What Is a Message Broker? Key Models and Benefits Explained

Modern applications are becoming more intelligent but also increasingly complex.

Engineers often encounter communication issues related to software components built in different programming languages while processing big data, resolving security risks, and simplifying complex infrastructures. Fortunately, some solutions help mitigate these issues.

A message broker is one of them.

Message brokers are a central component of any communication system that contains multiple actors, each with their own way of communicating. Message brokers, also known as message queue (MQ) software, are commonly used in systems like web services when communicating between different components in an application. Message brokers can act as a distributed communications layer, connecting services on various platforms.

Message brokers act as bridges between different message queues (MQs) and enable the distributed processing of messages across multiple cluster nodes. They provide scalability, reliability, transparency, automated management capabilities, and extendable security features, making them highly versatile platforms for building mission-critical applications.

Message brokers are included in messaging middleware or message-oriented middleware (MOM) systems. This form of middleware gives developers a consistent approach to handling data flow among an application's components and focuses on its core functionality. Message brokering is commonly performed using middleware technologies such as Apache Kafka, RabbitMQ, Apache ActiveMQ, and ZeroMQ.

Why use a message broker?

Message brokers are generally beneficial when message senders and receivers are spread across platforms or written in other programming languages. For example, a web application might send messages to a mobile application or a mobile application to a web application. In these cases, a message broker serves as a communication pipeline for different platforms.

Message brokers promote async communications among services so that the sending app doesn’t have to wait for the receiving app's response. This improves fault tolerance and resiliency in the infrastructure. Using message brokers also makes it easier to scale systems.

Message brokers can help create a standard integration mechanism to sustain cloud-native, microservices-based, serverless, and hybrid cloud architectures. They often handle transmissions between on-premises systems and cloud components in hybrid cloud environments.

Using an enterprise message broker allows for increased control over inter-service communications, ensuring data is sent securely, reliably, and efficiently between app components. Message brokers can play a similar role in integrating multi-cloud environments, enabling communication between workloads and runtimes on different platforms. They also work well with serverless computing, in which individual cloud-hosted services run as needed.

In short, message brokers help integrate applications and automate message transmission. They’re instrumental when fully automating the messaging needs of the sender and receiver is difficult.

How does a message broker work?

In synchronous processing, a source app submits a message to the target app, waits for it to finish its task, and accepts the response before continuing. This is a blocking request because the source app can’t take any further action until a response is delivered.

Message brokers introduce asynchronous communication in monolithic infrastructures. Asynchronous communication doesn’t require waiting for real-time responses, resulting in greater multitasking ability because one activity doesn’t need to wait for another to conclude. Email is a good example of asynchronous communication, where users can perform additional tasks without waiting for a response once an email is sent.

Message brokers rely on asynchronous messaging for inter-application communication. It prevents the loss of essential data and allows systems to continue operating even with intermittent connectivity or lag on public networks. Asynchronous messaging ensures that messages are sent only once (and only once) in the correct order.

The source application (producer) delivers a message to a message broker, which provides data marshaling, routing, message translation, persistence, and transmission to all suitable destinations (consumers). It is a software intermediary for different systems, allowing senders to transmit information without revealing consumers' location, status, or number. This facilitates the decoupling of operations and practices within systems.

Producers and consumers interact with the broker using standard or custom protocols. A message broker handles all client state management and tracking and offloads individual applications, integrating the intricacies of information transmission within its architecture.

Message brokers often require a substructure or component known as a message queue to ensure reliable message retention and assured delivery. Message queues store and arrange messages until consumer service can process them. Messages are held in a message queue in the order they were sent to until delivery is confirmed.

Message broker models

The primary function of a message broker is to route messages among apps. It’s analogous to a mediator or intermediary adjudicating between two parties if communication has broken down. Message brokers provide three essential message delivery methods or messaging styles.

1. Point-to-Point messaging model

The Point-to-Point (P2P) pattern helps message queues where producers and consumers have a one-to-one relationship. Each message in the queue is forwarded to and consumed by a single consumer. P2P messaging is useful when a message needs to be acted upon only once.

