Messaging Architecture Patterns

21 min readAug 5, 2023

Asynchronous Request-Reply Pattern:

Decouple backend processing from a frontend host, where backend processing needs to be asynchronous but the front end still needs a clear response.

Asynchronous request-reply is a communication pattern used in distributed systems to handle interactions between different components or services. Unlike synchronous communication, where the sender waits for a response before proceeding, asynchronous request-reply allows the sender to continue its operations while waiting for the response. This approach is particularly useful in scenarios where immediate responses are not required or when dealing with long-running tasks.

Here’s how asynchronous request-reply works:

1. Request: The client sends a request to the server, specifying the operation it wants to perform. The request contains a unique identifier (such as a correlation ID) to track the response associated with this particular request.

2. Acknowledgment: Upon receiving the request, the server acknowledges it immediately with a receipt or confirmation. This acknowledgement lets the client know that the request has been received and is being processed. The server then starts handling the request in the background.

3. Asynchronous Processing: The server processes the request asynchronously, meaning it continues its operations without blocking the client. This allows the server to perform time-consuming tasks or handle multiple requests concurrently.

4. Response: Once the server completes processing the request, it sends the response back to the client. The response contains the unique identifier received in the initial request, allowing the client to match it with the corresponding request.

5. Client Handling: The client, upon receiving the response, processes it accordingly based on the correlation ID. It may use this ID to associate the response with the original request, execute additional logic, or notify other components of the completed operation.

Advantages of Asynchronous Request-Reply:

1. Scalability: Asynchronous communication allows systems to handle a large number of requests efficiently, as processing requests doesn’t block the main thread or hinder the system’s responsiveness.

2. Fault Tolerance: Since clients don’t wait for immediate responses, they can handle server failures gracefully. The client can be programmed to handle timeouts and retries, ensuring the request is eventually processed even if the initial server handling the request becomes unavailable.

3. Improved Performance: Asynchronous processing is suitable for long-running tasks, such as file processing, batch operations, or tasks involving multiple steps. By utilizing asynchronous patterns, systems can optimize resource utilization and overall performance.

4. Loosely Coupled Architecture: Asynchronous request-reply fosters a loosely coupled communication model as the client and server are not directly tied to each other during request processing.

Claim Check Pattern:

Split a large message into a claim check and a payload to avoid overwhelming a message bus.

The “Claim Check” pattern is a design pattern used in distributed systems to optimize and reduce the size of messages when exchanging data between components or services. It is particularly useful when dealing with large data payloads or attachments that may cause communication overhead and inefficiency.

In essence, the Claim Check pattern involves removing the actual data from the main message and replacing it with a reference or identifier, commonly known as the “claim check.” The sender retains the actual data temporarily, typically in a shared storage location (e.g., a database, cache, or file system), and provides the recipient with the claim check to access the data when needed.

Here’s how the Claim Check pattern works:

1. Data Extraction: When a sender wants to send a large payload to a recipient, it first extracts the data and stores it in a shared storage location. The sender generates a unique identifier for this data, which will serve as the claim check.

2. Message Transformation: Instead of sending the large data payload directly within the main message, the sender replaces it with the claim check, which is usually a small reference or token containing the unique identifier.

3. Message Sending: The sender now sends the main message with the claim check to the recipient. Since the claim check is typically much smaller than the actual data, this reduces the size of the message being transmitted, leading to better performance and reduced network utilization.

4. Claim Check Usage: Upon receiving the message, the recipient can use the claim check to retrieve the actual data from the shared storage location. The recipient makes a request to the storage location using the claim check, and the storage location returns the corresponding data.

5. Data Cleanup: To ensure efficient use of resources, a mechanism should be in place to clean up the data from the shared storage location after the recipient has retrieved it. This could be done through an expiration policy or by periodically cleaning up unused data.

Benefits of the Claim Check Pattern:

1. Reduced Message Size: By replacing large data payloads with claim checks, the overall size of messages sent over the network is significantly reduced, leading to improved performance and reduced network traffic.

2. Improved Scalability: The pattern enables more efficient use of resources, especially when dealing with large volumes of data or numerous message exchanges.

3. Decoupling: The Claim Check pattern decouples the data processing from message transmission, as the actual data is stored independently of the message. This promotes a more loosely coupled architecture.

