Architecture Backwards: Engineering a Self-Defending System Before the UI Arrives

#rails #architecture #observability #postgres

Happy New Year!

As we kick off 2026, many of us are starting new projects or refining old ones. This year, I’ve decided to challenge the "industry standard" of MVP development. Instead of racing to build a login page, I’m spending my time in the trenches of the core engine.

Introduction

In the rush to launch a Minimum Viable Product (MVP), the standard advice is almost always: Start with the User. Build the login page, the profile, and the "Join Now" button. We often prioritise Authentication and Authorization, then move to the core logic, the actual reason the product exists, nearly as an afterthought.

This approach works, and it’s the industry standard. But what if I tell you there’s a more resilient way to architect a system? One that isn't as glamorous or visually aesthetic, but is infinitely more stable?

Lately, I’ve been building a new system where I’ve intentionally flipped the script. I am working backwards, starting with the "brain" of the engine rather than the face of the app. I am two weeks into development, and I haven't even touched a User module. Instead, I’ve spent my time building Self-Defence Mechanisms and Telemetry.

Hear me out, while authentication and authorisation in your system is vital, we have battle-tested gems like Devise and CanCanCan that can be dropped in later and will solve this for you in minutes. The core health of your system, however, cannot be "dropped in" as an afterthought and therefore needs a complete system architecture shift. Here is why the most scalable systems are built from the inside out using what I call the Sensor & Suitcase pattern, which I will introduce in the second section below.

1. The "Horizontal" Nature of Users

When working with a modern Ruby on Rails application, the User resource can be treated as a horizontal layer. I want you to think of the User layer as the front door to your house. It’s how people get in, and it’s what they see first. But before I invite guests over, I want to make sure the foundation is solid and the electricity is wired correctly. Because Rails emphasises Convention over Configuration architecture, adding a user_id to a record later is often just a simple migration away. I have the expensive luxury of treating "Identity" as a pluggable module because the framework and the core language, Ruby, are designed for developer happiness. I am actually happy working with this stack for the project.

However, when we talk about the core engine, the part that actually does the work, this should be seen as vertical. If that engine is fragile, it won't matter how many users you have or how beautiful your login page is.

If your core system cannot handle the "real world" problems that come with scale, such as race conditions, API rate limits, or cache stampedes, the system will collapse under the weight of its own success.

1.1 The Tortoise and the API

This might feel like a re-invention of the story of the Hare and the Tortoise. The "Hare" approach is to race toward a UI, a "Join Now" button, and a beautifully designed user dashboard. It looks fast, but it’s tripping over every unhandled exception along the way.

By building "Backwards," I am the Tortoise. I am building a system that knows how to protect itself first. I am ensuring that when the "Hare" (the User) finally arrives, the ground beneath them is solid, monitored, and reinforced.

2. Building the "Defensive By Design Perimeter."

Before a single user is able trigger any request through my new system, I needed to know that the system will protect its most expensive resource: External API Credits!

I am making use of what is called the Sensor & Suitcase pattern. By implementing the Sensor & Suitcase pattern, we shift the architecture from being reactive (fixing things when users complain) to proactive (handling failures before the user even notices).

Here is why this "Backwards" approach is a game-changer for your system:

1. Total Visibility (The Sensors): Most developers fly blind until an error log hits their inbox. By using ActiveSupport::Notifications to broadcast every “heartbeat”, you create a "Black Box" recorder for your app. Remember that a heartbeat is any user-defined activity and can be something like a user requesting a resource from my system. This way, you aren't just guessing how the system performs; the system is telling you exactly where it feels strain in real-time.

# The following snippets of code and all that follow are  
# intentionally simplified to communicate architectural  
# intent rather than production-ready implementations.
# Implementation of the 'Sensor' within the service layer
def fetch_resource(query, location)
  # Wrapping the core logic in a 'Sensor' block
  ActiveSupport::Notifications.instrument('<Module name>::external_call', {
    query: query,
    location: location
  }) do |payload|
    result = <Module name>::Client.execute(query, location)

    # Enrich the sensor data with metadata for real-time telemetry
    payload[:status] = result.status
    payload[:duration_ms] = result.duration

    result
  end
end

2. De-risking Through Isolation (The Suitcase): External APIs are often the "chaos variables" of web development. By packing those risks into SolidQueue background jobs, you decouple your app’s success from an outsider's failure. If a third-party service hangs for 10 seconds, your user doesn't see a spinning loading icon; they see a finished task because your "Suitcase" is handling the heavy lifting in the background.

