Forem: Fredrick Oladipupo

I couldn’t come up with a title

Fredrick Oladipupo — Thu, 31 Jul 2025 09:59:14 +0000

PS: This would skew more towards finding peace than loading up on aspiration/goals, and that’s a reflection of where I am in life. That may/may not apply to you, depending on where you are in your own journey.

Most of what you see in life is a reflection of your own life. A simple thing like your neighbourhood can shape your whole perception of the world. But the truth is, that is not the world; that is just your world. The world is so much bigger than your little town or what the Twitter algorithm feeds you. That means 👇
You live in a bubble. It is important to know this. For everything you know, there are a million things you don’t. For every feed you see on your social media, there is a billion you don’t know about. Your music playlist is a bubble. Your YouTube feed is a bubble. Your ideology is a bubble. The number of things you don’t know is so massive that there is no reason for you not to be humble. But that's okay because 👇
Most things don’t matter; they seem like they are, but they are not. Stay grounded in things that truly matter. Unfortunately, I'm unable to disclose what those are. That's the irony of life. In the end, when the ocean rises, we have to paddle our canoes; no one is going to do that for us. That is why it is important you 👇
Don’t major in minor things. A lot of things are important, but few are important to you. Even fewer should be important to you. Stay on those. Dedicate yourself to those.
Minor time: Talking about doing it, writing about doing it, telling people about doing it, presenting how you are going to do it, researching about it, thinking about doing it.
Major time: Doing it.
Action is the bottleneck. When attempting a big goal, People often assume what they need is more information, more research, more talks, more thoughts, a new course, more bookmarked YouTube video links. Information is not the problem anymore. Almost everything you would want to know is on the internet. Most universities have open courses available on their website. Action is the new gold. People have access to more information than they know what to do with. We spent all the time collating information, while we ought to be taking action. Every dream has a cost in action, and that's why 👇
The phrase You can be anything is a scam and a corporate ideology to get you to aspire for more, work for more, spend more, and keep the cycle going. You can’t be anything you want. This shouldn’t be surprising, by birth, you are already limited either by geography, genes, or family lineage. Comparing yourself to Elon Musk, Oprah, or a random person you saw their flashy life online is the gateway to misery. Focus on the step in front of you. Take that and then worry about the next one.
Every dream/goal is a judge. Everything you want is something you lack. Logically, this would mean the higher the distance between your dreams and your current self is, the more miserable/unsatisfied you are with your current self. You have two options: be realistic with your goals or improve your current life faster.
It’s okay to be Normal. Normal is actually where most people would end up based on several factors. Sometimes the hustle of fighting this is more hurtful than being Normal itself. Every goal/aspiration that is not backed up with the appropriate resources is a wish.
Do not be afraid to try. Change of tone from the previous ones. But do not be afraid to break things. I have tried at least 4 businesses over the past 2 years. It doesn’t matter how much of it is successful; in the end, you only need one. Explore new things. Listen to music outside your playlist(I am listening to Terry Apala while writing this, I’m serious). Read outside your field. Ask uninteresting questions. Break patterns. Go outside your norms, even if just for the fun of it.
Don’t live in isolation. Find love if you can, and embrace genuine friendship if you have one. At minimum, be at peace with your neighbour. Life is so much easier with the right people beside you. Sorry to say this, but your online friends/community is not the same. I couldn't care less about your number of followers if no one is there for you in your low moments.

Domain-Driven Architecture: Blueprint for Scalable Systems Part 2

Fredrick Oladipupo — Wed, 14 May 2025 16:14:00 +0000

In Part 1, we laid the groundwork, exploring what Domain-Driven Architecture (DDA) is, why it matters, and how concepts like Bounded Contexts, Ubiquitous Language, and Aggregates shape the structure of your system.

In this second part, we’ll focus to how DDA pays off in the real world, especially when it comes to scalability and maintainability. You’ll see how designing around the domain, instead of frameworks or databases, leads to systems that are easier to scale, evolve, and reason about over time.

We will explore:

How DDA improves Scalability and Maintainability
Real-World Example of organisation using DDA from Amazon to Netflix
Best Practices when Implementing DDA
Scenarios where DDA might not be the optimal choice

How DDA Improves Scalability and Maintainability

Clear System Boundaries: DDA encourage splitting systems into bounded contexts, each with its own domain logic and can be manage by independent Teams with clear boundaries and unpolluted domains. This aligns with micro-service and modular systems architectures.
Easier Scaling: Each service in DDA can be scaled independently based on demand.
Separation of concerns to reduce complexity: Each bounded context encapsulates its own data and logic. Each service is kept simple, this makes changes easier to isolate and test.
Lower Operational Costs: Systems remain leaner, reducing infrastructure overhead.

Real-World Example of Organisations using DDA

Netflix

Netflix began as a monolithic application but, after a major outage in 2008, embarked on a journey to decompose its system into microservices. DDD was central to this process, guiding the identification of business domains and the creation of bounded contexts-each mapped to a set of micro-services responsible for a specific business capability, such as user management, content catalog, recommendations, and playback

Uber

Uber’s journey from a monolithic architecture to a highly distributed system is a textbook example of how Domain-Driven Design (DDD) principles can be applied-and adapted-at extreme scale. Uber evolved from a single, monolithic codebase to a system with over 2,200 micro-services. As system complexity grew, Uber adopted a Domain-Oriented Microservice Architecture (DOMA), drawing heavily from DDD, Clean Architecture, and other established patterns. The process was documented here

