Message latency and message throughput, two of the main performance indicators, generally depend on the time it takes a typical message to complete various steps in the message delivery process. These steps are shown below for the case of a persistent, reliably delivered message. The steps are described following the illustration.
The message is delivered from producing client to broker.
The broker reads in the message.
The message is placed in persistent storage (for reliability).
The broker confirms receipt of the message (for reliability).
The broker determines the routing for the message.
The broker writes out the message.
The message is delivered from broker to consuming client.
The consuming client acknowledges receipt of the message (for reliability).
The broker processes client acknowledgment (for reliability).
The broker confirms that client acknowledgment has been processed.
Since these steps are sequential, any one of them can be a potential bottleneck in the delivery of messages from producing clients to consuming clients. Most of the steps depend on physical characteristics of the messaging system: network bandwidth, computer processing speeds, message service architecture, and so forth. Some, however, also depend on characteristics of the messaging application and the level of reliability it requires.
The following subsections discuss the effect of both application design factors and messaging system factors on performance. While application design and messaging system factors closely interact in the delivery of messages, each category is considered separately.
Application design decisions can have a significant effect on overall messaging performance.
The most important factors affecting performance are those that affect the reliability of message delivery. Among these are the following:
Other application design factors affecting performance are the following:
The sections that follow describe the effect of each of these factors on messaging performance. As a general rule, there is a tradeoff between performance and reliability: factors that increase reliability tend to decrease performance.
Table 11–1 shows how the various application design factors generally affect messaging performance. The table shows two scenarios—one high-reliability, low-performance, and one high-performance, low-reliability—and the choices of application design factors that characterize each. Between these extremes, there are many choices and tradeoffs that affect both reliability and performance.Table 11–1 Comparison of High-Reliability and High-Performance Scenarios
High-Reliability, Low-Performance Scenario
High-Performance, Low-Reliability Scenario
Use of transactions
AUTO_ACKNOWLEDGE or CLIENT_ACKNOWLEDGE
Use of selectors
No message filtering
Large number of small messages
Small number of large messages
Message body type
Complex body types
Simple body types
Persistent messages guarantee message delivery in case of broker failure. The broker stores the message in a persistent store until all intended consumers acknowledge they have consumed the message.
Broker processing of persistent messages is slower than for nonpersistent messages for the following reasons:
A broker must reliably store a persistent message so that it will not be lost should the broker fail.
The broker must confirm receipt of each persistent message it receives. Delivery to the broker is guaranteed once the method producing the message returns without an exception.
Depending on the client acknowledgment mode, the broker might need to confirm a consuming client’s acknowledgment of a persistent message.
For both queues and topics with durable subscribers, performance was approximately 40% faster for nonpersistent messages. We obtained these results using 10k-sized messages and AUTO_ACKNOWLEDGE mode
A transaction is a guarantee that all messages produced in a transacted session and all messages consumed in a transacted session will be either processed or not processed (rolled back) as a unit.
Message Queue supports both local and distributed transactions.
A message produced or acknowledged in a transacted session is slower than in a nontransacted session for the following reasons:
Additional information must be stored with each produced message.
In some situations, messages in a transaction are stored when normally they would not be (for example, a persistent message delivered to a topic destination with no subscriptions would normally be deleted, however, at the time the transaction is begun, information about subscriptions is not available).
Information on the consumption and acknowledgment of messages within a transaction must be stored and processed when the transaction is committed.
One mechanism for ensuring the reliability of JMS message delivery is for a client to acknowledge consumption of messages delivered to it by the Message Queue broker.
If a session is closed without the client acknowledging the message or if the broker fails before the acknowledgment is processed, the broker redelivers that message, setting a JMSRedelivered flag.
AUTO_ACKNOWLEDGE. The system automatically acknowledges a message once the consumer has processed it. This mode guarantees at most one redelivered message after a provider failure.
