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

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.
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.
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).
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:

there is no index to support the query, or
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:

Explicitly create the #targetIds table rather than using SELECT INTO...
For the #targetIds table, declare a clustered primary key on the column(s).
For the #batchIds table, declare a clustered primary key on the column(s).
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).

WHILE EXISTS (SELECT * FROM TableName WHERE Value <> 'abc1' AND Parameter1 = 'abc' AND Parameter2 = 123)
BEGIN
UPDATE TOP (1000) TableName
SET Value = 'abc1'
WHERE Parameter1 = 'abc' AND Parameter2 = 123 AND Value <> 'abc1'
END

I encountered this thread yesterday and wrote a script based on the accepted answer. It turned out to perform very slowly, taking 12 hours to process 25M of 33M rows. I wound up cancelling it this morning and working with a DBA to improve it.

The DBA pointed out that the is null check in my UPDATE query was using a Clustered Index Scan on the PK, and it was the scan that was slowing the query down. Basically, the longer the query runs, the further it needs to look through the index for the right rows.

The approach he came up with was obvious in hind sight. Essentially, you load the IDs of the rows you want to update into a temp table, then join that onto the target table in the update statement. This uses an Index Seek instead of a Scan. And ho boy does it speed things up! It took 2 minutes to update the last 8M records.

Batching Using a Temp Table

SET NOCOUNT ON

DECLARE @Rows INT,
        @BatchSize INT,
        @Completed INT,
        @Total INT,
        @Message nvarchar(max)

SET @BatchSize = 4000
SET @Rows = @BatchSize
SET @Completed = 0

-- #targetIds table holds the IDs of ALL the rows you want to update
SELECT Id into #targetIds 
FROM TheTable 
WHERE Foo IS NULL 
ORDER BY Id

-- Used for printing out the progress
SELECT @Total = @@ROWCOUNT

-- #batchIds table holds just the records updated in the current batch
CREATE TABLE #batchIds (Id UNIQUEIDENTIFIER);

-- Loop until #targetIds is empty
WHILE EXISTS (SELECT 1 FROM #targetIds)
BEGIN
    -- Remove a batch of rows from the top of #targetIds and put them into #batchIds
    DELETE TOP (@BatchSize)
    FROM #targetIds
    OUTPUT deleted.Id INTO #batchIds  

    -- Update TheTable data
    UPDATE t
    SET Foo = 'bar'
    FROM TheTable t
    JOIN #batchIds tmp ON t.Id = tmp.Id
    WHERE t.Foo IS NULL
    
    -- Get the # of rows updated
    SET @Rows = @@ROWCOUNT

    -- Increment our @Completed counter, for progress display purposes
    SET @Completed = @Completed + @Rows

    -- Print progress using RAISERROR to avoid SQL buffering issue
    SELECT @Message = 'Completed ' + cast(@Completed as varchar(10)) + '/' + cast(@Total as varchar(10))
    RAISERROR(@Message, 0, 1) WITH NOWAIT    

    -- Quick operation to delete all the rows from our batch table
    TRUNCATE TABLE #batchIds;
END

-- Clean up
DROP TABLE IF EXISTS #batchIds;
DROP TABLE IF EXISTS #targetIds;

Batching the slow way, do not use!

For reference, here is the original slower performing query:

SET NOCOUNT ON

DECLARE @Rows INT,
        @BatchSize INT,
        @Completed INT,
        @Total INT

SET @BatchSize = 4000
SET @Rows = @BatchSize
SET @Completed = 0
SELECT @Total = COUNT(*) FROM TheTable WHERE Foo IS NULL

WHILE (@Rows = @BatchSize)
BEGIN

    UPDATE t
    SET Foo = 'bar'
    FROM TheTable t
    JOIN #batchIds tmp ON t.Id = tmp.Id
    WHERE t.Foo IS NULL

SET @Rows = @@ROWCOUNT
SET @Completed = @Completed + @Rows
PRINT 'Completed ' + cast(@Completed as varchar(10)) + '/' + cast(@Total as varchar(10))

END

I want share my experience. A few days ago I have to update 21 million records in table with 76 million records. My colleague suggested the next variant. For example, we have the next table 'Persons':

Id | FirstName | LastName | Email            | JobTitle
1  | John      |  Doe     | [email protected]     | Software Developer
2  | John1     |  Doe1    | [email protected]     | Software Developer
3  | John2     |  Doe2    | [email protected]     | Web Designer

Task: Update persons to the new Job Title: 'Software Developer' -> 'Web Developer'.

1. Create Temporary Table 'Persons_SoftwareDeveloper_To_WebDeveloper (Id INT Primary Key)'

2. Select into temporary table persons which you want to update with the new Job Title:

INSERT INTO Persons_SoftwareDeveloper_To_WebDeveloper SELECT Id FROM
Persons WITH(NOLOCK) --avoid lock 
WHERE JobTitle = 'Software Developer' 
OPTION(MAXDOP 1) -- use only one core

Depends on rows count, this statement will take some time to fill your temporary table, but it would avoid locks. In my situation it took about 5 minutes (21 million rows).

3. The main idea is to generate micro sql statements to update database. So, let's print them:

DECLARE @i INT, @pagesize INT, @totalPersons INT
    SET @i=0
    SET @pagesize=2000
    SELECT @totalPersons = MAX(Id) FROM Persons

    while @i<= @totalPersons
    begin
    Print '
    UPDATE persons 
      SET persons.JobTitle = ''ASP.NET Developer''
      FROM  Persons_SoftwareDeveloper_To_WebDeveloper tmp
      JOIN Persons persons ON tmp.Id = persons.Id
      where persons.Id between '+cast(@i as varchar(20)) +' and '+cast(@i+@pagesize as varchar(20)) +' 
        PRINT ''Page ' + cast((@i / @pageSize) as varchar(20))  + ' of ' + cast(@totalPersons/@pageSize as varchar(20))+'
     GO
     '
     set @i=@i+@pagesize
    end

After executing this script you will receive hundreds of batches which you can execute in one tab of MS SQL Management Studio.

4. Run printed sql statements and check for locks on table. You always can stop process and play with @pageSize to speed up or speed down updating(don't forget to change @i after you pause script).

5. Drop Persons_SoftwareDeveloper_To_AspNetDeveloper. Remove temporary table.

Minor Note: This migration could take a time and new rows with invalid data could be inserted during migration. So, firstly fix places where your rows adds. In my situation I fixed UI, 'Software Developer' -> 'Web Developer'.

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

Batching Using a Temp Table

Batching the slow way, do not use!

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