DEV Community: Ahmed Omeiza

Cache Avalanche: When Your Cache Fails and Your Database Pays the Price

Ahmed Omeiza — Fri, 04 Sep 2026 07:37:44 +0000

Your cache is supposed to protect your database.

But what happens when thousands of cached items expire at almost the exact same time?

Your database suddenly receives a flood of requests it was never meant to handle.

That is a Cache Avalanche.

What Is a Cache Avalanche?

A Cache Avalanche happens when a large number of cache entries become unavailable simultaneously.

This can happen because:

Many cache keys have the same expiration time.
The cache server goes down.
A cache cluster becomes unavailable.

When the cache stops serving those requests, they all fall back to the database.

Example

Imagine your application has 100,000 product records cached.

You set them all to expire after exactly 1 hour:

product:1   → expires in 1 hour
product:2   → expires in 1 hour
product:3   → expires in 1 hour
...
product:100000 → expires in 1 hour

After one hour, a large number of these keys expire together.

Now users request those products.

The cache misses.

The application queries the database.

Thousands of requests suddenly hit the database at once.

The database slows down, requests start timing out, and your application can experience a cascading failure.

The cache problem has now become a system problem.

How to Prevent It

1. Add Random TTL Values

Instead of giving every cache entry the same expiration time, add some randomness.

Instead of this:

TTL = 60 minutes

Use something like:

TTL = 60 minutes + random(0–10 minutes)

Now your cache entries expire gradually instead of all at once.

This is often called TTL jitter.

2. Use a Highly Available Cache

If your entire application depends on one cache server, that server becomes a single point of failure.

Use replication, clustering, or a managed cache solution where appropriate.

The goal is simple: one cache failure should not take down your application.

3. Protect the Database

Your database should not blindly accept an unlimited flood of requests when the cache fails.

Consider using:

Rate limiting
Request throttling
Circuit breakers
Queues
Connection limits

These mechanisms help prevent a cache outage from overwhelming your database.

Cache Avalanche vs Cache Stampede

These two are related but different.

A Cache Avalanche happens when many cache keys become unavailable at the same time.

A Cache Stampede happens when many requests try to rebuild the same missing cache entry simultaneously.

For example:

1000 requests → cache miss for product:123

Without protection:

1000 requests → 1000 database queries

With a locking mechanism, one request rebuilds the cache while the others wait or receive a fallback response.

The Key Takeaway

A cache is not just about making your application faster.

You also need to think about what happens when the cache is unavailable.

If thousands of cache entries can expire at once, or your cache can fail without a fallback strategy, your database may become the next victim.

Spread out cache expiration, design for cache failures, and always protect your database from sudden traffic spikes.

REST vs SOAP: Two Ways APIs Talk, but Not the Same Way

Ahmed Omeiza — Thu, 03 Sep 2026 13:43:26 +0000

When building an API, you've probably heard of REST and SOAP.

Both allow applications to communicate with each other, but they take very different approaches.

The simplest way to think about it is this:

REST is flexible and lightweight. SOAP is strict and structured.

What is REST?

REST is an architectural style commonly used for web APIs.

It usually works with standard HTTP methods:

GET — retrieve data
POST — create data
PUT — update data
DELETE — remove data

For example:

GET /api/users/47

A typical response might look like:

{
  "id": 47,
  "name": "John",
  "email": "john@example.com"
}

REST commonly uses JSON because it is lightweight and easy for both humans and applications to work with.

What is SOAP?

SOAP is a protocol with strict rules for how messages are structured and exchanged.

SOAP messages are typically written in XML.

A request might look more like this:

<soap:Envelope>
  <soap:Body>
    <GetUser>
      <UserId>47</UserId>
    </GetUser>
  </soap:Body>
</soap:Envelope>

Compared to REST, SOAP can feel more verbose.

But that strict structure is intentional.

SOAP supports standardized features around security, reliability, and transactions, which is one reason it is still used in some enterprise and financial systems.

REST vs SOAP at a Glance

REST	SOAP
Architectural style	Protocol
Commonly uses JSON	Uses XML
Lightweight	More structured and verbose
Flexible	Strict standards
Usually uses HTTP	Can work with different transport protocols
Popular for modern web APIs	Common in enterprise and legacy integrations

So, Which One Should You Use?

For most modern web applications, REST is usually the simpler choice.

Building a frontend with React that communicates with a .NET API?

REST will likely feel natural:

GET /api/products
POST /api/products
DELETE /api/products/10

But if you're integrating with an enterprise system that requires strict contracts, advanced messaging standards, or SOAP-based security, then SOAP may be the requirement.

