My co-worker Steve Wright (blog | @SQL_Steve) prodded me with a question recently on a strange result he was seeing. In order to test some functionality in our latest tool, SQL Sentry Plan Explorer PRO, he had manufactured a wide and large table, and was running a variety of queries against it. In one case he was returning a lot of data, but STATISTICS IO was showing that very few reads were taking place.
One of the things that's simultaneously great and horrible about the Internet is that, once something gets posted out in the ether, it basically never goes away. (Some day, politicians will realize this. We can easily fact check their consistency.) Because of longevity of content posted to the Internet, a lot of performance tuning topics become "zombies." We shoot 'em dead, but they keep coming back!
SQL Server 2012 AlwaysOn Availability Groups require a database mirroring endpoint for each SQL Server instance that will be hosting an availability group replica and/or database mirroring session. This SQL Server instance endpoint is then shared by one or more availability group replicas and/or database mirroring sessions and is the mechanism for communication between the primary replica and the associated secondary replicas.
Depending on the data modification workloads on the primary replica, the availability group messaging throughput requirements can be non-trivial. This activity is also sensitive to traffic from concurrent non-availability group activity. If throughput is suffering due to degraded bandwidth and concurrent traffic, you may consider isolating the availability group traffic to its own dedicated network adapter for each SQL Server instance hosting an availability replica. This post will describe this process and also briefly describe what you might expect to see in a degraded throughput scenario.
Occasionally I see people try to "optimize" their update statements to avoid writing the same value to a particular column. My understanding has always been that if you're going to update a row, assuming all of the values are in-row, the costs of locking the row are much higher than the incremental cost of updating one, two or all columns in that row.