O'Reilly logo

Understanding Message Brokers by Jakub Korab

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Chapter 4. Messaging Considerations and Patterns

In the previous chapters we explored two very different approaches to broker-based messaging and how they handle concerns such as throughput, high availability and transactionality. In this chapter we are going to focus on the other half of a messaging system—the client logic. We will discuss some of the mechanical complexities that message-based systems need to address and some patterns that can be applied to deal with them. To round out the discussion, we will discuss how to reason about messaging products in general and how they apply to your application logic.

Dealing with Failure

A system based on messaging must be able to deal with failures in a graceful way. Failures come in many forms at various parts of the application.

Failures When Sending

When producing messages, the primary failure that needs to be considered is a broker outage. Client libraries usually provide logic around reconnection and resending of unacknowledged messages in the event that the broker becomes unavailable.

Reconnection involves cycling through the set of known addresses for a broker, with delays in-between. The exact details vary between client libraries.

While a broker is unavailable, the application thread performing the send may be blocked from performing any additional work if the send operation is synchronous. This can be problematic if that thread is reacting to outside stimuli, such as responding to a web service request.

If all of the threads in a web server’s thread pool are suspended while they are attempting to communicate with a broker, the server will begin rejecting requests back to upstream systems, typically with HTTP 503 Service Unavailable. This situation is referred to as back-pressure, and is nontrivial to address.

One possibility for ensuring that an unreachable broker does not exhaust an application’s resource pool is to implement the circuit breaker pattern around messaging code (Figure 4-1). At a high level, a circuit breaker is a piece of logic around a method call that is used to prevent threads from accessing a remote resource, such as a broker, in response to application-defined exceptions.

Circuit Breaker
Figure 4-1. Circuit breaker

A circuit breaker has three states:


Traffic calls are routed into the method as normal.


An error state around the resource has been detected; subsequent method calls are routed to alternative logic, such as the formatting of an immediate error message.


Triggered after a period of time to allow threads to retest the protected reource. From here, the circuit breaker will either be closed, allowing application logic to continue as normal, or reopened.

Circuit breaker implementations vary in terms of functionality and can take into consideration concerns such as timeouts and error thresholds. In order for message sends to work correctly with circuit breakers, the client library must be configured to throw an exception at some point if it cannot send. It should not attempt to reconnect infinitely.

Asynchronous sends, such as those performed by Kafka, are not in themselves a workaround for this issue. An asynchronous send will involve messages being placed into a finite in-memory buffer that is periodically sent by a background thread to the broker. In the case of Kafka’s client library, if this buffer becomes full before the client is able to reconnect, then any subsequent sends will be blocked (i.e., become synchronous). At this time the application thread will wait for some period until eventually the operation is abandoned and a TimeoutException is thrown. At best, asynchronous sends will delay the exhaustion of an application’s thread pool.

Failures When Consuming

In consumption there are two different types of failures:


The message that you are consuming will never be able to be processed.


The message would normally be processed, but this is not possible at this time.

Permanent failures

Permanent failures are usually caused by an incorrectly formed payload or, less commonly, by an illegal state within the entity that a message refers to (e.g., the betting account that a withdrawal is coming from being suspended or canceled). In both cases, the failure is related to the application, and if at all possible, this is where it should be handled. Where this is not possible, the client will often fall back to broker redelivery.

JMS-based message brokers provide a redelivery mechanism that is used with transactions. Here, messages are redispatched for processing by the consumer when an exception is thrown. When messages are redelivered, the broker keeps track of this by updating two message headers:

  • JMSRedelivered set to true to indicate redelivery

  • JMSXDeliveryCount incremented with each delivery

Once the delivery count exceeds a preconfigured threshold, the message is sent to a dead-letter queue or DLQ. DLQs have a tendency to be used as a dumping ground in most message-based systems and are rarely given much thought. If left unconsumed, these queues can prevent the cleanup of journals and ultimately cause brokers to run out of disk space.

So what should you do with these queues? Messages from a DLQ can be quite valuable as they may indicate corner cases that your application had not considered or actions requiring human intervention to correct. As such they should be drained to either a log file or some form of database for periodic inspection.