The important thing is that this is not really about choosing the “new” technology over the “old” one.

It's about choosing the communication style that fits the system.

Key Takeaway

REST focuses on simplicity, flexibility, and resource-based APIs.

SOAP focuses on strict contracts and standardized messaging.

Use REST when you want a lightweight and flexible API. Use SOAP when your system requires the strict standards and enterprise-level features SOAP is designed for.

Latency vs Throughput: They Are Not the Same Thing

Ahmed Omeiza — Wed, 02 Sep 2026 11:52:18 +0000

When developers talk about performance, latency and throughput are often mentioned together. But they measure two completely different things.

Latency is how long one operation takes.

If you click a button and the API responds in 100ms, that is latency.

Throughput is how much work a system can handle over time.

If your API processes 10,000 requests per second, that is throughput.

The easiest way to remember it is:

Latency = How fast?
Throughput = How much?

Here is where things get interesting: a system can have high throughput and still feel slow.

Imagine your server processes thousands of requests every second, but each request takes 5 seconds to complete. Your system handles a lot of work, but users are still waiting.

On the other hand, an API might respond in 50ms but only handle 100 requests per second. It feels fast with a few users but may struggle when traffic increases.

So:

Low latency means individual requests are fast.
High throughput means the system handles more work.
Good performance often requires both.

When optimizing latency, focus on things like faster database queries, caching, and reducing unnecessary network calls.

When optimizing throughput, focus on removing bottlenecks, scaling workers, and processing work efficiently.

The key takeaway?

A fast response does not mean your system can handle heavy traffic.

Handling heavy traffic does not mean your system gives users a fast experience.

Latency is about speed. Throughput is about capacity.

A good engineer knows which one the system needs most—and when it needs both.

WebSocket vs Webhook: Stop Confusing Them

Ahmed Omeiza — Mon, 31 Aug 2026 12:31:35 +0000

At first glance, WebSockets and Webhooks seem similar.

Both help systems communicate when something happens.

But the way they work is completely different.

If you understand one simple idea, the confusion disappears:

WebSocket keeps the conversation open. Webhook sends a message when an event happens.

Let's break it down.

What is a WebSocket?

A WebSocket creates a persistent, two-way connection between a client and a server.

Once the connection is established, both sides can send messages at any time.

Think of it like a phone call.

You call someone, they answer, and the line stays open.

You can speak.

They can speak.

You don't need to call again every time you want to say something.

Example

Imagine a chat application.

You send:

"Hello"

The server receives it immediately.

The other user receives it immediately.

Then they reply.

This is exactly the type of problem WebSockets solve well.

Common WebSocket use cases:

Chat applications
Live notifications
Multiplayer games
Real-time dashboards
Live stock or crypto prices
Collaborative applications

WebSocket is about continuous real-time communication.

What is a Webhook?

A Webhook is an HTTP request sent automatically when a specific event happens.

There is no permanent connection.

Instead, one system says:

"When this event happens, send a request to this URL."

Think of it like a doorbell.

You don't stay connected to the person at the door all day.

When they arrive, they press the bell.

You get notified.

That's it.

Example

Imagine you integrate your application with a payment provider.

A customer completes a payment.

The payment provider sends an HTTP POST request to your webhook endpoint:

POST /api/webhooks/payment

Your application receives the notification and updates the payment status.

The connection ends.

Common Webhook use cases:

Payment completed
User registered
Order shipped
Subscription renewed
Git push events
Third-party integrations

Webhook is about event-driven notifications between systems.

The Biggest Difference

The simplest way to understand them is this:

WebSocket

"Let's stay connected so we can talk anytime."

Webhook

"Don't contact me until something important happens."

That is the core difference.

Feature	WebSocket	Webhook
Connection	Persistent	Temporary
Communication	Two-way	Usually one-way
Protocol	WebSocket protocol	HTTP
Best for	Real-time interaction	Event notifications
Connection starts	Client connects	Event triggers request
Example	Chat app	Payment notification

A Real-World Example

Imagine you are building an e-commerce application.

A customer opens their order tracking page.

You want the page to update instantly when the delivery status changes.

WebSocket makes sense.

Your server can push this message directly to the customer:

"Your order is now out for delivery."

Now imagine your payment provider confirms a successful payment.

Your application needs to know about it.

Webhook makes sense.

The payment provider sends your backend an HTTP request:

"Payment successful. Here are the transaction details."

Different problems.

