Pub-Sub (Publish-Subscribe) is a messaging pattern used in event-driven systems with message brokers. It simplifies communication by separating publishers, who generate events, from subscribers, who consume them.
To make this simple, let’s make an analogy with an email subscription service. When we register to an email list, we will receive their messages. In this case, the email list will be the publisher, we will be the subscribers, and the means (downloaded a book, mini-course, mini-quiz) you chose to get subscribed in the list is the topic. Depending on the topic you register for, you will receive different emails.
This decoupling allows for scalable and adaptable systems, as publishers and subscribers are unaware of each other. Publishers emit events without knowing who will receive them, while subscribers express interest in specific events. The Pub-Sub system acts as a mediator, efficiently distributing events to the relevant subscribers.
In this article, we delve into the fundamentals of Pub-Sub, exploring its benefits and real-world applications. We discuss how Pub-Sub facilitates loose coupling, enabling components to be added or removed dynamically without impacting the overall system. We also examine features such as event categorization and selective consumption, which enhance efficiency and reduce unnecessary processing. Whether you’re new to event-driven systems or seeking to optimize your existing architecture, understanding Pub-Sub will empower you to build scalable and resilient applications. Join us as we uncover the simplicity and power of Pub-Sub in streamlining event-based communication.
Publisher
A message broker publisher is like a sender or a source of messages. It is responsible for creating and sending messages to the message broker. Think of it as someone who writes a letter and puts it in an envelope to be delivered through a postal service. The publisher decides what information or events to send and when. Once the publisher sends a message to the message broker, it becomes available for consumption by interested subscribers.
The other example is a newsletter. If you are subscribed to the newsletter from Java Challengers, you will receive emails according to your needs. In this case, Java Challengers is the publisher, and you are the subscriber.
Subscriber
A message broker subscriber is like a receiver or a listener for messages. It is interested in specific types of messages and wants to be notified whenever they are available. Think of it as someone eagerly waiting for a letter to arrive in their mailbox. The subscriber informs the message broker about the types of messages it wants to receive, and the message broker delivers those messages to the subscriber when they become available. The subscriber can then process or react to the received messages according to its specific needs or requirements.
As mentioned before, if you are subscribed to a newsletter service, you will be the subscriber and will be receiving messages from the publisher whenever available.
Topic
A message broker topic is like a channel or category that messages can be organized into. It helps to group related messages together based on a common theme or subject. Think of it as different sections or topics in a newspaper where articles of similar content are grouped together. When a publisher sends a message to the message broker, it assigns the message to a specific topic. Subscribers can then choose to subscribe to one or more topics they are interested in. This way, when a message is published to a topic, all subscribers interested in that topic will receive the message.
Topics allow for selective message consumption, ensuring that subscribers only receive the messages they are interested in and ignore the rest.
Multiple Topics
Multiple topics refer to the ability to organize messages into different categories or channels based on their subject or content. Instead of having a single topic, a message broker allows for the creation and management of multiple topics.
Think of it like a bookstore with different sections for various genres such as fiction, non-fiction, science fiction, and mystery. Each section represents a different topic. Similarly, in a message broker, multiple topics serve as distinct channels for messages to be published and consumed.
Publishers can choose the appropriate topic for each message they send, ensuring that it aligns with the subject matter. Subscribers, on the other hand, have the flexibility to subscribe to one or more topics based on their interests. By subscribing to specific topics, subscribers receive only the messages relevant to those topics, filtering out irrelevant information.
This capability of multiple topics allows for efficient message distribution, as it enables publishers to target specific audiences and subscribers to receive only the messages they are interested in. It also promotes modularity and scalability, as new topics can be added or removed without impacting the overall messaging system.
Message Broker Sharding
Sharding is a technique commonly used in distributed systems to horizontally partition data across multiple nodes or databases. It is primarily employed to improve scalability and performance by distributing the data workload across multiple resources.