Amazon

Amazon, both as a retailer and as a cloud provider (AWS), has embraced Domain-Driven Design (DDD) principles to manage the complexity of its vast, evolving systems. DDD has played a key role in Amazon’s shift from monolithic architectures to distributed micro-services, helping align software with business domains and enabling scalable, resilient, and agile systems

Best Practices for Implementing DDA

Define Clear Boundaries

Ensure each bounded context has distinct responsibilities. Create boundaries between your domain model and external systems. Keep your domain clean and focused, prevent non-domain concepts from polluting your model.
Establish a Ubiquitous Language

Create a shared language that is used consistently by developers, domain experts, and stakeholders. Use this language in the code, documentation, and discussions to avoid misunderstandings and ensure alignment. This minimises misunderstandings and ensures software model matches the business use-cases.
Collaborate with Domain Experts

Work directly with domain experts to gain a deep understanding of the business domain. In fact, it is a futile effort to try and implement DDA without a domain expert.
Use Domain events and Event-Driven Communication: To avoid redundant and unnecessary coupling, use domain events to track and react to significant changes within the domain while Keeping services loosely coupled for better scalability.
Optimize with CQRS: Separate read and write concerns to boost performance.
Leverage Aggregates Smartly: Group entities logically to maintain data integrity.
Document Everything: A well-documented domain model ensures teams stay aligned.
Do not over-engineer - Don’t apply DDD to simple projects with little domain complexity. It can introduce unnecessary overhead.

When DDA Might Not Be the Best Choice

While Domain-Driven Design (DDA) is powerful and provides structure and scalability, it’s not a silver bullet and sometimes DDA might not be the right choice for your project and could introduce unnecessary complexity.

A non-exhaustive list of scenarios where DDA might not be necessary—or even problematic:

1. Small or Simple Applications

Example: A basic CRUD (Create, Read, Update, Delete) application with no complex business logic.

DDA introduces extra complexity (bounded contexts, aggregates, domain events, etc.), which is an overkill for a straightforward app.
A simple MVC or layered architecture is more efficient.
Use a traditional monolithic approach with clear service layers instead of a full-fledged DDA structure.

2. Early-Stage Startups or Fast Prototyping

Example: A startup building an MVP (Minimum Viable Product) that needs rapid iteration.

DDA slows down development due to the need for extensive domain modeling and bounded contexts.
The business logic might change frequently, making the initial DDA investment wasteful.
Start with a modular monolith and introduce DDA concepts gradually if complexity increases.

3. When the Business Domain Is Not Well Understood

Example: A new industry or business model where the rules, processes, and workflows are still evolving.

DDA requires deep domain knowledge upfront.
If the business keeps changing, the domain model might need constant restructuring, leading to wasted effort.
Begin with a simpler, iterative approach, and introduce domain-driven concepts once the business logic stabilises.

4. Performance-Critical, High-Throughput Systems

Example: A real-time stock trading platform or video streaming service that needs ultra-low latency.

DDA prioritises domain consistency over performance, which can introduce overhead (extra layers, event processing, etc.).
Aggregates and repositories may slow down high-volume, high-speed operations.
Use a data-driven architecture or event-sourcing to optimize for speed, and selectively apply DDA where business logic is critical.

5. Teams Without Strong DDA Experience

A team unfamiliar with aggregates, bounded contexts, and domain events trying to implement DDA for the first time.

Steep learning curve - misuse of DDA can add confusion instead of solving problems.
Over-engineering risk - developers might introduce unnecessary complexity without clear benefits.
Start with basic domain modeling and introduce DDA principles gradually rather than applying them everywhere at once.

Conclusion

Domain-Driven Design isn’t just a technical approach, it’s a mindset shift. By structuring software based on business realities, teams can build scalable, maintainable, and resilient systems.

For large, evolving applications, DDA serves as a blueprint for long-term success. But like any tool, its effectiveness depends on when and how it’s applied.

Domain-Driven Architecture: A Blueprint for Scalable Systems - PART 1

Fredrick Oladipupo — Tue, 15 Apr 2025 10:45:22 +0000

As systems grow in complexity, traditional development approaches struggle to keep up. Codebases become tangled, business logic gets scattered across layers, and scaling introduces bottlenecks. Domain-Driven Architecture (DDA) offers a structured approach to tackling this complexity, ensuring that software effectively mirrors real-world business problems.

DDA is not suitable for simple applications. However, when building medium to large scale systems or enterprise applications, adopting DDA can make your system scalable, maintainable, and aligned with business goals.

When working with Domain-driven architecture, Your goal isn’t just to build a CRUD system. You’re modeling how the physical business shop works.

Throughout this article, I will use DDA or DDD as the shorthand for Domain-driven architecture/design. To make things a bit easier, I will use Fred and son’s shoe an imaginary e-commerce company as a case study. Fred and son’s shoe is a one stop shop for shoes in Ilora, Nigeria.

This topic is broken down into two articles. In this article, we will explore the core concepts of DDA:

Domains
Sub-domains
Bounded Context
Entities
Value Objects
Aggregates and aggregates roots
Domain events
Command Query Responsibility Segregation (CQRS)

Core Concepts of Domain-Driven Architecture

1. Domains

A domain represents the core problem space a business operates in. That’s a fancy way of saying Domain is the core idea your business is built on. The problem you are solving for your customer. For Fred and sons shoe, our domain is shoe sales.

Your domain is the core business logic that rules the software — not the tech stack, not the UI, but the rules, goals, and behaviour of the organisation or problem you're addressing. The most important part of your business that gives you competitive advantage.

