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The Typo That Broke Production  And Accidentally Created Spring Cloud Contract  Marcin Grzejszczak & Jakub Pilimon thumbnail

The Typo That Broke Production And Accidentally Created Spring Cloud Contract Marcin Grzejszczak & Jakub Pilimon

Published 5 May 2026

Duration: 00:30:49

A deep dive into microservices challenges, server-driven contract testing via Spring Cloud Contract, AI-driven contract generation with human oversight, observability strategies, and balancing automation with foundational knowledge and community feedback to avoid tooling pitfalls.

Episode Description

This interview was recorded for GOTO Unscripted. https://gotopia.tech Marcin Grzejszczak - Software Engineer at HeroDevs & Java Champion Jakub Pilimon...

Overview

The podcast episode features an interview with Marcin Grzejszczak, a software developer with extensive experience in open-source projects, distributed systems, and microservices architecture. He discusses his work on tools like Spring Cloud Contract, initially named Accurist, which addresses challenges in consumer-driven contract testing for microservices. Grzejszczak highlights the importance of server-driven contracts to ensure consistency between client and server behaviors, automating validation of breaking changes, and leveraging foundational texts like Building Microservices by Sam Newman. He also reflects on past projects, including a notable mistake where a minor API typo caused production issues, emphasizing the need for robust testing and pipelines. The conversation delves into the evolution of Spring Cloud Contract, challenges in balancing public contributions with personal growth, and the role of community feedback in refining open-source tools.

Technical themes include the limitations of manual contract testing, the cognitive load of managing contracts with diverse tools, and proposals for AI-assisted contract generation using production traffic as a data source. Grzejszczak advocates for human oversight in AI-generated contracts and outlines a three-pillar approach to observability: logs, metrics, and traces, stressing the need to correlate them rather than analyze them in isolation. He also explores the potential of AI to reduce the burden of contract management, shifting from developers writing contracts to AI proposing them for review. The discussion underscores the importance of context in system analysis, the impact of poor design choices (e.g., a poorly structured class in Spring Cloud Contract), and the value of learning from both successes and failures in software development. Observability tools like Micrometer and the transition of Spring Cloud Sleuth to micrometer are also mentioned as key contributions.

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