Optimize Large SQL Server Inserts, Updates, Deletes with SQL Batching Processing

Problem

Sometimes you must perform DML processes (insert, update, delete, or combination) on large SQL Server tables. If your database has a high concurrency, these types of processes can lead to blocking or filling up the transaction log, even if you run these processes outside of business hours. So maybe you were tasked to optimize some processes to avoid large log growths and minimize locks on tables. Let’ see how this can be done with SQL batch processing.

Solution

We will do these DML processes using batches with the help of @@ROWCOUNT. This also give you the ability to implement custom “stop-resume” logic. We will show you a general method, so you can use it as a base to implement your own processes.

Note: we will not focus on indexes on this tip. Of course, this can help queries. However, I want to show a worst-case scenario. Index creation is another topic.

Basic Algorithm for SQL Batch Processing

The basic batch process is something like this:

DECLARE @id_control INT
DECLARE @batchSize INT
DECLARE @results INT
 
SET @results = 1 --stores the row count after each successful batch
SET @batchSize = 10000 --How many rows you want to operate on each batch
SET @id_control = 0 --current batch 
 
-- when 0 rows returned, exit the loop
WHILE (@results > 0) 
BEGIN
   -- put your custom code here
   SELECT * -- OR DELETE OR UPDATE
   FROM 
   WHERE 
   (
      AND  > @id_control
      AND  <= @id_control + @batchSize
   )
   -- very important to obtain the latest rowcount to avoid infinite loops
   SET @results = @@ROWCOUNT
 
   -- next batch
   SET @id_control = @id_control + @batchSize
END

We use a WHILE loop and run our statements inside the loop. We set a batch size (numeric value) to indicate how many rows we want to operate on each batch.

For this approach, I assume the primary key is either an int or a numeric data type. You will need that type of key for this algorithm to work. For alphanumeric or GUID keys, this approach won’t work. However, you can implement other types of custom batch processing with additional coding.

With the batch size and the key control variable, we validate that the rows in the table are within the range.

Important Note: Your process must always operate on at least some rows in each batch.  If a batch does not operate on any rows, the process will end as row count will be 0. If you have a situation where only some rows from a large table will be affected, it is better and more secure to use the index/single DML approach. Another approach for these cases is to use a temporary table to filter the rows to be processed and then use this temp table in the loop to control the process.

Example Setup for Batch Processing in SQL

We will use a test table [MyTestTable] with this definition:

CREATE TABLE [dbo].[MyTestTable](
   [id] [bigint] IDENTITY(1,1) NOT NULL,
   [dataVarchar] [nvarchar](50) NULL,
   [dataNumeric] [numeric](18, 3) NULL,
   [dataInt] [int] NULL,
   [dataDate] [smalldatetime] NULL,
 CONSTRAINT [PK_MyTestTable] PRIMARY KEY CLUSTERED 
(
   [id] ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY]
GO

It contains random information and 6,000,000 records.

Executing a SELECT statement

Here we execute a simple SELECT statement over the entire table. Note: I enabled statistics IO and cleared the data cache first, so we have better results for comparison.

DBCC DROPCLEANBUFFERS 
SET STATISTICS IO ON
SELECT *
FROM [dbo].[MyTestTable]
WHERE dataInt > 600sq

These are the IO results:

Table 'MyTestTable'. Scan count 1, logical reads 65415, physical reads 2, read-ahead reads 65398, 
lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

The SELECT took 1:08 minutes and retrieved 2,395,317 rows.

SELECT Statement Using SQL Batch Processing

For the same SELECT, we implement the following process to do it in batches:

DBCC DROPCLEANBUFFERS 
 
SET STATISTICS IO ON
 
DECLARE @id_control INT
DECLARE @batchSize INT
DECLARE @results INT
 
SET @results = 1
SET @batchSize = 100000
SET @id_control = 0
 
WHILE (@results > 0)
BEGIN
   -- put your custom code here
   SELECT *
   FROM [dbo].[MyTestTable]
   WHERE dataInt > 600
   AND id > @id_control
      AND id <= @id_control + @batchSize
   
   -- very important to obtain the latest rowcount to avoid infinite loops
   SET @results = @@ROWCOUNT
 
   -- next batch
   SET @id_control = @id_control + @batchSize
END

The IO results (for each batch):

Table 'MyTestTable'. Scan count 1, logical reads 1092, physical reads 0, read-ahead reads 1088, 
lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

If we multiply it for 60 batches performed, it should be around 65,500 logical reads. This is approximately the same as before, which makes sense since it is the same data we are accessing.

However, looking at the overall execution time, it improves by around 10 seconds, with the same number of rows:

A SELECT statement is probably not the best way to demonstrate this, so let’s proceed with an UPDATE statement.

SQL Batch Update Example

Let’s do an UPDATE on a varchar field with random data (so our test is more real). After clearing the cache, we will execute the code.

This is a screenshot of the transaction log before the operation:

DBCC DROPCLEANBUFFERS 
 
BEGIN TRAN;
 
UPDATE [dbo].[MyTestTable]
SET dataVarchar = N'Test UPDATE 1'
WHERE dataInt > 200;
 
COMMIT TRAN;

The execution took 37 seconds on my machine.

To find the rows affected, we perform a simple count and we get 4,793,808 rows:

SELECT COUNT(1)
FROM [dbo].[MyTestTable]
WHERE dataVarchar = N'Test UPDATE 1'

Checking the log size again, we can see it grew to 1.5 GB. It then released the space since the database is in SIMPLE mode:

Let’s execute the same UPDATE statement in batches.  Change the text Test UPDATE 1 for Test UPDATE 2, and this time use the batch process. I also shrunk the transaction log to its original size and performed a cache cleanup before executing.

