Export (0) Print
Expand All
This topic has not yet been rated - Rate this topic
SQL Q&A Row-Overflow, Differential Backups, and More
Paul S. Randal


Q I recently upgraded an application to run on SQL Server 2005. One of the things I've taken advantage of is the ability to have rows greater than 8,060 bytes so I can allow users to create longer data fields without getting an error from SQL Server. Now that this application is in production, we're having performance issues for some scan queries that used to run fine before the schema change. I've checked the fragmentation of the various indexes and everything is OK. Why are the queries running slowly on SQL Server 2005?
A The feature you are using, row-overflow, is great for allowing the occasional row to be longer than 8,060 bytes, but it is not well suited for the majority of rows being oversized and can lead to a drop in query performance, as you are experiencing.
The reason for this is that when a row is about to become oversized, one of the variable-length columns in the row is pushed "off-row." This means the column is taken from the row on the data or index page and moved to a text page. In place of the old column value, a pointer is substituted that points to the new location of the column value in the data file.
This is exactly the same mechanism used to store regular LOB (Large Object) columns, such as XML, text, image, or varchar(max). Note that if the table schema contains multiple variable-length columns, there is no guarantee that the same column will be pushed off-row when multiple rows become oversized.
This mechanism can create a performance problem. Suddenly a query to retrieve a variable-length column from a single row in a table might need an additional I/O if the column has been pushed off-row (to read in the text page containing the off-row location of the value). If multiple rows are oversized, a query to retrieve the same variable-length column from multiple rows could have unpredictable performance, depending on how many of the values have been pushed off-row.
In your case, a query performing a range scan or table scan for a select list that includes a variable-length column is suffering from poor performance due to row-overflow and its effects. It doesn't matter whether the indexes are perfectly fragmented—when a variable-length column has been pushed off-row, the previously efficient scan is essentially interrupted since a random I/O is necessary to read the text page containing the off-row value.
Row-overflow is still very useful for occasional oversized rows. However, if query performance is critical, it should not be a heavily exploited component in your design.

Q We've just introduced database mirroring between two failover clusters as a way of getting geo-redundancy for less than the cost of storage area network (SAN) replication. The datacenters are within the same city, so we're able to use synchronous mirroring. The problem is that when a failover occurs on the local cluster, the mirrored database fails over to the remote cluster, which is not the behavior we want. How can we stop this from happening? We want the failover to happen only if the local cluster is unavailable.
A For increased availability, mirroring is set up with a witness server so failovers occur automatically if the principal becomes unavailable. The idea is that if the entire local cluster goes down, database mirroring will failover to the second cluster and the application can continue.
The problem occurs when a cluster failover happens. The failover takes longer to occur than the default time-out setting of database mirroring. The witness server and mirror server (meaning the active SQL Server instance on the second cluster) agree that they cannot see the principal, and then the mirror server initiates a mirroring failover to the second cluster.
The easiest method for preventing this is to remove the witness server so that database mirroring does not automatically failover if the local cluster goes down. Of course, this reduces availability, as a human is then needed to initiate a failover.
The second option is to alter the default timeout setting of database mirroring. This is the number of once-per-second "pings" that the principal must fail to respond to before it is declared unavailable. This setting is called the partner timeout and has a default value of 10. The current timeout value for the database can be found using the following code:
SELECT [mirroring_connection_timeout]
  FROM master.sys.database_mirroring 
  WHERE [database_id] = DB_ID ('mydbname');
GO

The timeout value can be changed using the following code:
ALTER DATABASE mydbname 
  SET PARTNER TIMEOUT <timeoutvalue>;
GO

For this scenario, the partner time-out needs to be set higher than the usual time it takes a cluster failover to occur on the local cluster. This may be a little tricky to determine given the variability in the time it takes to run recovery on the mirrored database when the cluster failover occurs, but you should be able to determine an upper bound. The problem with this method is that the timeout value may have to be minutes, which may be unacceptable for when a real disaster occurs.

