Overview
This article discusses how LinkedIn scales its compatibility testing process to accommodate its rapidly growing ecosystem of 12,000 Multirepo codebases. It highlights the challenges faced by developers and the strategies implemented to enhance developer productivity through improved debugging, stability, and performance in compatibility testing.
What You'll Learn
1
How to implement compatibility testing in a CI pipeline
2
Why semantic versioning is crucial for library producers and consumers
3
How to enhance debugging processes for compatibility testing failures
4
When to apply failure thresholds in compatibility testing
Prerequisites & Requirements
- Understanding of Continuous Integration (CI) and Continuous Delivery (CD) practices
- Familiarity with semantic versioning concepts
Key Questions Answered
What is compatibility testing and how does it work at LinkedIn?
Compatibility testing at LinkedIn is a required check in the CI pipeline that ensures library changes do not break existing consumers. It runs post-merge validation jobs that test the library against its direct consumers, using build artifacts uploaded to a temporary remote storage.
What challenges does LinkedIn face with compatibility testing at scale?
LinkedIn faces significant challenges due to its rapid growth, including long wait times for feedback on code submissions and difficulties in debugging failures caused by flaky tests in consumer multiproducts. These issues can lead to inefficiencies and hinder developer productivity.
How has LinkedIn improved the performance of its compatibility testing?
LinkedIn reduced the compatibility testing execution time for the gradle-jvm library from 14 hours to 2 hours by optimizing job scheduling, moving preparation tasks to the server-side, and implementing timely enforcement of compatibility testing success criteria.
What strategies does LinkedIn use to enhance the stability of compatibility testing?
To enhance stability, LinkedIn allows library producers to set failure thresholds, ignore known flaky consumers, and require certain stable consumers to pass. This approach helps mitigate the impact of non-deterministic tests on the compatibility testing process.
Key Statistics & Figures
Number of multiproducts at LinkedIn
12,000
This number reflects the scale of LinkedIn's development ecosystem and the complexity of managing compatibility across numerous codebases.
Reduction in compatibility testing time for gradle-jvm
From 14 hours to 2 hours
This significant improvement was achieved through various optimizations in the CI pipeline.
Percentage of consumers allowed to fail in compatibility testing for gradle-jvm
4%
This threshold allows library updates to proceed even if some consumers encounter issues, balancing stability with development speed.
Technologies & Tools
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Backend
Kafka
Used for emitting pass/fail results of compatibility testing validation jobs.
Backend
Rest.li
Used for efficient data retrieval during compatibility testing preparation.
Backend
Orca
LinkedIn's internal job orchestration service that manages job scheduling in the CI system.
Key Actionable Insights
1Implement failure thresholds in your compatibility testing to allow for some consumer failures without blocking library updates.This strategy can help maintain development velocity while ensuring that critical consumers are still validated, reducing the burden on library producers.
2Enhance your CI pipeline with detailed failure reports to improve debugging efficiency.Providing actionable insights and links to relevant logs can significantly reduce the time developers spend troubleshooting issues, leading to faster resolution and improved productivity.
3Utilize semantic versioning rigorously to communicate changes effectively between library producers and consumers.Adhering to semantic versioning helps prevent breaking changes from impacting consumers unexpectedly, fostering a more reliable development ecosystem.
Common Pitfalls
1
Relying solely on CI feedback without implementing effective debugging tools can lead to prolonged development cycles.
When developers face compatibility testing failures, they often spend excessive time sifting through logs. Implementing better debugging tools can streamline this process and enhance productivity.
Related Concepts
Continuous Integration (ci)
Semantic Versioning
Multirepo Development
Developer Productivity