Payroll and financial transaction processing are two examples of P2P messaging. Senders and receivers in these systems require assurance that each payment will happen only once, and P2P ensures this.

2. Publish/Subscribe messaging model

Publish/Subscribe messaging, or the pub/sub model, allows a producer to send messages to a topic. In this approach, the producer is known as a publisher, while the consumer is referred to as a subscriber. Different publishers can write on the same topic, and different subscribers can receive messages from one or more publishers.

Subscribers linked to topics receive timely communication submitted to the topic. This subscriber-centric architecture enables easy interest-driven delivery depending on the topics that apps subscribe to. It can also facilitate adopting an event-driven architecture-based system with fewer dependencies among apps.

3. Hybrid messaging model

Hybrid messaging models sometimes exist as server-based message brokers. For example, when multiple entities want a copy of a message while requiring permanence and durability to message loss.

In such a case, messages are distributed according to topics. This way, only interested consumer groups receive the messages, and they can choose which others receive the messages.

Examples of message brokers

Redis, RabbitMQ, Amazon SQS, Apache Kafka, and Amazon Simple Notification Service (SNS) are some of the most popular and effective message brokers. When it comes to low load, there isn't much difference between these tools. However, they work differently in large-scale operations and complex scenarios. 

Here are some of their main characteristics:

• RabbitMQ is an open-source message broker software that provides advanced and complex routing configurations. It offers four different forms of exchange. An exchange is a component of the message broker that routes messages to the appropriate queues. In RabbitMQ, messages are first forwarded to exchanges, and not queues. This is best suited for environments that require lower throughput.
• Amazon MQ is a cloud-based message broker and a part of Amazon Web Services (AWS). It provisions and maintains a message broker for businesses and reduces their routine tasks.
• Apache Kafka is a distributed messaging system that originally tracked website activity. Tracking requires a system to store a large amount of data for a long time. And this is precisely where Kafka excels, especially when it comes to real-time data storage.
• Redis is an in-memory data structure store and a high-performance message broker. With Redis, you can’t guarantee that a system will retain messages. It’s an excellent solution if you have a setup that demands a fast broker but can survive with some data loss.
• Amazon SNS is a push notification from Amazon. It’s an offering from AWS. One of its primary benefits is its low-cost infrastructure. It automatically scales the workload inside your application. SNS uses a publish-subscribe messaging model to transmit individual messages.
• Amazon Simple Queue Service (SQS) is a fully controlled queuing solution from Amazon. It's a component of AWS, much like Amazon SNS. SQS can automatically resize to fit the workload. It also provides a free tier for a limited number of message queues and is highly cost-effective. The message consumption process is different for SQS than SNS. SNS uses a push technique to offer messages to consumers without retaining them. Amazon SQS, on the other hand, uses a pull approach, requiring consumers to grab messages from SQS queues manually.

Message brokers vs. APIs

When building a microservices architecture, it's important to understand how your services will interact. Many businesses create an application programming interface (API) for each service and have the other apps invoke it using a standard Hypertext Transfer Protocol (HTTP) client.

Representational State Transfer (REST) APIs are often used for communication. They're the rules and limitations developers use while creating web services. Any system that follows these rules can communicate with other systems using a set of standard stateless operators and requests.

REST APIs interact via HTTP. However, since HTTP is a request-response protocol, it’s best used in synchronous communication. In this case, developers must configure services making queries via REST APIs to expect an instant response. 

If the consumer receiving the message is unavailable, the producer service will stop while waiting for the response. Both services should be set up with built-in failover and error handling logic.

On the other hand, message brokers provide asynchronous communication between services. This means the producer doesn’t have to wait for a response from the receiving service, which increases systems' fault tolerance and robustness.

Furthermore, message brokers facilitate system scaling since they use a pub/sub messaging structure that can easily handle fluctuating service levels. They also keep track of consumer statuses.

Message brokers vs. event streaming platforms

Message brokers support a variety of messaging styles, including message queues and pub/sub. Event streaming platforms, on the other hand, only support pub/sub-style distribution patterns. They’re easy to scale since they’re designed to work with large volumes of messages. They can label streams of records into topics and store them for a set period.

Event streaming services cannot ensure message delivery or track which subscribers have received messages.