4. Optimal Use of Memory: In-memory messaging systems can benefit from the Claim Check pattern by avoiding memory bottlenecks caused by large message payloads.

However, it’s essential to consider the trade-offs, as using the Claim Check pattern introduces additional complexity due to the need for shared storage and data cleanup mechanisms. Additionally, accessing the data through the claim check might introduce some latency compared to having the data directly embedded in the message. As with any design pattern, the suitability of the Claim Check pattern depends on the specific requirements and constraints of the distributed system in question.

Choreography Pattern:

Here’s how asynchronous request-reply works:

Advantages of Asynchronous Request-Reply:

4. Loosely Coupled Architecture: Asynchronous request-reply fosters a loosely coupled communication model as the client and server are not directly tied to each other during request processing.

Asynchronous request-reply can be implemented using various communication mechanisms, such as message queues, publish-subscribe patterns, or asynchronous APIs. It’s crucial to handle scenarios like retries, duplicate requests, and error handling to ensure reliable communication in distributed systems.

Competing Consumer Patterns:

Enable multiple concurrent consumers to process messages received on the same messaging channel.

Competing Consumers is a messaging pattern used in distributed systems to process messages efficiently and in parallel. In this pattern, multiple consumer instances compete to process messages from a shared message queue. It is particularly useful when there is a need to distribute the workload among multiple consumers to achieve better scalability, fault tolerance, and performance.

The Competing Consumers pattern is often applied in scenarios where a producer generates messages and puts them into a queue, and multiple consumers read from the queue and process the messages concurrently. Each message is delivered to only one of the available consumers, and they work in parallel to process the messages.

Key features of the Competing consumer pattern:

1. Shared Message Queue: All messages are placed into a shared message queue, which acts as a buffer between the producer and the consumer.

2. Parallel Processing: Multiple consumer instances run concurrently and process messages from the queue in parallel. This allows for efficient utilization of resources and faster message processing.

3. Load Balancing: The message queue evenly distributes messages among the available consumers, ensuring that the workload is balanced among them.

4. Scalability: As the number of messages increases or the system load grows, additional consumers can be added to the system to handle the increased workload.

5. Fault Tolerance: If one consumer instance fails or becomes unavailable, other consumers can continue processing messages, ensuring high availability and fault tolerance.

6. Acknowledgment and Message Removal: Once a consumer successfully processes a message, it acknowledges the processing to the message queue, and the message is then removed from the queue. If a consumer fails to process a message, the message can be re-queued for processing by another consumer.

Use cases of the Competing Consumers pattern:

1. Task Distribution: In a task processing system, tasks can be distributed among multiple worker instances using a message queue to achieve parallel processing.

2. Event Handling: Events generated by different sources can be processed concurrently by multiple consumers, ensuring that events are processed in a timely and efficient manner.

3. Data Ingestion: In data processing pipelines, multiple consumers can read from a data queue and process data records in parallel.

4. Load Leveling: The pattern helps to distribute incoming requests evenly among multiple services, preventing overload on a single service.

It’s important to design the Competing Consumer's pattern carefully to handle potential challenges such as message duplication, ordering requirements, and processing idempotency. Additionally, the number of consumers and their processing capacity should be adjusted based on the system’s workload and performance requirements to ensure optimal utilization of resources.

Pipes and Filters Pattern:

Break down a task that performs complex processing into a series of separate elements that can be reused.

Pipes and Filters are a design pattern used in software engineering and distributed systems to process data in a modular and reusable way. It involves breaking down a complex task or data processing flow into a series of smaller, independent processing steps (filters) that are connected together using pipes to form a data processing pipeline.

Each filter is responsible for a specific operation or transformation of the data, and the data flows through the pipeline from one filter to the next. This pattern promotes the separation of concerns, modularity, and code reusability, making it easier to develop, test, and maintain complex data processing tasks.

Key elements of the Pipes and Filters pattern:

1. Filters: Filters are the individual components responsible for specific data processing tasks. Each filter takes an input, performs a transformation or operation on the data, and produces an output. Filters are designed to be reusable and independent, making them easy to replace or add to the pipeline.

2. Pipes: Pipes are the channels that connect the output of one filter to the input of the next filter in the pipeline. They enable the flow of data between filters, allowing the data processing to proceed sequentially.

