Michael J. Swart

January 26, 2021

Avoid This Pitfall When Using sp_getapplock

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 11:10 am

Takeaway: For most use cases, using sp_releaseapplock is unnecessary. Especially when using @LockOwner = 'Transaction (which is the default).

The procedure sp_getapplock is a system stored procedure that can be helpful when developing SQL for concurrency. It takes a lock on an imaginary resource and it can be used to avoid race conditions.

But I don’t use sp_getapplock a lot. I almost always depend on SQL Server’s normal locking of resources (like tables, indexes, rows etc…). But I might consider it for complicated situations (like managing sort order in a hierarchy using a table with many different indexes).

In that case, I might use it something like this:

BEGIN TRAN
 
exec sp_getapplock 
    @Resource = @LockResourceName, 
    @LockMode = 'Exclusive', 
    @LockOwner = 'Transaction';
 
/* read stuff (e.g. "is time slot available?") */
/* change stuff (e.g. "make reservation") */
 
exec sp_releaseapplock
    @Resource = @LockResourceName, 
    @LockOwner = 'Transaction';
 
COMMIT

But there’s a problem with this pattern, especially when using RCSI. After sp_releaseapplock is called, but before the COMMIT completes, another process running the same code can read the previous state. In the example above, both processes will think a time slot is available and will try to make the same reservation.

What I really want is to release the applock after the commit. But because I specified the lock owner is 'Transaction'. That gets done automatically when the transaction ends! So really what I want is this:

BEGIN TRAN
 
exec sp_getapplock 
    @Resource = @LockResourceName, 
    @LockMode = 'Exclusive', 
    @LockOwner = 'Transaction';
 
/* read stuff (e.g. "is time slot available?") */
/* change stuff (e.g. "make reservation") */
 
COMMIT -- all locks are freed after this commit

January 18, 2021

Pushing SQL Server Higher

Filed under: Miscelleaneous SQL,SQLServerPedia Syndication — Michael J. Swart @ 9:39 am

I sometimes get frustrated with SQL Server. It’s a tricky and challenging product to work with and some days I feel like I’ve only scratched the surface.

But sometimes I’m really impressed with what it can do. It really is a good (if expensive) product.

With the increase in online learning, last week we broke a record for a single SQL Server. 254,000 batch requests per second! That’s the first time we’ve pushed it above the quarter million mark.

(The screen shot was from a day earlier, but you get the idea)

I’d like to hear from others that have reached these kinds of numbers in prod to talk about their experiences. Find me on twitter (@MJSwart).

Find Out More About Our Experience

I’ll be presenting to the Ohio North User Group this February 2nd called How High Can We Scale SQL Server? Feel free to sign up!
I have fond memories of presenting to folks in Cleveland. Why they host their SQL Saturday’s in February is still a mystery. And so I’m glad to present virtually this February to keep up my streak.

January 8, 2021

Collect Wait Stats Regularly in Order To Report On Them Over Time

Filed under: Miscelleaneous SQL,SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 12:48 pm

I find wait stats so useful that I’ve got Paul Randal’s SQL Server Wait Statistics (or please tell me where it hurts…) bookmarked and I visit it frequently.

But that gives the total amount of waits for each wait type accumulated since the server was started. And that isn’t ideal when I’m troubleshooting trouble that started recently. No worries, Paul also has another fantastic post Capturing wait statistics for a period of time.

Taking that idea further, I can collect data all the time and look at it historically, or just for a baseline. Lot’s of monitoring tools do this already, but here’s what I’ve written:

Mostly I’m creating these scripts for me. I’ve created a version of these a few times now and some reason, I can’t find them each time I need them again!

This stuff can be super useful, especially, if you combine it with a visualization tool (like PowerBI or even Excel).
For example, here’s a chart I made when we were experiencing the XVB_LIST spinlock issues I wrote about not too long ago. Good visualizations can really tell powerful stories.

A visualization of a spinlock graph

I’m talking here about spins and not waits of course, but the idea is the same and I’ve included the spinlock monitoring scripts in the same repo on github.

Also a quick reminder wait stats aren’t everything. Don’t neglect monitoring resources as Greg Gonzales pointed out last year.

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.

October 16, 2020

Maximum Simultaneous User Connections

Filed under: SQLServerPedia Syndication — Michael J. Swart @ 12:00 pm
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.

A Really Busy September

It’s been a whirlwind of a month. The first day of school, or a new semester is always a busy time for online education. And in 2020 that’s an understatement. This year, we had to answer how busy could one SQL Server get?

We’ve always approached this question using the usual techniques

  • Cache like crazy (the cheapest query is the one that doesn’t have to be run)
  • Tackle expensive queries (CPU, IO)
  • Tackle large wait categories (so much easier said than done)
  • Offload reporting queries to other servers.

