In our production environment (SQL Server 2008 Enterprise), we've been having issues with CPU usage and network utilization. Based on the specs of the (multiple) machines we have and the general functionality of our software product, load should not be an issue.
As a developer, I figured the problem must lie elsewhere, probably in the queries that are being run. I thought it would be a good idea to attach the SQL Profiler to a production instance to figure out which queries (or even code) are the hotspots, and work on optimizing those areas first. (None of our hardware/network guys have done this before.)
I attached the profiler to one of our development instances for a couple hours, and determined we have a few problem areas:
- Queries being executed inside loops -- it was common to see the same few queries being executed tens of thousands of times.
- Queries that don't get cached -- I saw a few
SELECT *queries show up; there were also quite a few that are non-parameterized. This is a large subset of #1, where a query would only vary by a single
- Long-running queries -- ones that are actually taxing the CPU; these may be inefficiently written, not utilizing indexes, etc. The hit count on these was much lower.
Based on the summary statistics I generated, the biggest problem queries are of type #1 and #2. However, I can't immediately take those results and start working because the development instance queries are essentially what the other devs are working on at the time, not what is going to be run most in production.
I've been reading that attaching the SQL Server Profiler to an instance is problematic because of overhead. For obvious reasons, attaching in a production environment should be as lightweight as possible.
What I need to know is this -- given the three different types of queries I need to spot, how do I set up the profiler to have a minimal impact on performance? Is there another tool that I could use to accomplish this?
- Our production environment has definite on- and off-peak times, so it may be possible to get a decent sample by capturing only 30-60 minutes of data during a peak time.
- I'd like to log to a database -- after playing around with the profiler, that log format is the easiest to consume later on.