# The 'Suitcase' - Isolating external volatility from the main request thread
class <Module name>::IngestJob < ApplicationJob
  queue_as :external_api

  # Defensive retries with exponential backoff to handle provider downtime
  retry_on <Module name>::ExternalError, wait: :exponentially_longer, attempts: 3

  def perform(signal_id)
    signal = <Module name>::Signal.find(signal_id)

    # Perform the 'heavy lifting' away from the user's eyes
    <Module name>::Ingestor.new(signal).call
  rescue StandardError => e
    # Log the failure for the 'Black Box' recorder
    <Module name>::Telemetry.record_failure(e)
    raise e
  end
end

3. The "Snappiness" Factor: In a Rails 8 monolith, speed is often about what you don't do during the request-response cycle. This pattern strips the main thread down to its bare essentials, ensuring your UI stays lightning-fast while the complex logic executes safely behind the scenes.

The Core Benefit here is that you aren't just building a feature; you are building a self-healing perimeter. Your system defends its own performance metrics, regardless of how the outside world (or the user) behaves.

3. Solving the "Thundering Herd" Problem

One of the most satisfying parts of this "Backwards" approach was preparing for a "Thundering Herd" before it ever happened. This refers to the moment when multiple requests hit your system at once for the same resource.

Usually, this is where most systems start to show off their glamorously covered-up cracks. In a situation where 10 users request the same data at the exact same millisecond, a standard app might fire off 10 expensive, redundant calls to an external API.

However, by focusing on the "Self-Defence" layer first, I solved this before a single user ever signed up on my system. I used atomic increments in Redis and PostgreSQL row locking to make the system smarter. Now, when those 10 requests hit at the same time, the system identifies the "herd" and performs one expensive API call, while the other nine requests simply wait for a heartbeat to share that single result.

Instead of 10 separate stresses on the engine, it’s just one. This is the difference between a panicked crowd and an organised queue.

# Utilizing PostgreSQL Advisory Locks to organize the 'Herd'
def find_or_create_resource(query, location)
  # Create a unique lock key based on the specific search parameters
  lock_key = Digest::SHA256.hexdigest("#{query}:#{location}")

  # Ensure only ONE process handles the expensive external call
  <Module name>::Database.with_advisory_lock(lock_key) do
    # Re-check cache inside the lock; the first worker filled it already
    return cached_result if cached_result.present?

    # This is the single 'Tortoise' worker performing the expensive API trip
    execute_expensive_external_call(query, location)
  end
end

4. The Admin-First Dashboard

Because I haven't spent a single minute working on a "User Profile" page, I had the time to build something much more valuable: a Telemetry Dashboard.

Right now, this dashboard isn't for a customer; it’s for the system (and, okay, it's for me to geek out on until I go live! xD). Instead of looking at user avatars or bios, I am looking at the "System’s Efficiency." I’ve built a metric I call the Vault Ratio. This metric tracks how many requests are served from the "Vault" (my local, optimized data) versus how many require a slow, expensive trip to an external API.

By seeing these numbers move in real-time, I can tune the engine while it’s still on the workbench. I’m not guessing if my caching strategy works; I’m watching the "Vault" grow stronger every day. By the time the first user signs up, they aren't entering a construction site; they are stepping into a fully furnished house.

# The core logic behind the Telemetry Dashboard
def today_snapshot
  date = Date.current
  {
    discovery_requests:  <Module name>::Metrics.read('discovery.requests', date),
    cache_hits:          <Module name>::Metrics.read('discovery.cache_hits', date), # Local Rails cache
    vault_hits:          <Module name>::Metrics.read('discovery.vault_hits', date), # Persistent optimized data
    external_calls:      <Module name>::Metrics.read('discovery.external_calls', date), # Expensive credit usage
    signals_created:     <Module name>::Metrics.read('signals.created', date)
  }
end

The Lesson I have learned so far: Embrace the trenches

In the past, I was always afraid of staying in the backend trenches for too long. There is a constant pressure to "show progress" through UI and buttons. But if there is one thing I’ve learned from systems that I didn’t architect well in the past, it’s this: Don’t be afraid to stay in the back-end longer than it feels comfortable.

Remember the story of the tortoise and the hare. The slow, intentional pace you take now is what prevents the dreaded 3:00 AM headaches down the line.

When you build an engine that is self-defending and observable from day one, adding the "User" front-facing layer isn't going to be a stressful launch; it will be just a normal Tuesday. This approach gives you the confidence to sleep soundly, whether your system is handling 10 users or 10,000 users concurrently.

Conclusion

As a parting shot and a last word of advice from an engineer implementing best software practices that help global businesses thrive, to build software that helps businesses truly thrive, we have to prioritize the "brain" over the "face." Focus on your system’s core logic and bulletproof it first. Make it exist, make it resilient, and then make it pretty.

The users might not see the Redis locks or the background workers, but they will certainly feel the stability.

Is the "Sensor & Suitcase" pattern something you or your team would consider for your next project? I’d love to hear how you handle the "Defensive" side of your builds in the comments!