This year we smashed records and faced some interesting challenges along the way. I want to talk about those challenges in the next few blog posts. This first post is about user connections. Something I’ve never really paid much attention to before now.

A Metric I’d Never Thought I’d Have To Worry About

The maximum number of user connections that SQL Server can support is 32,767. That’s it. That’s the end of the line. You can buy faster I.O. or a server with more CPUs but you can’t buy more connections.

I actually mentioned this limit in the post where I introduced Swart’s 10% rule: “If you’re using over 10% of what SQL Server restricts you to, you’re doing it wrong” In that post, I was guarded about that statement as it applied to the user connection limit. But I’d like to upgrade that to elevated.

With such a hard limit, it’s important to watch this metric carefully. You can do that with the performance counter SQLServer: General Statistics – User Connections or with this query:

SELECT   ISNULL(DB_NAME(database_id), 'Total On Server') AS DatabaseName, 
         COUNT(*) AS Connections,
         COUNT(DISTINCT host_name) ClientMachines
FROM     sys.dm_exec_sessions
WHERE    host_name IS NOT NULL
GROUP BY ROLLUP( database_id )

The Maximum is 32,767
If you haven’t changed the maximum number of user connections by some method like sp_configure 'user connections', then the default is 0 and @@MAX_CONNECTIONS will return 32,767. I think the UI for this property is a bit misleading, 0 is absolutely not equivalent to unlimited.

What Does Trouble Look Like?

The issue shows up on the client when it’s unable to establish a connection to the server. There’s a variety of errors you might see such as:

  • Client unable to establish connection because an error was encountered during handshakes before login. Common causes include client attempting to connect to an unsupported version of SQL Server, server too busy to accept new connections or a resource limitation (memory or maximum allowed connections) on the server.
  • A connection was successfully established with the server, but then an error occurred during the pre-login handshake. (provider: TCP Provider, error: 0 – An existing connection was forcibly closed by the remote host.)
  • Or simply the generic:
  • A network-related or instance-specific error occurred while establishing a connection to SQL Server. The server was not found or was not accessible. Verify that the instance name is correct and that SQL Server is configured to allow remote connections. (provider: TCP Provider, error: 40 – Could not open a connection to SQL Server)

It’s Almost Always a Symptom

It’s often a symptom and not the root cause. For example, imagine you have a busy server with lots of clients and there’s a sudden slowdown.

Perhaps the slowdown is an increase in a certain wait category like blocking (LCK_M_X) or perhaps the slowdown is an increase in CPU because of a bad query plan. Either way, queries suddenly require a longer duration. They can’t complete quickly and remain open. So any new queries that come along have to make brand new connections (because there are no available connections in the connection pool). Maybe THREADPOOL waits start to pile up. And now even more sessions are waiting and more sessions get created. Then you might reach the maximum number of connections in this scenario.

But decent monitoring will often identify the actual root cause here (the blocking or whatever). And it’s actually easy to ignore the user connection limit because it wasn’t really the bottleneck.

But Sometimes it’s the Root Cause

Once we’ve tackled all the other scaling challenges, and the demand gets cranked up to 11. Then we can see trouble.

In our case. We have an elastic scaling policy that allows us to spin up client machines in response to demand. So hundreds of web servers times the number connection pools times the number of connections in each pool can really add up fast. So those web servers scale really nicely, but without a decent sharding strategy, SQL Server doesn’t.

On one of our servers, this is where we’re sitting now, and the number of connections is uncomfortably high. There’s not a lot of room to tolerate bursts of extra activity here:

The red lines indicate 10% and 100% of the maximum connection count.

What We Can Do About It

  • Monitor the number of connections that is typical for your servers using the query above for a point in time. Or use the performance counter: SQLServer: General Statistics – User Connections
  • Make sure you’re not leaking any connections. This SQLPerformance post tells you how to find that.
  • Use connection pooling efficiently. This Microsoft article SQL Server Connection Pooling (ADO.NET) has some great tips on avoiding pool fragmentation. The article describes pool fragmentation as a web server issue. But the tips are also appropriate to minimizing the total user connections on the server.
  • Keep the variety of connection strings used by your application small. With connection pooling, connections are only made when they’re needed. But still there’s a little bit of overhead. It takes 4 to 8 minutes for an idle connection to be released by the pool. So minimizing the number of connection pools actually does help.
  • Queries should be as quick as possible. Get in and out.
    • So be quick about reading the data you asked for (i.e. avoid C#’s yield if you can)
    • Offload reads as much as possible to other servers. Availability groups have read-only routing features but be careful how you implement this. If you have some connection strings that use ApplicationIntent=ReadOnly and some that don’t, then that’s two different connection pools. If you want to defer configuring AGs until after the connection strings are done, then there can be some tricky overlapping scenarios. It’s complicated, and it just highlights the importance of monitoring.
    • When tuning queries. The metric to focus on is the total duration of queries. Try to minimize that number. That’s the total_elapsed_time column in sys.dm_exec_query_stats. Or the elapsed time query in http://michaeljswart.com/go/top20. This is an interesting one. I’ve always preferred to focus on optimizing CPU or logical reads. But in this case, connection count is most sensitive to long running queries no matter the reason.