As previously discussed, Kafka provides no mechanism for transactional consumption and therefore no built-in mechanism for message redelivery on error. It is the responsibility of your client code to provide redelivery logic and send messages to dead-letter topics if needed.

Temporary failures

Temporary failures in message consumption fall into one of two categories:


Affecting all messages. This includes situations such as a consumer’s backend system being unavailable.


The current message cannot be processed, but other messages on the queue can. An example of this is a database record relating to a message being locked and therefore temporarily not being updateable.

Failures of this type are by their nature transient and will likely correct themselves over time. As such, the way they need to be handled in significantly different ways to handle permanent failures. A message that cannot be processed now is not necessarily illegitimate and should not end up on a DLQ. Going back to our deposit example, not being able to credit a payment to an account does not mean that the payment should just be ignored.

There are a couple of options that you may want to consider on a case by case basis, depending on the capabilities of your messaging system:

  • If the problem is local, perform retries within the message consumer itself until the situation corrects itself. There will always be a point at which you give up. Consider escalating the message for human intervention within the application itself.

  • If the situation is global, then relying on a redelivery mechanism that eventually pushes messages into a DLQ will result in a succession of perfectly-legitimate messages being drained from the source queue and effectively being discarded. In production systems, this type of situation is characterized by DLQs accumulating messages in bursts. One solution to this situation is to turn off consumption altogether until the situation is rectified through the use of the Kill Switch pattern (Figure 4-2).

A Kill Switch operates by catching exceptions related to transient issues and pausing consumption. The message currently being processed should either be rolled back if using a transaction, or held onto by the consuming thread. In both cases, it should be possible to reprocess the message later.

Kill Switch sequence diagram
Figure 4-2. Kill Switch sequence diagram

The consumer should trigger a background checker task to periodically determine whether the issue has gone away. If the issue is a web service outage, the check might be a poll of a URL that simply acknowledges that the service is up. If the issue is a database outage, then the check might consist of a dummy SQL query being run (e.g., SELECT 1 FROM DUAL on Oracle). If the check operation succeeds, then the checker task reactivates the message consumer and terminates itself.

Preventing Duplicate Messages with Idempotent Consumption

Previously we discussed that systems based on queues must deal with the possibility of duplicate messages. In the event of a consumer system going offline unexpectedly, there may be a situation where messages were processed but had not yet been acknowledged. This applies regardless of whether you are using a transaction-capable broker and did not yet commit, or in the case of Kafka did not move the consumed offset forward. In both cases, when the client is restarted, these unacknowledged messages will be reprocessed.

Duplication may also occur when a system that is upstream of a broker reissues the same payloads. Consider the scenario where a system has its inputs reloaded into it after an outage involving data loss. The replay of the data into the system causes a side effect of sending messages into a queue. The messages are technically different from those that were sent in the past (they have different message IDs or offsets), but they trigger the same consumer logic multiple times.

To avoid processing the message multiple times, the consumption logic needs to be made idempotent. The Idempotent Consumer pattern acts like a stateful filter that allows logic wrapped by it to be executed only once. Two elements are needed to implement this:

  • A way to uniquely identify each message by a business key.

  • A place to store previously seen keys. This is referred to as an idempotent repository. Idempotent repositories are containers for a durable set of keys that will survive restarts of the consumer and can be implemented in database tables, journals, or similar.

Consider the following JSON message which credits an account:

    "timestamp" : "20170206150030",
    "accountId" : "100035765",
    "amount" : "100"

When a message arrives, the consumer needs to uniquely identify it. As discussed earlier, built-in surrogate keys such as a message ID or offset are not adequate to protect from upstream replays. In the message above, a good candidate for this key is a combination of the timestamp and account fields of the message, as it is unlikely that two deposits for the same account happen at the same time.

The idempotent repository is checked to see whether it contains the key, and if it does not, the logic wrapped by it is executed, otherwise it is skipped. They key is stored in the idempotent repository according to one of two strategies:


Before the wrapped logic is executed. In this case, the consumer needs to remove the key if the wrapped logic throws an error.


After the logic is executed. In this situation, you run the risk of duplicate processing if the key is not stored due to a system crash.

