Tag: Performance

Page Latch Waits

Posted on by sqljared

Reducing waits is a great way to improve the performance of your SQL Servers. Minimizing PAGELATCH_EX and PAGELATCH_SH wait types are more involved than most. There are generally two causes; one of which is largely solved in recent versions, and one which requires real thought and planning to resolve.

Tempdb Contention

Tempdb contention is caused when there is a high rate of object creation in tempdb. There are specific pages (the GAM, SGAM, and PFS pages) that are locked exclusively when allocating space for the new objects. This creates a bottleneck by serializing part of the process.

This shows up as a PAGELATCH_xx wait type, but to differentiate this from the other major cause of page latch contention, check the wait description. If you see three numbers separated by colons and the first number is a 2, you’re seeing tempdb contention. Consider this wait description: 2:1:1.

  • This three-part number indicates the db_id, file_id, and page_id this thread is waiting for access to.
  • db_id 2 corresponds to tempdb.
  • The file_id of 1 indicates the first data file for that database. If there are additional tempdb files any of them could be referenced. Adding data files reduces the contention by providing another set of key pages, so 1 is the most likely number you will see.
  • Most often the page_id will be 1 or 3 (the PFS or SGAM pages). As a file grows, additional PFS, GAM, and SGAM pages will be created at regular intervals, so if you see much larger numbers, it’s still the same issue.

There were a few options to mitigate this in older versions, but there have been changes since SQL Server 2016 that made this easier or automatic. This has been essentially solved in SQL Server 2022.

For my full post on this topic including a list of the changes in each version, see https://www.sqljared.com/blog/tempdb-contention-in-2023/.

Page Latch and the Hot Page Issue

If you see page latch waits that are not tempdb related, you are likely experiencing the hot page issue. This happens when many different connections are attempting to update the same page in memory.

This can happen with any write operation, but it’s probably easiest to understand when inserting new rows into a table. Imagine inserting new records one at a time into a table with a clustered index based on an IDENTITY(1,1) column.

The IDENTITY value for the next row will be the highest in the table, and the row will be placed in the last page. But that will be the case for each new row. If many different threads are trying to insert into the table simultaneously, they will block each other since they need exclusive access to the last page.

Even though the page is in memory and access is faster, it is not instantaneous.

Most documentation on the subject will focus on the clustered index, and I will also focus on it here. You can see page latch waits when there is a nonclustered index on an IDENTITY (or other sequential) column, but it will typically require an order of magnitude more inserts before you will see the contention on a nonclustered (and typically smaller) index.

So, how do we address this? There are several possibilities.

Change the clustered index

The issue here is the clustered index is based on our IDENTITY column. If you made this a nonclustered index instead, you could create a clustered index on another column. The order of the table would be based on the column(s) of the new clustered index.

This could be a composite index, which could still include the IDENTITY column. However the first column will determine the order of the rows in the table, and the goal would be for us to insert new rows randomly throughout the table.

I would prefer to use a small column for this as the clustered indexes columns get added to all other indexes in the table, duplicating that data. I also would choose a column that has many different values. If you chose a BIT field, you would go from having one hot page to having two. That may not improve matters.

You should also be careful not to use another column that would be, in practice, sequential. For example, a column that has the DATETIME for when the row was created would not be an IDENTITY column, but its values would be sequential and you would always insert it into the final page of the index.

I should also mention that randomizing the location of our inserts also means increasing our fragmentation. We will be getting page splits where we are inserting. That doesn’t happen if we insert it to the end of the index, we create a new empty page. This isn’t a huge concern for me, but it is a valid point.

You could also not have a clustered index at all, and the table will be stored in a heap with no order.

Use a computed column

If you don’t see a column you want to use for your clustered index, you can always create a new one.

ALTER TABLE Sales.OrderLines
ADD HashValue AS (CONVERT(tinyint, (OrderLineID%10))) PERSISTED NOT NULL;

This creates a new column that uses the last digit from the OrderLineID in the same table, so a number from 0-9. If you make this the first column of your clustered index, each new row will be inserted based on the HashValue.

