Part 8: Case Study Matrix – The Monuments of the Modular Monolith

Numerous debates about architectural design often lead to dead ends due to a lack of quantitative, real-world numbers. There is a common misconception that: “Only Microservices can withstand web-scale loads.”

To conclude this Playbook series, we will look at the Case Study Matrix – a compilation of the greatest Modular Monolith systems, ranging from massive e-commerce platforms to billion-user chat applications.

1. Shopify: 284 Million Requests/Minute with Ruby On Rails

When discussing the Monolith, one cannot ignore Shopify. This e-commerce system faces brutally massive traffic spikes during every Black Friday.

  • The Numbers: Handled over 173 billion requests during Black Friday/Cyber Monday, peaking at 284 million requests/minute.
  • Architecture: The entire core of Shopify remains a massive Ruby on Rails Modular Monolith application (over 3 million lines of code, contributed to by thousands of developers).
  • How they Scale:
    • They did not fracture the application. Instead, they protected code boundaries using the Packwerk library.
    • To solve the performance equation, Shopify invested heavily in YJIT (a Just-In-Time compiler for Ruby) helping the application run 15% faster.
    • The database is heavily sharded to distribute write loads.

2. Notion: Sharding Postgres (200 Billion Blocks) on a Node.js Monolith

Notion is clear proof that “The bottleneck is always in the Database, not the Application Logic.”

  • The Numbers: Stores over 200 billion data blocks, equivalent to tens of Terabytes.
  • Architecture: Node.js Monolith.
  • How they Scale: When Postgres became overloaded, Notion did not shatter the Node.js system into Microservices. They focused entirely on Database Sharding. They scaled from 32 to 96 physical Postgres servers using a simple routing algorithm workspace_id % 480. The Node.js application remained intact as a Monolith handling centralized logic.

3. WhatsApp: 2 Million Concurrent Connections on ONE Server

In 2014, WhatsApp served hundreds of millions of daily active users with only about… 50 engineers.

  • The Numbers: 2 million concurrent TCP connections on a single physical server.
  • Architecture: Erlang Monolith.
  • How they Scale: The WhatsApp team pursued physical system optimization to an extreme degree (Vertical Scaling). They tuned everything from the FreeBSD kernel to Erlang’s BEAM runtime to achieve massive connection capacity on each network node. The simplicity of the Monolith design helped the system operate smoothly and with far less risk than a distributed approach.

4. 37signals (HEY/Basecamp): Saving $1.5 Million USD by Dropping the Cloud

The company belonging to the creator of the Ruby on Rails framework (DHH) holds strong views on the Majestic Monolith.

  • The Event: The “Cloud Exit” campaign (abandoning the AWS cloud) to bring their Monolith applications (HEY, Basecamp) to run on physical bare-metal servers purchased by the company.
  • How they executed: They utilized the open-source tool Kamal to simplify deploying Docker containers directly onto Bare-metal servers with Zero-downtime deployment capabilities. They didn’t need to employ the complexity of Kubernetes.
  • The Result: Saved $1.5 million USD in cloud server rental costs in just the first year.

5. Target: Consolidating Micro-APIs to Accelerate Mobile Experience

The retail giant Target once applied Microservices extensively for its mobile backend.

  • The Problem: To load a checkout screen, the Mobile App had to call back and forth across dozens of Micro-APIs. The latency generated from HTTP handshakes severely degraded the user experience.
  • How they executed: Target decided to Consolidate these Micro-APIs back into a larger Monolithic API Backend.
  • The Result: Completely eradicated internal network hops, reducing the average latency of requests by over 120ms – a critical timeframe for E-commerce conversion rates.

6. Stack Overflow: The Art of In-Memory Caching

As mentioned throughout this Series, Stack Overflow serves billions of page views every month with only 9 primary Web servers.

  • Architecture: C# .NET Monolith.
  • The Secret: They store the entire Tag Engine in the RAM of each Web Server. Instead of querying a caching Microservice or a database, retrieving a list of articles by Tag only takes a few nanoseconds reading from internal memory. This speed is unrivaled.

Conclusion of the Entire Series

The resurgence of the Modular Monolith in the years 2024-2026 is not a technological step backward. It is a “Correction” or maturation. The software industry has finally realized that: Separating systems over a TCP/IP network is the most expensive, most complex, and most failure-prone solution to solve problems related to people (Organization).

By rigorously designing hard Domain boundaries using tools (Packwerk, ArchUnit), consolidating databases to avoid fragmentation, and fully leveraging the power of modern hardware through internal Caching, the Modular Monolith helps us save FinOps costs, simplify CI/CD, and return the inherent Developer Velocity back to engineers.

“Start with a Monolith. If the boundaries are good enough, you can always split it into Microservices someday… but 90% of projects will never need that day.”

Thank you for joining the Modular Monolith Architecture Playbook. Apply this framework to your organization’s next system design to gain the maximum advantage in speed and cost!