

In testing we were finally able to push a 96 CPU machine up to 100% CPU on every core and only then did throughput plateau. No tempdb contention in sight.

In production we also saw the same behavior and we were able to sustain over 200K batch requests per second. No tempdb contention in sight.

Those numbers are nice, tempdb contention has been such a thorn in my side for so long, it’s such a relief to squash that issue once and for all. I now get to focus on the next bottleneck and can leave tempdb contention in the past.

October 19, 2020

How to Alter User Defined Table Types (Mostly) Online

Filed under: Miscelleaneous SQL,SQL Scripts,Technical Articles — Michael J. Swart @ 2:41 pm

Last year, Aaron Bertrand tackled the question, How To Alter User Defined Table Types. Aaron points out that “There is no ALTER TYPE, and you can’t drop and re-create a type that is in use”. Aaron’s suggestion was to create a new type and then update all procedure to use the new type.

I think I’ve got a bit of improvement based on sp_rename and sp_refreshmodule. Something that works well with

  • blue-green deployments,
  • both ad-hoc queries and procedures,
  • imperfectly understood schemas, like schemas that may have suffered from a little bit of schema drift.

Example

Say I have… I don’t know, let’s pick an example out of thin air. Say I have a simple table type containing one BIGINT column that I want to make memory optimized:

What I’ve Got

CREATE TYPE dbo.BigIntSet 
  AS TABLE ( 
    Value BIGINT NOT NULL INDEX IX_BigIntSet );

What I Want

CREATE TYPE dbo.BigIntSet 
  AS TABLE ( 
    Value BIGINT NOT NULL INDEX IX_BigIntSet )
  WITH (MEMORY_OPTIMIZED=ON);

I can’t directly ALTER this table type, but I can do this three-card monte trick using sp_rename to put the BigIntSet in its place.

The Migration Script

IF NOT EXISTS (
  SELECT * 
  FROM sys.table_types
  WHERE name = 'BigIntSet' 
  AND is_memory_optimized = 1
)
BEGIN
 
  CREATE TYPE dbo.BigIntSet_MO 
    AS TABLE ( 
      Value bigint NOT NULL 
      INDEX IX_BigIntSet ) 
    WITH (MEMORY_OPTIMIZED=ON);
 
  -- the switcheroo!
  EXEC sp_rename 'dbo.BigIntSet', 'zz_BigIntSet';
  EXEC sp_rename 'dbo.BigIntSet_MO', 'BigIntSet';
 
  --refresh modules
  DECLARE @Refreshmodulescripts TABLE (script nvarchar(max));
 
  INSERT @Refreshmodulescripts (script)
  SELECT 'EXEC sp_refreshsqlmodule ''' + QUOTENAME(referencing_schema_name) + '.' + QUOTENAME(referencing_entity_name) + ''';'
  FROM sys.dm_sql_referencing_entities('dbo.BigIntSet', 'TYPE');
 
  DECLARE @SQL NVARCHAR(MAX) = N'';
  SELECT @SQL = @SQL + script FROM @Refreshmodulescripts;
 
  EXEC sp_executesql @SQL;
 
END

But Is This Online?

Mostly. All queries that are in progress (whether ad-hoc or via procedures), continue to execute with no issues. However, there may be an issue with other queries that begin their execution during this migration.

If someone sends a query that uses the table type in the split second between the two sp_rename statements, then the query may fail.

If someone executes a procedure in the time between the first sp_rename and when sp_executesql gets around to refreshing that procedure, then the procedure may fail.

In practice, even on a busy server, I saw no such errors the few times I’ve tried this method, but of course, that’s no guarantee. In my case, even when refreshing close to 300 modules, this script takes about one second with no issues.

I actually tried adding a transaction around this whole migration script, and I did in fact see issues on a busy server. The schema modification lock that needs to be taken and held on all 300 objects was too much. It caused excessive blocking and I had to abandon that approach. In practice, I avoided trouble by ditching the explicit transaction.

June 19, 2020

Problem Solving by Cheating

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:34 am

Solving real-world problems is different than answering interview questions or twitter polls. The biggest difference is that real problems aren’t always fair. There’s not always a right answer.

