Voice of the DBA

Tag: Common SQL Server Mistakes

Common SQL Server Mistakes – Multi Row DML Triggers

How often have you seen someone write a DML trigger like this:

create trigger mytrigger on Mytable for insert as

declare @id int
select @id = id from inserted

update xx set yyy = zz
where id = @id

return

There seems to be this common misconception that a trigger fires for each change in a row (insert/update/delete), and that’s not true. As noted in Books Online, triggers fire once for the insert/update/delete. Typically this is an implicit transaction for the statement. If you have multiple statements inside an explicit transaction, the trigger fires once for each insert/update/delete statement in the transaction.

That means that if I change two rows and I have the trigger above, I won’t get the behavior I expect from the trigger. Let’s say that I want to update my inventory table each time I change an order. Imagine that I have this in my orders table:

and this in my inventory table.

If I now write this trigger:

alter trigger orders_update_inventory on orders
  for update
  as

  declare @qty int
     ,    @product int

  select @qty = a.qty – b.qty
    from inserted a
       inner join deleted b
         on a.orderid = b.orderid
  select @product = productid from inserted

  update inventory
   set onhand = onhand – @qty
   where productid = @product

return

and execute this:

update orders
  set qty = 2
   where orderid = ’59CD85CE-984C-4D33-9E23-5F6159848277′

I will find that my inventory table looks like this:

That appears to work, but what happens if we execute this?

update orders
  set qty = qty+1
  where[CustomerID int] = 2

Then we find that the inventory is

In this case productID 1 has had its inventory reduced by 1, but not product ID 2. Why not?
When the trigger fires, there are actually this data in the tables:

Inserted
OrderID OrderDate CustomerID int qty         productid
——– ———- ————– ———– ———–
59CD…    2010-09-22 3              3           1
01B6…    2010-09-22 2              2           2

and deleted

OrderID OrderDate CustomerID int qty         productid
——– ———- ————– ———– ———–
59CD85… 2010-09-22 2              2           1
01B66E… 2010-09-22 2              1           2

However the trigger, in setting the variables to the result of a query could have picked either of the rows, but only one row. SQL Server doesn’t guarantee order without an ORDER BY, so either product ID could have been chosen. As a result, only one of the products had the inventory updated.

A proper trigger would look like this

alter trigger orders_update_inventory on orders
  for update
  as

  select @qty = a.qty – b.qty
    from inserted a
       inner join deleted b
         on a.orderid = b.orderid
  select @product = productid from inserted

  update inventory
   set onhand = onhand – ( a.qty – b.qty)
   from inserted a
       inner join deleted b
         on a.orderid = b.orderid
   where inventory.productid = i.productid

return

Triggers should always be written to handle multiple rows, using the inserted and deleted tables for joins instead of variables. Even if you always just update single rows, coding this way will prevent issues if there is a multiple row change.

September 23, 2010
Common SQL Server Mistakes – Shrinking Databases

I don’t like there being an easy command to shrink databases, and I especially don’t like seeing the shrink option as a part of the default maintenance plans.
However it seems that this technique for managing sizes is used quite often, and even given as advice by some people. A few comments about this feature:
First, don’t regularly shrink databases. Actually, don’t shrink databases at all if you don’t understand what it does. Paul Randal, who managed the storage engine team, wrote a blog about why not: Here’s a good reason not to run SHRINKDATABASE. The bottom line is that this fragments your indexes, which raises reads and decreases performance.
If you are concerned about space usage, you have two choices: add less data or buy more space.
SQL Server database files aren’t like a Word or Excel file. They don’t allocate space on disk as it’s needed. Well, they do if you have autogrow turned on, but really the files and server expect to have free space in the data files for data growth, change to data (and potential page splits/new extent allocations), and for maintenance.
If you rebuild indexes regularly, and you ought to if they become fragmented, you need free space in your server. An index rebuild copies the entire index to a new, un-fragmented set of pages, and then drops the old index. So you need double your disk space for rebuilds.
Managing space proactively is something you should do, and that means that you want to leave a pad inside your data files to allow for data growth. If you don’t have enough disk space, buy more. You need the space for data, and for performance.

