One of the SQL scripts that I keep handy in my toolbox is Truncate All Transaction Logs.sql. While I would never recommend running this script in a Production environment (PROD), I find it to be very helpful for periodically freeing up disk space in shared Development environments (DEV) and especially on my local VMs. True, I could instead choose to schedule periodic backups, but then I’d still have to periodically delete the backup files (or, I suppose, I could schedule that as well), but, honestly, I really don’t care to put that much effort into managing these environments — especially since I tend to periodically “nuke” them from time to time to start fresh.
Here is the script:
DROP TABLE #CommandQueue CREATE TABLE #CommandQueue ( ID INT IDENTITY ( 1, 1 ) , SqlStatement VARCHAR(1000) ) INSERT INTO #CommandQueue ( SqlStatement ) SELECT 'BACKUP LOG [' + name + '] WITH TRUNCATE_ONLY' FROM sys.databases WHERE name NOT IN ( 'master', 'model', 'msdb', 'tempdb' ) DECLARE @id INT SELECT @id = MIN(ID) FROM #CommandQueue WHILE @id IS NOT NULL BEGIN DECLARE @sqlStatement VARCHAR(1000) SELECT @sqlStatement = SqlStatement FROM #CommandQueue WHERE ID = @id PRINT 'Executing ''' + @sqlStatement + '''...' EXEC (@sqlStatement) DELETE FROM #CommandQueue WHERE ID = @id SELECT @id = MIN(ID) FROM #CommandQueue END
As you can see, there really isn’t much to this script. However, what I really wanted to cover in this post is how I implemented the script, and why I think this is a good pattern for scripts that perform some operation on an arbitrary number of objects in a SQL Server database.
Notice that the first thing that I do is create a temporary table that I call the “command queue.” Okay, actually the first thing I do is drop the temporary table if it exists, just in case I need to run the script multiple times in a single session (not necessarily all that helpful when truncating transaction logs, but remember this is more about the pattern than this particular script).
While I could certainly choose to bypass the “command queue” and simply execute the SQL statements directly, I find this approach to be much more robust in terms of error handling. If something unexpected happens during the execution of the script, I can easily resume processing after recovering from the error (without having to completely start over again).
The rest of the script really requires no explanation. You can see that once I’ve “queued” up all of the commands to be run, I simply process them one at a time in a FIFO (First-In-First-Out) manner — nothing special there.
In my next post, I share another useful script that follows the same pattern and potentially frees up even more disk space than simply truncating the transaction logs.