mochivi/distributed-file-system

AI Summary: Implement a reusable `FlushingBuffer` in Go to improve memory efficiency and performance of the distributed file system's download functionality. This involves creating a thread-safe buffer that flushes data to disk when a configurable threshold is reached, integrating it with the existing downloader, and potentially extending its use to other streaming components. The solution should be configurable and include monitoring metrics.

AI Summary: Implement a standardized validation layer for API requests in a Go-based distributed file system. This involves creating a custom error type for validation failures, modifying existing Protobuf-to-internal-type conversion functions to include validation, and updating the gRPC handlers to translate validation errors into user-facing errors.

AI Summary: Refactor the `internal/storage` Go package and its sub-packages to use custom, package-specific errors instead of generic errors. This involves creating a new `errors.go` file, defining specific error types (sentinel and custom), modifying package functions to return these new errors, and updating calling code to handle them appropriately using `errors.Is()` and `errors.As()`. The goal is to improve error handling clarity, predictability, and decoupling within the distributed file system.

AI Summary: Refactor the Go code in the `internal/datanode` package to use custom, package-specific errors instead of generic errors. This involves creating new error types, modifying existing functions to return these new errors, and updating calling functions to handle them appropriately. The goal is to improve error handling clarity and decouple the `internal/datanode` package from the application's main error handling mechanism.

AI Summary: Refactor the Go code in the `internal/cluster/shared` package to use custom, package-specific errors instead of generic errors. This involves creating new error types, modifying existing functions to return these new errors, and updating the calling code to handle them appropriately. The goal is to improve error handling clarity and decouple the package from the application's main error handling mechanism.

AI Summary: Refactor the Go code in the `internal/cluster` package to use custom, package-specific errors instead of generic errors. This involves creating new error types, modifying existing functions to return these new errors, and updating the calling code to handle them appropriately. The goal is to improve error handling clarity and maintain package decoupling, aligning with a larger project-wide error handling strategy.

AI Summary: Refactor the `internal/coordinator` Go package to use custom, package-specific errors instead of generic errors. This involves creating a new `errors.go` file, defining specific error types (sentinel and custom), modifying package functions to return these new errors, and updating calling code to handle them appropriately using `errors.Is()` and `errors.As()`. The goal is to improve error handling clarity and decouple the package from the application's main error handling mechanism.

AI Summary: Refactor the Go code in the `internal/clients` package to use custom, package-specific errors instead of generic errors. This involves creating new error types, modifying existing functions to return these new errors, and updating the calling code to handle them appropriately. The goal is to improve error handling clarity and decouple the package from the application's main error handling mechanism.

AI Summary: Refactor the Go code in the `internal/cluster/state` package to use custom, package-specific errors instead of generic errors. This involves creating new error types, modifying existing functions to return these new errors, and updating the calling code to handle them appropriately. The goal is to improve error handling clarity and decouple the package from the application's main error handling mechanism.

AI Summary: Refactor the DataNode service in a distributed file system (written in Go) to separate its gRPC transport logic from its core business logic. This involves creating a new Service interface, implementing it in a separate service layer, and adapting the existing gRPC handlers to act as thin wrappers around this service. The goal is to improve testability and maintainability by adhering to the principle of separation of concerns.

AI Summary: Refactor the Go code for a distributed file system's coordinator to separate the gRPC transport layer from the business logic, improving testability and maintainability. This involves creating a `Service` interface, moving the core logic into a new `service.go` file, and refactoring the existing gRPC handlers to act as thin adapters.

AI Summary: Implement a centralized error handling mechanism for a Go gRPC service using a custom error type and a unary interceptor. This involves creating a new error package, an interceptor to handle errors consistently, and refactoring existing gRPC handlers to use the new error types. The goal is to improve error logging, standardize error messages, and decouple error handling from the gRPC transport layer.

AI Summary: Refactor the Go codebase of a distributed file system to centralize logging keys and component names into a constants file (`internal/common/constants.go`) for improved consistency, readability, and maintainability. This involves identifying all occurrences of hardcoded strings representing these identifiers and replacing them with the newly defined constants.

AI Summary: Refactor the logging system of a Go-based distributed file system to use context.Context for logger propagation, standardize logging keys using constants, and implement gRPC interceptors for automatic context injection. This improves log traceability and reduces boilerplate.

AI Summary: Implement a background goroutine in the Go distributed file system's `streamingSessionManager` to periodically check for and remove expired upload/download sessions. This addresses a memory leak by proactively cleaning up orphaned sessions that are not explicitly deleted after creation.

AI Summary: Implement a garbage collection mechanism in the MetadataSessionManager of a Go-based distributed file system to periodically remove expired upload sessions from an in-memory map. This involves adding a background goroutine that iterates through sessions and removes those past their expiration time, preventing memory leaks. Unit tests should be added to verify the functionality.

AI Summary: Implement validation checks in the `MetadataSessionManager.commit` function to ensure data integrity during file uploads. This involves verifying replica node validity, matching chunk IDs, and validating chunk header information. Tests must also be updated to cover the new validation logic.

AI Summary: Implement a robust garbage collection (GC) mechanism for orphaned chunks in a distributed file system. This involves integrating the GC controller with a persistent, cluster-wide metadata store (etcd) to ensure reliable orphaned chunk detection and deletion, handling failures gracefully, and potentially adding features like progress persistence and a failure queue. The task is blocked until persistent metadata storage (#14) is implemented.

AI Summary: The task requires replacing the in-memory metadata store in a Go-based distributed file system with a persistent, distributed key-value store like etcd. This involves refactoring the metadata interface to interact with etcd, ensuring atomicity of operations, and implementing watch-based updates for efficient data synchronization among nodes. The goal is to achieve fault tolerance, scalability, and support for multiple coordinators.