What to note when defining your domain

Your domain must reflects business goals and rules. When defining domain, this is not the time to think about the technical details, the focus should be on what the problems are and how to go about solving them. Technical concerns like tech stack, database choice or infra decisions can wait. The focus is here is core problem definition
Domains should be defined by business expert, People who understood what the primary focus of the business is. They can be supported by developers to ensure accurate representation and language.
Try to use common set of terms shared by devs and domain experts as much as possible (e.g., "Order", "Customer", "Customer discount Points"). This will make communication across teams and units easier and processes faster.
Domain is not just data, but behaviour — e.g., “Apply discount,” “Mark order as fulfilled,” “Calculate tax.”, “Cancel order”
Domain can evolve. You are not stuck with a definition forever - As the business grows, domain **evolves too — e.g. adding delivery support, new pricing strategies, or international shipping.

A well defined domain will guide developers in building systems that aligns with the business goals. The business should drive the technical requirements not the other way around.

2. Subdomains

A subdomain is a distinct, smaller and focused part of the main domain that manages a specific business function. DDA advocates breaking down domain into subdomains to handle complexity and better align code with business logic.

Each subdomain operates independent yet interconnected, enabling teams to work on specific areas without disrupting the broader system. Subdomain must have a clear and defined boundary - that separates it from other Subdomains.

Using our e-commerce example, here are some subdomains we could come up with:

Product Catalog Subdomain: Handles product listings, categories, attributes (e.g., size, color), and availability.
Example: Add new product, Filter by category, Show out-of-stock items
Ordering Subdomain: Manages the shopping cart, order creation, and order tracking.
Example: Add to cart, Place an order, Track order status
Payment Subdomain: Handles payment processing, refunds, and invoices.Example: Process credit card, Initiate refund, Send invoice email.
Shipping Subdomain: Deals with delivery options, tracking shipments, and managing logistics. Example: Calculate shipping fee, Update delivery status.
Customer Management Subdomain: Stores customer profiles, preferences, and support interactions.Example: Update address, View order history, Open support ticket

2. Bounded Contexts:

A bounded context defines clear (technical) boundaries within a system, ensuring that each subdomain has its own independent logic, rules, and data models. A model within a specific context behaves consistently across all use cases within the boundary.

This is probably the most task intensive part of DDA. It requires effort and time to map domain to the appropriate context and even more energy and skills to ensure teams respect the boundaries and avoid function overlap (A situation where two or more part of a domain is doing the same thing).

Example:

The Ordering service tracks items and quantities.
The Payments service only deals with transactions and invoices.
They communicate via APIs or message queue services but don’t share databases.

When bounded context is done properly it ensures

Separation of Concerns: It isolates different parts of a system to reduce complexity and improve maintainability.
Clarity: Each context uses a consistent language (Ubiquitous Language), avoiding ambiguities in terminology.
Modularity: Enables independent evolution, scaling, and deployment of components, especially in micro services architecture
It reduces code sprawl

How is bounded context different from subdomains?

As we get deeper, I would like to take a pause and clarify this before it gets confusing. Many times, bounded contexts and subdomains are misinterpreted and can lead to confusion. Below are the core differences between the two.

Problem space vs Solution Space
A Subdomain exists in the problem space. It represents specific areas of the business domain (e.g., billing, inventory) that need to be taken care of. On the other hand, Bounded Context exists in the solution space, it defines technical boundaries where a consistent domain model is applied to solve problems within a subdomain.
Purpose
Sub domain goal is to divides the core domain into smaller manageable parts. While bounded context ensures integrity of domain by defining clear boundaries, preventing ambiguity in terms.
Hierarchy:
Subdomains are conceptual and often considered “above” bounded contexts because they represent business concerns. Bounded Contexts are design to implement subdomains in code, making them more technical and solution-oriented

3. Entities and Value Objects

Entities

Entities are part of the core concept in a domain, They are identified by their unique identities and so they are distinguishable from other objects even if their attributes changed over their lifecycle, Their unique identities remain the same.

Few details that can help you understand entities

Entities are domain objects defined by unique identifier e.g ID, UUID. This identity is persisted regardless of changes to their attributes
They are persisted over time (e.g., a User, Order, or Product).
They are mutable and they change over their life cycle.
Their identities and continuity of the object are crucial, take tracking the same customer across transaction for example.
Example is “a customer” entity with a unique customer ID or a customer wallet entity with a unique wallet ID

Value Objects

On the other hand, value objects are immutable. This mean they do not change throughout their lifecycles. They do not have identities, and are defined solely by their attributes. Two value objects with the same attributes are considered equal. Example: Two addresses with the same street name, postcode, and country are considered the same.

Characteristics of value objects

Value objects are the descriptive part of the domain.
They are defined by their attributes.
Because of their lack of identity, they are interchangeable, value objects with the same attributes can be technically identify as one.
They are immutable - They cannot change once created
When thinking of value objects, think of currency, or a price of a product, or address of a customer.

How do you decide whether to use an entity or a value object in your domain model?

When you are confused on what to use, ask your self the question below

Identity:
- Entity - Has a unique identifier
- Value objects - No unique identifier, it is defined by its attributes
Mutable
- Entity - They are mutable (changed)
- Value objects - Immutable, once created they remain same through their life cycle
Equality
- Entity - Equality is based on identity
- Value objects - Equality is based on attribute values

4. Aggregates and Aggregate Roots

An aggregate is a cluster of entities and value objects that are related and are treated as a single unit. Aggregate encapsulates all the details of the unit and only expose required functionalities to the rest of the system through the aggregate root.

Aggregate root is the main entry point for interacting with data in an aggregate. Likewise, only aggregate root interacts with external systems. This ensures consistency and enforces business rules across the unit.