Answer this multiple choice question:

Which of the following SQL statements is used to modify existing data in a table?
A) SELECT
B) INSERT
C) DELETE

Give it some thought. Which option would you pick? The correct answer is UPDATE but it wasn’t one of the options listed and that’s not fair. But neither is real life. Many real problems don’t have an easy answer and some real problems are impossible to solve. That can be discouraging.

A blue shell from MarioKart is about to attack a runner. Sometimes life just isn't fair.

Real Problems Allow For Creativity

But if your problems are unfair, then maybe you’re allowed to cheat too.

“None of the above” is always an option. Understand the goal so that you can stretch or ignore requirements.

Example – Changing an INT to a BIGINT

I have a table that logs enrollments into courses. It’s append only and looks something like this:

CREATE TABLE dbo.LOG_ENROLL (
    LogId INT IDENTITY NOT NULL,  -- This identity column is running out of space
    UserId INT NOT NULL,
    CourseId INT NOT NULL,
    RoleId INT NULL,
    EnrollmentType INT NOT NULL,
    LogDate DATETIME NOT NULL DEFAULT GETUTCDATE(),
 
    INDEX      IX_LOG_ENROLL_CourseId    CLUSTERED    ( CourseId, UserId ),
    CONSTRAINT PK_LOG_ENROLL PRIMARY KEY NONCLUSTERED ( LogId ),
    INDEX      IX_LOG_ENROLL_UserId      NONCLUSTERED ( UserId, CourseId ),
    INDEX      IX_LOG_ENROLL_LogDate     NONCLUSTERED ( LogDate, LogId )
);

The table has over 2 billion rows and it looks like it’s going to run out of space soon because the LogId column is defined as an INT. I need to change this table so that it’s a BIGINT. But changing an INT to a BIGINT is known as a “size of data” operation. This means SQL Server has to process every row to expand the LogId column from 4 to 8 bytes. But it gets trickier than that.

The biggest challenge is that the table has to remain “online” (available for queries and inserts).

Compression?
Gianluca Sartori (spaghettidba) had the idea of enlarging the columns with no downtime using compression. It’s promising, but I discovered that for this to work, all indexes need to be compressed not just the ones that contain the changed column. Also, any indexes which use the column need to be disabled for this to work.

Cheating
I gave up on solving this problem in general and constrained my focus to the specific problem I was facing. There’s always some context that lets us bend the rules. In my case, here’s what I did.

Ahead of time:

  • I removed extra rows. I discovered that many of the rows were extraneous and could be removed. After thinning out the table, the number of rows went from 2 billion down to 300 million.
  • I compressed two of the indexes online (IX_LOG_ENROLL_UserId and IX_LOG_ENROLL_CourseId) because I still want to use the compression trick.

But I’m not ready yet. I still can’t modify the column because the other two columns depend on the LogId column. If I tried, I get this error message:


Msg 5074, Level 16, State 1, Line 22
The index ‘IX_LOG_ENROLL_LogDate’ is dependent on column ‘LogId’.
Msg 5074, Level 16, State 1, Line 22
The object ‘PK_LOG_ENROLL’ is dependent on column ‘LogId’.
Msg 4922, Level 16, State 9, Line 22
ALTER TABLE ALTER COLUMN LogId failed because one or more objects access this column.

So I temporarily drop those indexes!

  • Drop the constraint PK_LOG_ENROLL and the index IX_LOG_ENROLL_LogDate
  • Do the switch! ALTER TABLE LOG_ENROLL ALTER COLUMN LogId BIGINT NOT NULL; This step takes no time!
  • Recreate the indexes online that were dropped.

Hang on, that last step is a size of data operation. Anyone who needs those indexes won’t be able to use them while they’re being built.
Exactly, and this is where I cheat. It turns out those indexes were used for infrequent reports and I was able to co-ordinate my index rebuild around the reporting schedule.

You can’t always make an operation online, but with effort and creativity, you can get close enough. I have found that every real problem allows for a great degree of creativity when you’re allowed to bend the rules or question requirements.

January 28, 2020

What Tables Are Being Written To The Most?

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:38 am

You have excessive WRITELOG waits (or HADR_SYNC_COMMIT waits) and among other things, you want to understand where.