Effectively, we would have 10 pages where we insert instead of one. So there will be less contention on each of those pages. You could change the mod to be higher to spread out the inserts more, but that would also lead to more fragmentation.

Partition the table

If you partitioned the table, you effectively have multiple b-tree structures for the clustered index. If there are 10 partitions, each of them has a hot page. This is another way of going from one hot page where all new rows are inserted to using multiple pages.

Partitioning wouldn’t be my first approach. Unless I want the table to be partitioned for other purposes, like switching or truncating partitions, I wouldn’t want to introduce the complications of partitioning.

If your queries are not aligned (containing and filtering on the partitioning key), your queries will suddenly be reading all of the b-tree structures for the aligned index. This could be a substantial increase in reads and can increase duration.

OPTIMIZE_FOR_SEQUENTIAL_KEY

This is an option you can use when creating or rebuilding your indexes. This attempts to increase throughput by limiting the number of threads that can request the latch and favoring threads that are likely to complete their work in a single quantum.

With this enabled, you will see a new wait type, BTREE_INSERT_FLOW_CONTROL. You may see an increase in overall waits and some threads may see more latency if they are delayed for favored threads, but the result should be an increase in throughput.

For more details, see this blog from Pam Lahoud.

Reduce transaction size Tune your writes

My initial thought was to encourage you to look at the entire transaction containing the write that is running into page latch contention. The idea would be to shrink the transaction as a whole to allow its latches to be released sooner, but a little research showed a problem.

Latches are not held for the entire transaction, only for the operation that requires them. So if you are inserting a new row as part of a larger transaction, you will hold locks related to that INSERT for the entire transaction while latches are released when the INSERT is complete. For reference, please see the comparison of latches and locks in this Microsoft article.

Still, anything we can do to speed up that statement will release the latch sooner and allow greater throughput. If your operation reads a lot of data, optimize your execution plan as best you can. Add hints if that will make performance more consistent. Perhaps you can read the data in a separate statement to get the IDs of the rows to write so that your write operation can be simplified (using Manual Halloween).

Read Committed Snapshot

SELECT statements also require latches while accessing data, and they can add to the contention when running an INSERT or UPDATE on a hot page. If you see your writes operations wait on PAGELATCH_SH, you may want to consider using READ_COMMITTED_SNAPSHOT.

This is a database-level setting, so you will need to consider its impact on all operations in the database. But the benefit is that your read and write processes no longer block each other. If you are experiencing hot page contention, your reads will at least stop contributing to the problem.

Batch your operations

I’ve saved this for last because it’s probably the most effective solution and one requiring the most work. Instead of having 50 connections each calling the same procedure to insert one row each, why not make one call using a TVP input to insert all 50 rows?

If you work in a batch, you reduce the number of threads operating on a given table while processing the same number of rows. Reducing threads reduces contention.

The difficulty is the change to your application. Does it receive a large amount of data to insert all at once? If so, changing to a new procedure would be relatively simple. Do encourage your app developers to keep the batch size reasonable, given the size of one row in your TVP.

If the application only receives individual values to insert, you could queue them to insert a batch at once. This may be more complicated on the code side.

I have an example from a recent presentation where I inserted 500 rows of data into a table in the WideWorldImporters database in three different ways. The results are below.

The first method used a simple procedure to insert 1 row into the table. I ran this from SSMS 500 times. Of course, this meant I was inserting the values serially, and there was no contention from multiple threads running at once. This took 23 microseconds per row.

The second method used the same single-insert procedure, but SQLQueryStress allowed me to run 50 executions across 10 threads. I included sys.query_store_wait_stats in my query against Query Store so we can see “Buffer Latch” waits. Each row took 119 microseconds on average, and interestingly our CPU time for each insert was significantly higher (about 3x).

The third method used a different stored procedure taking a memory-optimized TVP as input and inserting all 500 rows in one call. The batched approach took 4.2 microseconds per row.