Think of an aggregate as a mansion with lots of goodies inside (Entities and value objects). This mansion has a single golden gate (Aggregate root) which is the only entrance. To get something out or interact with the mansion you need to pass through this gate.

Now a bit technical - Using Delivery as an example in Fred’s shoe.
Delivery Manager is an aggregate - entities are: delivery items, customer details, and address, total price, discount code as value objects. You wouldn’t let the outside world change individual Delivery item or price directly. Instead, they go through the aggregate root DeliveryManager.

DeliveryManager.addItem(product, quantity)
DeliveryManager.removeItem(productId)
DeliveryManager.applyDiscount(code)
DeliveryManager.changeDeliveryTime(newDateTime)

This is an important concepts in DDA and it helps to:

Protects your data from being changed in weird ways.
Keeps your system easy to reason about.
Makes your code more reliable in big systems.
Prevents inconsistent writes and race conditions in high-load environments.

5. Domain Events

What are domain events ?
Event are occurrence that has happened in the past. Like your birthday or in our e-commerce example, a shoe purchase. Think of domain events in the same way. Domain events are events that capture significant business occurrences in a domain that other aggregates/parts of the system might need to react to.

Domain events enable better separation of concerns among different parts of a domain. Instead of tightly coupling services, events enable loose coupling and asynchronous processing.

How are domain events different from traditional message events ?
Domain events are similar to event message style but also different in one way. With event messaging service like: message queue, message broker or even a service bus (link to the previoud article here), messages are always sent asynchronously and communication are available to different process and machine.

They are not bounded by the boundary context of the domain. On the other hand, with domain event, domain events are raise from a domain operation in progress and wants side effects/reaction to it by other process operating within the same domain.

Likewise, in a traditional messaging system, messages can be produced without a consumer or a reaction. Domain events are quite different as you most certainly only emit events because you want a reaction. In some instances, you also want to treat those reactions as a database transaction where either all the operations related to a domain event finish successfully or none of them do.

For example - Imagine a situation at Fred’s shoe store - An order was placed using a gift card and a discount code was used and it emits an event - You want reactions to the event to: Update the stock count, nullify the discount code so it is not re-used and also manage the customer gift card balance among other things.

Lastly, Domain events and their reactions (the actions triggered afterwards that are managed by event handlers) should occur almost immediately, and within the same domain. Domain event can handled synchronously if necessary.

In DDA, use domain events to explicitly implement side effects across one or multiple aggregates within a domain.

6. Command Query Responsibility Segregation (CQRS)

Command Query Responsibility Segregation (CQRS) is a design pattern that segregates write and read actions for a data store into separate data models. This means a single data store would have two model interfaces. One to manager write operations and the other for read operations. It is important to note that CQRS is not limited to DDA. Other software pattern and architecture can leverage CQRS if the project demands it.

Disclaimer: While CQRS is a powerful tool for DDA, it introduces more complexity to your system. It is only beneficial and it is advisable to only use it if your system requires it.

Only use CQRS if your your system meets one or more of the following:

Your domain is complex, requiring distinct models for handling reads and writes.
Scalability and performance are critical concerns.
Your system requires flexibility to evolve over time without disrupting existing functionality.
You are not building for a simple domain or CRUD-style applications CQRS may introduce unnecessary complexity
You have separation of development concerns within the same model. With CQRS a team can work on complex business logic in the write model, and another team develops the read model.
Divergent Data Requirements: In large applications, there are instances where read and write operations have different data requirements. To place an order at Fred’s shoe you need the customerId, productId and the quantity. On the other hand, to read an order, you need the full customerDTO and the productDTO. These two operations demand to different model from the same data store.

Example: A banking system may have **one database for handling transactions (writes). Another optimised database for quickly retrieving account balances (reads).This pattern optimises API performance and reduces database contention.

This is the first installment of the two part series on DDA
In the second part, we will talk about:

How DDA Improves Scalability and Maintainability
Examples of Companies using DDA and how they are using it
Best Practices for Implementing DDA
When DDA might not be the best choice
A personal story

Further Reading & Resources
📖 Books:

Domain-Driven Design by Eric Evans
Implementing Domain-Driven Design by Vaughn Vernon

🔗 Articles & Guides:

Domain-Driven Design in Micro-services
Jan Stenberg. Domain Events and Eventual Consistency
Event-Driven Architecture & DDA
DDA in Microservices - Martin Fowler
Greg Young on CQRS & Event Sourcing
Greg Young. What is a Domain Event?
Domain events: Design and implementation in .Net
Photo by Joakim Nådell on Unsplash

API PERFORMANCE

Fredrick Oladipupo — Fri, 11 Apr 2025 08:46:02 +0000

Fredrick Oladipupo

Mar 31 '25

Optimizing API Performance Part 3: Asynchronous Processing & Queues

#webdev #webperf #programming #backenddevelopment

Comments

5 min read

Optimizing API Performance Part 3: Asynchronous Processing & Queues

Fredrick Oladipupo — Mon, 31 Mar 2025 12:47:42 +0000

This is the 3rd instalment on the API performance and optimisation series. It is targeted at medium scale to enterprise level applications. We have explore Caching in part 1 of the series, and load balancing in part 2. In this article, we will dive into asynchronous processing and how it can help makes your API faster, scalable and more resilient

We will explore:

Why Asynchronous Processing Matters
Benefits of Asynchronous Processing and queues to your API
How Asynchronous Processing works with queues
Popular Message Queues for Asynchronous Processing
Practical Use Cases of Asynchronous Processing
Best Practices for Asynchronous Processing
Resources for further reads

1. The problem statement (Why Asynchronous Processing Matters).

Most applications start with synchronous REST APIs, where Service A makes a request and waits for Service B to respond. However, as application grows this becomes a problem.

