Parameter sniffing is a feature in SQL Server where the query optimizer examines (or “sniffs”) the parameter values the first time a stored procedure or parameterized query executes. It uses these specific values to create an execution plan optimized for those particular parameters. The plan is then cached and reused for subsequent executions, even when different parameter values are passed in.
This behavior can be efficient if the initial parameters are representative of typical queries, but it can also cause performance problems if later calls use very different parameter values that make the cached plan inefficient.
How Parameter Sniffing Works
When you execute a stored procedure or parameterized query for the first time, SQL Server looks at the parameter values you provided. It uses these values along with statistics on your tables and indexes to estimate how many rows the query will return and which execution plan will be most efficient.
This is usually beneficial as it allows SQL Server to generate highly optimized plans based on actual data distributions. But it can also cause serious performance problems when the first set of parameters isn’t representative of typical queries, resulting in a cached plan that works well for some parameter values but poorly for others.
When Parameter Sniffing is Beneficial
While parameter sniffing can sometimes cause issues, it’s actually designed to be helpful. When your data is relatively uniform or your parameter values have similar distributions, having a plan optimized for actual data is far better than a generic plan.
If most of your queries return similar result set sizes, parameter sniffing lets SQL Server create targeted, efficient plans. A stored procedure that looks up individual customer records will consistently benefit from index seeks, and parameter sniffing ensures the optimizer chooses that approach.
The benefit is that SQL Server doesn’t need to recompile the execution plan every time you run the query. Plan compilation has overhead, which includes examining statistics, evaluating index options, calculating costs, etc. Caching plans saves that work and improves overall system performance.
When Parameter Sniffing Causes Problems
Parameter sniffing becomes problematic when your data has skewed distributions and the “wrong” parameter value gets cached. If some parameter values return a handful of rows while others return thousands, no single execution plan works well for all cases. Basically, there’s a mismatch between the parameter values that create your cached plans and the parameter values you actually query most often.
The issue isn’t just that some parameter values return more rows than others. The real problem occurs when the wrong parameter value gets cached.
Consider a product search stored procedure. Searching for a rare specialty item might return 10 rows, which is perfect for an index seek. Searching for a common category might return 50,000 rows, which might be better served by a table scan or different index strategy. If the cached plan was optimized for the rare item, queries for common categories will suffer. And if most queries are for the common categories, then it’s fair to say that the wrong parameter was cached, and the query plan is not as efficient as it could be. However, if most queries are for the rare items, then it’s likely that the most efficient plan was cached.
Another common scenario involves date ranges. A query that searches the last 7 days might return 100 rows and work great with an index seek. But if that same cached plan is used for someone querying the last 5 years, performance tanks because the optimizer didn’t anticipate scanning millions of rows.
Unpredictable performance can be an indicator of such parameter sniffing problems. The same query runs fast sometimes and slow other times, depending on which parameters happened to execute first and get cached.
Solutions to Parameter Sniffing Problems
Several approaches can mitigate parameter sniffing issues. The best solution depends on your specific situation.
Suppose we create the following stored procedure:
CREATE PROCEDURE GetSalesByRegion
@Region VARCHAR(50)
AS
BEGIN
SELECT sale_id, sale_date, product_name, amount
FROM regional_sales
WHERE region = @Region
ORDER BY sale_date DESC;
END;And we find that it results in parameter sniffing problems. Below are some options for dealing with this.
Option 1: Use OPTION (RECOMPILE)
Adding OPTION (RECOMPILE) to a query forces SQL Server to create a fresh execution plan every time, using the current parameter values:
CREATE OR ALTER PROCEDURE GetSalesByRegion
@Region VARCHAR(50)
AS
BEGIN
SELECT sale_id, sale_date, product_name, amount
FROM regional_sales
WHERE region = @Region
ORDER BY sale_date DESC
OPTION (RECOMPILE);
END;This eliminates parameter sniffing problems but adds compilation overhead. Use this when plan generation is cheap compared to the cost of a bad cached plan.
Option 2: Use OPTION (OPTIMIZE FOR)
If you know which parameter values are most common, you can tell SQL Server to optimize for specific values:
CREATE OR ALTER PROCEDURE GetSalesByRegion
@Region VARCHAR(50)
AS
BEGIN
SELECT sale_id, sale_date, product_name, amount
FROM regional_sales
WHERE region = @Region
ORDER BY sale_date DESC
OPTION (OPTIMIZE FOR (@Region = 'West'));
END;This creates a plan optimized for ‘West’ regardless of which parameter value is passed first. Works well when you have a clear “typical” case.
Option 3: Use local variables
Assigning parameters to local variables prevents parameter sniffing because SQL Server doesn’t sniff local variable values:
CREATE OR ALTER PROCEDURE GetSalesByRegion
@Region VARCHAR(50)
AS
BEGIN
DECLARE @LocalRegion VARCHAR(50) = @Region;
SELECT sale_id, sale_date, product_name, amount
FROM regional_sales
WHERE region = @LocalRegion
ORDER BY sale_date DESC;
END;This forces SQL Server to create a generic plan based on average statistics rather than specific parameter values. The plan might not be optimal for any single case, but it won’t be terrible for any case either.
Option 4: Use OPTION (OPTIMIZE FOR UNKNOWN)
This hint tells SQL Server to ignore the parameter values and use average density statistics:
CREATE OR ALTER PROCEDURE GetSalesByRegion
@Region VARCHAR(50)
AS
BEGIN
SELECT sale_id, sale_date, product_name, amount
FROM regional_sales
WHERE region = @Region
ORDER BY sale_date DESC
OPTION (OPTIMIZE FOR UNKNOWN);
END;Similar to the local variable approach but more explicit about the intent.
Identifying Parameter Sniffing Issues
If you suspect parameter sniffing problems, look for these symptoms:
- Queries that run fast sometimes and slow other times with no obvious pattern
- Performance that improves dramatically when you clear the plan cache or recompile the procedure
- Execution plans that seem inappropriate for the amount of data being processed
- Significant variance in execution times for the same stored procedure with different parameters
You can examine cached plans to see what parameter values were used during compilation:
SELECT
st.text,
cp.usecounts,
qp.query_plan,
qs.creation_time,
qs.last_execution_time
FROM sys.dm_exec_cached_plans cp
CROSS APPLY sys.dm_exec_sql_text(cp.plan_handle) st
CROSS APPLY sys.dm_exec_query_plan(cp.plan_handle) qp
INNER JOIN sys.dm_exec_query_stats qs ON cp.plan_handle = qs.plan_handle
WHERE st.text LIKE '%GetSalesByRegion%'
AND st.text NOT LIKE '%sys.dm_exec%';Look at the query plan XML to see the parameter values that were sniffed during compilation. If those values don’t represent typical usage, you’ve likely found your parameter sniffing issue.
When to Leave Parameter Sniffing Alone
Not every performance issue is parameter sniffing, and not every parameter sniffing situation needs fixing. If your queries perform consistently well, the feature is working as designed. The overhead of recompilation or the trade-offs of generic plans might be worse than occasional suboptimal executions.
Consider the frequency and impact. If a rarely-used procedure occasionally runs slower, that might be acceptable. If a high-traffic procedure causes timeouts and user complaints, intervention is warranted.
Monitor your system’s actual behavior before implementing solutions. Parameter sniffing is a feature, not a bug – it becomes a problem only when the cached plans consistently hurt more than they help.