Topics are useful because they allow several event producers and consumers to exist simultaneously, allowing a service to scale easily. They’re also immutable records that last as long as the use case requires. 

Unlike conventional message brokers that discard messages once a consumer handles them, topics save records of events via event streams. These records can be replayed when rebuilding an app's state, incorporating a new feature, or fixing bugs.

Event streaming systems are easier to scale than message brokers but have fewer fault tolerance features (such as message resending) and less message routing and queueing flexibility.

Message broker vs. enterprise service bus

An enterprise service bus (ESB) is an architectural style often used in service-oriented infrastructures. ESB integrates communication protocols and data structures into a "shared language" that all infrastructural apps and services can access. For example, ESBs can interpret requests from one protocol, such as extensible markup language (XML), to another, such as JavaScript object notation (JSON).

ESB software automatically converts a message’s content. This unified software platform handles other orchestration logic, such as connection, routing, and request data processing. An ESB allows consumers to interact via various protocols (HTTP, FTP, SMTP, etc.) and trigger operations residing outside the organization. In contrast, message queue solutions are often built in-house.

ESB systems are complicated and can be hard to integrate and manage. When challenges arise in production environments, ESBs are difficult to debug and scale and require time-consuming patching.

Message brokers are lightweight substitutes for ESBs that provide comparable functionality and simple and cost-effective inter-service connectivity. They're well-suited for use in microservices architectures to decouple endpoints, which have grown in popularity as ESBs have fallen out of favor.

Benefits of a message broker

Message brokers are essential to building scalable software systems and workflows. Message broker software offers various benefits.

• Enhanced interaction among asynchronous programs: Producers can send messages about whether or not the consumers are present. All they need is a working message broker. Consumers work similarly.
• Improved system performance: Async processing segregates high-demand tasks into independent processes, improving application speed and user experience.
• Greater reliability by ensuring message delivery: Message brokers provide a redelivery function. In the event of a consumer failure, they can redeliver the message immediately or at a specified time. Message brokers also effectively route undeliverable messages, a dead-letter mechanism.

Challenges of a message broker

Message brokers add a layer of asynchronous processing to your infrastructure. Users face difficulties initiating asynchronous operations, a significant disadvantage of using a message broker.

• Increased system complexity: Integrating a message broker into your system adds a new component to the overall infrastructure. This requires addressing additional factors, such as supporting the network between parts or security risks. Message brokers can also lead to system inconsistencies as some elements may not have current data until the messages are transmitted and interpreted.
• Debugging can be cumbersome: Suppose you take several steps to handle a single request via the message broker. You submitted a message but didn’t receive a response. Identifying the error source can be challenging because each service has its own logs. Therefore, it’s good to provide message-tracing features when developing systems that use message brokers.
• Steeper learning curve: Message brokers require many setup and configuration options and aren't as easy to implement. Queue and message sizes, queue behavior, delivery parameters, and message time-to-live (TTL) are just a few variables that users can choose from.

Message broker use cases

A message broker is a great solution to various business needs across sectors and corporate computing infrastructures. It supports reliable inter-application communication and message delivery.

Some other use cases of message brokers are:

• Monetary transactions and payment services: Payment transfers should happen only once. Message brokers guarantee payment information is not lost or unintentionally duplicated, confirm receipt, and allow systems to interact consistently even when intermediate services are inactive.
• E-commerce order processing and fulfillment: Online businesses' strength is determined by the durability of their websites and e-commerce platforms. Message brokers improve fault tolerance and ensure that messages are only consumed once, making them an obvious choice for processing online orders. This also enhances order fulfillment.
• Safeguarding extremely sensitive information in transit and at rest: If your business is heavily regulated or faces substantial data security threats, look for a message broker software that offers end-to-end encryption.

Async communication is the key to success

A message broker is a simple solution that offers various applications in a solid and scalable producer-consumer architecture. Message brokers are becoming increasingly important as businesses revamp their systems in anticipation of the cloud. Simply put, message brokers are essential for modern, technologically developing societies.

Want to try message brokers? Here is the top free message queue software from which to choose.

This article was originally written in 2022. It has been updated with new information.