Conclusion

It’s been a while since my last post. I was focusing my attention on my presentation for the PASS Data Community Summit in November, which went very well and was a great experience.

And then I changed jobs at the start of December. This has been a good move for me, but it involved a lot of thought and emotion as it meant leaving a team with great people.

Things have settled now, and hopefully, I’ll get back to writing on a more regular cadence.

My social media links are above. Please contact me if you have questions, and I’m happy to consult with you if you have a more complex performance issue. Also, let me know if you have any suggestions for a topic for a new blog post.

Variant Queries in Query Store

Posted on by sqljared

I wrote about Parameter Sensitive Plan Optimization in my last blog. In this post, I want to talk about a specific problem you may see in Query Store, depending on how to get information from it.

A Query Store Example

I use Query Store frequently, and I tend to be working on a specific stored procedure at a time. Either I’m evaluating a procedure to see how we can improve its performance, or I’m testing\validating the improvements to that procedure. Here’s an example Query Store script I might use.

SELECT 
	qsq.query_id,
	qsp.plan_id,
	CAST(qsp.query_plan as XML) AS query_plan,
	qt.query_sql_text,
	rsi.end_time,
	(rs.avg_duration * rs.count_executions) as total_duration,
	rs.avg_duration,
	rs.count_executions,
	rs.avg_cpu_time,
	rs.avg_logical_io_reads,
	rs.avg_rowcount
FROM sys.query_store_query qsq
INNER JOIN sys.query_store_plan qsp
	ON qsp.query_id = qsq.query_id
INNER JOIN sys.query_store_query_text qt
	ON qt.query_text_id = qsq.query_text_id
INNER JOIN sys.query_store_runtime_stats rs
	ON rs.plan_id = qsp.plan_id
INNER JOIN sys.query_store_runtime_stats_interval rsi
	ON rsi.runtime_stats_interval_id = rs.runtime_stats_interval_id
WHERE
	qsq.object_id = OBJECT_ID('dbo.User_GetByReputation')
	AND rsi.end_time > DATEADD(DAY,-2, GETUTCDATE())

This query gets the execution stats from the procedure I used in my last blog post against the StackOverflow2013 database. Any executions in the last two days will be included in the results.

Or it should. When I run this I get “(0 rows affected)”.

But I just ran this procedure, so what’s the issue?

sys.query_store_query_variant

This is an example of a query that needs to be updated for SQL Server 2022 with Parameter Sensitive Plan Optimization in place, and the reason has to do with changes made to allow variant queries.

There is a new table in Query Store that is essential to the PSPO feature: sys.query_store_query_variant.

The table is something of a stub, with only three columns. This establishes the relationship between the parent query, variant queries, and the dispatcher plan.

You can see in this case there are two variants for the same parent_query_id. So, for a given query you could LEFT JOIN to sys.query_store_query_variant to find any variant queries it may have, then join back to sys.query_store_query to get the rest of the details for that variant query.

Parent Queries Don’t Execute

But why did my query have no results?

The first issue is that the parent queries and the plan associated with them don’t execute. Joining the tables that give the query, plan, and text is fine, but when we INNER JOIN sys.query_store_runtime_stats and sys.query_store_runtime_stats_interval we lose our results.

Running the same query with LEFT JOINs shows the execution stats are all NULL.

While we are here, if I click on the link for the plan I will see the dispatcher plan. This isn’t a full execution plan, but there is some information about the use of PSPO (the boundaries and details of the statistic used) in the XML.

