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I am a Journalist-turned-Software Engineer. I love coding and the associated grind of learning every day. A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from. This is my contribution back to the community.
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📖 Introduction In modern AI applications, communication between clients and tools isn’t always as simple as calling an API. The Model Context Protocol (MCP) provides a standardized way for clients to exchange information, invoke tools, and maintain ...
If you’re curious about how to build an intelligent agent using Model Context Protocol (MCP), you’re in the right place. In this post, I’ll walk you through how to: Create an MCP Server using FastMCP Expose a tool that calculates BMI Build a Clien...
In our previous tutorial, we built a restaurant customer support chatbot using OpenAI's Agents SDK. In this follow-up, we’ll explore guardrails—a critical feature that enhances AI chatbot safety and reliability. What Are Guardrails in AI Agents? Guar...
Video https://www.youtube.com/watch?v=UPBMkFSJdBI The Problem at Hand Uber's data platform processes approximately 1.2 million interactive queries monthly, with 36% of these coming from the operations organization. This group—comprising engineers...
Video https://www.youtube.com/watch?v=FgRGwnpd2HY Google's recent white paper on "Agents" has created quite a buzz. The paper explores the concept of AI agents and delves into their architecture and potential. Let's break down what this white paper...
I am Zahiruddin Tavargere (Zahere). A social-learner, here to learn, share and grow with the tech community.
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I am Zahiruddin Tavargere (Zahere). A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from - this is my contribution back to the community.
In this edition of the newsletter, we will look at different architectural patterns that are practical and have several use cases.
The Pipe and Filters architecture pattern is a design pattern that provides a structured approach to processing and transforming data.
It breaks down complex tasks into a series of smaller, independent processing steps, or filters, which are connected together through pipes.
Each filter performs a specific operation on the data, and the output is passed along to the next filter in the pipeline.
One way to think about the Pipe and Filters architecture pattern is to imagine a production line in a manufacturing plant.
On a production line, raw materials go through a series of machines or filters, each performing a specialized operation.
The materials pass through the machines in a sequential manner, with each machine adding value and refining the product.
Similarly, in the Pipe and Filters pattern, data flows through a sequence of filters, with each filter performing a specific processing task, ultimately producing the desired output.
High-level Pros and Cons
✅ Pros
Modularity: Filters can be developed and maintained independently, which allows for better code organization and reusability.
Scalability: New filters can be added or existing ones modified easily, making it simpler to accommodate changing requirements and scale the system.
Flexibility: Filters can be combined and rearranged to support different data processing flows, offering adaptability to various scenarios.
Testability: Individual filters can be tested in isolation, facilitating unit testing and ensuring the correctness of each processing step.
🚫 Cons
Overhead: The pattern introduces additional communication overhead between filters, which may impact performance in highly latency-sensitive systems.
Ordering Dependencies: The order of filters may be critical in some cases, requiring careful consideration during the design phase.
Data Format Consistency: Filters need to agree on a standardized data format, which may pose challenges when dealing with multiple data sources or formats.
🎯 Use Cases:
Data processing pipelines: ETL (Extract, Transform, Load) processes, data validation, and data enrichment pipelines.
Image and video processing: Applying filters, transformations, or effects to media files.
Message processing systems: Filtering, transforming, or routing messages based on specific criteria.
Event-driven systems: Handling events through a sequence of filters for processing and analysis.
Space-based Architecture draws inspiration from the vastness and resilience of outer space, this architectural pattern enables applications to handle large volumes of data and efficiently process complex tasks across a network of interconnected nodes.
Space-Based Architecture leverages a network of interconnected nodes, each processing and storing data independently.
Just as celestial bodies collaborate harmoniously to shape the cosmos, these nodes collaborate to perform tasks and share information in a decentralized manner, creating a scalable and fault-tolerant system.
High-level Pros and Cons
✅ Pros
🌟 Scalability: By distributing data and processing across multiple nodes, this pattern allows for horizontal scaling, accommodating growing workloads seamlessly.
🌟 Fault-Tolerance: With its decentralized nature, Space-Based Architecture enhances system resilience. If a node fails, the workload and data can be dynamically redistributed to other nodes, ensuring continuity of operations and reducing the impact of failures.
🌟 Elasticity: The architecture pattern supports dynamic resource allocation, enabling the system to scale up or down based on demand.