Using e-commerce applications as a case study, a click to purchase an item can trigger calls to: process payments, send emails, update stock count and inventory, update suggested products data, update dashboard reports in real-time. Without any form of asynchronous processing, The API process everything synchronously before returning a response.

This often:

Degrades application performance.
Increase server load – More CPU and memory usage.
Results in slow API response times – Users wait longer for a response.
Lead to potential failures – A delay in one step (e.g., slow email service) can crash the entire request.
Creates bottlenecks – A delayed response from your email provider will put rest of the processes on hold.

The Solution: Offloading Heavy and non urgent Tasks to a Queue

Instead of making users wait, APIs can process tasks asynchronously:

API receives a request (e.g., "Place Order").

The API queues background tasks (e.g., email notifications, inventory update, analytics).
API immediately responds to the user.
Background tasks are processed separately, without slowing the API.
This makes APIs blazing fast, scalable, and resilient under heavy load.

2. Benefits of Asynchronous Processing and queues to your applications

Improved Performance & Responsiveness

Offloading time-consuming tasks (e.g., sending emails, processing payments) to background workers ensures your API responds instantly to users instead of making them wait.

Better Scalability

Queues help distribute workloads across multiple workers, preventing bottlenecks and allowing your system to handle more requests without overwhelming resources.

Fault Tolerance & Reliability

If a request fails due to a temporary issue (e.g., network failure), queued tasks can be retried automatically, reducing the risk of data loss or failed operations.

Efficient Resource Utilization

Instead of blocking API threads with long-running operations, background workers process tasks at a controlled pace, optimising CPU and memory usage.

Decoupled Architecture

Queues enable micro-services and different system components to communicate asynchronously, improving modularity and reducing dependencies between services.

Improved User Experience

Users don’t have to wait for slow operations (like file processing or third-party API calls) to complete before getting a response, making applications feel faster and smoother.

3. How Message Queues Work

We have highlighted that asynchronous processing allows applications become more resilient by handling important/urgent tasks immediately and queue the rest for a later time. They do this by storing tasks and relevant data in a message queue system like RabbitMQ or Apache Kafka to be processed or read at a later time.

Message queue systems are robust and serve different use cases. Apache Kafka for example ensure reliability and persistence of data even across reads. However, Kafka doesn’t care whether there is a consumer for a message or if consumer reads a message. On other hand, RabbitMQ is more involved in the life span of the message, ensuring it gets to the right consumer in the right order.

Irrespective of the type of message queues you are working with, there are three common themes:

A producer - application that sends messages.
Queue (RabbitMQ, AWS SQS) - a buffer that stores messages.
A consumer - application that receives/read messages.

Using our order process example - The order service produce the message to send email, update inventory, and so on. The respective services receive the message or read the message from the broker then perform actions as required of them.

Note: Message Queue systems are capable of handling variety of process complexity. From simple process like a pub/sub messaging system to a complex streaming and Remote procedure call (RPC). However, your use case would determine how and which to use.

4. Popular Message Queues:

Redis (for lightweight, fast task queues).
RabbitMQ (for enterprise-grade messaging)
Apache Kafka (for large-scale event streaming) - think something big like a sport-book service.
AWS SQS (fully managed message queue)
Others - Apache ActiveMQ, KubeMQ, Celery, IBM MQ etc.

5. Practical Use Cases of Asynchronous Processing

1. Sending Emails Without Blocking the API

Instead of waiting for an email service to respond, queue the task and return a response immediately.

Example (Using Node.js & Redis Queue – BullMQ)

const Queue = require("bullmq").Queue;
const emailQueue = new Queue("emailQueue");

app.post("/send-email", async (req, res) => {
  await emailQueue.add("sendEmail", { userEmail: req.body.email });
  res.json({ message: "Email request received!" });
});

// Worker: Processes emails in the background
emailQueue.process(async (job) => {
  try {
    const { userEmail } = job.data;
    await sendEmail(userEmail);
  } catch (error) {
    console.error(`Failed to send email: ${error.message}`);
  }
});

2. Processing Payments Without Slowing Checkout

E-commerce platforms queue payment tasks so API responses are instant.

Steps:

API places order and queues payment processing.
The response is immediate (user doesn’t wait).
A background worker handles the payment asynchronously.

Example: Shopify processes millions of orders daily, especially on Black Friday, where traffic surges 10x. By queuing payments, they reduced checkout times by 40%, preventing crashes and improving conversions.

3. Generating Reports in the Background

Instead of making users wait, APIs can:

Accept the request & enqueue report generation.
Send a notification/email when the report is ready.

Example: Google Analytics queues data processing so reports are generated without blocking real-time insights.

4. Updating stock Inventory

Instead of making users wait, APIs can:

Accept the request & enqueue report generation.
Send a notification/email when the report is ready.

6. Best Practices for Asynchronous Processing

Choose the right queue – Redis for speed, RabbitMQ for reliability, Kafka for streaming.
Set task priorities – If possible, Process urgent tasks first.
Monitor failed jobs – Implement retries and logging.
Auto-scale workers – Handle increased load dynamically.

Conclusion: Make Your APIs Lightning Fast

Asynchronous processing and queues remove bottlenecks, improve scalability, and enhance user experience. Whether you're handling emails, payments, or heavy computations, queues make APIs smoother and more resilient.

How do you handle background tasks in your APIs? Drop a comment below!

