How to update large table with millions of rows in SQL Server?

Question

I've an UPDATE statement which can update more than million records. I want to update them in batches of 1000 or 10000. I tried with @@ROWCOUNT but I am unable to get desired result.

Just for testing purpose what I did is, I selected table with 14 records and set a row count of 5. This query is supposed to update records in 5, 5 and 4 but it just updates first 5 records.

Query - 1:

SET ROWCOUNT 5  UPDATE TableName  SET Value = 'abc1'  WHERE Parameter1 = 'abc' AND Parameter2 = 123  WHILE @@ROWCOUNT > 0 BEGIN     SET rowcount 5      UPDATE TableName      SET Value = 'abc1'      WHERE Parameter1 = 'abc' AND Parameter2 = 123      PRINT (@@ROWCOUNT) END  SET rowcount 0 

Query - 2:

SET ROWCOUNT  5  WHILE (@@ROWCOUNT > 0) BEGIN     BEGIN TRANSACTION      UPDATE TableName      SET Value = 'abc1'      WHERE Parameter1 = 'abc' AND Parameter2 = 123      PRINT (@@ROWCOUNT)      IF @@ROWCOUNT = 0     BEGIN         COMMIT TRANSACTION          BREAK     END      COMMIT TRANSACTION END  SET ROWCOUNT  0 

What am I missing here?

Answer 1

1. You should not be updating 10k rows in a set unless you are certain that the operation is getting Page Locks (due to multiple rows per page being part of the UPDATE operation). The issue is that Lock Escalation (from either Row or Page to Table locks) occurs at 5000 locks. So it is safest to keep it just below 5000, just in case the operation is using Row Locks.

2. You should not be using SET ROWCOUNT to limit the number of rows that will be modified. There are two issues here:

   1. It has that been deprecated since SQL Server 2005 was released (11 years ago):

      Using SET ROWCOUNT will not affect DELETE, INSERT, and UPDATE statements in a future release of SQL Server. Avoid using SET ROWCOUNT with DELETE, INSERT, and UPDATE statements in new development work, and plan to modify applications that currently use it. For a similar behavior, use the TOP syntax

   2. It can affect more than just the statement you are dealing with:

      Setting the SET ROWCOUNT option causes most Transact-SQL statements to stop processing when they have been affected by the specified number of rows. This includes triggers. The ROWCOUNT option does not affect dynamic cursors, but it does limit the rowset of keyset and insensitive cursors. This option should be used with caution.

   Instead, use the TOP () clause.

3. There is no purpose in having an explicit transaction here. It complicates the code and you have no handling for a ROLLBACK, which isn't even needed since each statement is its own transaction (i.e. auto-commit).

4. Assuming you find a reason to keep the explicit transaction, then you do not have a TRY / CATCH structure. Please see my answer on DBA.StackExchange for a TRY / CATCH template that handles transactions:

   Are we required to handle Transaction in C# Code as well as in Store procedure

I suspect that the real WHERE clause is not being shown in the example code in the Question, so simply relying upon what has been shown, a better model ^{(please see note below regarding performance)} would be:

DECLARE @Rows INT,         @BatchSize INT; -- keep below 5000 to be safe      SET @BatchSize = 2000;  SET @Rows = @BatchSize; -- initialize just to enter the loop  BEGIN TRY       WHILE (@Rows = @BatchSize)   BEGIN       UPDATE TOP (@BatchSize) tab       SET    tab.Value = 'abc1'       FROM  TableName tab       WHERE tab.Parameter1 = 'abc'       AND   tab.Parameter2 = 123       AND   tab.Value <> 'abc1' COLLATE Latin1_General_100_BIN2;       -- Use a binary Collation (ending in _BIN2, not _BIN) to make sure       -- that you don't skip differences that compare the same due to       -- insensitivity of case, accent, etc, or linguistic equivalence.        SET @Rows = @@ROWCOUNT;   END; END TRY BEGIN CATCH   RAISERROR(stuff);   RETURN; END CATCH; 

By testing @Rows against @BatchSize, you can avoid that final UPDATE query (in most cases) because the final set is typically some number of rows less than @BatchSize, in which case we know that there are no more to process (which is what you see in the output shown in your answer). Only in those cases where the final set of rows is equal to @BatchSize will this code run a final UPDATE affecting 0 rows.

I also added a condition to the WHERE clause to prevent rows that have already been updated from being updated again.

NOTE REGARDING PERFORMANCE

I emphasized "better" above (as in, "this is a better model") because this has several improvements over the O.P.'s original code, and works fine in many cases, but is not perfect for all cases. For tables of at least a certain size (which varies due to several factors so I can't be more specific), performance will degrade as there are fewer rows to fix if either:

1. there is no index to support the query, or
2. there is an index, but at least one column in the WHERE clause is a string data type that does not use a binary collation, hence a COLLATE clause is added to the query here to force the binary collation, and doing so invalidates the index (for this particular query).

This is the situation that @mikesigs encountered, thus requiring a different approach. The updated method copies the IDs for all rows to be updated into a temporary table, then uses that temp table to INNER JOIN to the table being updated on the clustered index key column(s). (It's important to capture and join on the clustered index columns, whether or not those are the primary key columns!).

Please see @mikesigs answer below for details. The approach shown in that answer is a very effective pattern that I have used myself on many occasions. The only changes I would make are:

1. Explicitly create the #targetIds table rather than using SELECT INTO...
2. For the #targetIds table, declare a clustered primary key on the column(s).
3. For the #batchIds table, declare a clustered primary key on the column(s).
4. For inserting into #targetIds, use INSERT INTO #targetIds (column_name(s)) SELECT and remove the ORDER BY as it's unnecessary.

So, if you don't have an index that can be used for this operation, and can't temporarily create one that will actually work (a filtered index might work, depending on your WHERE clause for the UPDATE query), then try the approach shown in @mikesigs answer (and if you use that solution, please up-vote it).