DBCC DROPCLEANBUFFERS
 
DECLARE @id_control INT
DECLARE @batchSize INT
DECLARE @results INT
 
SET @results = 1
SET @batchSize = 1000000
SET @id_control = 0
 
WHILE (@results > 0)
BEGIN
   -- put your custom code here
   BEGIN TRAN;
 
   UPDATE [dbo].[MyTestTable]
   SET dataVarchar = N'Test UPDATE 2'
   WHERE dataInt > 200
   AND id > @id_control
   AND id <= @id_control + @batchSize
 
   -- very important to obtain the latest rowcount to avoid infinite loops
   SET @results = @@ROWCOUNT
 
   COMMIT TRAN;
   
   -- next batch
   SET @id_control = @id_control + @batchSize
END

This time the query execution was 18 seconds, so there was an improvement in time.

As we can see, there was an improvement with the log spaced used, which grew to 0.43 GB.

The last thing to verify is the number of rows affected. We can see we have the same row count as the UPDATE above, 4,793,808 rows.

As you can see, for very large DML processes, running smaller batches can help execution time and transaction log use.

The only drawback to this method is that your key must be a sequential number. Also, there must be at least one row in each batch. This is needed so that the process does not end before being applied to all data.

Handling transactions and errors

Imagine your process involves complex validations, complex calculations, and multiple tables for each batch iteration. To handle potential issues, we can use a BEGIN – COMMIT transaction on each batch. If not implemented properly and if any error occurs on a batch run, it can leave your system unresponsive or running in a forever loop.

A good approach for error handling and transaction management for each batch is this one (taking one table from WideWorldImporters demo database):

(I purposely made a division by zero so the batch will fail, this is for demonstration purposes.)

SET XACT_ABORT ON;
 
USE [WideWorldImporters]
 
DECLARE @id_control INT
DECLARE @batchSize INT
DECLARE @results INT
 
SET @results = 1 --stores the row count after each successful batch
SET @batchSize = 100 --How many rows you want to operate on each batch
SET @id_control = 0 --current batch 
 
-- when 0 rows returned, exit the loop
WHILE (@results > 0) 
BEGIN
   BEGIN TRY
      BEGIN TRAN
         UPDATE [Sales].[Invoices]
         SET  [TotalChillerItems] = 1/0
         WHERE [TotalDryItems] > 1
         AND [InvoiceID] > @id_control
         AND [InvoiceID] <= @id_control + @batchSize
   
         -- very important to obtain the latest rowcount to avoid infinite loops
         SET @results = @@ROWCOUNT
 
      COMMIT TRAN
   END TRY
 
   BEGIN CATCH
      IF @@TRANCOUNT >= 1
      BEGIN
         THROW 51000, 'User defined error, on this case divide by zero', 1;  
         ROLLBACK TRAN
         BREAK;
      END
      SET @results = 0; -- failsafe so we can exit loop if any other issue occurs
   END CATCH
   -- next batch
   SET @id_control = @id_control + @batchSize
END 

Explaining the code:

The process implemented for batch processing is the same we discussed before, but we added a few things:

• XACT_ABORT ON: We use this to rollback any unhandled transaction if the TRY..CATCH is not able to capture it. According to Microsoft Documentation, there are some errors that are not captured by it.
• TRY..CATCH block: It will help catch any error in our batch process (duplicated key, invalid datatype, NULL insert, etc.). Also, the CATCH block will allow us to take different actions as required. The last line of code I like to put inside the CATCH block is assigning the @results parameter 0. So, in case we have an unhandled exception (something we did not consider), it will act as a failsafe and avoid entering the WHILE loop again.
• BEGIN..COMMIT TRAN: This section is inside the TRY block. It will help to commit or discard the batch if we work with multiple tables or statements.
• ROLLBACK TRAN: This occurs in the CATCH block. If any issue happens, we first validate that the transaction count is greater or equal to zero. Then we throw a single, generic error or validate other rules and flags, being as descriptive as we want. Then we rollback any open transaction and finally, we break the WHILE loop in case needed.

Errors Generated

If we execute this code block, we can see that the error is properly caught and thrown. And the batch is finished:

Note that this error-handling method should only be used as a base. You must implement other options such as logging, retry-on-error logic, or more complex logic. A robust process is necessary, especially if it must be implemented on a system that runs regularly (like ETL loads or processes).

Next Steps

• You can determine if your process can use this batch method. Run the SELECT statements and compare the number of expected rows with the results.
• You can increase or decrease the batch size to suit your needs. However, for it to have meaning, the batch size must be less than 50% of the expected rows to be processed.
• This process can be adapted to implement a “stop-retry” logic. This allows already processed rows to be skipped if you decide to cancel the execution.
• It also supports multi-statement processes. (In fact, this is the real-world use of this approach.) You can achieve this with a “control” table having all the records to work with and update accordingly.
• If you want to implement an “execution log” you can achieve this by adding PRINT statements. Just now that this could slow down some processes, especially for very small batch sizes.

Eduardo Pivaral

Eduardo Pivaral is an MCSA, SQL Server Database Administrator and Developer with over 15 years of experience working in large environments.

• MSSQLTips Awards: Trendsetter (25+ tips) – 2020 | Author of the Year Contender – 2019 | Rookie of the Year – 2018