Plugin Dependency Graph

The graph is a Directed Acyclic Graph (DAG), meaning edges have a direction (Plugin A depends on Plugin B) and contain no cycles. This structure is essential because:

• It allows for topological sorting to determine a valid load order.
• It prevents circular dependencies that would make initialization impossible.
• Nodes represent plugins, and directed edges represent dependency relationships.

The host system performs dependency resolution by analyzing the graph to find a valid execution sequence. This is typically done via a topological sort algorithm (e.g., Kahn's algorithm).

• The algorithm outputs an order where every plugin is loaded only after all its dependencies.
• This ensures required services and APIs are available before a plugin initializes.
• Failed resolution indicates an unresolvable circular dependency, preventing system startup.

Dependencies are declared declaratively, not discovered at runtime. Each plugin's manifest file (e.g., plugin.json) explicitly lists its required plugins by a unique identifier and often a semantic versioning range.

• Example: "dependencies": { "database-connector": "^2.1.0", "auth-provider": "1.x" }
• This allows the host to construct the complete graph before loading any code.
• It enables static analysis for conflict detection and compatibility checks.

The graph inherently manages transitive dependencies. If Plugin A depends on Plugin B, and Plugin B depends on Plugin C, then Plugin A has an implicit, transitive dependency on Plugin C.

• The host system must ensure Plugin C is loaded before Plugin B, and Plugin B before Plugin A.
• This prevents dependency hell where multiple versions of the same plugin are requested.
• Sophisticated systems may implement version conflict resolution strategies.

The graph directly informs the plugin lifecycle. The host invokes lifecycle hooks (e.g., init(), start(), stop()) in the order defined by the topological sort.

• Initialization proceeds from the graph's roots (plugins with no dependencies) outward.
• Deactivation or unloading typically proceeds in reverse order.
• This guarantees that a plugin's dependencies are active and ready when it starts, and fully stopped only after all dependent plugins have stopped.

The graph enables advanced conflict detection. The host can identify:

• Direct conflicts: Two plugins declare incompatible versions of the same dependency.
• Diamond dependency problems: The same plugin is required via different paths with different version constraints.
• Resource contention: Plugins that are not directly dependent but may contend for the same system resource.
• This analysis allows for pre-runtime warnings, sandboxing decisions, or the enforcement of a capability model to isolate plugins.

A metadata file (JSON/YAML) that declares a plugin's identity, capabilities, and requirements to the host system. It is the primary data source for building the dependency graph.

• Declares dependencies: Lists other plugins or specific versions the plugin requires.
• Defines extension points: Specifies which host interfaces the plugin implements.
• Provides configuration schema: Describes valid settings for the plugin.

A formal versioning convention (MAJOR.MINOR.PATCH) critical for dependency resolution. The dependency graph uses SemVer to determine compatibility between plugins.

• MAJOR change: Incompatible API changes. A plugin requiring [email protected] cannot use [email protected] or [email protected].
• MINOR change: Backwards-compatible new functionality.
• PATCH change: Backwards-compatible bug fixes.

Graph algorithms must resolve version ranges (e.g., ^1.2.3) to specific, compatible releases.

The core graph algorithm used to order plugins for loading and initialization. Given a directed acyclic graph (DAG) of dependencies, it produces a linear sequence where every plugin appears after all its dependencies.

• Ensures correct initialization: Prevents a plugin from starting before the services it depends on are ready.
• Detects circular dependencies: If the graph contains a cycle, a topological order is impossible, and the sort will fail, alerting the system to a configuration error.
• Foundation for execution order: Also used to determine the safe order for unloading or updating plugins.

Design patterns where a plugin's required services are provided ('injected') by the host framework. The dependency graph is the blueprint for the DI container.

• Framework-managed lifecycle: The host, not the plugin, creates and wires together dependent objects.
• Enables testing: Dependencies can be replaced with mocks or stubs.
• Tight integration with graph: After topological sort, the host uses the resolved order to instantiate and inject dependencies into each plugin.

A minimalist architectural pattern where a small, stable core provides only essential services (like communication and plugin loading). All other functionality is implemented as plugins. The dependency graph is a first-class concern in this architecture.

• Core responsibilities: Include plugin lifecycle management and inter-plugin communication buses.
• Graph as core service: The microkernel itself often maintains and resolves the plugin dependency graph to coordinate the extended system.

Security models that work in tandem with dependency graphs. While the graph defines what a plugin needs, capabilities define permissions.

• Capability declaration: A plugin's manifest lists required capabilities (e.g., network_access, write_storage).
• Sandbox enforcement: The host grants a restricted execution environment based on granted capabilities.
• Dependency-aware security: A plugin may be denied a capability if a critical dependency lacking that capability could be exploited through it.