Optimizing API Performance – Part 2: Load Balancing

Fredrick Oladipupo — Tue, 25 Mar 2025 11:04:53 +0000

APIs are the backbone of modern applications, handling thousands (or even millions) of requests daily. But as traffic grows, a single server can quickly become overwhelmed, leading to slow responses, timeouts, crashes and ultimately loss in business revenue.

This is where load balancing comes in. Load balancers are capable of distributing traffic across multiple servers, ensuring your API remains fast, scalable, and highly available. This tutorial is part of a series on Optimising api performance. You can read part 1 of this series on Caching here

In this guide, we’ll explore:

When you should consider load balancing in your application
How load balancing works
Load balancing strategies
Real-World Case Study: PayU
Common load balancing issues and fixes
Best practices
Conclusion & Resources

1. When Should I consider Load Balancing?

Not every API needs load balancing from day one. But you should strongly consider it if:

Your API receives 1,000+ requests per second.
Your server CPU usage consistently exceeds 70%.
You need high availability (e.g., financial transactions, streaming)
You’ve experienced downtime due to traffic spikes - Those graphs are telling you something
Reliability - Robust Load balancer like Elastic load balancer are capable of performing health checks on upstreams servers before distributing requests - helping maximise performance and resiliency.
You need an added layer of Security - Load balancers are capable of playing crucial role in security. They are capable of acting as a buffer against DDoS attacks, and enabling features like SSL encryption and WAF integration.
Likewise, Load balancers can be instructed to drop requests from suspicious source. Ensuring these malicious requests do not reach your core application resources.

If any of these apply to your system, it’s time to implement a load balancer.

2. How Load Balancing Works

A load balancer acts as a middle layer between clients and backend servers. It distributes incoming networks and or application requests across multiple servers or "targets" based on predefined rules.

Load balancer prevents overload and improve performance, availability, and scalability of an application. Load balancer acts as a single point of contact for clients, routing requests to healthy servers and monitoring their health.

Example:

Without load balancing: Requests go to one server, which can get overloaded.
With load balancing: Requests are spread across multiple servers, improving speed and reliability and make it easy to scale.

Here’s a basic visualisation of how it works:

Client Requests  --->  Load Balancer  --->  Server 1
                                      --->  Server 2
                                      --->  Server 3

Nginx as a Load Balancer *

http {
    upstream backend_servers {
        server api-server-1.local;
        server api-server-2.local;
    }

    server {
        listen 80;

        location / {
            proxy_pass http://backend_servers;
        }
    }
}

This setup distributes requests between api-server-1.local and api-server-2.local.

3. Load Balancing Strategies

Load balancing strategies are methods or techniques load balancer uses in distributing network or application traffic across multiple targets. This strategy defines the rules to optimize resource utilisation, improve performance, and enhance reliability of the system as a whole. We will explore few of the common strategies below.

1. Round Robin (Default & Simple)

Distributes requests evenly across all servers.
Best for: APIs with similar server capacities.
Nginx illustration:

upstream backend {
    server api1.example.com;
    server api2.example.com;
}

2. Least Connections (Great for Heavy Requests)

Sends traffic to the server with the fewest active connections.
Best for: APIs with long-running or high-latency requests.
Nginx illustration:

upstream backend {
    least_conn;
    server api1.example.com;
    server api2.example.com;
}

3. IP Hash (Good for Session Persistence)

Assigns each client’s request to the same server based on their IP.
Best for: APIs that need session consistency (e.g. authentication, sticky sessions).
Nginx illustration:

upstream backend {
    ip_hash;
    server api1.example.com;
    server api2.example.com;
}

4. Weighted Load Balancing (Fine-Tuned Traffic Control)

Some servers are more powerful than others—this method sends more traffic to stronger servers.
Best for: APIs running on mixed-capacity servers.
Example:

upstream backend {
    server api1.example.com weight=3;
    server api2.example.com weight=1;
}

Here:

api1.example.com gets 3x more traffic than api2.example.com.

4. Real-World Case Study: PayU

PayU one of India's leading payments and fintech companies *serves over 450,000 merchants using over 100+ payment methods * and cannot afford API slowdowns. PayU uses application load balancers to scale their service 10x to meet demands during peak periods.

5. Common Load Balancing Issues & Fixes

Issue	Fix
Some servers get more traffic than others	Use Least Connections instead of Round-Robin.
High latency even with load balancing	Enable Geo-DNS to route users to nearby servers.
Users randomly get logged out	Use session persistence (sticky sessions).
Unhealthy target servers	Implement health checks and use auto scaling groups to ensure new servers are spin up when one or more servers becomes unhealthy.

6. Best Practices for Load Balancing APIs

Choose the right strategy – Round Robin is simple, but Least Connections or Weighted Load Balancing may be better.
Enable health checks – Automatically remove failing servers from the pool.
Use cache alongside load balancing – Combining both boosts performance further.
Monitor and tweak – Use tools like Prometheus & Grafana to track load balancer health.

7. Conclusion & Resources

Load balancing is essential for scaling APIs and ensuring high availability. Whether you’re handling a small SaaS app or a global platform like PayU, the right load balancing strategy prevents slowdowns, reduces costs, and improves user experience.

Continue with the 3rd part of this series Asynchronous Processing & Queues here

Resources

Optimizing API Performance - Part 1: Caching

Fredrick Oladipupo — Tue, 18 Mar 2025 10:42:02 +0000

Why Caching Matters for API Performance

Ever waited too long for an app to load? Slow APIs frustrate users and cost businesses millions in lost revenue. The good news? Caching can cut response times from seconds to milliseconds.