<ShowPlanXML xmlns="http://schemas.microsoft.com/sqlserver/2004/07/showplan" Version="1.564" Build="16.0.1050.5">
  <BatchSequence>
    <Batch>
      <Statements>
        <StmtSimple StatementText="SELECT &#xD;&#xA;		u.AccountId,&#xD;&#xA;		u.DisplayName,&#xD;&#xA;		u.Views,&#xD;&#xA;		u.CreationDate&#xD;&#xA;	FROM dbo.Users u&#xD;&#xA;	WHERE &#xD;&#xA;		u.Reputation=@Reputation" StatementId="1" StatementCompId="3" StatementType="MULTIPLE PLAN" RetrievedFromCache="false" QueryHash="0x08FD84B17223204C" QueryPlanHash="0x86241E8431E63362">
          <Dispatcher>
            <ParameterSensitivePredicate LowBoundary="100" HighBoundary="1e+06">
              <StatisticsInfo LastUpdate="2023-05-19T14:04:14.91" ModificationCount="0" SamplingPercent="100" Statistics="[IX_User_Reputation]" Table="[Users]" Schema="[dbo]" Database="[StackOverflow2013]" />
              <Predicate>
                <ScalarOperator ScalarString="[StackOverflow2013].[dbo].[Users].[Reputation] as [u].[Reputation]=[@Reputation]">
                  <Compare CompareOp="EQ">
                    <ScalarOperator>
                      <Identifier>
                        <ColumnReference Database="[StackOverflow2013]" Schema="[dbo]" Table="[Users]" Alias="[u]" Column="Reputation" />
                      </Identifier>
                    </ScalarOperator>
                    <ScalarOperator>
                      <Identifier>
                        <ColumnReference Column="@Reputation" />
                      </Identifier>
                    </ScalarOperator>
                  </Compare>
                </ScalarOperator>
              </Predicate>
            </ParameterSensitivePredicate>
          </Dispatcher>
        </StmtSimple>
      </Statements>
    </Batch>
  </BatchSequence>
</ShowPlanXML>

But if we didn’t execute the dispatcher plan for the parent query, we should have executed the plan for the variant query. Why didn’t we see that in our results?

Variant Queries’ Object_ID = 0 (Adhoc)

The second issue is that variant queries have an object_id of 0 in sys.query_store_query; the same as an ad-hoc query.

I was filtering on the object_id of my procedure to only get results for that procedure, but that doesn’t include our variant queries.

But I can query from the sys.query_store_query_variant table to sys.query_store_query based on the query_variant_query_id to get the details for my variant query, then join to other tables to get the stats I was looking for.

SELECT 
	var.*,
	qsq.query_id,
	qsp.plan_id,
	CAST(qsp.query_plan as XML) AS query_plan,
	qt.query_sql_text,
	rsi.end_time,
	(rs.avg_duration * rs.count_executions) AS total_duration,
	rs.avg_duration,
	rs.count_executions,
	rs.avg_cpu_time,
	rs.avg_logical_io_reads,
	rs.avg_rowcount
FROM sys.query_store_query_variant var
INNER JOIN sys.query_store_query qsq
	ON qsq.query_id = var.query_variant_query_id
INNER JOIN sys.query_store_plan qsp
	ON qsp.query_id = qsq.query_id
LEFT JOIN sys.query_store_query_text qt
	ON qt.query_text_id = qsq.query_text_id
LEFT JOIN sys.query_store_runtime_stats rs
	ON rs.plan_id = qsp.plan_id
LEFT JOIN sys.query_store_runtime_stats_interval rsi
	ON rsi.runtime_stats_interval_id = rs.runtime_stats_interval_id
	AND rsi.end_time > DATEADD(DAY,-2, GETUTCDATE());

Getting the runtime statistics isn’t the hard part, it’s just identifying which queries we care about.

Silent Failure

If you use Query Store regularly, and especially if you have any tools or automation built on that information, you’ll need to account for the two points above. Because your existing scripts won’t fail, they will give you incomplete results. This is a case where an actual error would be more helpful; you’d know something had broken.

So, how do we get all the execution details for our procedure? First, let’s see the parent query and its children (updated per the addendum).

