Michael J. Swart

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.

May 15, 2020

Cross Database Transactions on One Server

Filed under: SQLServerPedia Syndication — Michael J. Swart @ 11:03 am

So check out this code, what’s going on here?

begin transaction
 
insert d1.dbo.T1 values (1);
insert d2.dbo.T1 values (1);
 
commit

The transaction is touching two different databases. So it makes sense that the two actions should be atomic and durable together using the one single transaction.

However, databases implement durability and atomicity using their own transaction log. Each transaction log takes care of its own database. So from another point of view, it makes sense that these are two separate transactions.

Which is it? Two transaction or one transaction?

Two Vs. One

It’s One Transaction (Mostly)

Microsoft’s docs are pretty clear (Thanks Mladen Prajdic for pointing me to it). Distributed Transactions (Database Engine) says:

A transaction within a single instance of the Database Engine that spans two or more databases is actually a distributed transaction. The instance manages the distributed transaction internally; to the user, it operates as a local transaction.

I can actually see that happening with this demo script:

use master
if exists (select * from sys.databases where name = 'D1')
begin
    alter database D1 set single_user with rollback immediate;
    drop database D1;
end
go
 
if exists (select * from sys.databases where name = 'D2')
begin
    alter database D2 set single_user with rollback immediate;
    drop database D2;
end
go
 
create database d1;
go
 
create database d2;
go
 
create table d1.dbo.T1 (id int);
create table d2.dbo.T1 (id int);
go
 
use d1;
 
CHECKPOINT;
go
 
begin transaction
 
insert d1.dbo.T1 values (1);
insert d2.dbo.T1 values (1);
 
commit
 
select [Transaction ID], [Transaction Name], Operation, Context, [Description]
from fn_dblog(null, null);

That shows a piece of what’s going on in the transaction log like this:

Transaction log output

If you’re familiar with fn_dblog output (or even if you’re not), notice that when a transaction touches two databases, there are extra entries in the transaction log. D1 has LOP_PREP_XACT and LOP_FORGET_XACT and D2 only has LOP_PREP_XACT. Grahaeme Ross wrote a lot more about what this means in his article Understanding Cross-Database Transactions in SQL Server

Well that’s good. I can count on that can’t I?

Except When …

You Break Atomicity On Purpose
Well, they are two databases after all. If you want to restore one database to a point in time before the transaction occurred but not the other, I’m not going to stop you.

Availability Groups
But there’s another wrench to throw in with Availability Groups. Again Microsoft’s docs are pretty clear on this (Thanks Brent for pointing me to them). In Transactions – availability groups and database mirroring they point out this kind of thing is pretty new:

In SQL Server 2016 SP1 and before, cross-database transactions within the same SQL Server instance are not supported for availability groups.

There’s support in newer versions, but the availability group had to have been created with WITH DTC_SUPPORT = PER_DB. There’s no altering the availability group after it’s been created.

It’s also interesting that availability groups’ older brother, database mirroring is absolutely not supported. Microsoft says so several times and wants you to know that if you try and you mess up, it’s on you:

… any issues arising from the improper use of distributed transactions are not supported.

Long story short:

  • Cross DB Transactions in the same server are supported with Availability Groups in SQL Server 2017 and later
  • Cross DB Transactions are not supported with mirrored databases at all

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.

April 3, 2019

Finding Tables with Few Dependencies

Filed under: SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 10:00 am

A couple weeks ago, I wrote about how to find lonely tables in Sql Server. This is a follow up to that post. I’m now going to talk about small sets of tables that are joined to eachother, but no-one else.

It’s Not Just Me
It seems everyone’s talking about this.

So as I was writing this post and code I noticed an amazing coincidence. I saw the same ideas I was writing about being discussed on twitter by Kelly Sommers, Ben Johnson and others.

They discuss Uber’s microservice graph. When visualized, it’s a big mish-mash of dependencies. Kelly points out how hard it is to reason about and Ben points to a small decoupled piece of the system that he wants to work on.

Me too Ben! And I think that’s the value of that visualization. It can demonstrate to others how tangled your system is. It can also identify small components that are not connected to the main mess. When I tie it to my last post and consider this idea in the database world, I can expand my idea of lonely tables to small sets of tables that are never joined to other tables.

I want to find them because these tables are also good candidates for extraction but how do I find them? I start by visualizing tables and their joins.

Visualizing Table Joins

I started by looking for existing visualizations. I didn’t find exactly what I wanted so I coded my own visualization (with the help of the d3 library). It’s always fun to code your own physics engine.

Here’s what I found

A monolith with some smaller isolated satellites

An example that might be good to extract

That ball of mush in the middle is hard to look at, but the smaller disconnected bits aren’t! Just like Ben, I want to work on those smaller pieces too! And just like the lonely tables we looked at last week, these small isolated components are also good candidates for extracting from SQL Server.

Try It Yourself

I’ve made this visualization available here:

https://michaeljswart.com/show_graph/show_graph.html

There’s a query at the end of this post. When you run it, you’ll get pairs of table names and when you paste it into the Show Graph page, you’ll see a visualization of your database.

(This is all client-side code, I don’t collect any data).