Microsoft’s advice Diagnosing Transaction Log Performance Issues and Limits of the Log Manager remains a great resource. They tell you to use perfmon to look at the log bytes flushed/sec counter (in the SQL Server:Databases object) to see which database is being written to so much.

After identifying a database you’re curious about, you may want to drill down further. I wrote about this problem earlier in Tackle WRITELOG Waits Using the Transaction Log and Extended Events. The query I wrote for that post combines results of an extended events session with the transaction log in order to identify which procedures are doing the most writing.

But it’s a tricky kind of script. It takes a while to run on busy systems. There’s a faster way to drill into writes if you switch your focus from which queries are writing so much to which tables are being written to so much. Both methods of drilling down can be helpful, but the table approach is faster and doesn’t require an extended event session and it might be enough to point you in the right direction.

Use This Query

use [specify your databasename here]
 
-- get the latest lsn for current DB
declare @xact_seqno binary(10);
declare @xact_seqno_string_begin varchar(50);
exec sp_replincrementlsn @xact_seqno OUTPUT;
set @xact_seqno_string_begin = '0x' + CONVERT(varchar(50), @xact_seqno, 2);
set @xact_seqno_string_begin = stuff(@xact_seqno_string_begin, 11, 0, ':')
set @xact_seqno_string_begin = stuff(@xact_seqno_string_begin, 20, 0, ':');
 
-- wait a few seconds
waitfor delay '00:00:10'
 
-- get the latest lsn for current DB
declare @xact_seqno_string_end varchar(50);
exec sp_replincrementlsn @xact_seqno OUTPUT;
set @xact_seqno_string_end = '0x' + CONVERT(varchar(50), @xact_seqno, 2);
set @xact_seqno_string_end = stuff(@xact_seqno_string_end, 11, 0, ':')
set @xact_seqno_string_end = stuff(@xact_seqno_string_end, 20, 0, ':');
 
WITH [Log] AS
(
  SELECT Category, 
         SUM([Log Record Length]) as [Log Bytes]
  FROM   fn_dblog(@xact_seqno_string_begin, @xact_seqno_string_end)
  CROSS  APPLY (SELECT ISNULL(AllocUnitName, Operation)) AS C(Category)
  GROUP  BY Category
)
SELECT   Category, 
         [Log Bytes],
         100.0 * [Log Bytes] / SUM([Log Bytes]) OVER () AS [%]
FROM     [Log]
ORDER BY [Log Bytes] DESC;

Results look something like this (Your mileage may vary).
A screenshot of the results

Notes

  • Notice that some space in the transaction log is not actually about writing to tables. I’ve grouped them into their own categories and kept them in the results. For example LOP_BEGIN_XACT records information about the beginning of transactions.
  • I’m using sp_replincrementlsn to find the current last lsn. I could have used log_min_lsn from sys.dm_db_log_stats but that dmv is only available in 2016 SP2 and later.
  • This method is a little more direct measurement of transaction log activity than a similar query that uses sys.dm_db_index_operational_stats

January 20, 2020

Watching SQL Server Stuff From Performance Monitor

Taking a small break from my blogging sabbatical to post one script that I’ve found myself writing from scratch too often.
My hope is that the next time I need this, I’ll look it up here.

The User Settable Counter

Use this to monitor something that’s not already exposed as a performance counter. Like the progress of a custom task or whatever. If you can write a quick query, you can expose it to a counter that can be plotted by Performance Monitor.

Here’s the script (adjust SomeMeasurement and SomeTable to whatever makes sense and adjust the delay interval if 1 second is too short:

declare @deltaMeasurement int = 0;
declare @totalMeasurement int = 0;
 
while (1=1)
begin
 
  select @deltaMeasurement = SomeMeasurement - @totalMeasurement
  from SomeTable;
 
  set @totalMeasurement += @deltaMeasurement;
 
  exec sp_user_counter1 @deltaMeasurement;
  waitfor delay '00:00:01'
end

Monitoring

Now you can monitor “User Counter 1” in the object “SQLServer:User Settable” which will look like this:
Example of monitoring a performance counter using Performance Monitor

Don’t forget to stop the running query when you’re done.

