Baseline Use Patterns

Once a messaging application is deployed and running, it is important to establish baseline use patterns. You want to know when peak demand occurs and you want to be able to quantify that demand. For example, demand normally fluctuates by number of end users, activity levels, time of day, or all of these.

To establish baseline use patterns you need to monitor your message service over an extended period of time, looking at data such as the following:

• Number of connections

• Number of messages stored in the broker (or in particular physical destinations)

• Message flows into and out of a broker (or particular physical destinations)

• Numbers of active consumers

You can also use average and peak values provided in metrics data.

It is important to check these baseline metrics against design expectations. By doing so, you are checking that client code is behaving properly: for example, that connections are not being left open or that consumed messages are not being left unacknowledged. These coding errors consume broker resources and could significantly affect performance.

The base-line use patterns help you determine how to tune your system for optimal performance. For example:

• If one physical destination is used significantly more than others, you might want to set higher message memory limits on that physical destination than on others, or to adjust limit behaviors accordingly.

• If the number of connections needed is significantly greater than allowed by the maximum thread pool size, you might want to increase the thread pool size or adopt a shared thread model.

• If peak message flows are substantially greater than average flows, that might influence the limit behaviors you employ when memory runs low.

In general, the more you know about use patterns, the better you are able to tune your system to those patterns and to plan for future needs.