When it comes to message brokers, sharding can be applied to achieve similar benefits. By sharding the message broker, the overall system can handle a higher volume of messages and increased throughput.
In a sharded message broker setup, the messages are partitioned across multiple message broker instances or shards. Each shard is responsible for storing and managing a subset of the messages. This allows for parallel processing and distribution of the message load.
Sharding can be implemented in different ways depending on the specific message broker technology being used. Some message brokers provide built-in sharding capabilities, while others may require manual partitioning and distribution of messages across multiple instances.
One common sharding approach is based on message topics or queues. Messages with the same topic or belonging to the same queue are directed to a specific shard. This ensures that related messages are stored together and can be efficiently processed by the corresponding shard.
Sharding a message broker can enhance scalability and performance by allowing message processing to be distributed across multiple nodes or instances. It enables the system to handle higher message volumes, increases throughput, and improves overall system resilience. However, sharding also introduces additional complexities, such as managing shard assignments, ensuring data consistency, and handling shard failures.
It’s worth noting that the decision to shard a message broker depends on the specific requirements and characteristics of the messaging workload. Sharding is typically employed in scenarios where the message volume exceeds the capacity of a single message broker instance or when high availability and fault tolerance are critical.
Idempotent Operation
An idempotent operation in the context of a message broker refers to an action or operation that can be safely repeated multiple times without altering the final result. In simpler terms, performing the same operation multiple times produces the same outcome as performing it just once. Idempotent operations are crucial in message broker systems to ensure data consistency and prevent unintended side effects. They are particularly important in scenarios where message delivery or processing can be unreliable or subject to duplication.
By designing the consumer logic to handle duplicate messages gracefully, idempotent operations help maintain system integrity. This is often achieved by using unique identifiers or sequence numbers to track and identify duplicate messages, allowing the consumer to detect and discard duplicates to avoid unintended modifications or inconsistencies in the system. Implementing idempotent operations in a message broker system provides reliability and consistency in message processing, making the system more resilient to potential message duplication or reprocessing scenarios and ensuring data integrity throughout the process.
Non-idempotent operation
In a non-idempotent message broker, the operations or actions performed by the broker are not inherently designed to be repeatable without altering the final outcome. This means that if the same operation is executed multiple times, it can result in different or unintended effects.
In such a scenario, duplicate or repeated messages can lead to undesired consequences or inconsistencies within the system. For example, if a non-idempotent message broker processes a duplicate message, it may perform the associated action multiple times, leading to data duplication, incorrect state changes, or unintended side effects.
To address the challenges of non-idempotent message processing, additional measures need to be taken. This may involve implementing deduplication mechanisms or introducing additional checks and validations to prevent the processing of duplicate messages. These measures help ensure that the non-idempotent operations are handled in a controlled and deterministic manner, minimizing the risks associated with duplicate messages.
Designing and implementing a non-idempotent message broker requires careful consideration of the application’s specific requirements and the potential impact of duplicate message processing. It is essential to establish safeguards and mechanisms to handle duplicates appropriately and maintain data consistency and system integrity.
Deduplication
Deduplication in a message broker is the process of identifying and removing duplicate messages to ensure that each message is processed and delivered only once. It involves assigning unique identifiers to incoming messages and comparing them with previously processed messages. If a duplicate message is detected, it is discarded or ignored. Deduplication helps maintain data consistency, prevents unintended side effects caused by duplicate processing, and improves the reliability and efficiency of the message broker system.
Order of messages
In a message broker, the order of messages refers to the sequence in which they are delivered and processed. Maintaining message order can be important when messages need to be processed in a specific sequence. However, it can be challenging in distributed systems. Message brokers aim to preserve message order within a channel or topic, but factors like network latency and parallel processing can cause variations.
Some brokers offer features to prioritize order, but in some cases, scalability and performance may take precedence over strict message ordering. Overall, maintaining message order depends on system requirements and trade-offs between ordering, scalability, and performance.
