Avoiding the Halloween Problem in SQL Server with Safer UPDATE Patterns

The Halloween Problem is a classic relational database behavior that can quietly affect performance and query plans during UPDATE or DELETE operations. While SQL Server includes internal protections to prevent it, relying solely on the engine can lead to unpredictable execution plans and unnecessary overhead. Fortunately, with a few simple patterns, you can avoid the issue entirely.

What Is the Halloween Problem?

The Halloween Problem occurs when an UPDATE statement modifies a column that’s part of the index used to locate the rows being updated. If SQL Server isn’t careful, it might re-encounter the same row after it’s been updated—leading to multiple updates of the same row in a single statement.

For example:

UPDATE Employees
SET Salary = Salary + 10000
WHERE Salary < 80000;

If Salary is indexed, SQL Server might use that index to find rows. But as it updates the Salary, the row could move within the index and be picked up again.

How SQL Server Avoids the Halloween Problem

To prevent this, SQL Server inserts a Table Spool (Eager Spool) into the execution plan to avoid multiple updates to the same row. This operator materializes the qualifying rows into a temporary structure before the update begins. It ensures that even if a row’s position in the index changes, it won’t be reprocessed.

But now we are left with a spool in our plan. 🙁

Why Relying on Spools for Halloween Protection Can Hurt Performance

While SQL Server’s optimizer is smart enough to detect and prevent this behavior, the protection comes at a cost:

• Extra memory and I/O for the spool operation
• Less predictable execution plans
• Potential performance degradation under load

A Better Pattern: Materialize First, Then Update

To avoid this, you can rewrite your UPDATE to separate the read and write phases. Two common approaches are:

Example 1. Using a CTE

;WITH TargetRows AS (
    SELECT EmployeeID
    FROM Employees
    WHERE Salary < 80000
)

UPDATE E
SET Salary = Salary + 10000
FROM Employees E
JOIN TargetRows T ON E.EmployeeID = T.EmployeeID;

Example 2. Using a Temp Table

SELECT EmployeeID
INTO #ToUpdate
FROM Employees
WHERE Salary < 80000;

UPDATE E
SET Salary = Salary + 10000
FROM Employees E
JOIN #ToUpdate T ON E.EmployeeID = T.EmployeeID;

Both approaches ensure that the set of rows to be updated is materialized before the update begins, eliminating the need for Halloween protection mechanisms.

CTEs and temp tables have their own problems, but this pattern opens up more opportunities to control batch sizes in large bulk updates. Let’s talk about that next.

Batch Updates to Control Locking

Another benefit of this pattern is that it makes batch updates easy. You can limit the number of rows updated in each batch to reduce the risk of lock escalation. This gives you fine-grained control over concurrency and resource usage.

WHILE 1 = 1
BEGIN
    ;WITH TargetRows AS (
        SELECT TOP (1000) EmployeeID
        FROM Employees
        WHERE Salary < 80000
    )
    UPDATE E
    SET Salary = Salary + 10000
    FROM Employees E
    JOIN TargetRows T ON E.EmployeeID = T.EmployeeID;
    IF @@ROWCOUNT = 0
        BREAK;
END

This simple loop updates rows in batches of 1,000, avoids the Halloween “fix,” reduces lock escalation, and improves concurrency and performance.

How to Detect Halloween Protection

For Developers

Use Actual Execution Plans in SSMS or Azure Data Studio:

• Look for a Table Spool (Eager Spool) operator.
• This could indicate SQL Server is applying Halloween Protection.

For DBAs and Developers

Use Query Store to find plans with spools:

SELECT
    qsqt.query_sql_text,
    CAST(REPLACE(CAST(qsp.query_plan AS NVARCHAR(MAX)), NCHAR(160), N' ') AS XML) AS query_plan_xml,
    qsp.plan_id,
    qsq.query_id,
    qsp.last_execution_time
FROM sys.query_store_plan qsp
JOIN sys.query_store_query qsq
    ON qsp.query_id = qsq.query_id
JOIN sys.query_store_query_text qsqt
    ON qsq.query_text_id = qsqt.query_text_id
WHERE CAST(REPLACE(CAST(qsp.query_plan AS NVARCHAR(MAX)), NCHAR(160), N' ') AS XML).exist(
    '
    declare namespace p="http://schemas.microsoft.com/sqlserver/2004/07/showplan";
    //p:RelOp[contains(@PhysicalOp, "Spool")]
    '
) = 1;

This helps identify queries and plans that have spools. Plug the query_id into the Query Store Tracked Queries report to see its performance over time.

Additional Reading

• Halloween Problem – Wikipedia
• SQL Server 2025: introducing optimized Halloween protection | Microsoft Community Hub