The Query

use [your database name goes here];
 
select
    qs.query_hash,
    qs.plan_handle,
    cast(null as xml) as query_plan
into #myplans
from sys.dm_exec_query_stats qs
cross apply sys.dm_exec_plan_attributes(qs.plan_handle) pa
where pa.attribute = 'dbid'
and pa.value = db_id();
 
with duplicate_queries as
(
    select ROW_NUMBER() over (partition by query_hash order by (select 1)) r
    from #myplans
)
delete duplicate_queries
where r > 1;
 
update #myplans
set query_plan = qp.query_plan
from #myplans mp
cross apply sys.dm_exec_query_plan(mp.plan_handle) qp
 
;WITH XMLNAMESPACES (DEFAULT 'http://schemas.microsoft.com/sqlserver/2004/07/showplan'),
mycte as
(
    select q.query_hash,
           obj.value('(@Schema)[1]', 'sysname') AS schema_name,
           obj.value('(@Table)[1]', 'sysname') AS table_name
    from #myplans q
    cross apply q.query_plan.nodes('/ShowPlanXML/BatchSequence/Batch/Statements/StmtSimple') as nodes(stmt)
    CROSS APPLY stmt.nodes('.//IndexScan/Object') AS index_object(obj)
)
select query_hash, schema_name, table_name
into #myExecutions
from mycte
where schema_name is not null
and object_id(schema_name + '.' + table_name) in (select object_id from sys.tables)
group by query_hash, schema_name, table_name;
 
select DISTINCT A.table_name as first_table,
       B.table_name as second_table
from #myExecutions A
join #myExecutions B
on A.query_hash = B.query_hash
where A.table_name < B.table_name;

March 12, 2019

Lonely Tables in SQL Server

Filed under: SQL Scripts,SQLServerPedia Syndication,Technical Articles — Michael J. Swart @ 12:00 pm

Takeaway: I provide a script that looks at the procedure cache and reports tables that are never joined to other tables.

Recently, I’ve been working hard to reduce our use of SQL Server as much as possible. In other words, I’ve been doing some spring cleaning. I pick up a table in my hands and I look at it. If it doesn’t spark joy then I drop it.

If only it were that easy. That’s not quite the process I’m using. The specific goals I’m chasing are about reducing cost. I’m moving data to cheaper data stores when it makes sense.

So let’s get tidying. But where do I start?

Getting rid of SQL Server tables should accomplish a couple things. First, it should “move the needle”. If my goal is cost, then the tables I choose to remove should reduce my hardware or licensing costs in a tangible way. The second thing is that dropping the table is achievable without 10 years of effort. So I want to focus on “achievability” for a bit.

Achievable

What’s achievable? I want to identify tables to extract from the database that won’t take years. Large monolithic systems can have a lot of dependencies to unravel.

So what tables in the database have the least dependencies? How do I tell without a trustworthy data model? Is it the ones with the fewest foreign keys (in or out)? Maybe, but foreign keys aren’t always defined properly or they can be missing all together.

My thought is that if two tables are joined together in some query, then they’re related or connected in some fashion. So that’s my idea. I can look at the procedure cache of a database in production to see where the connections are. And when I know that, I can figure out what tables are not connected.

Lonely Tables

This script gives me set of tables that aren’t joined to any other table in any query in cache

use [your db name here];
 
SELECT qs.query_hash,
       qs.plan_handle,
       cast(null as xml) as query_plan
  INTO #myplans
  FROM sys.dm_exec_query_stats qs
 CROSS APPLY sys.dm_exec_plan_attributes(qs.plan_handle) pa
 WHERE pa.attribute = 'dbid'
   AND pa.value = db_id();
 
WITH duplicate_queries AS
(
  SELECT ROW_NUMBER() OVER (PARTITION BY query_hash ORDER BY (SELECT 1)) n
  FROM #myplans
)
DELETE duplicate_queries
 WHERE n > 1;
 
UPDATE #myplans
   SET query_plan = qp.query_plan
  FROM #myplans mp
 CROSS APPLY sys.dm_exec_query_plan(mp.plan_handle) qp;
 
WITH XMLNAMESPACES (DEFAULT 'http://schemas.microsoft.com/sqlserver/2004/07/showplan'),
my_cte AS 
(
    SELECT q.query_hash,
           obj.value('(@Schema)[1]', 'sysname') AS [schema_name],
           obj.value('(@Table)[1]', 'sysname') AS table_name
      FROM #myplans q
     CROSS APPLY q.query_plan.nodes('/ShowPlanXML/BatchSequence/Batch/Statements/StmtSimple') as nodes(stmt)
     CROSS APPLY stmt.nodes('.//IndexScan/Object') AS index_object(obj)
)
SELECT query_hash, [schema_name], table_name
  INTO #myExecutions
  FROM my_cte
 WHERE [schema_name] IS NOT NULL
   AND OBJECT_ID([schema_name] + '.' + table_name) IN (SELECT object_id FROM sys.tables)
 GROUP BY query_hash, [schema_name], table_name;
 
WITH multi_table_queries AS
(
    SELECT query_hash
      FROM #myExecutions
     GROUP BY query_hash
    HAVING COUNT(*) > 1
),
lonely_tables as
(
    SELECT [schema_name], table_name
      FROM #myExecutions
    EXCEPT
    SELECT [schema_name], table_name
      FROM #myexecutions WHERE query_hash IN (SELECT query_hash FROM multi_table_queries)
)
SELECT l.*, ps.row_count
  FROM lonely_tables l
  JOIN sys.dm_db_partition_stats ps
       ON OBJECT_ID(l.[schema_name] + '.' + l.table_name) = ps.object_id
 WHERE ps.index_id in (0,1)
 ORDER BY ps.row_count DESC;

Caveats

So many caveats.
There are so many things that take away from the accuracy and utility of this script that I hesitated to even publish it.
Here’s the way I used the script. The list of tables was something that helped me begin an investigation. For me, I didn’t use it to give answers, but to generate questions. For example, taking each table in the list, I asked: “How hard would it be to get rid of table X and what would that save us?” I found it useful to consider those questions. Your mileage of course will vary.

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
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