Guaranteed to be delivered at least once
A “guaranteed to be delivered at least once” message broker ensures that messages sent through it will reach the intended recipients without being lost or missed. It uses acknowledgments and retries to make sure messages are received. The broker stores messages and tries again if there are failures or network issues. This reliability is important for critical processes like financial transactions or real-time events. However, it may introduce some delays. In summary, this type of message broker provides confidence that messages will be reliably delivered.
Dead-letter Queue
A dead letter queue (DLQ) is a special queue in messaging systems designed to handle messages that cannot be successfully processed or delivered. When a message encounters an error or fails to be processed, it is moved to the dead letter queue for further analysis and resolution.
The purpose of the DLQ is to capture problematic messages without disrupting the normal flow. Messages end up in the DLQ when they exceed retry limits, cannot be routed correctly, or encounter processing errors. Administrators or developers can then review and analyze the messages in the DLQ, identify the causes of failure, resolve any issues, and potentially reprocess the messages or take appropriate corrective actions. By using a dead letter queue, messaging systems can effectively manage and address problematic messages, prevent data loss, and ensure reliable message processing.
Message Broker Technologies
There are several popular message broker technologies available, each offering different features and capabilities to facilitate reliable and efficient messaging. Here are brief explanations of some commonly used message broker technologies:
Apache Kafka: Apache Kafka is a distributed streaming platform known for its high-throughput, fault-tolerant, and scalable architecture. It provides persistent, publish-subscribe messaging, where data is stored in a distributed commit log. Kafka supports real-time event streaming, fault tolerance, and horizontal scalability, making it suitable for use cases such as real-time data processing, event-driven architectures, and data integration.
RabbitMQ: RabbitMQ is a widely adopted open-source message broker that implements the Advanced Message Queuing Protocol (AMQP). It supports various messaging patterns like publish-subscribe, request-reply, and work queues. RabbitMQ offers features such as message acknowledgments, flexible routing, and message persistence. It is known for its ease of use, extensibility, and support for multiple programming languages.
Apache ActiveMQ: Apache ActiveMQ is an open-source message broker that supports the Java Message Service (JMS) API and other protocols like MQTT and STOMP. It provides reliable messaging, message persistence, and clustering capabilities. ActiveMQ is feature-rich and widely used in enterprise applications for reliable messaging and integration scenarios.
Amazon Simple Queue Service (SQS): Amazon SQS is a fully managed message queuing service provided by Amazon Web Services (AWS). It offers reliable, scalable, and highly available message queuing with no upfront infrastructure management required. SQS supports both standard and FIFO (First-In-First-Out) queues, provides message durability, and integrates well with other AWS services.
Microsoft Azure Service Bus: Azure Service Bus is a cloud-based messaging service offered by Microsoft Azure. It provides features such as queuing, publish-subscribe messaging, and message sessions. Azure Service Bus supports multiple protocols and offers capabilities like message ordering, transactions, and dead-lettering.
Conclusion
Message broker technologies facilitate reliable and efficient messaging between applications and systems.
Key concepts in message brokers include:
- Publish-Subscribe: Messages are published to topics or channels and consumed by interested subscribers.
- Message Queues: Messages are sent to queues and consumed by consumers in a sequential manner.
- Message Routing: Messages are directed to specific destinations based on predefined rules or criteria.
- Message Persistence: Messages are stored durably to survive system failures or restarts.
- Acknowledgments: Senders receive acknowledgments from brokers upon successful message delivery.
- Retry Mechanisms: Failed message deliveries are retried to ensure successful processing.
- Dead Letter Queues: Messages that cannot be processed or delivered are moved to a special queue for analysis and resolution.
- Popular message broker technologies include Apache Kafka, RabbitMQ, Apache ActiveMQ, Amazon SQS, and Azure Service Bus.
Message brokers are used in various scenarios such as real-time data processing, event-driven architectures, and enterprise integration.