SELECT 
	qsq.query_id,
	qsp.plan_id,
	qsp.plan_type_desc,
	vr.parent_query_id,
	vr.query_variant_query_id,
	qv.query_id,
	qvp.plan_id,
	qvp.plan_type_desc
FROM sys.query_store_query qsq
INNER JOIN sys.query_store_plan qsp
	ON qsp.query_id = qsq.query_id
LEFT JOIN sys.query_store_query_variant vr
	ON vr.parent_query_id = qsq.query_id
    AND vr.dispatcher_plan_id = qsp.plan_id
LEFT JOIN sys.query_store_query qv
	ON qv.query_id = vr.query_variant_query_id
LEFT JOIN sys.query_store_plan qvp
	ON qvp.query_id = qv.query_id
WHERE
	qsq.object_id = OBJECT_ID('dbo.User_GetByReputation');

Also, note that sys.query_store_plan has two new columns that are relevant to us; plan_type_desc and plan_type. Query_id 48 is the parent query related to the “Dispatcher Plan”; the variant plan is marked as “Query Variant Plan”. A normal plan would be a “Compiled Plan”.

A Suggested Solution

We could return both, but we don’t really need to. The parent query has no related performance statistics, but we need the parent query to find all the variant queries that allow us to get the execution statistics.

There is one more issue to consider; sys.query_store_query_variant does not exist in a SQL Server instance below 2022. So if we want a procedure that can run on our un-upgraded instances, we’ll need two paths.

Oh, we also want to make sure we don’t miss plans for any queries not using PSPO.

Here’s a simple procedure that does that (which has been updated per the addendum).

USE StackOverflow2013 
GO
CREATE OR ALTER PROCEDURE dbo.QS_GetProcedurePerfDetails
  @Schema_Object NVARCHAR(100),
  @StartDate DATETIME2
AS
	DECLARE 
		@c_level INT,
		@obj_id INT;

	CREATE TABLE #QueryList (
		query_id INT,
		plan_id INT,
		query_plan NVARCHAR(MAX),
		query_text_id INT,
		plan_type_desc NVARCHAR(120) NULL
	);

	SELECT 
		@c_level = db.compatibility_level  
	FROM sys.databases db
	WHERE 
		db.database_id = DB_ID();

	SET @obj_id = OBJECT_ID(@Schema_Object);

	-- Based on the compatability level, get IDs for relevant queries and plans
	IF (@c_level < 160)
	BEGIN 
		INSERT #QueryList
		SELECT 
			qsq.query_id,
			qsp.plan_id,
			qsp.query_plan,
			qsq.query_text_id,
			NULL AS plan_type_desc
		FROM sys.query_store_query qsq
		INNER JOIN sys.query_store_plan qsp
			ON qsp.query_id = qsq.query_id
		WHERE
			qsq.object_id = @obj_id;
	END
	ELSE
	BEGIN
		INSERT #QueryList
		SELECT 
			ISNULL(qv.query_id,qsq.query_id) AS query_id,
			ISNULL(qvp.plan_id,qsp.plan_id) AS plan_id,
			ISNULL(qvp.query_plan,qsp.query_plan) AS query_plan,
			ISNULL(qv.query_text_id,qsq.query_text_id) AS query_text_id,
			ISNULL(qvp.plan_type_desc,qsp.plan_type_desc) AS plan_type_desc
		FROM sys.query_store_query qsq
		INNER JOIN sys.query_store_plan qsp
			ON qsp.query_id = qsq.query_id
		LEFT JOIN sys.query_store_query_variant vr
			ON vr.parent_query_id = qsq.query_id
            AND vr.dispatcher_plan_id = qsp.plan_id
		LEFT JOIN sys.query_store_query qv
			ON qv.query_id = vr.query_variant_query_id
		LEFT JOIN sys.query_store_plan qvp
			ON qvp.query_id = qv.query_id
		WHERE
			qsq.object_id = @obj_id;
	END;

