The performance of a messaging application is affected not only by application design, but also by the message service performing the routing and delivery of messages.
The following sections discuss various message service factors that can affect performance. Understanding the effect of these factors is key to sizing a message service and diagnosing and resolving performance bottlenecks that might arise in a deployed application.
The most important factors affecting performance in a Message Queue service are the following:
The sections below describe the effect of each of these factors on messaging performance.
For both the Message Queue broker and client applications, CPU processing speed and available memory are primary determinants of message service performance. Many software limitations can be eliminated by increasing processing power, while adding memory can increase both processing speed and capacity. However, it is generally expensive to overcome bottlenecks simply by upgrading your hardware.
Because of the efficiencies of different operating systems, performance can vary, even assuming the same hardware platform. For example, the thread model employed by the operating system can have an important effect on the number of concurrent connections a broker can support. In general, all hardware being equal, Solaris is generally faster than Linux, which is generally faster than Windows.
The broker is a Java process that runs in and is supported by the host JVM. As a result, JVM processing is an important determinant of how fast and efficiently a broker can route and deliver messages.
In particular, the JVM’s management of memory resources can be critical. Sufficient memory has to be allocated to the JVM to accommodate increasing memory loads. In addition, the JVM periodically reclaims unused memory, and this memory reclamation can delay message processing. The larger the JVM memory heap, the longer the potential delay that might be experienced during memory reclamation.
The number and speed of connections between client and broker can affect the number of messages that a message service can handle as well as the speed of message delivery.
All access to the broker is by way of connections. Any limit on the number of concurrent connections can affect the number of producing or consuming clients that can concurrently use the broker.
The number of connections to a broker is generally limited by the number of threads available. Message Queue can be configured to support either a dedicated thread model or a shared thread model (see Thread Pool Management).
The dedicated thread model is very fast because each connection has dedicated threads, however the number of connections is limited by the number of threads available (one input thread and one output thread for each connection). The shared thread model places no limit on the number of connections, however there is significant overhead and throughput delays in sharing threads among a number of connections, especially when those connections are busy.
Message Queue software allows clients to communicate with the broker using various low-level transport protocols. Message Queue supports the connection services (and corresponding protocols) described in Connection Services.
The choice of protocols is based on application requirements (encrypted, accessible through a firewall), but the choice affects overall performance.
Our tests compared throughput for TCP and SSL for two cases: a high-reliability scenario (1k persistent messages sent to topic destinations with durable subscriptions and using AUTO_ACKNOWLEDGE acknowledgment mode) and a high-performance scenario (1k nonpersistent messages sent to topic destinations without durable subscriptions and using DUPS_OK_ACKNOWLEDGE acknowledgment mode).
In general we found that protocol has less effect in the high-reliability case. This is probably because the persistence overhead required in the high-reliability case is a more important factor in limiting throughput than the protocol speed. Additionally:
TCP provides the fastest method to communicate with the broker.
SSL is 50 to 70 percent slower than TCP when it comes to sending and receiving messages (50 percent for persistent messages, closer to 70 percent for nonpersistent messages). Additionally, establishing the initial connection is slower with SSL (it might take several seconds) because the client and broker (or Web Server in the case of HTTPS) need to establish a private key to be used when encrypting the data for transmission. The performance drop is caused by the additional processing required to encrypt and decrypt each low-level TCP packet.
HTTP is slower than either the TCP or SSL. It uses a servlet that runs on a Web server as a proxy between the client and the broker. Performance overhead is involved in encapsulating packets in HTTP requests and in the requirement that messages go through two hops--client to servlet, servlet to broker--to reach the broker.
HTTPS is slower than HTTP because of the additional overhead required to encrypt the packet between client and servlet and between servlet and broker.
As the number of clients connected to a broker increases, and as the number of messages being delivered increases, a broker will eventually exceed resource limitations such as file descriptor, thread, and memory limits. One way to accommodate increasing loads is to add more broker instances to a Message Queue message service, distributing client connections and message routing and delivery across multiple brokers.
In general, this scaling works best if clients are evenly distributed across the cluster, especially message producing clients. Because of the overhead involved in delivering messages between the brokers in a cluster, clusters with limited numbers of connections or limited message delivery rates, might exhibit lower performance than a single broker.
You might also use a broker cluster to optimize network bandwidth. For example, you might want to use slower, long distance network links between a set of remote brokers within a cluster, while using higher speed links for connecting clients to their respective broker instances.
For more information on clusters, see Chapter 9, Working With Broker Clusters
The message throughput that a broker might be required to handle is a function of the use patterns of the messaging applications the broker supports. However, the broker is limited in resources: memory, CPU cycles, and so forth. As a result, it would be possible for a broker to become overwhelmed to the point where it becomes unresponsive or unstable.
The Message Queue message broker has mechanisms built in for managing memory resources and preventing the broker from running out of memory. These mechanisms include configurable limits on the number of messages or message bytes that can be held by a broker or its individual physical destinations, and a set of behaviors that can be instituted when physical destination limits are reached.
With careful monitoring and tuning, these configurable mechanisms can be used to balance the inflow and outflow of messages so that system overload cannot occur. While these mechanisms consume overhead and can limit message throughput, they nevertheless maintain operational integrity.
Message Queue supports both file-based and JDBC-based persistence modules. File-based persistence uses individual files to store persistent data. JDBC-based persistence uses a Java Database Connectivity (JDBC™) interface and requires a JDBC-compliant data store. File-based persistence is generally faster than JDBC-based; however, some users prefer the redundancy and administrative control provided by a JDBC-compliant store.
In the case of file-based persistence, you can maximize reliability by specifying that persistence operations synchronize the in-memory state with the data store. This helps eliminate data loss due to system crashes, but at the expense of performance.
The Message Queue client runtime provides client applications with an interface to the Message Queue message service. It supports all the operations needed for clients to send messages to physical destinations and to receive messages from such destinations. The client runtime is configurable (by setting connection factory attribute values), allowing you to control aspects of its behavior, such as connection flow metering, consumer flow limits, and connection flow limits, that can improve performance and message throughput. See Client Runtime Message Flow Adjustments for more information on these features and the attributes used to configure them.