🚫 Cons
⚠️ Complexity: Implementing Space-Based Architecture requires careful planning and design. Coordinating data distribution, ensuring consistency, and managing node failures may introduce additional complexity to the system.
⚠️ Network Overhead: The communication overhead between nodes can impact performance. Efficient network design and optimization are crucial to minimize latency and maximize throughput.
Are you looking to build highly extensible and adaptable software systems? If so, the microkernel architecture pattern may be the solution you need.
Microkernel architecture, also known as plug-in architecture, is a software design pattern that separates a system's core functionalities from non-core functionalities.
An oversimplified example of microkernel architecture is an IDE.
Though not a microkernel architecture in the purest form, an IDE, like VS code, can be a simple example to help understand the microkernel architecture pattern.
The core system of VS Code provides basic functionality such as text editing, syntax highlighting, and debugging, while additional functionality is provided by a wide range of plugins and extensions.
High-level Pros and Cons
✅ Pros
Plug-in modules can be added or removed without making significant changes to the core system, allowing it to react to changes in the modules while minimizing modifications to the core system.
Compared to a layered architecture, using plug-in modules makes deployment easier and faster, which reduces downtime.
Testing is simplified since each module can be tested individually and in isolation from the others.
Although not typically recommended for high-performance applications, the plug-in architecture can perform well because it allows the application to be customized by including only the necessary features.
🚫 Cons
The plug-in architecture is typically suited for smaller applications that are not designed to be highly scalable.
Before implementing a plug-in architecture, thorough design analysis is necessary, including considerations such as contract versioning, internal plug-in registries, plug-in granularity, and the available options for plug-in connectivity.
What if your application architecture has evolved to a point where the communication between several services has become tangled?
Event-bus architecture might just be the solution you've been searching for. 🌟
Also known as publish/subscribe architecture, is a design pattern that enables decoupled and asynchronous communication between various components of a system.
Instead of components directly communicating with each other, they exchange messages through a central "event bus."
🚌 This bus acts as a mediator, ensuring seamless information flow without components needing to have explicit knowledge of one another.
✨ Highlevel Pros and Cons:
✅ Pros
🌟 Loose Coupling: Components are decoupled, allowing them to evolve independently without impacting other parts of the system.
🌟 Scalability: New components can be easily added to the system, and existing components can subscribe to relevant events, enabling flexibility and scalability.
🌟 Asynchronous Communication: Components can operate independently and asynchronously, enhancing performance and responsiveness.
🌟 Extensibility: By using event-driven communication, it becomes easier to introduce new features and functionalities without disrupting the existing system.
🚫 Cons
❗️ Complexity: Implementing and managing an event-bus infrastructure requires careful design and maintenance, which can introduce additional complexity to the system.
❗️ Event Handling: Developers need to design robust event handling mechanisms to ensure reliable delivery and prevent issues like event loss or delayed processing.
❗️ Debugging and Testing: With distributed communication, debugging and testing can become more challenging, as the flow of events may not be linear and predictable.
Are you looking for a powerful design pattern that improves communication and collaboration between components in your software architecture?
The Broker Pattern is a widely used architectural pattern that facilitates communication between components by introducing a central coordinator known as the broker.
Instead of direct communication between components, the broker acts as an intermediary, enabling decoupling and flexibility in your software design.
By centralizing communication, the pattern reduces dependencies and enhances the scalability, maintainability, and extensibility of your system.
👍 Highlevel Pros and Cons:
✅ Pros
🌟 Decoupling: Components communicate through the broker, reducing direct dependencies and enabling individual components to work independently.
🌟Flexibility: The broker can dynamically adapt to changes in the system, allowing for easy addition or removal of components.
🌟Scalability: With the broker acting as a centralized point for communication, scaling your system becomes more manageable and efficient.
🌟 Maintainability: By abstracting the communication logic to the broker, it becomes easier to modify or replace components without affecting the overall system.
🚫 Cons
⚠️Increased complexity: Introducing a central broker adds an additional layer of complexity to the system.
⚠️ Single point of failure: If the broker fails, it can disrupt the communication between components, potentially impacting the entire system.
⚠️ Performance overhead: The use of a broker can introduce some performance overhead due to the additional processing required for message passing.
