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Architectural decision Record 0002: Adoption of gRPC
ADR-0002: Adoption of gRPC for Communication Between Frontend and Backend
Status
Accepted
Context
FrankenScore's architecture involves separate frontend and backend Clojure applications. This separation necessitates a robust, efficient, and flexible communication mechanism between these two components. We need to choose a protocol that can handle complex data structures, provide type safety, and offer high performance for real-time interactions with large musical scores.
Decision
We will use gRPC (gRPC Remote Procedure Call) as the primary communication protocol between FrankenScore's frontend and backend applications.
Rationale
1. Performance:
- gRPC uses Protocol Buffers, a lightweight binary serialization format.
- Offers significantly faster serialization and deserialization compared to JSON.
- Supports streaming, crucial for real-time updates of large scores.
2. Type Safety:
- Strongly typed interface definitions via Protocol Buffers.
- Reduces errors related to data type mismatches between frontend and backend.
3. Language Agnostic:
- While we're using Clojure for both frontend and backend, gRPC's language-agnostic nature provides flexibility for future expansions or integrations.
4. Bi-directional Streaming:
- Allows for efficient, real-time updates from backend to frontend.
- Crucial for live collaboration features and real-time score rendering.
5. Code Generation:
- Automatic client and server code generation from service definitions.
- Reduces boilerplate and ensures consistency between client and server implementations.
6. Compression:
- Built-in support for data compression, beneficial for transferring large musical scores.
7. Multiplexing:
- Supports multiple simultaneous requests over a single connection.
- Improves efficiency, especially important for complex score manipulations.
8. Community and Ecosystem:
- Strong community support and a growing ecosystem.
- Well-documented and actively maintained.
9. Cross-platform Support:
- Works well across different operating systems, aligning with FrankenScore's cross-platform goals.
10. Future-proofing:
- Scalable architecture that can support future cloud-based deployments if needed.
Consequences
Positive:
Negative:
Implementation Approach
Alternatives Considered
Notes
While gRPC is our chosen solution, we should monitor its performance and developer experience in practice. If it introduces unexpected complexities or performance issues, we may need to reevaluate this decision.
We should also keep an eye on the evolution of gRPC and related technologies to ensure we're using the most efficient communication method as the project grows.
Accepted
Context
FrankenScore's architecture involves separate frontend and backend Clojure applications. This separation necessitates a robust, efficient, and flexible communication mechanism between these two components. We need to choose a protocol that can handle complex data structures, provide type safety, and offer high performance for real-time interactions with large musical scores.
Decision
We will use gRPC (gRPC Remote Procedure Call) as the primary communication protocol between FrankenScore's frontend and backend applications.
Rationale
1. Performance:
- gRPC uses Protocol Buffers, a lightweight binary serialization format.
- Offers significantly faster serialization and deserialization compared to JSON.
- Supports streaming, crucial for real-time updates of large scores.
2. Type Safety:
- Strongly typed interface definitions via Protocol Buffers.
- Reduces errors related to data type mismatches between frontend and backend.
3. Language Agnostic:
- While we're using Clojure for both frontend and backend, gRPC's language-agnostic nature provides flexibility for future expansions or integrations.
4. Bi-directional Streaming:
- Allows for efficient, real-time updates from backend to frontend.
- Crucial for live collaboration features and real-time score rendering.
5. Code Generation:
- Automatic client and server code generation from service definitions.
- Reduces boilerplate and ensures consistency between client and server implementations.
6. Compression:
- Built-in support for data compression, beneficial for transferring large musical scores.
7. Multiplexing:
- Supports multiple simultaneous requests over a single connection.
- Improves efficiency, especially important for complex score manipulations.
8. Community and Ecosystem:
- Strong community support and a growing ecosystem.
- Well-documented and actively maintained.
9. Cross-platform Support:
- Works well across different operating systems, aligning with FrankenScore's cross-platform goals.
10. Future-proofing:
- Scalable architecture that can support future cloud-based deployments if needed.
Consequences
Positive:
- High-performance communication suitable for real-time score editing.
- Strong typing reduces runtime errors related to data transfer.
- Efficient handling of large data structures (complex musical scores).
- Streamlined development process with code generation.
- Flexibility for future expansion to other platforms or languages.
Negative:
- Learning curve for developers not familiar with gRPC and Protocol Buffers.
- Slightly more complex setup compared to REST APIs.
- Potential challenges in debugging due to binary nature of communication.
Implementation Approach
- Define Protocol Buffer messages for all shared data structures (e.g., musical elements, layout information).
- Design gRPC services for all backend operations (e.g., score manipulation, rendering requests).
- Implement bi-directional streaming for real-time updates (e.g., live score changes).
- Set up code generation pipelines for both frontend and backend.
- Implement error handling and retry mechanisms for robust communication.
- Create abstractions in Clojure to work seamlessly with generated gRPC code.
- Develop comprehensive testing suites for the gRPC communication layer.
Alternatives Considered
- REST API with JSON: Rejected due to performance concerns with large datasets and lack of native streaming support.
- WebSockets: Considered for its real-time capabilities but rejected due to lack of built-in type safety and less structured approach to API definition.
- Apache Thrift: Similar to gRPC but rejected due to gRPC's better language support and more active community.
Notes
While gRPC is our chosen solution, we should monitor its performance and developer experience in practice. If it introduces unexpected complexities or performance issues, we may need to reevaluate this decision.
We should also keep an eye on the evolution of gRPC and related technologies to ensure we're using the most efficient communication method as the project grows.
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FrankenScore is a modern, open-source music notation software designed to handle complex musical scores with ease. It is designed to be a flexible and powerful music notation software tool providing professional, extremely high-quality results. The core functionality includes inputting music notation, formatting scores and their parts, and printing them. Additional features can be added as plugins, allowing for a modular and customizable user experience.
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