3. Data Flow: Data flows through the pipeline, passing through each filter in the order defined by the pipeline configuration. The output of one filter becomes the input to the next filter.

4. Sequential Execution: Filters in the pipeline are executed sequentially, and the entire data processing flow moves forward step by step.

Benefits of the Pipes and Filters pattern:

1. Reusability: Filters are designed to be self-contained and reusable, which encourages a modular design and reduces duplication of code.

2. Separation of Concerns: Each filter is responsible for a specific task, making the overall system design easier to understand and maintain.

3. Flexibility: Since filters are independent, it’s straightforward to modify or replace them without affecting the rest of the pipeline.

4. Scalability: The modular nature of this pattern allows for parallel processing and easy distribution of the workload across multiple instances of filters.

Examples of the Pipes and Filters pattern:

1. Data Processing Pipelines: Pipes and Filters can be used in data processing pipelines, such as in ETL (Extract, Transform, Load) systems, where data is extracted from a source, transformed through various filters, and loaded into a destination.

2. Image and Video Processing: In image or video processing, different filters can be applied in sequence to achieve various effects or enhancements.

3. Compiler Design: Compilers often use the Pipes and Filters pattern to process source code through multiple stages, such as lexical analysis, syntax analysis, and code generation.

4. Data Stream Processing: In in-stream processing systems, data streams can be processed through a series of filters to perform real-time analytics or event processing.

Overall, the Pipes and Filters pattern is a powerful and flexible approach to designing data processing systems, providing a clear separation of responsibilities and promoting code reusability.

Priority Queue Pattern:

Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority.

The Priority Queue pattern is a design pattern used to manage a collection of elements with different priorities. It ensures that elements are processed or retrieved in order of their priority, with higher-priority elements being processed before lower-priority ones. Priority queues are commonly used in various computer science applications, including scheduling tasks, event processing, and Dijkstra’s algorithm for finding the shortest path in a graph.

Key characteristics of the Priority Queue pattern:

1. Elements with Priorities: Each element in the priority queue is associated with a priority value. The priority can be numerical, like an integer, or defined by a custom comparison function.

2. Priority-Based Ordering: Elements in the priority queue are organized based on their priority values. Higher-priority elements are placed at the front of the queue, and lower-priority elements are placed towards the back.

3. Operations: Common operations supported by a priority queue include inserting elements with their respective priorities, extracting the element with the highest priority (peeking), and removing the element with the highest priority.

4. Data Structure: Implementations of the priority queue pattern typically use specialized data structures, such as binary heaps, Fibonacci heaps, or balanced binary search trees, to efficiently maintain the priority order.

Usage scenarios for the Priority Queue pattern:

1. Task Scheduling: In task scheduling systems, tasks with different priorities are added to a priority queue. The scheduler dequeues and executes tasks in order of their priorities, ensuring higher-priority tasks are completed first.

2. Event Processing: In event-driven systems, events with different levels of importance or urgency can be placed in a priority queue. The system processes events based on their priorities to ensure critical events are handled promptly.

3. Shortest Path Algorithms: Priority queues are commonly used in graph algorithms like Dijkstra’s algorithm to efficiently extract the vertex with the smallest distance during the graph traversal.

4. Resource Management: In resource management scenarios, priority queues can be used to allocate resources to tasks or processes based on their priorities.

Examples of Priority Queue implementations:

1. Binary Heap: A binary heap is a binary tree-based data structure that satisfies the heap property, ensuring that the parent node’s priority is greater than or equal to its children’s priorities.

2. Fibonacci Heap: A more advanced type of heap that provides better time complexity for some priority queue operations, making it suitable for certain algorithms.

3. Balanced Binary Search Tree: Data structures like AVL trees or Red-Black trees can be adapted to maintain a priority queue, ensuring efficient insertion, deletion, and extraction of the highest-priority element.

In summary, the Priority Queue pattern is a powerful tool for managing elements with different priorities, providing efficient ways to process or retrieve the highest-priority elements from the collection.

Publisher-Subscriber Pattern:

Enable an application to announce events to multiple interested consumers asynchronously without coupling the senders and receivers.