APIs handle thousands (or even millions) of requests daily, often fetching the same data repeatedly. Speed is a value on its own and without caching, APIs can suffer from:

High database load – Unnecessary queries for frequently accessed data.
Slow API response times – Every request has to process everything from scratch, boot your whole application and framework service providers.
Increased infrastructure costs – More server power needed to handle the same traffic.

By implementing caching, APIs can return precomputed responses instantly, reducing workload and boosting speed.

Types of Caching in APIs

1. Client-Side Caching (Browser & Frontend Cache)

APIs can instruct clients (browsers, mobile apps) to temporarily store data instead of refetching it repeatedly. This is done via HTTP headers like:

Cache-Control – Defines how long the client should keep the response.
ETag (Entity Tag) – Helps determine if data has changed before refetching.

Minimal Example: Client-Side Caching with Local Storage

const cacheKey = "popular_articles";
let cachedData = localStorage.getItem(cacheKey);

if (!cachedData) {
  fetch("/api/popular-articles")
    .then(response => response.json())
    .then(data => {
      localStorage.setItem(cacheKey, JSON.stringify(data)); // Cache response
    });
} else {
  console.log(JSON.parse(cachedData)); // Use cached data
}

Use Case: Reducing unnecessary API calls for static content like product details or user profiles.

2. Server-Side Caching (Memory-Based Cache)

Instead of querying the database every time, APIs can store frequently requested data in-memory using Redis or Memcached or Memory based cache of your choice.

Minimal Example: Server-Side Caching with Redis (Node.js)

const redis = require("redis");
const client = redis.createClient();

app.get("/api/popular-articles", async (req, res) => {
  client.get("popular_articles", async (err, data) => {
    if (data) {
      return res.json(JSON.parse(data)); // Return cached response
    } else {
      const articles = await fetchPopularArticlesFromDB();
      client.setex("popular_articles", 3600, JSON.stringify(articles)); // Cache for 1 hour
      return res.json(articles);
    }
  });
});

Use Cases:

Caching user authentication sessions to reduce database lookups.
Storing popular API responses (e.g., trending articles, weather forecasts).
Reducing third-party API calls by storing results instead of re-fetching.

💡 Real-World Example: Twitter caches user timelines in Redis to reduce database queries by 95% while keeping user feeds responsive.

3. Database Query Caching

Instead of executing expensive SQL queries every time, APIs can cache query results in memory.

Minimal Example: MySQL Query Caching with Redis (PHP)

$cacheKey = "exchange_rates";
$cachedRates = $redis->get($cacheKey);

if (!$cachedRates) {
    $result = $db->query("SELECT * FROM exchange_rates");
    $rates = $result->fetchAll();
    $redis->setex($cacheKey, 3600, json_encode($rates)); // Cache for 1 hour
} else {
    $rates = json_decode($cachedRates, true);
}

Best practices:

Cache frequently accessed but rarely changing data (e.g., country lists, exchange rates).
Set expiration times (e.g., 1 hour) to prevent serving stale data.
Use cache invalidation to refresh data when updates occur.

Use Case: An e-commerce platform caching product categories to avoid database overload.

4. Full-Page Caching (Reverse Proxy Caching)

For read-heavy traffic, solutions like Varnish, Nginx, or Cloudflare can cache entire API responses and serve them instantly.

Minimal Example: Reverse Proxy Caching with Nginx

location /api/ {
    proxy_pass http://backend_server;
    proxy_cache my_cache;
    proxy_cache_valid 200 1h;
}

💡 Real-World Example: A news API used Cloudflare's cache to serve headlines, cutting response times from 300ms to under 50ms globally.

When Not to Use Caching

While caching is powerful, it’s not a silver bullet. Avoid caching:

Highly dynamic data (e.g., real-time stock prices, chat messages, frequently changing data, financial data - wallet balance, transaction history).
User-specific content unless using per-user cache keys.
Data that must always be fresh (e.g., real-time analytics, transaction records).

Best Practices for Implementing API Caching

Cache strategically – Not everything should be cached.
Set expiration policies – Define how long data stays cached before refreshing.
Handle cache invalidation – Clear outdated data when updates occur.
Monitor cache hit rates – Track performance to ensure caching is effective.

- Use multiple cache layers – Combine client-side, memory-based, and database caching for maximum speed.

Conclusion

Caching isn’t just an optimization—it’s a necessity for scaling APIs. Whether you’re building a small app or a global platform, smart caching can make the difference between an API that struggles and one that scales.

Resources

🚀 Next up in the API Performance Series: Load Balancing read here!

Have you used caching in your projects? What worked (or failed)? Let’s discuss in the comments! 🚀

Amazon S3 Security in Enterprise Applications: A Comprehensive Guide with Case Studies

Fredrick Oladipupo — Sat, 15 Mar 2025 16:15:20 +0000

Introduction: Why S3 Security Matters for Enterprises

Amazon S3 is one of the most widely used cloud storage solutions, handling data for businesses across industries like finance, healthcare, and e-commerce. However, misconfigured S3 buckets have been responsible for some of the biggest data breaches in history. In the last 10 years there has been at least 20 data leaks from S3 due to bucket misconfigurations.

For enterprises, data security is not optional—it is essential for:

Regulatory Compliance (GDPR, HIPAA, PCI-DSS, CCPA)
Preventing Data Leaks that damage trust and cause financial losses
Protecting Against Ransomware & Insider Threats
Ensuring Business Continuity during regional outages or cyberattacks

This guide walks through essential Amazon S3 security best practices, backed by real-world case studies to illustrate what can go wrong when these measures aren’t followed.

