Overview
The article discusses the challenges of managing software dependencies at scale within LinkedIn's engineering environment, which includes over 10,000 separate software codebases. It outlines the evolution from a legacy dependency management system to a new service designed to improve dependency resolution and management, ensuring reliability and scalability.
What You'll Learn
1
How to implement a robust dependency management service for large-scale software projects
2
Why transitioning from a graph database to a NoSQL document store can enhance scalability
3
How to leverage dependency graphs for effective CI/CD pipeline integration
Prerequisites & Requirements
- Understanding of software dependency management concepts
- Familiarity with build tools like Gradle and Maven(optional)
- Experience with large-scale software development environments
Key Questions Answered
What are the limitations of the legacy dependency management solution at LinkedIn?
The legacy solution managed dependencies at the product level, which led to issues like version conflicts not being resolved correctly and a lack of support for multiple programming languages. This caused developer frustrations, including false errors and warnings.
How does the new dependency service improve dependency management?
The new service provides a fine-grained, fully resolved dependency graph at the module level, captures data using build tools like Gradle, and supports importing dependency graphs from various programming languages. This enhances accuracy and reduces false negatives in dependency-related errors.
What is the scale of data managed by the new dependency service?
The new service manages over 10,000 multiproducts, translating to approximately 1.3 billion nodes and 4.8 billion edges in the dependency graphs, demonstrating its capability to handle large-scale data efficiently.
What improvements have been observed since implementing the new dependency service?
Since the new service went live, the number of false negatives for circular dependency errors has decreased by 60%, significantly improving developer productivity and the accuracy of dependency-related queries.
Key Statistics & Figures
Number of multiproducts managed
10,000
This reflects the scale of LinkedIn's software ecosystem.
Nodes in the dependency graphs
1.3 billion
Indicates the complexity and size of the data being managed.
Edges in the dependency graphs
4.8 billion
Demonstrates the extensive interdependencies among the software products.
Reduction in false negatives for circular dependency errors
60%
Highlights the improvement in developer productivity since the new service implementation.
Technologies & Tools
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Database
Espresso
Used as the NoSQL document store for managing dependency graphs.
Build Tool
Gradle
Utilized for resolving and generating dependency graphs for Java-based products.
API Framework
Rest.li
Provides the service API for the new dependency management service.
Messaging
Kafka
Used for messaging within the dependency management service.
Key Actionable Insights
1Implement a dependency management service that captures fine-grained details at the module level to improve accuracy in dependency resolution.This approach minimizes version conflicts and enhances the reliability of the CI/CD pipeline, especially in complex software environments.
2Transition from traditional graph databases to NoSQL document stores for better scalability and performance in managing large datasets.This change allows for more efficient data retrieval and management, which is crucial as the number of software products and their dependencies grow.
3Utilize dependency graphs to inform CI/CD processes and automate testing based on actual module usage.This can lead to more efficient testing cycles and reduce the overhead of managing outdated or unused dependencies.
Common Pitfalls
1
Managing dependencies at a coarse granularity can lead to significant issues, such as unresolved version conflicts and inaccurate dependency paths.
This often results in developer frustration and inefficiencies in the build process, as tools may provide false errors or warnings.