The Publisher-Subscriber pattern, also known as the Pub/Sub pattern, is a messaging pattern used in distributed systems to facilitate communication between multiple components or services. It enables loose coupling and asynchronous communication among different parts of the system. In this pattern, components can act as publishers that send messages (events) to a central message broker, and other components can act as subscribers that receive and react to these events.

Here’s how the Publisher-Subscriber pattern works:

1. Publisher: Publishers are components or services that generate events. When a publisher has new information or data to share, it sends the event to the message broker.

2. Subscriber: Subscribers are components or services that express interest in specific types of events. They register with the message broker to receive events of interest.

3. Message Broker: The message broker is a central intermediary that manages the flow of events. It receives events from publishers and forwards them to relevant subscribers based on their subscriptions.

4. Asynchronous Communication: The communication between publishers and the message broker, as well as between the message broker and subscribers, is typically asynchronous. This allows subscribers to process events independently and at their own pace.

Benefits of the Publisher-Subscriber pattern:

1. Decoupling: Publishers and subscribers are decoupled from each other. Publishers do not need to know who the subscribers are, and vice versa. This loose coupling promotes flexibility and modularity in the system.

2. Scalability: The pattern scales well as the number of publishers and subscribers increases. The message broker acts as a central point of coordination, allowing for efficient distribution of events to relevant subscribers.

3. Event-Driven Architecture: The pattern naturally lends itself to event-driven architectures, where components react to events rather than being tightly coupled to each other’s behavior.

4. Flexibility: New components can easily be added as publishers or subscribers without affecting the existing components, making it easy to extend the system’s functionality.

5. Fault Tolerance: The decoupling of publishers and subscribers makes the system more resilient to failures. If a subscriber goes down, the message broker can continue to queue events for that subscriber until it comes back online.

Examples of Publisher-Subscriber pattern implementations:

1. Message Queues: Message queue systems (e.g., RabbitMQ, Apache Kafka) can be used as the message broker to implement the Pub/Sub pattern.

2. Event Bus: An event bus or event channel is a common implementation of the Pub/Sub pattern, acting as the central communication mechanism.

3. Observer Pattern: In object-oriented programming, the Observer pattern can be seen as a simplified form of the Publisher-Subscriber pattern.

The Publisher-Subscriber pattern is widely used in various scenarios, such as real-time data processing, event notification systems, logging, and distributed systems with microservice architecture.

Queue-Based Load Leveling Pattern:

Use a queue that acts as a buffer between a task and the service that it invokes in order to smooth intermittent heavy loads.

Queue-Based Load Leveling is a design pattern used in distributed systems to manage and balance the load between multiple components or services, preventing system overload and ensuring optimal resource utilization. It involves using a message queue to smooth out workload peaks and valleys by decoupling the production of work (tasks) from their execution.

The Queue-Based Load Leveling pattern is especially useful when there is a significant difference between the rate at which tasks are produced and the rate at which they can be processed. Using a message queue as an intermediate buffer allows the system to handle bursts of tasks more efficiently without overwhelming the processing components.

Here’s how the Queue-Based Load Leveling pattern works:

1. Task Producer: The task producer is responsible for generating tasks or jobs that need to be processed by the system. These tasks are put into a message queue.

2. Message Queue: The message queue acts as an intermediary between the task producer and the task consumers (processing components). It stores the tasks until they can be picked up and processed by the consumers.

3. Task Consumers: The task consumers are responsible for retrieving tasks from the message queue and processing them. They pick up tasks from the queue in a controlled and steady manner, avoiding sudden spikes in workload.

4. Load Balancing: The message queue serves as a load balancer, distributing tasks evenly to the available consumers. This helps prevent overloading any single consumer and ensures efficient utilization of resources.

5. Asynchronous Processing: The pattern promotes asynchronous communication between the task producer and consumers, allowing them to work independently and at their own pace.

Benefits of the Queue-Based Load Leveling Pattern:

1. Load Smoothing: By using a message queue, the pattern helps smooth out the workload spikes, preventing system overload during peak times.

2. Fault Tolerance: The decoupling provided by the message queue enhances fault tolerance. If a consumer fails or becomes unavailable temporarily, the tasks remain in the queue and can be picked up by another available consumer.

3. Scalability: The pattern facilitates horizontal scalability by allowing additional consumers to be added easily as the workload increases.