1. S3 Block Public Access: Preventing Unauthorized Exposure

Why It’s Important

One of the biggest mistakes companies make is leaving S3 buckets publicly accessible. This can lead to data leaks, compliance violations, and security breaches.

AWS provides a Block Public Access feature that allows organizations to centrally prevent public access at both the account and bucket level.

Best Practices

Enable S3 Block Public Access at the account level to prevent any bucket from being made public, even by mistake.
Use IAM policies and bucket policies to enforce strict access controls.
Monitor with AWS Config Rules to flag publicly accessible buckets.

bash
CopyEdit
aws s3api put-public-access-block \
    --bucket my-secure-bucket \
    --public-access-block-configuration BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true

Case Study: The Public S3 Bucket Disaster

On October 3rd, 2017, UpGuard Director of Cyber Risk Research Chris Vickery discovered an Amazon Web Services S3 cloud storage bucket configured for public access, allowing any web user entering the repository’s URL to access and download the bucket’s contents. The bucket’s subdomain, “crm-mvp,” likely refers to “customer record management” or “customer relationship management,” theories seemingly corroborated by the repository’s contents: forty-seven thousand files, most of them PDF and text documents, containing the sensitive information of National Credit Federation customers. Read full security report here

📌 Lesson Learned: Always block public access by default and enforce strict IAM policies to avoid accidental exposure.

2. S3 Object Lock: Preventing Accidental and Malicious Deletion

Why It’s Important

Without proper safeguards, accidental deletions or ransomware attacks can wipe out critical data. S3 Object Lockhelps by preventing deletion or modification of objects for a specified retention period.

Best Practices

Enable S3 Object Lock for critical compliance data and audit logs.
Use Governance Mode (admins can override) or Compliance Mode (data is fully locked).
Combine Object Lock with Versioning for stronger data protection.

bash
CopyEdit
aws s3api put-object-retention \
    --bucket my-secure-bucket \
    --key audit-logs.json \
    --retention-mode COMPLIANCE \
    --retain-until-date 2030-01-01T00:00:00Z

Case Study: The Ransomware Attack on Cloud Backups

A financial services company stored audit logs in S3 but failed to enable Object Lock. Hackers gained access and deleted all logs, erasing valuable compliance records.

Lesson Learned: Use Object Lock with Compliance Mode to prevent data loss from cyberattacks or human errors.

3. S3 Versioning: Protecting Against Data Loss

Why It’s Important

Even with the best security policies, human errors happen. S3 Versioning ensures you can recover previous versions of an object in case of:

Accidental deletions or overwrites
Application bugs corrupting stored data
Security incidents like insider threats

Best Practices

Enable Versioning on all critical S3 buckets.
Use Lifecycle Policies to delete older versions automatically and save storage costs.
Combine with MFA Delete to prevent unauthorized deletions.

bash
CopyEdit
aws s3api put-bucket-versioning \
    --bucket my-secure-bucket \
    --versioning-configuration Status=Enabled

Case Study: The Insider Threat – Accidental Data Deletion

A healthcare organization accidentally deleted a large dataset during maintenance. Because Versioning was disabled, the data was lost permanently, causing compliance violations.

Lesson Learned: Always enable S3 Versioning to recover from accidental deletions or malicious activities.

4. Encryption: Ensuring Data Security at Rest and in Transit

Why It’s Important

Regulatory bodies like GDPR, HIPAA, and PCI-DSS require data encryption to protect sensitive information.

Best Practices

Use Default S3 Encryption (SSE-S3) to ensure all stored data is encrypted.
Leverage SSE-KMS for tighter control using AWS Key Management Service (KMS).
Enforce encryption policies with AWS Service Control Policies (SCPs).

bash
CopyEdit
aws s3api put-bucket-encryption \
    --bucket my-secure-bucket \
    --server-side-encryption-configuration \
    '{"Rules": [{"ApplyServerSideEncryptionByDefault": {"SSEAlgorithm": "AES256"}}]}'

Case Study: The Compliance Violation – Unencrypted Data Breach

A company storing customer PII in S3 failed a GDPR audit because their data wasn’t encrypted. This led to a hefty fine and loss of customer trust.

Lesson Learned: Always encrypt data at rest using SSE-S3 or KMS to meet compliance and security standards.

5. S3 Cross-Region Replication: Disaster Recovery & High Availability

Why It’s Important

Relying on a single AWS region creates a single point of failure. If a region goes down, your entire system could become unavailable.

Best Practices

Enable Cross-Region Replication (CRR) to store redundant copies of data.
Use Lifecycle Policies to automatically archive older data.
Replicate to a different AWS account for additional security.

bash
CopyEdit
aws s3api put-replication-configuration \
    --bucket my-secure-bucket \
    --replication-configuration file://replication.json

Case Study: The Regional Outage – Data Loss Without Redundancy

A retail company stored all its product catalog data in a single AWS region. When that region suffered an extended outage, they lost access to critical data, affecting sales and operations.

Lesson Learned: Always replicate critical data across multiple AWS regions for disaster recovery.

Conclusion: Building a Secure S3 Strategy

Securing Amazon S3 requires a multi-layered approach:

✔ Block Public Access – Prevents unauthorized exposure.

✔ Object Lock & Versioning – Protects against deletions.

✔ Encryption – Ensures compliance and secures data.

✔ Replication – Improves resilience against failures.

✔ IAM Policies & Access Controls – Restricts data access.

By learning from real-world security failures, enterprises can fortify their cloud storage against breaches, compliance risks, and disasters.

Have you experienced S3 data leaks before, How did you handle it?
What other security practices do you use in securing your data in S3 buckets? Let’s talk in the comments.