Cloud Chaos and Microservices Mayhem

Are your microservices secretly a distributed monolith? Learn how contract testing lets you refactor and release services safely and independently.

#1about 4 minutes

The evolving role of developers in the cloud era

Modern developers must expand beyond just writing apps to include operations, security, and financial management (FinOps) to succeed in the cloud.

#2about 6 minutes

Adapting infrastructure practices for the cloud environment

Cloud adoption requires new approaches to instance management to avoid zombie servers, externalizing logs, and rethinking application packaging with containers.

#3about 3 minutes

Optimizing Java performance for cloud-native applications

Cloud environments shift the focus from long-term throughput to rapid startup and low memory usage, making ahead-of-time (AOT) compilation with frameworks like Quarkus highly effective.

#4about 4 minutes

Aligning release schedules with microservices architecture

Adopting microservices without changing organizational processes like infrequent release schedules negates the benefits and leads to cargo culting without achieving agility.

#5about 6 minutes

The dangers of the distributed monolith anti-pattern

Microservices can easily become a distributed monolith with hidden coupling and increased complexity, losing the benefits of compile-time safety without gaining true independence.

#6about 3 minutes

Using the test pyramid for microservices

The test pyramid provides a strategy for microservices testing, with contract tests offering a balance between the low cost of unit tests and the high confidence of integration tests.

#7about 6 minutes

Demonstrating hidden coupling with a simple refactoring

A live coding demo shows how a seemingly safe backend refactoring can break the frontend, revealing hidden coupling between services that is not caught by the compiler.

#8about 8 minutes

Implementing consumer-driven contract tests with Pact

Using the Pact framework, a consumer-driven contract test is created to define expectations, which then automatically verifies the provider and catches breaking API changes.