In addition to timings, when developing idempotent repositories you need to be aware that they may be accessed by multiple consumer instances at the same time.


The Apache Camel project is a Java-based integration framework that includes an implementation of numerous integration patterns, including the Idempotent Consumer. The project’s documentation provides a good starting point for implementing this pattern in other environments. It includes many idempotent repository implementations for storing keys in files, databases, in-memory data grids, and even Kafka topics.

What to Consider When Looking at Messaging Technologies

Message brokers are a tool, and you should aim to use the right one for the job. As with any technology, it is difficult to make objective decisions unless you know what questions to ask. Your choice of messaging technology must first and foremost be led by your use cases.

What sort of a system are you building? Is it message-driven, with clear relationships between producers and consumers, or event-driven where consumers subscribe to streams of events? A basic queue-based system is enough for the former, while there are numerous options for the latter involving persistent and non-persistent messaging, the choice of which will depend on whether or not you care about missing messages.

If you need to persist messages, then you need to consider how that persistence is performed. What sorts of storage options does the product support? Do you need a shared filesystem, or a database? Your operating environment will feed back into your requirements. There is no point looking at a messaging system designed for independent commodity servers with dedicated disks if you are forced to use a storage area network (SAN).

Broker storage is closely related to the high availability mechanism. If you are targeting a cloud deployment, then highly available shared resources such as a network filesystem will likely not be available. Look at whether the messaging system supports native replication at the level of the broker or its storage engine, or whether it requires a third-party mechanism such as a replicated filesystem. High availability also needs to be considered over the entire software to hardware stack—a highly available broker is not really highly available if both master and slave can be taken offline by a filesystem or drive failure.

What sort of message ordering guarantees does your application require? If there is an ordering relationship between messages, then what sort of support does the system provide for sending these related messages to a single consumer?

Are certain consumers in your applications only interested in subsets of the overall message stream? Does the system support filtering messages for individual consumers, or do you need to build an external filter that drops unwanted messages from the stream?

Where is the system going to be deployed? Are you targeting a private data center, cloud, or a combination of the two? How many sites do you need to pass messages between? Is the flow unidirectional, in which case replication might be enough, or do you have more complex routing requirements? Does the messaging system support routing or store-and-forward networking? Is the replication handled by an external process? If so, how is that process made highly available?

For a long time, marketing in this field was driven by performance metrics, but what does it matter if a broker can push thousands of messages per second if your total load is going to be much lower than that? Get an understanding of what your real throughput is likely to be before being swayed by numbers. Large message volumes per day can translate to relatively small numbers per second. Consider your traffic profile—are the messages going to be a constant 24-hour stream, or will the bulk of traffic fall into a smaller timeframe? Average numbers are not particularly useful—you will get peaks and troughs. Consider how the system will behave on the largest volumes.

Perform load tests to get a good understanding of how the system will work with your use cases. A good load test should verify system performance with:

  • Estimated message volumes and sizes—use sample payloads wherever possible.

  • Expected number of producers, consumer, and destinations.

  • The actual hardware that the system will run on. This is not always possible, but as we discussed, it will have a substantial impact on performance.

If you are intending to send large messages, check how the system deals with them. Do you need some form of additional external storage outside of the messaging system, such as when using the Claim Check pattern? Or is there some form of built-in support for streaming? If streaming very large content like video, do you need persistence at all?

Do you need low latency? If so, how low? Different business domains will have different views on this. Intermediary systems such as brokers add processing time between production and consumption—perhaps you should consider brokerless options such as ZeroMQ or an AMQP routing setup?

Consider the interaction between messaging counterparties. Are you going to be performing request-response over messaging? Does the messaging system support the sorts of constructs that are required, i.e., message headers, temporary destinations, and selectors?

Are you intending on spanning protocols? Do you have C++ servers that need to communcate with web clients? What support does the messaging system provide for this?

The list goes on…transactions, compression, encryption, duration of message storage (possibly impacted by local legislation), and commercial support options.

It might initially seem overwhelming, but if you start looking at the problem from the point of view of your use cases and deployment environment, the thought process behind this exercise will steer you naturally.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required