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This section provides some background information on performance tuning.
The performance you get out of a messaging application depends on the interaction between the application and the Message Queue service. Hence, maximizing performance requires the combined efforts of both the application developer and the administrator.
The process of optimizing performance begins with application design and continues on through tuning the message service after the application has been deployed. The performance tuning process includes the following stages:
Defining performance requirements for the application
Designing the application taking into account factors that affect performance (especially tradeoffs between reliability and performance)
Establishing baseline performance measures
Tuning or reconfiguring the message service to optimize performance
The process outlined above is often iterative. During deployment of the application, a Message Queue administrator evaluates the suitability of the message service for the application’s general performance requirements. If the benchmark testing meets these requirements, the administrator can tune the system as described in this chapter. However, if benchmark testing does not meet performance requirements, a redesign of the application might be necessary or the deployment architecture might need to be modified.
In general, performance is a measure of the speed and efficiency with which a message service delivers messages from producer to consumer. However, there are several different aspects of performance that might be important to you, depending on your needs.
The number of message producers, or message consumers, or the number of concurrent connections a system can support.
The number of messages or message bytes that can be pumped through a messaging system per second.
The time it takes a particular message to be delivered from message producer to message consumer.
The overall availability of the message service or how gracefully it degrades in cases of heavy load or failure.
The efficiency of message delivery; a measure of message throughput in relation to the computing resources employed.
These different aspects of performance are generally interrelated. If message throughput is high, that means messages are less likely to be backlogged in the broker, and as a result, latency should be low (a single message can be delivered very quickly). However, latency can depend on many factors: the speed of communication links, broker processing speed, and client processing speed, to name a few.
In any case, the aspects of performance that are most important to you generally depends on the requirements of a particular application.
Benchmarking is the process of creating a test suite for your messaging application and of measuring message throughput or other aspects of performance for this test suite.
For example, you could create a test suite by which some number of producing clients, using some number of connections, sessions, and message producers, send persistent or nonpersistent messages of a standard size to some number of queues or topics (all depending on your messaging application design) at some specified rate. Similarly, the test suite includes some number of consuming clients, using some number of connections, sessions, and message consumers (of a particular type) that consume the messages in the test suite’s physical destinations using a particular acknowledgment mode.
Using your standard test suite you can measure the time it takes between production and consumption of messages or the average message throughput rate, and you can monitor the system to observe connection thread usage, message storage data, message flow data, and other relevant metrics. You can then ramp up the rate of message production, or the number of message producers, or other variables, until performance is negatively affected. The maximum throughput you can achieve is a benchmark for your message service configuration.
Using this benchmark, you can modify some of the characteristics of your test suite. By carefully controlling all the factors that might have an effect on performance (see Application Design Factors Affecting Performance), you can note how changing some of these factors affects the benchmark. For example, you can increase the number of connections or the size of messages five-fold or ten-fold, and note the effect on performance.
Conversely, you can keep application-based factors constant and change your broker configuration in some controlled way (for example, change connection properties, thread pool properties, JVM memory limits, limit behaviors, file-based versus JDBC-based persistence, and so forth) and note how these changes affect performance.
This benchmarking of your application provides information that can be valuable when you want to increase the performance of a deployed application by tuning your message service. A benchmark allows the effect of a change or a set of changes to be more accurately predicted.
As a general rule, benchmarks should be run in a controlled test environment and for a long enough period of time for your message service to stabilize. (Performance is negatively affected at startup by the just-in-time compilation that turns Java code into machine code.)
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.