4. Flexibility: Task producers and consumers can be modified or replaced independently without affecting other parts of the system.

5. Delay Handling: If necessary, the message queue can introduce delays before sending tasks to consumers, which can be useful for tasks that require specific timing or scheduling.

Examples of Queue-Based Load Leveling implementations:

1. Task Processing: In task processing systems, tasks can be queued and processed asynchronously by multiple consumers, allowing for better load distribution.

2. Message Queue Systems: Message queue systems like RabbitMQ, Apache Kafka, or AWS SQS can be used to implement the message queue and enable load leveling.

3. Batch Processing: In batch processing scenarios, tasks can be collected and processed in batches, leveraging the queue to control the batch sizes and improve processing efficiency.

Overall, the Queue-Based Load Leveling pattern is an effective way to manage workload imbalances and ensure stable and efficient operation of distributed systems, especially in scenarios with varying processing rates and bursty workloads.

SagaPattern

Manage data consistency across microservices in distributed transaction scenarios. A saga is a sequence of transactions that updates each service and publishes a message or event to trigger the next transaction step.

The Saga Pattern is a design pattern used in distributed systems to manage long-lived and complex transactions without the need for a traditional two-phase commit protocol. It helps to maintain data consistency across multiple services and ensures that if a transaction fails at any point, the system can roll back the changes made during the transaction.

In a microservices architecture, where different services handle separate parts of a business process, a single transaction can span multiple services. The Saga Pattern breaks down this distributed transaction into a series of smaller, isolated, and reversible steps or actions, called “saga steps.” Each saga step represents a local transaction within an individual service and is responsible for making a single change or update.

Key features of the Saga Pattern:

1. Local Transactions: Each service involved in the saga performs a local transaction within its own database or scope. This makes each individual transaction easier to manage and ensures better performance and scalability.

2. Compensating Actions: If any step in the saga fails or an error occurs, the pattern includes compensating actions to undo the changes made by the failed step. These compensating actions are designed to return the system to a consistent state.

3. Saga Orchestrator: The saga is coordinated by an external component called the “saga orchestrator” or “saga manager.” The orchestrator is responsible for initiating and coordinating the saga steps, ensuring they are executed in the correct order.

4. Sagas as Finite State Machines: Sagas can be viewed as finite state machines, where each state represents the progress of the saga as it moves from one step to another. The saga orchestrator manages the state transitions.

5. Eventual Consistency: The Saga Pattern ensures eventual consistency, meaning that the system might be temporarily inconsistent during the saga’s execution but will eventually reach a consistent state after all saga steps have been completed successfully or failed with compensating actions.

Benefits of the Saga Pattern:

1. Distributed Transaction Management: The Saga Pattern provides an alternative to traditional distributed transactions, which can be challenging to implement and can suffer from blocking and performance issues.

2. Decentralization: Each service’s local transaction maintains its own database, reducing contention for shared resources and promoting system scalability.

3. Fault Tolerance: The pattern allows the system to gracefully handle failures and errors, as it can roll back and compensate for any failed saga steps.

4. Modularity: Services involved in the saga are loosely coupled, promoting modularity and independent development.

5. Improved Performance: By avoiding distributed transactions, the pattern can enhance the system’s overall performance and throughput.

However, it’s essential to carefully design and handle compensating actions to ensure that the system can recover properly from errors and maintain data integrity.

Overall, the Saga Pattern is a valuable tool for managing distributed transactions in a microservices architecture, promoting scalability, fault tolerance, and flexibility.

Scheduler Agent Supervisor Pattern:

Coordinate a set of actions across a distributed set of services and other remote resources.

The Scheduler-Agent-Supervisor Pattern is a design pattern used in distributed systems to manage and coordinate the execution of tasks across multiple nodes or agents. It is especially useful in scenarios where tasks need to be scheduled and executed in a distributed and decentralized manner. The pattern involves three main components: the Scheduler, Agents, and Supervisor.

1. Scheduler: The Scheduler is responsible for managing the overall task scheduling and distribution process. It receives task requests and decides how and when to distribute them among the available Agents based on certain scheduling algorithms or criteria. The Scheduler may use load-balancing techniques to evenly distribute tasks across Agents.

