Filter Out Your Variables
Filtering thousands of points of data sometimes can be the only way to make the complex simple. Filtering can be as easy as dropping all the transactions of a specific type, within a specific slice of time, or based on specific error codes you receive for those transactions. Sometimes you want to look at the raw data, sometime just the timers. Sometimes you want to see the requests, sometimes just the responses. Try them all.
Filtering focuses on multiple factors at a time. Another approach is to take one factor at a time. Turn on different variables individually and look at their patterns. This technique can be especially effective if you have a specific type of transaction that recurs on a regular basis or occurs for all users.
Changing the colors of the different variables can also be a very effective way of identifying patterns. Take the scatter chart in Figure 1, for example. I bet the mass of blue dots doesn’t mean much to you, but how about the version in Figure 2?
Figure 2 Use different colors for each transaction type.
By changing the colors, we can clearly see that we need to be concerned about only three transactions—Login, Service Call 2, and Service Call 3—because all are above 200 seconds in response time. The other transactions may need some tuning as well, but more than likely fixing these hideously slow transactions will affect the entire system in a positive way.