October 26, 2018

Uncovering Hidden Complexity

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 12:15 pm

The other day, Erin Stellato asked a question on twitter about the value of nested SPs. Here’s how I weighed in:

Hidden complexity has given me many problems in the past. SQL Server really really likes things simple and so it’s nice to be able to uncover that complexity. Andy Yun has tackled this problem for nested views with his sp_helpexpandview.

Here’s what I came up with for nested anything. It helps unravel a tree of dependencies based on information found in sys.triggers and sys.dm_sql_referenced_entities. With it, you can see what’s involved when interacting with objects. Here’s what things look like for Sales.SalesOrderDetail in AdventureWorks2014. A lot of the resulting rows can be ignored, but there can be surprises in there too.

A lot in there

DECLARE @object_name SYSNAME = 'Sales.SalesOrderDetail';
 
WITH dependencies AS
(
    SELECT @object_name AS [object_name],
           CAST(
             QUOTENAME(OBJECT_SCHEMA_NAME(OBJECT_ID(@object_name))) + '.' + 
             QUOTENAME(OBJECT_NAME(OBJECT_ID(@object_name)))
             as sysname) as [escaped_name],
           [type_desc],
           object_id(@object_name) AS [object_id],
           1 AS is_updated,
           CAST('/' + CAST(object_id(@object_name) % 10000 as VARCHAR(30)) + '/' AS hierarchyid) as tree,
           0 as trigger_parent_id
      FROM sys.objects 
     WHERE object_id = object_id(@object_name)
 
    UNION ALL
 
    SELECT CAST(OBJECT_SCHEMA_NAME(o.[object_id]) + '.' + OBJECT_NAME(o.[object_id]) as sysname),
           CAST(QUOTENAME(OBJECT_SCHEMA_NAME(o.[object_id])) + '.' + QUOTENAME(OBJECT_NAME(o.[object_id])) as sysname),
           o.[type_desc],
           o.[object_id],
           CASE o.[type] when 'U' then re.is_updated else 1 end,
           CAST(d.tree.ToString() + CAST(o.[object_id] % 10000 as VARCHAR(30)) + '/' AS hierarchyid),
           0 as trigger_parent_id
      FROM dependencies d
     CROSS APPLY sys.dm_sql_referenced_entities(d.[escaped_name], default) re
      JOIN sys.objects o
           ON o.object_id = isnull(re.referenced_id, object_id(ISNULL(re.referenced_schema_name,'dbo') + '.' + re.referenced_entity_name))
     WHERE tree.GetLevel() < 10
       AND re.referenced_minor_id = 0
       AND o.[object_id] <> d.trigger_parent_id
       AND CAST(d.tree.ToString() as varchar(1000)) not like '%' + CAST(o.[object_id] % 10000 as varchar(1000)) + '%'
 
     UNION ALL
 
     SELECT CAST(OBJECT_SCHEMA_NAME(t.[object_id]) + '.' + OBJECT_NAME(t.[object_id]) as sysname),
            CAST(QUOTENAME(OBJECT_SCHEMA_NAME(t.[object_id])) + '.' + QUOTENAME(OBJECT_NAME(t.[object_id])) as sysname),
            'SQL_TRIGGER',
            t.[object_id],
            0 AS is_updated,
            CAST(d.tree.ToString() + CAST(t.object_id % 10000 as VARCHAR(30)) + '/' AS hierarchyid),
            t.parent_id as trigger_parent_id
       FROM dependencies d
       JOIN sys.triggers t
            ON d.[object_id] = t.parent_id
      WHERE d.is_updated = 1
        AND tree.GetLevel() < 10
        AND CAST(d.tree.ToString() as varchar(1000)) not like '%' + cast(t.[object_id] % 10000 as varchar(1000)) + '%'
)
SELECT replicate('—', tree.GetLevel() - 1) + ' ' + [object_name], 
       [type_desc] as [type],
       tree.ToString() as dependencies       
  FROM dependencies
 ORDER BY tree

June 15, 2018

ORDER BY newid() is an Unbiased Way To Randomize

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 9:47 am

Mike Bostock is a data-visualization specialist. And it really shows in his blog. Every article is really well designed (which makes sense… many of the articles are about design).

