Part 4: CI/CD Simplified & The Power of Atomic Deployments

One of the biggest drivers pushing teams toward Microservices is the promise of “Independent Deployment.” In theory, team A can deploy service A without caring about team B. But reality is often much crueler: The existence of “Dependency Hell.”

If Service A changes its API payload, Service B is forced to update accordingly. The organization must design complex pipelines, use API contracts (Contract Testing with tools like Pact), and coordinate release schedules (Release coordination) to avoid bringing down the system. Actual velocity doesn’t increase; it is bottlenecked by synchronization costs.

Conversely, the Modular Monolith uses the Atomic Deployments model, providing a much safer, cheaper, and more reliable Release management approach.

1. What Are Atomic Deployments?

Atomic Deployment means the application is released as a single block, at a single point in time. In a Modular Monolith, the application logic code and the database structure definitions (Database Schema/Migrations) always travel together in a single Commit Hash. When you deploy a new version, all modules are updated simultaneously.

  • You never encounter the error: “Service A’s API version does not match Service B’s.”
  • You don’t have to rack your brain managing scenarios like: What happens if Service A deploys successfully but Service B fails and has to Rollback? In a Monolith, either everyone moves forward together, or everyone rolls back together (Rollback as a whole). The system state is always consistent.

2. The Challenge of Monolith CI/CD: The Test Time Nightmare

Although Atomic Deployments eliminate the complexity of the release process, they create a different challenge in the Continuous Integration (CI) phase: If the company’s entire code resides in one repository (Monorepo/Monolith codebase), does the system have to re-run hundreds of thousands of Unit Tests every time a Pull Request is created? If so, the Feedback loop could take hours.

The solution to keeping a Modular Monolith agile is to apply Pipeline Optimization and Smart Testing.

3. CI Optimization Lessons from Shopify (Buildkite)

Shopify owns one of the largest Ruby on Rails Modular Monoliths in the world, maintained by thousands of developers. To ensure Developer Velocity, they restructured their CI/CD process brilliantly:

  1. Static Analysis & Selective Testing: By using static analysis tools like Sorbet and Packwerk (as mentioned in Part 3), Shopify’s CI system can automatically calculate exactly which Modules (Packs) are affected by a Pull Request. As a result: The pipeline only runs Unit Tests belonging to the changed modules or modules that directly depend on them, skipping 90% of irrelevant tests.

  2. Parallel Execution with Buildkite: Shopify uses Buildkite combined with massive cloud computing infrastructure to parallelize test tasks (Test parallelization). Hundreds of thousands of tests can be distributed across hundreds of worker nodes to execute simultaneously.

  3. Merge Queue: Instead of everyone merging code into the main branch and causing breakages, Shopify uses Shipit (an internal deployment tool) integrated with a Merge Queue. The system automatically groups Pull Requests, runs consolidated tests, and deploys in batches.

[!TIP] The Result: Through a combination of Static Analysis and Parallelization, Shopify maintains a P95 response time (95% of runs) of under 10 minutes. An astonishing speed for a Monolith system containing over 3 million lines of code!

4. The Shift in Focus: From DevOps Engineer to Pipeline Engineer

When operating Microservices, an organization needs a large DevOps/SRE team just to manage Helm charts, Kubernetes YAML, Ingress controllers, and Service Mesh configurations for dozens of separate projects.

With a Modular Monolith, the Platform Engineering team’s efforts are restructured:

  • There is only one deployment process to maintain (maintain 1 excellent CI/CD script instead of 100 terrible ones).
  • The Kubernetes/DevOps infrastructure budget is reallocated toward renting extremely powerful servers to parallelize CI tests, delivering direct value to developer Velocity.

Simplifying CI/CD alone can save an organization countless work hours. However, when the system goes into Production, how do we track errors? In a distributed architecture, we need highly expensive Distributed Tracing. In a Monolith, this problem is much simpler and more effective. Discover how in Part 5: Observability in Memory.