Different tools.

Can You Use Both?

Absolutely.

In fact, many real applications do.

For example:

A payment provider sends a Webhook to your backend when payment succeeds.
Your backend processes the payment.
Your backend sends a WebSocket message to the user's app.
The user instantly sees:

"Payment successful!"

The webhook handles system-to-system communication.

The WebSocket handles real-time communication with the connected user.

Which One Should You Use?

Use WebSocket when you need:

Real-time, continuous, two-way communication.

Use Webhook when you need:

A system to notify another system when an event occurs.

Don't choose WebSocket simply because your application needs an update.

And don't choose a Webhook because you hear the word "real-time."

Ask one question instead:

Do I need an open connection for continuous communication, or do I only need a notification when an event happens?

Your answer will usually tell you exactly what to use.

Final Thought

WebSockets and Webhooks are not competitors.

They solve different problems.

A WebSocket is like keeping a phone call open.

A Webhook is like receiving a call only when something important happens.

Once you understand that, choosing between them becomes much easier.

Top-Down vs Bottom-Up Design: Where Should You Start?

Ahmed Omeiza — Thu, 27 Aug 2026 15:54:48 +0000

When designing software, one question can shape everything that follows:

Do you start with the big picture or the small details?

That is the difference between Top-Down and Bottom-Up design.

Top-Down Design: Start With the Why

Top-down design begins with the big picture.

You start by asking:

What problem are we solving?

Then you break the system into smaller parts until every component has a clear responsibility.

For example, imagine you're building an e-commerce application:

E-Commerce System
→ User Management
→ Product Management
→ Order Management
→ Payment System

Then each of those areas is broken down further.

The idea is simple:

Big problem → Smaller problems → Smaller components

Why it works

Top-down design gives you direction. Before writing code, you understand how the major parts of the system should fit together.

It is especially useful when:

Building a new system from scratch
Designing system architecture
Working with complex business requirements
You need a clear structure before implementation

The biggest advantage? You don't get lost in the details too early.

But there is a downside.

You can spend too much time designing the perfect architecture before proving that the smaller pieces will actually work.

Bottom-Up Design: Start With What You Can Build

Bottom-up design takes the opposite approach.

Instead of starting with the entire system, you start by building small, reusable components.

For the same e-commerce application, you might first build:

Authentication service
Product repository
Payment integration
Notification service

Then you connect those pieces together to form larger features.

The idea becomes:

Small components → Larger modules → Complete system

Why it works

Bottom-up design is practical.

You focus on building and validating real components early. This is useful when you already have reusable modules, existing libraries, or well-understood technical building blocks.

It works especially well when:

Reusing existing components
Building on an existing system
Developing libraries or frameworks
The low-level requirements are already clear

The downside?

You might build excellent components that don't fit together well because you didn't define the bigger picture early enough.

So, Which One Is Better?

Neither.

The best software engineers usually use both.

Start top-down to understand the problem and define the architecture.

Then work bottom-up to build, test, and improve the individual components.

Think of it like building a house.

You wouldn't start laying bricks without knowing what kind of house you're building.

But you also can't build the entire house from an architectural drawing alone.

You need the vision first and the building blocks second.

A practical approach

Top-Down:

Understand the business problem
Define the major features
Identify the main components
Design how they interact

Bottom-Up:

Build the individual components
Test them independently
Combine them into larger modules
Integrate everything into the complete system

The Real Lesson

Top-down thinking prevents you from building the wrong system.

Bottom-up thinking helps you build the system correctly.

One gives you direction.

The other gives you execution.

Great software design is knowing when to zoom out and see the entire system and when to zoom in and focus on one component at a time.

Don't just ask, "How do I build this?"

Sometimes the better question is:

"What exactly am I building, and how do all the pieces fit together?"

SoftwareEngineering #SystemDesign #SoftwareArchitecture #Programming #DotNet #CleanArchitecture

5 Basic Security Principles Every Developer Should Know

Ahmed Omeiza — Tue, 25 Aug 2026 12:55:17 +0000

Security can feel complicated.

There are firewalls, authentication systems, encryption algorithms, vulnerability scanners, penetration tests, and countless security tools.

But before you worry about advanced security techniques, you need to get the basics right.

Here are 5 fundamental security principles that should influence how you design and build software.

1. Minimize Attack Surface Area

Every feature, endpoint, dependency, port, and service you expose is another potential entry point for an attacker.

The more you expose, the more you have to protect.

For example, if your application only needs to expose:

GET /api/products
POST /api/orders

don't expose administrative or internal endpoints publicly just because they're available.