One of his articles, Visualizing Algorithms has some thoughts on shuffling at https://bost.ocks.org/mike/algorithms/#shuffling.

He says that sorting using a random comparator is a rotten way to shuffle things. Not only is it inefficient, but the resulting shuffle is really really biased. He goes on to visualize that bias (again, I really encourage you to go see his stuff).

Ordering by random reminded me of the common technique in SQL Server of ORDER BY newid(). So I wondered whether an obvious bias was present there. So I shuffled 100 items thousands of times and recreated the visualization of bias in a heat map (just like Mike did).

Here is the heatmap. If you can, try to identify any patterns.

Order By NewID Bias

Where:

    columns are the position before the shuffle,
    rows are the position after the shuffle,
    green is a positive bias and
    red is a negative bias.

I don’t think there is any bias here. The problem that introduces bias in Mike Bostock’s example is that his “random comparator” that he defined does not obey transitivity. His words. “A comparator must obey transitivity: if a > b and b > c, then a > c.”
But in SQL Server, because each row is assigned a newid(), ORDER BY newid() doesn’t have that flaw and so it doesn’t have that bias.

But Be Careful

Although the method is unbiased, ORDER BY newid() is still inefficient. It uses a sort which is an inefficient way of shuffling. There are alternative shuffle algorithms that are more efficient.
ORDER BY newid() is good for quick and dirty purposes. But if you value performance, shuffle in the app.

April 6, 2018

Are There Any System Generated Constraint Names Lurking In Your Database?

Names for constraints are optional meaning that if you don’t provide a name when it’s created or cannot afford one, one will be appointed to you by the system.
These system provided names are messy things and I don’t think I have to discourage you from using them. Kenneth Fisher has already done that in Constraint names, Say NO to the default.

But how do you know whether you have any?

Here’s How You Check

SELECT SCHEMA_NAME(schema_id) AS [schema name],
       OBJECT_NAME(object_id) AS [system generated object name],
       OBJECT_NAME(parent_object_id) AS [parent object name],
       type_desc AS [object type]
  FROM sys.objects
 WHERE OBJECT_NAME(object_id) LIKE 
         type + '\_\_' + LEFT(OBJECT_NAME(parent_object_id),8) + '\_\_%' ESCAPE '\'
       OR
       OBJECT_NAME(object_id) LIKE 
          REPLACE(sys.fn_varbintohexstr(CAST(object_id AS VARBINARY(MAX))), '0x', '%\_\_') ESCAPE '\'

This will find all your messy system-named constraints.
For example, a table defined like this:

create table MY_TABLE
(
  id INT IDENTITY PRIMARY KEY,
  id2 INT REFERENCES MY_TABLE(id) DEFAULT 0,
  UNIQUE(id),
  CHECK (id >= 0)
)

Will give results like this:

Happy hunting.

Update: April 9, 2018
We can get this info from the system views a little easier as Rob Volk pointed out. I’ve also included the parent object’s type.

SELECT OBJECT_SCHEMA_NAME(id) AS [schema name],
       OBJECT_NAME(constid) AS [system generated constraint name],
       (select type_desc from sys.objects where object_id = constid) as [constraint type],
       OBJECT_NAME(id) AS [parent object name],
       (select type_desc from sys.objects where object_id = id) as [parent object type]
  FROM sys.sysconstraints
 WHERE status & 0x20000 > 0
   AND OBJECT_NAME(id) NOT IN (N'__RefactorLog', N'sysdiagrams')
 ORDER BY [parent object type], [parent object name], [system generated constraint name];

March 26, 2018

T-SQL Options for Comparing “Distinctness”

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 8:40 am

I had the privilege of listening to Itzik Ben Gan talk about “distinctness” in a talk he gave at PASS Summit. Distinctness is a relationship or comparison between two variables, just like equals (=). But unlike equality, distinctness treats NULLs in a more intuitive way (NULL is not distinct from NULL).

There’s often confusion because equality in SQL is not like equality in mathematics. In particular equality in SQL doesn’t follow the reflexive property (∀A, A=A).

Clear right?

I explore different syntax options to test whether values are distinct or not. Each method has its pros and cons.