2. Agents: Agents are distributed nodes or workers that execute the tasks assigned to them by the Scheduler. They continuously poll or listen for new task assignments from the Scheduler and report the task’s status or completion back to the Supervisor.

3. Supervisor: The Supervisor is responsible for overseeing the execution of tasks and handling the results reported by the Agents. It monitors the progress of tasks, takes appropriate actions in case of failures or timeouts, and may also aggregate the results for further processing.

Key features of the Scheduler-Agent-Supervisor Pattern:

1. Decentralized Task Execution: The pattern allows tasks to be executed in a distributed and decentralized manner, with each Agent responsible for executing its assigned tasks autonomously.

2. Dynamic Task Distribution: The Scheduler can dynamically distribute tasks to Agents based on their availability, load, or other criteria. This allows for efficient load balancing and resource utilization.

3. Fault Tolerance: Since tasks are distributed among multiple Agents, the pattern provides inherent fault tolerance. If one Agent fails, the Scheduler can reassign the task to another available Agent.

4. Scalability: The pattern can scale easily by adding more Agents to handle an increasing number of tasks and workloads.

5. Asynchronous Communication: The communication between the Scheduler, Agents, and Supervisor is typically asynchronous, allowing Agents to work independently and not block each other.

Example Use Case:

A common use case for the Scheduler-Agent-Supervisor Pattern is in a distributed data processing system. The Scheduler receives data processing requests, and it distributes the tasks among available Agents responsible for processing different parts of the data. The Agents process the data in parallel, and the Supervisor oversees the progress and collects the results.

It’s essential to design the pattern carefully, considering factors like task distribution policies, fault handling mechanisms, and communication protocols between the Scheduler, Agents, and Supervisor, to ensure the efficient execution of tasks and proper handling of failures.

Sequential Convoy Pattern:

Process a set of related messages in a defined order without blocking the processing of other groups of messages.

The Sequential Convoy Pattern is a design pattern used in distributed systems to handle a sequence of related tasks or events that need to be processed in a specific order. It ensures that the tasks are processed sequentially, one after another, despite the distributed and parallel nature of the system. This pattern is particularly useful when a set of tasks must follow a specific order to maintain consistency and correctness.

The name “convoy” in this pattern refers to the concept of a group of elements moving together in a sequence, much like a convoy of vehicles moving in a single file.

Key characteristics of the Sequential Convoy Pattern:

1. Task Dependency: The tasks in the sequence are dependent on each other, and their order of execution is crucial to the overall process.

2. Task Queuing: Each task is enqueued into a central queue or a shared resource that acts as a synchronization point.

3. Sequential Execution: A single “convoy leader” is responsible for executing the tasks in the queue sequentially. The convoy leader ensures that only one task is processed at a time.

4. Hand-off: After the convoy leader completes processing a task, it hands off control to the next eligible participant in the convoy, allowing them to process the next task.

5. Blocking or Locking: The convoy leader may use blocking or locking mechanisms to ensure that only one task is processed at a time and that the convoy remains synchronized.

Use cases of the Sequential Convoy Pattern:

1. Distributed Transactions: In distributed transactions, the pattern can be used to ensure that multiple sub-transactions are executed in the correct order to maintain data consistency.

2. Workflow Processing: In workflow systems, the pattern ensures that the steps in a workflow are executed sequentially, adhering to the defined process flow.

3. Order Processing: In systems dealing with order processing, the pattern can be applied to ensure that order fulfillment and related tasks are executed in the right sequence.

Example Scenario:

Consider an order processing system where each order needs to go through a sequence of steps before it is fulfilled:

1. Order Validation: Validate the order details and check for correctness and availability of items.

2. Payment Processing: Process the payment for the order.

3. Inventory Update: Update the inventory to reflect the items’ sale.

4. Shipping: Initiate the shipping process.

In the Sequential Convoy Pattern, each order is enqueued into a shared queue. A single convoy leader takes an order from the queue, processes it through the sequential steps, and then passes control to the next convoy leader for the next order.

The pattern ensures that orders are processed in the correct order and that no two orders go through the steps simultaneously, preventing race conditions and ensuring consistency.

It’s important to manage the convoy leader hand-off and use proper synchronization mechanisms to prevent bottlenecks and efficiently process the tasks in the correct sequence.