The same applies to infrastructure.

Ask yourself:

"Does this need to be exposed?"

If the answer is no, remove it, disable it, or restrict access to it.

Less exposure = fewer opportunities to attack.

2. Principle of Least Privilege

Give users, services, and applications only the permissions they actually need.

Nothing more.

A reporting service that only needs to read data shouldn't have permission to delete records.

A frontend application shouldn't have direct access to your database credentials.

An employee who only manages customer support shouldn't have administrator privileges.

This principle limits the damage when something goes wrong.

If an account gets compromised, the attacker inherits that account's permissions.

So instead of asking:

"What permissions can I give this user?"

Ask:

"What is the minimum permission required to do this job?"

That's least privilege.

3. Secure Defaults

Your application should be secure before the user changes anything.

Don't make security something users have to manually enable.

For example:

New accounts should not automatically become administrators.
APIs should require authentication by default.
Debug mode shouldn't be enabled in production.
Sensitive endpoints shouldn't be publicly accessible.
Passwords shouldn't be stored in plaintext.
CORS shouldn't blindly allow every origin.

A good rule is:

If the user does nothing, the system should still be reasonably secure.

Security shouldn't depend on someone remembering to turn it on.

4. Encrypt Sensitive Data

Some data should never be readable if someone gains unauthorized access to your storage or network traffic.

Think about:

Passwords
API keys
Authentication tokens
Financial information
Personal information
Confidential business data

Use encryption where appropriate.

For passwords, however, don't encrypt them for storage—hash them using a strong password-hashing algorithm such as Argon2, bcrypt, or PBKDF2.

For data transmitted over a network, use HTTPS/TLS.

For sensitive data stored in databases or files, consider encryption at rest and proper key management.

And remember:

Encryption is only as strong as how you manage the keys.

Putting your encryption key directly inside your source code isn't security.

5. Maintain Security Updates

One of the easiest ways to improve security is also one of the easiest to ignore:

Keep your software updated.

Your application depends on frameworks, libraries, operating systems, databases, containers, and other infrastructure.

Vulnerabilities are discovered constantly.

That means yesterday's secure dependency might become tomorrow's security problem.

Don't just update because you want the latest features.

Update because security vulnerabilities get fixed.

A practical approach is to:

Monitor dependencies for known vulnerabilities.
Remove unused dependencies.
Apply critical security patches quickly.
Keep your runtime and operating system updated.
Test updates before deploying them to production.

An outdated dependency can become an open door into an otherwise well-designed system.

The Bigger Picture

These principles aren't complicated.

The difficult part is applying them consistently.

When building your next application, ask:

1. What can I remove?
→ Minimize the attack surface.

2. What permissions does this actually need?
→ Least privilege.

3. What happens if nobody changes the configuration?
→ Secure defaults.

4. What happens if someone gets access to this data?
→ Encrypt sensitive data.

5. Am I running software with known vulnerabilities?
→ Maintain security updates.

Security isn't one feature you add at the end of development.

It's a mindset that should influence every decision you make while building software.

Build secure by default. Reduce what can go wrong. And assume that eventually, something will.

JIT vs AOT Compilation: What’s the Difference?

Ahmed Omeiza — Fri, 14 Aug 2026 19:28:06 +0000

When a program is written in a high-level language, the code usually needs to be translated into machine code before the processor can execute it. Two common approaches for doing this are Just-In-Time (JIT) compilation and Ahead-of-Time (AOT) compilation.

The main difference is simple:

JIT compiles code at runtime, while AOT compiles code before the program runs.

But the difference becomes more interesting when we look at how each approach handles performance and optimization.

What is JIT Compilation?

Just-In-Time (JIT) compilation translates code into machine code while the program is running.

Instead of compiling everything before execution, a JIT compiler observes the application as it runs. It identifies frequently executed sections of code, often called hot spots, and compiles those sections into optimized machine code.

This allows the compiler to make optimization decisions based on what is actually happening during execution.

For example, a JIT compiler might notice that a particular function is being called thousands of times with similar types of data. It can then optimize that function specifically for that situation.

This can lead to significant performance improvements.

JIT in JavaScript

Modern JavaScript engines use JIT compilation extensively.

A browser may initially execute JavaScript using a relatively fast compiler that prioritizes quick startup. While the program runs, the engine collects information about how the code behaves.

If certain parts become "hot," they can be passed to a more advanced compiler that performs stronger optimizations. The optimized version can then replace the earlier version.