Transaction Log Files

Now the transaction log files are a slightly different story. You still want to size them correctly, and some good reasons from Mr. Randal on this. You should set your log file size based on the frequency of your backups. The backups are scheduled based on your risk tolerance. Basically, more frequent backups, less transaction log space needed.
However regularly shrinking your log files doesn’t introduce fragmentation, but it is dumb. Maybe not dumb, but it’s a waste of resources. Your server needs a t-log file size of xx to handle the regular activity on your server. Shrinking it at night and having it grow the next day to handle load is silly. And a waste of disk writes.
Set your log file, manage it as needed, don’t shrink it.

When to Shrink

So should you never shrink? No, you can shrink, but the feature there is for emergencies or one-time events. If I get a load of 500GB on my 1TB data once a year, I might get crazy log growth. I might plan for that by expanding my log in advance, and then shrinking the log afterward, back to the size that I normally use.
The same thing could occur in a database. Perhaps you move some data to a read only db and want to get the space down to data + largest index. Then you can shrink, rebuild indexes, and leave the log there. You can’t shrink to just data without fragmenting, so don’t try.
When you shrink, use SHRINKFILE, and target specific files, for a specific reason. Not as part of regular maintenance.

September 14, 2010
Common SQL Server Mistakes – SELECT *
I’ve been trying to work on some new presentations so that I have a variety, including some spares, when I go to events. One of the topics that I think has some value, especially for .NET and sysadmin groups, is a list of common mistakes, how to fix them, and why they’re bad.
I was going to call this Common Developer Mistakes, but I’m not sure that would go over well at Developer events, and I see DBAs making these mistakes along with Windows admins.
I decided to build a series of blog posts as I work through the presentation to document some of the issues, and help me work through speaking points. Please feel free to comment.
SELECT * Is For Short Term Use Only
The first mistake that I often see in application code is that too often people write things like
```
SELECT * 
FROM Sales.Customer
```
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What does this do? If I run this in my 2008 AdventureWorks database, I get something like this:

You can see that I end up with multiple columns (CustomerID, TerritoryID, CustomerType, rowguid, ModifiedDate). That’s handy, and cool, and allows me to get all the data in the table.

But do I really need it?

In most applications, my guess is that we don’t. Why do we need TerritoryID? That’s a foreign key to the SalesTerritory table, and typically what I want instead is the SalesTerritory.Name column instead of the ID value.

I could do this:
```
SELECT *
FROM Sales.Customer c
INNER JOIN Sales.SalesTerritory t
ON c.TerritoryID = t.TerritoryID
```
but that’s any better. Now I’ve returned even more columns, 10 more to be exact, including TerritoryID twice, once from each table. In AdventureWorks, this is 19k rows, and at a minimum this query has returned 19k rows x 8 bytes (int data type) too much data. That doesn’t sound like a lot, but what if this runs in your application 500 times a day? That’s a lot of wasted:
- bandwidth
- disk access
- memory from caching
- CPU work on the server AND client
I would also guess that most of the time when you access a customer, you don’t even want all the rows. Likely you want to filter this somehow, and you will with a WHERE clause, but it’s still wasted time and resources.

We know that the database often is a bottleneck. It’s a shared resource, it’s one machine, and it doesn’t scale as easy as multiple clients or web servers, or even developers, so we should avoid wasting resources when we don’t have to.

What Do You Do?

Here’s what I recommend:

You can write this, and it’s what I often do:
```
SELECT TOP 2
*
FROM Sales.Customer
```
And I get a limited result set:

Why is this better? I do this so I can easily see the column names. I can then include those in my SELECT statement, with a quick rewrite.

I could also quickly use the Object Explorer to find the columns like this:

And you can right click, and choose “script” and “as SELECT”

and paste the code into your query window. The results would look something like this:

Alternatively, my employer, Red Gate Software, makes a fantastic product called SQL Prompt that will help you quickly grab columns. For me, I can do an SSF, and get a SELECT * FROM and then choose the table:

Not that I see the columns to the right. I could also just select the table with a tab and then if I remove my askterisk, I get a list of columns I can easily pick:

SQL Prompt makes this easier, but it isn’t that hard to just do this by hand. You could easily grab the columns you need from SSMS and add them to queries.

The database is a limited resource, even if you have a 256 core server with 1024GB of RAM. You still want to query the data you need and only return what’s necessary. A little more effort when building code will pay off later with much better performing applications.

References
A few links from other people that see this as an issue as well.
September 1, 2010