Q My backup strategy involves full and log backups, but I've heard that I should add differential backups to decrease restore time. I take a full backup once a week and hourly log backups. I tried adding daily differential backups, but one odd thing I've noticed is that the differential backups at the end of the week are close to the same size as the weekly full backup. I was under the impression that they are incremental, just like log backups. Am I missing something?
A The misunderstanding here is around the nature of differential backups. Unlike log backups, differential backups are not incremental. A differential backup contains all the changed data file extents since the previous full backup (and this applies to database, filegroup, and file level backups).
When an extent (a logical group of eight contiguous data file pages) is changed in any way, it is marked in a special bitmap page called the differential map (or more commonly known as the diff map). There is a diff map for each 4GB chunk of each data file. When a differential backup is taken, the backup subsystem scans all the diff maps and copies all the changed extents, but the diff maps are not reset. This means that if more extents are changed between successive differential backups, the later backups will be larger. The diff maps are only reset when a full backup is performed.
If the application workload is such that the database contents are extensively changed within a short period of time (say, within a week), then a weekly full backup will be almost the same size as a differential backup that was taken just before the next full backup. This explains the behavior that you're observing.
You are correct in thinking that differential backups offer a way to reduce restore time in a disaster-recovery situation. If the backup strategy is to take weekly full backups and hourly log backups, an up-to-the-minute restore would require the following:
  • Take a tail-of-the-log backup (all the logs generated since the most recent log backup).
  • Restore the most recent full database backup.
  • Restore all log backups, in sequence, since the most recent full database backup.
  • Restore the tail-of-the-log backup.
This could require a lot of log backups to be restored, especially if the disaster occurs just before the next full backup is due. (A worst-case scenario would mean 24 + 24 + 24 + 24 + 24 + 24 + 23 log backups to be restored!) By adding daily differential backups to this strategy, the restore sequence changes to this:
  • Take a tail-of-the-log backup (all the logs generated since the most recent log backup).
  • Restore the most recent full database backup.
  • Restore the most recent differential backup.
  • Restore all log backups, in sequence, since the most recent differential backup.
  • Restore the tail-of-the-log backup.
This could remove the need for restoring a lot of log backups, as restoring a differential backup is essentially the same as restoring all the log backups in the period covered by the differential backup.
The very worst case in a scenario where a daily differential backup is performed would be 23 log backups, even on the last day of the week. The one downside of differential backups not being incremental is that they can take more space, but that's almost always a worthwhile trade-off to reduce restore time.

Q I have a two-node failover cluster. Each node is running a single instance of SQL Server 2005. I'm following the common advice of setting each instance to only use 50% of the available memory. Now I'm having issues because the workload on both instances needs more memory to maintain the same performance levels. If I remove the memory limitation, or make it higher, I think I'll run into problems when one of the instances fails over and they are both running on just one node. What do you recommend?
A I'll answer this question for the two-node, two-instance case, but everything below also applies to other multi-instance setups (N-1 failover clusters, where there are N nodes and N-1 SQL Server instances).
Many people experience a high workload (consuming more than 50% of server memory) on both instances and don't take into account the effect on the workloads when both instances end up running on a single node after a failover occurs. Without any special configuration, it's possible for the memory distribution between the instances to become disproportionate, so one workload runs fine and the other slows to a crawl.
With SQL Server 2000, the recommendation is to limit each instance to a maximum of 50% of cluster node memory. This is because the memory manager in SQL Server 2000 does not respond to memory pressure—if SQL Server takes, say, 80% of the node's memory, it will not give it back. In a failover situation, this means another instance just starting up would only have 20% of the memory available. By limiting both instances to a maximum of 50% of a node's memory, a fail-over instance is guaranteed 50% of the memory. The problem with this is that the workload on each instance is also limited to 50% of the memory.
With SQL Server 2005 (and SQL Server 2008), the memory manager can respond to memory pressure so the 50% maximum is no longer appropriate. But without some kind of limitation, if two instances are running on one cluster node, they may pressure each other until a disproportionate memory distribution is reached.
The answer is to set each instance to have a minimum amount of memory so they cannot be pressured to release too much memory. A common setting for a two-node, two-instance setup is to have each instance configured for a minimum of 40% of the memory. This means that when each instance is running on a separate node, they can consume as much memory as they want. When a failover occurs, each instance is guaranteed a certain amount of memory to preserve a set level of workload performance, with a little left over to be shared between them. Though this means that the performance of both workloads may drop in a failover situation (as expected), they won't be limited at all for the vast majority of the time when each instance is running on a separate cluster node.

Paul S. Randal is the Managing Director of SQLskills.com and a SQL Server MVP. He worked on the SQL Server Storage Engine team at Microsoft from 1999 to 2007. Paul wrote DBCC CHECKDB/repair for SQL Server 2005 and was responsible for the Core Storage Engine during SQL Server 2008 development. Paul is an expert on disaster recovery, high availability, and database maintenance and is a regular presenter at conferences around the world. He blogs at SQLskills.com/blogs/paul.

Did you find this helpful?
(1500 characters remaining)
Thank you for your feedback
Show:
© 2014 Microsoft. All rights reserved.