So, a simplified JIT process looks like this:

Source Code → Initial Compilation → Execute → Profile → Detect Hot Code → Optimize → Execute Optimized Code

The major advantage is that optimization can be based on real runtime behavior.

What is AOT Compilation?

Ahead-of-Time (AOT) compilation compiles code before the application runs, usually during the build process.

Instead of waiting until runtime to generate machine code, the compiler produces a native executable or other lower-level output in advance.

Languages such as C and C++ traditionally follow this model. AOT compilation can also compile intermediate representations, such as Java bytecode or .NET's Common Intermediate Language (CIL), into native machine code before execution.

The process looks more like:

Source Code → Compilation → Optimization → Machine Code → Execute

Because most of the compilation work happens before the application starts, the runtime has less compilation work to perform.

JIT vs AOT: Where Does the Optimization Happen?

This is where the difference gets more interesting.

AOT compilers can perform aggressive optimizations before the program starts. They have more time during the build process to analyze the application and generate optimized code.

However, they usually don't know exactly how the application will behave in production.

A compiler might know what the program could do, but not necessarily what it will do most frequently.

JIT has a different advantage.

Because the program is already running, a JIT compiler can collect profiling information from actual execution. It can identify which functions are frequently called, what types of values are being passed around, and which paths through the program are most common.

This gives JIT compilers access to information that traditional AOT compilation doesn't have at compile time.

The Trade-Off

Neither approach is simply "better." They make different trade-offs, and it is easier to understand them when we separate them into categories.

*- Performance Timing
*
JIT may introduce delays during execution because compilation happens at runtime, which can impact performance during program startup or when hot code is first encountered. In contrast, AOT moves all compilation work to build time, resulting in faster startup and more predictable runtime performance since the code is already fully compiled before execution begins.

- Optimization Quality
JIT can optimize based on real runtime behavior, making it highly adaptive and often more precise in long-running applications. AOT relies on static analysis and compile-time assumptions, but it can still apply powerful optimizations when given sufficient information during the build process.

- Runtime Overhead
JIT requires additional CPU and memory resources during execution for profiling and dynamic compilation. AOT, on the other hand, has minimal runtime overhead because the code is already compiled into machine instructions before execution begins.

- Flexibility
JIT can adapt dynamically to changing workloads and usage patterns, continuously refining performance as the program runs. AOT produces a fixed binary that behaves consistently once deployed, offering stability but less adaptability.

- Startup Behavior
JIT often has slower startup performance because execution begins before full optimization is complete and compilation happens progressively. AOT typically starts faster since all compilation and optimization are completed ahead of time.

- Long-Term Performance
JIT can improve performance over time as it learns from actual execution patterns and optimizes hot paths. AOT performance remains stable and predictable, but it does not evolve or adapt after deployment.

JIT essentially says:

"Let's observe the application and optimize based on what actually happens."

AOT says:

"Let's do as much optimization as possible before the application starts."

Can AOT Get the Best of Both Worlds?

The limitations of AOT aren't absolute.

AOT compilers can also use profiling data to make better optimization decisions. A program can be run in a controlled environment, its behavior can be profiled, and the collected data can then be used during compilation.

This is often referred to as Profile-Guided Optimization (PGO).

The challenge is that the profiling environment may not perfectly represent real production workloads.

For example, an application might behave differently depending on its users, traffic patterns, hardware, or data.

JIT compilation has an advantage here because it can profile the application in the actual environment where it is running.

The Interesting Possibility

This raises an interesting question:

What if we could use AOT compilation while still taking advantage of runtime profiling?

One possible approach is to:

Compile the application ahead of time.
Run it and collect profiling information.
Use that data to produce a more optimized version.
Switch to the optimized version at runtime.

This could provide some of the benefits of JIT optimization while keeping the advantages of AOT compilation.

The idea isn't necessarily new, and similar techniques have existed in different forms. However, it isn't as common as the traditional JIT or AOT approaches.

Final Thoughts

JIT and AOT are two different strategies for turning code into something a processor can execute efficiently.

JIT compiles and optimizes during execution, giving it access to real runtime behavior.

AOT compiles before execution, allowing more work to be done ahead of time and reducing runtime compilation overhead.

The choice ultimately depends on what matters most for the application: startup time, runtime adaptability, compilation overhead, deployment environment, or the ability to optimize based on real-world behavior.

And the boundary between JIT and AOT isn't as strict as it might seem. With techniques such as profiling-guided optimization and runtime code replacement, it's possible to combine ideas from both approaches.