CLIENT_ACKNOWLEDGE. The application controls the point at which messages are acknowledged. All messages processed in that session since the previous acknowledgment are acknowledged. If the broker fails while processing a set of acknowledgments, one or more messages in that group might be redelivered.
DUPS_OK_ACKNOWLEDGE. This mode instructs the system to acknowledge messages in a lazy manner. Multiple messages can be redelivered after a provider failure.
(Using CLIENT_ACKNOWLEDGE mode is similar to using transactions, except there is no guarantee that all acknowledgments will be processed together if a provider fails during processing.)
Acknowledgment mode affects performance for the following reasons:
Extra control messages between broker and client are required in AUTO_ACKNOWLEDGE and CLIENT_ACKNOWLEDGE modes. The additional control messages add additional processing overhead and can interfere with JMS payload messages, causing processing delays.
In AUTO_ACKNOWLEDGE and CLIENT_ACKNOWLEDGE modes, the client must wait until the broker confirms that it has processed the client’s acknowledgment before the client can consume additional messages. (This broker confirmation guarantees that the broker will not inadvertently redeliver these messages.)
The Message Queue persistent store must be updated with the acknowledgment information for all persistent messages received by consumers, thereby decreasing performance.
Subscribers to a topic destination fall into two categories, those with durable and nondurable subscriptions.
Durable subscriptions provide increased reliability but slower throughput, for the following reasons:
The Message Queue message service must persistently store the list of messages assigned to each durable subscription so that should a broker fail, the list is available after recovery.
Persistent messages for durable subscriptions are stored persistently, so that should a broker fail, the messages can still be delivered after recovery, when the corresponding consumer becomes active. By contrast, persistent messages for nondurable subscriptions are not stored persistently (should a broker fail, the corresponding consumer connection is lost and the message would never be delivered).
We compared performance for durable and nondurable subscribers in two cases: persistent and nonpersistent 10k-sized messages. Both cases use AUTO_ACKNOWLEDGE acknowledgment mode. We found an effect on performance only in the case of persistent messages which slowed durables by about 30%
Application developers often want to target sets of messages to particular consumers. They can do so either by targeting each set of messages to a unique physical destination or by using a single physical destination and registering one or more selectors for each consumer.
A selector is a string requesting that only messages with property values that match the string are delivered to a particular consumer. For example, the selector NumberOfOrders >1 delivers only the messages with a NumberOfOrders property value of 2 or more.
Creating consumers with selectors lowers performance (as compared to using multiple physical destinations) because additional processing is required to handle each message. When a selector is used, it must be parsed so that it can be matched against future messages. Additionally, the message properties of each message must be retrieved and compared against the selector as each message is routed. However, using selectors provides more flexibility in a messaging application.
Message size affects performance because more data must be passed from producing client to broker and from broker to consuming client, and because for persistent messages a larger message must be stored.
However, by batching smaller messages into a single message, the routing and processing of individual messages can be minimized, providing an overall performance gain. In this case, information about the state of individual messages is lost.
In our tests, which compared throughput in kilobytes per second for 1k, 10k, and 100k-sized messages to a queue destination and AUTO_ACKNOWLEDGE acknowledgment mode, we found that nonpersistent messaging was about 50% faster for 1k messages, about 20% faster for 10k messages, and about 5% faster for 100k messages. The size of the message affected performance significantly for both persistent and nonpersistent messages. 100k messages are about 10 times faster than 10k, and 10k are about 5 times faster than 1k.
JMS supports five message body types, shown below roughly in the order of complexity:
BytesMessage contains a set of bytes in a format determined by the application.
TextMessage is a simple Java string.
StreamMessage contains a stream of Java primitive values.
MapMessage contains a set of name-value pairs.
ObjectMessage contains a Java serialized object.
While, in general, the message type is dictated by the needs of an application, the more complicated types (MapMessage and ObjectMessage) carry a performance cost: the expense of serializing and deserializing the data. The performance cost depends on how simple or how complicated the data is.
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.