	SELECT 
		ql.query_id,
		ql.plan_id,
		ql.plan_type_desc,
		CAST(ql.query_plan as XML),
		qt.query_sql_text,
		rsi.end_time,
		(rs.avg_duration * rs.count_executions) as total_duration,
		rs.avg_duration,
		rs.count_executions,
		rs.avg_cpu_time,
		rs.avg_logical_io_reads,
		rs.avg_rowcount
	FROM #QueryList ql
	INNER JOIN sys.query_store_query_text qt
		ON qt.query_text_id = ql.query_text_id
	LEFT JOIN sys.query_store_runtime_stats rs
		ON rs.plan_id = ql.plan_id
	LEFT JOIN sys.query_store_runtime_stats_interval rsi
		ON rsi.runtime_stats_interval_id = rs.runtime_stats_interval_id
	WHERE
		rsi.end_time > @StartDate
	ORDER BY
		qt.query_sql_text,
		rsi.end_time;
GO

EXEC dbo.QS_GetProcedurePerfDetails
  @Schema_Object = 'dbo.User_GetByReputation',
  @StartDate = '2023-05-24';
GO

I’m checking to see if the compatibility level is at least 160 as a way to see if we are on SQL Server 2022 or not. Of course, we could be using SQL Server 2022 with a lower compatibility level, but the sys.query_store_query_variant table would be empty in that case.

The key is to have an initial step to get the queries and plans we want. This has a LEFT JOIN to sys.query_store_query_variant in case there are variant queries to consider. We join to get the other details for the variant (the query_text_id, plan_id, and the plan itself) if they exist.

I’m throwing those initial results in a temp table to use in the final query. I’ve also found in my testing that splitting this operation in two helps to prevent the plan from getting too large and sluggish.

The temp table is populated with the details of the variant query and its “Query Variant Plan”, if present, but if they are NULL we use the details of what must be a normal query with a “Compiled Plan”.

From here, you can also get more complex and include options to aggregate the Query Store details or include more columns, but this should solidify how to incorporate sys.query_store_query_variant into your scripts.

In Summary

This is something I’ve been working on for a while. It became obvious to me months ago that we needed to include this logic at work so that our own Query Store aggregation wouldn’t suddenly miss a lot of executions.

Hopefully, this post will help some readers to avoid this pitfall.

You can follow me on Twitter (@sqljared) and contact me if you have questions. My other social media links are at the top of the page. Also, let me know if you have any suggestions for a topic for a new blog post.

Addendum

As I pointed out in my next post, the join I originally made to sys.query_store_query_variant was incorrect. If you only join based on the query_id, the result set gets multiplied. Each variant query is shown as related to all plans for the parent query, even “compiled plan” type plans which do not use PSPO at all.

I’ve updated the scripts above in two places, but wanted to call that out.

Parameter Sensitive Plan Optimization

Posted on by sqljared

Parameter sniffing (for the uninitiated)

Parameter sniffing is a well-known issue within SQL Server. Imagine you have a query that can return a wildly varying number of rows. For example, a query that returns all orders in your system for a given customer. Most of your customers have a few orders, several have a lot, and one huge customer has tons of orders.

When the query is executed for the first time, SQL Server will estimate the number of rows the query will return based on the customer_id and create a plan based on that estimate.

If you wanted the details for a small account, the plan will be optimized for a small number of rows (assuming your statistics are accurate) and will be more likely to have nested loops and key lookups. Those operations are fine for small result sets but less efficient for large result sets.

If you called to get details for your largest account, you’ll get a plan that is optimized for a huge number of rows; likely using an index\table scan or maybe a hash match join instead of nested loops.

But the plan will be compiled for the first execution, and hopefully reused after that. If the plan is geared toward a small result set, it will perform poorly for the large account, and vice versa.

You can minimize the effect of parameter sniffing using WITH RECOMPILE or OPTIMIZE FOR hints, but look at an example of parameter sniffing using WideWorldImporters.

USE WideWorldImporters
GO
CREATE OR ALTER PROCEDURE Sales.GetOrders
	@CustomerID INT
AS

	SELECT *
	FROM Sales.Orders so
	WHERE
		so.CustomerID = @CustomerID;
GO

I added some more data to the table, all under one CustomerID, to change its statistics. CustomerID 90 has been very busy.