I’d love to hear about others who have tackled this problem. Let me know what strategies you came up with.

July 17, 2020

Monitoring Identity Columns for Room To Grow

Filed under: SQLServerPedia Syndication — Michael J. Swart @ 12:01 pm

My friend pointed out an interesting RCA by Github where a

database table’s auto-incrementing ID column exceeded [maxint]. When we attempted to insert larger integers into the column, the database rejected the value

This led to a discussion about setting up monitoring for this kind of problem in our software. We have a place for monitoring and health-checks for all our databases. We just need to know how to define them.

So how do I create a script that reports any tables whose current identity values are above a certain threshold? This is what I came up with. Maybe you’ll find it useful too.

Find Tables in SQL Server Running Out Of Identity Room

declare @percentThreshold int = 70;
 
select t.name as [table],
       c.name as [column],
       ty.name as [type],
       IDENT_CURRENT(t.name) as [identity],
       100 * IDENT_CURRENT(t.name) / 2147483647 as [percent full]
from   sys.tables t
join   sys.columns c
       on c.object_id = t.object_id
join   sys.types ty
       on ty.system_type_id = c.system_type_id
where  c.is_identity = 1
and    ty.name = 'int'
and    100 * IDENT_CURRENT(t.name) / 2147483647 > @percentThreshold
order  by t.name

Other Notes

  • I really prefer sequences for this kind of thing. Monitoring goes along similar lines
  • I only worry about ints. Bigints are just too big.

July 10, 2020

DROP TABLE Could Be Better

Filed under: SQLServerPedia Syndication — Michael J. Swart @ 11:05 am

I was looking at the docs for DROP TABLE and I noticed this in the syntax: [ ,...n ]. I never realized that you can drop more than one table in a statement.

You Still Have to Care About Order

I think that’s great. When dropping tables one at a time. You always had to be careful about order when foreign keys were involved. Alas, you still have to care about order. The docs say:

    If both the referencing table and the table that holds the primary key are being dropped in the same DROP TABLE statement, the referencing table must be listed first.

That means that when you run

CREATE TABLE A ( id INT PRIMARY KEY );
CREATE TABLE B ( id INT FOREIGN KEY REFERENCES A(id) );
 
DROP TABLE IF EXISTS A, B;

It fails with

    Msg 3726, Level 16, State 1, Line 4
    Could not drop object ‘A’ because it is referenced by a FOREIGN KEY constraint.

But this ordering

DROP TABLE IF EXISTS B, A;

succeeds.

I think that order shouldn’t matter here. It’s not very SQL-like. If you think so too, vote for this suggestion In DROP TABLE statement, make table order irrelevant.

DROP TABLE IF EXISTS fails

This is a suggestion made by Matt Smith. Currently DROP TABLE behaves this way:

  • DROP TABLE IF EXISTS succeeds if the table exists and is deleted.
  • DROP TABLE IF EXISTS succeeds when there is no object with that name.
  • DROP TABLE IF EXISTS fails when that object name refers to an object that is not a table

For example, this script

CREATE VIEW C AS SELECT 1 AS One;
go
DROP TABLE IF EXISTS C;

gives the error

    Msg 3705, Level 16, State 1, Line 9
    Cannot use DROP TABLE with ‘C’ because ‘C’ is a view. Use DROP VIEW.

It’s really not in the spirit of what was intended with “IF EXISTS”. If you want to vote for that suggestion, it’s here DROP TABLE IF EXISTS fails

DROP TABLE is not Atomic

I’ve gotten really used to relying on atomic transactions. I know that when I update a set of rows, I can rely on the fact that all of the rows are updated, or in the case of an error, none of the rows are updated. There’s no situation where some of the rows are updated. But a DROP TABLE statement that tries to drop multiple tables using the [ ,...n ] syntax doesn’t behave that way. If there’s an error, SQL Server continues with the list dropping the tables that it can.

We can see that with the first example. Here it is again:

CREATE TABLE A ( id INT PRIMARY KEY );
CREATE TABLE B ( id INT FOREIGN KEY REFERENCES A(id) );
 
DROP TABLE IF EXISTS A, B;
-- Could not drop object 'A' because it is referenced by a FOREIGN KEY constraint.
-- B is dropped

That example throws an error and drops a table.

The same nonatomic behavior is seen in a simpler example:

CREATE TABLE D ( id INT );
 
DROP TABLE E, D;
-- Invalid object name 'E'
-- D is dropped

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.

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