When to use index or single DML in batching?

When to use index or single DML in batching?

Important Note: Your process will need to 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.

How to calculate batch size in SQL Server?

The basic batch process is something like this: To explain the code, we use a WHILE loop and run our statements inside the loop and we set a batch size (numeric value) to indicate how many rows we want to operate on each batch.

Can you increase or decrease the batch size?

You can increase/decrease the batch size to suit your needs, but 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 so already processed rows can be skipped if you decide to cancel the execution.

How to Test SQL update 2 with batching?

[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

Are there any drawbacks to batching in SQL?

The only drawback of this method is that your key must be a sequential number and there must ne at least one row in each batch, so the process does not end before being applied to all data. You can determine if your process can use this batch method just running the SELECT statements and comparing the number of expected rows with the results.

What happens if there are no rows in a batch?

So, with the batch size and the key control variable, we validate the rows in the table are within the range. Important Note: Your process will need to 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.

Are there drawbacks to batching in DML?

As you can see, for very large DML processes, running in smaller batches can help on execution time and transaction log use. The only drawback of this method is that your key must be a sequential number and there must ne at least one row in each batch, so the process does not end before being applied to all data.

How to optimize SQL Server DML processes by using batches?

BEGIN TRAN; UPDATE test_items SET [description] = ‘PROCESSED IN 1 BATCHES!’ WHERE category1 in (2,7,9) COMMIT TRAN; The execution took 55 seconds on my lab machine. There were 2,738,899 rows updated. And the log usage? Checking the log size again we can see it grow 1.69 GB (and then released the space since the database is in SIMPLE mode):

How are parallel execution mechanisms used in DML?

Parallel DML (parallel insert, update, merge, and delete) uses parallel execution mechanisms to speed up or scale up large DML operations against large database tables and indexes. You can also use INSERT SELECT statements to insert rows into multiple tables as part of a single DML statement.

What are the IO results of batching SQL?

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.