If I run this simple procedure for CustomerID 13, we’ll get a plan well suited to it, and the estimates are accurate.

EXEC Sales.GetOrders @CustomerID = 13; -- 13 or 90
GO

If we try again but with CustomerID 90, we use the same plan and estimate the same number of rows. But nested loops and a key lookup probably shouldn’t be the plan when we are returning over 100,000 rows.

This is an example of parameter sniffing. Our plan is going to be defined largely by the parameters we use when the procedure gets compiled.

Variant Queries

Parameter Sensitive Plan Optimization attempts to solve the parameter sniffing issue by allowing a query to have different plans that are used based on the cardinality of one important parameter. The parent query has a plan that is really just a stub; there can be up to three variant queries that have full execution plans. When the query is executed, the optimizer chooses which variant query and plan to use based on the cardinality of the parameter’s value.

The plans for the parent and variant queries all indicate that PSPO is in use, and show the boundaries for the parameter. There is a low boundary and a high boundary. If the value provided for the parameter should return a small number of rows, less than the low boundary, the appropriate plan is used. If the cardinality for that value is between the two boundaries, another plan is used. If the cardinality is above the high boundary, the third plan is used.

Example

I tried several times but was unable to get PSPO to kick in on this table in WideWorldImporters, so I shifted to using the StackOverflow2013 database. 

USE StackOverflow2013;
GO
ALTER DATABASE CURRENT SET COMPATIBILITY_LEVEL = 160; /* 2022 */
GO
ALTER DATABASE StackOverflow2013 SET QUERY_STORE = ON;
GO
ALTER DATABASE StackOverflow2013 SET QUERY_STORE (OPERATION_MODE = READ_WRITE, QUERY_CAPTURE_MODE = ALL, INTERVAL_LENGTH_MINUTES = 15);
GO
EXEC DropIndexes;
GO
IF NOT EXISTS(
	SELECT 1
	FROM sys.indexes si
	WHERE
		name = 'IX_User_Reputation'
)
BEGIN
	CREATE INDEX IX_User_Reputation ON dbo.Users(Reputation);
END;
GO
CREATE OR ALTER PROCEDURE dbo.User_GetByReputation
  @Reputation int
AS
	SELECT 
		u.AccountId,
		u.DisplayName,
		u.Views,
		u.CreationDate
	FROM dbo.Users u
	WHERE 
		u.Reputation=@Reputation;
GO

I’ve seen a few other blogs use Reputation as an example, and it seems its histogram is in a good state for the optimizer to choose PSPO.

You can see from the query text at the top of the plan that the system has added an OPTION hint for “PLAN PER VALUE”. This is specifying which of the variant plans to use for this parameter.

If you look in the XML, you will also see a block like this one when PSPO is in use:

<StmtSimple StatementCompId="4" StatementEstRows="7173" StatementId="1" StatementOptmLevel="FULL" CardinalityEstimationModelVersion="160" StatementSubTreeCost="21.8786" StatementText="SELECT &#xD;&#xA;		u.AccountId,&#xD;&#xA;		u.DisplayName,&#xD;&#xA;		u.Views,&#xD;&#xA;		u.CreationDate&#xD;&#xA;	FROM dbo.Users u&#xD;&#xA;	WHERE &#xD;&#xA;		u.Reputation=@Reputation option (PLAN PER VALUE(ObjectID = 1237579447, QueryVariantID = 2, predicate_range([StackOverflow2013].[dbo].[Users].[Reputation] = @Reputation, 100.0, 1000000.0)))" StatementType="SELECT" QueryHash="0x08FD84B17223204C" QueryPlanHash="0x2127C7766B9DDB3C" RetrievedFromCache="true" StatementSqlHandle="0x0900A6524A0ECC5A61EA55C6320D3963E1D20000000000000000000000000000000000000000000000000000" DatabaseContextSettingsId="1" ParentObjectId="0" StatementParameterizationType="1" SecurityPolicyApplied="false">
          <StatementSetOptions ANSI_NULLS="true" ANSI_PADDING="true" ANSI_WARNINGS="true" ARITHABORT="true" CONCAT_NULL_YIELDS_NULL="true" NUMERIC_ROUNDABORT="false" QUOTED_IDENTIFIER="true" />
          <Dispatcher>
            <ParameterSensitivePredicate LowBoundary="100" HighBoundary="1000000">
              <StatisticsInfo Database="[StackOverflow2013]" Schema="[dbo]" Table="[Users]" Statistics="[IX_User_Reputation]" ModificationCount="0" SamplingPercent="100" LastUpdate="2023-05-19T14:04:14.91" />
              <Predicate>
                <ScalarOperator ScalarString="[StackOverflow2013].[dbo].[Users].[Reputation] as [u].[Reputation]=[@Reputation]">
                  <Compare CompareOp="EQ">
                    <ScalarOperator>
                      <Identifier>
                        <ColumnReference Database="[StackOverflow2013]" Schema="[dbo]" Table="[Users]" Alias="[u]" Column="Reputation" />
                      </Identifier>
                    </ScalarOperator>
                    <ScalarOperator>
                      <Identifier>
                        <ColumnReference Column="@Reputation" />
                      </Identifier>
                    </ScalarOperator>
                  </Compare>
                </ScalarOperator>
              </Predicate>
            </ParameterSensitivePredicate>
          </Dispatcher>

The “ScalarOperator ScalarString” shows us the column and parameter our plan optimization is focusing on. We can see the index\statistic being used after the StatisticsInfo clause a few lines above; it also shows the sampling rate for that statistic.

The LowBoundary and HighBoundary values define the three ranges for parameter-sensitive plan optimization. If the optimizer estimates rows below the LowBoundary (which has always been 100 in examples I have seen), we will use the first variant. If the estimate is between the boundaries, we use the second. If it’s above the HighBoundary (which has been 100,000 or 1,000,000 in every case I’ve seen), we use the third.

In this case, we used VariantID 2 because we estimated 7173 rows.

One Problem

But as Brent Ozar points out here, there is still parameter sniffing going on. And if you are reading about the subject here, you should also read Brent’s post.

The low boundary I’ve always seen is 100, but there may be some variety there. So, if a query is executed using PSPO with a parameter that has a cardinality of less than 100, SQL Server will use the plan for that smallest range. If the parameter leads to an estimate of 5 rows, we’ll compile and execute that query and reuse the plan for subsequent low cardinality parameters. If the query is executed again with a parameter that should read 80 rows, we’ll use the same plan. But I would expect the optimal plan for those two parameters to be the same, or at least fairly similar. Nested loops and a key lookup won’t hurt here, since we aren’t talking about many rows in either case.

I would think the same for the high boundary, which I’ve seen as 100,000 or 1,000,000 in every case I’ve seen so far. Let’s assume the high boundary is 100,000. If the query is executed, and we estimate 250,000 rows to be returned, we’re likely to have a plan with a scan or hash match in it; maybe we’ll see parallelism. If the query is executed again and 1.5 million rows are expected, the plan is likely to be similar even if the memory allocation is too low.

My concern is for the middle range. A plan intended when we estimate 200 rows to be returned should be very different from a plan expecting 90,000 rows. So it seems more likely that there will be parameter sniffing in that middle range because there’s a large relative difference between the cardinality values within that range. So if parameter sniffing has a minimal effect on the high and low ranges, the improvement made by PSPO for a given query will depend on how many values fall into that middle range.

Summary

I wanted to do a post on the topic as is, before going into a problem that is potentially serious. I’ll cover that in the next post where I will talk about the changes in Query Store related to Parameter Sensitive Plan Optimization. There are changes in other system tables, but I will focus on Query Store because I use it daily.

You can follow me on Twitter (@sqljared) and contact me if you have questions. My other social media links are at the top of the page. Also, let me know if you have any suggestions for a topic for a new blog post.