The Setup

Consider this table.

CREATE TABLE Tasks
(
    TaskId INT NOT NULL IDENTITY CONSTRAINT PK_TASKS PRIMARY KEY,
    AssignedTeamId INT NULL,
    AssignedSubTeamId INT NULL,
    TaskDetails NVARCHAR(2000) NOT NULL,
    INDEX IX_TASKS_ASSIGNEDTEAMID (AssignedTeamId)
);

When a task is unassigned, the AssignedTeamId and AssignedSubTeamId columns can both be null.
Our goal will be to select an arbitrary TaskId given parameters @TeamId, @SubTeamId. And when parameters @TeamId and @SubTeamId are both null, I want to return an unassigned task.

The Equality Join (doesn’t compare distinctness)

I just want to post this here as an example that doesn’t work.

DECLARE @TeamId bigint = NULL,
    @SubTeamId bigint = NULL;
 
-- this will never return any rows when the parameters are null:
SELECT TOP 1 TaskId
  FROM Tasks
 WHERE AssignedTeamId = @TeamId
   AND AssignedSubTeamId = @SubTeamId

PROS: The syntax looks nice and clean.
CONS: It doesn’t work for nulls.

The Expanded WHERE Clause

Well, let’s just write it all out then.

DECLARE @TeamId bigint = NULL,
    @SubTeamId bigint = NULL;
 
SELECT TOP 1 TaskId
  FROM Tasks
 WHERE ( (AssignedTeamId IS NULL AND @TeamId IS NULL) OR AssignedTeamId = @TeamId )
   AND ( (AssignedSubTeamId IS NULL AND @SubTeamId IS NULL) OR AssignedSubTeamId = @SubTeamId )

There’s no way that syntax is sarg-able. But it turns out that it is. SQL Server works hard and says “I see what you’re trying to do there, I’ll help you out”.
PROS: It works and it’s sarg-able.
CONS: That syntax is sooo awkward.

Using INTERSECT Syntax

This is a tip that I got straight from Itzik Ben Gan who says he got the idea from Paul White. The idea is that INTERSECT doesn’t use the idea of equality, but of distinctness for it’s comparison. We can use this to create slightly nicer syntax.

DECLARE @TeamId bigint = NULL,
    @SubTeamId bigint = NULL;
 
SELECT TOP 1 TaskId
FROM tasks
WHERE EXISTS (
    SELECT assignedTeamId, assignedSubTeamId
    INTERSECT
    SELECT @TeamId, @SubTeamId
)

The syntax is slightly less awkward, and it’s sarg-able. Or should be… But there’s a problem with this query (see if you can find it before reading further). Compare the two query plans. First the expanded where clause:

The Expanded where clause produces an efficient seek.


Here’s what the query with the INTERSECT syntax produces:

The INTERSECT syntax produces an inefficient scan

The secret to this mystery lies in that filter operator. There’s an implicit conversion there from int to bigint and that can cause a scan of the entire index. With the expanded syntax, SQL Server can handle the conversion gracefully. With the INTERSECT syntax it cannot. This was a really hard-earned lesson for us this week.

Change the parameters @TeamId and @SubTeamId to INT to match and the query becomes sarg-able again.

PROS: More elegant syntax and sarg-able (when you’re careful)
CONS: This syntax causes performance issues with mismatched types. Take extra-special care to make sure types match up.

IS NOT DISTINCT FROM

Check it:

DECLARE @TeamId bigint = NULL,
    @SubTeamId bigint = NULL;
 
SELECT TOP 1 TaskId
FROM tasks
WHERE assignedTeamId IS NOT DISTINCT FROM @TeamId
  AND assignedSubTeamId IS NOT DISTINCT FROM @SubTeamId

Talk about elegant! That’s what we wanted from the beginning. It’s part of ANSI’s SQL 1999 standard. Paul White tells us it’s implemented internally as part of the query processor, but it’s not part of T-SQL! There’s a connect item for it… err. Or whatever they’re calling it these days. Go read all the comments and then give it a vote. There are lots of examples of problems that this feature would solve.

PROS: Super-elegant!
CONS: Invalid syntax (vote to have it included).

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