🏛️ Architecture Overview

Pyvider implements a sophisticated, layered architecture that seamlessly bridges Python code with Terraform's Plugin Protocol v6. This document provides a comprehensive understanding of how Pyvider transforms your Python classes into fully functional Terraform providers.

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📊 High-Level Architecture

graph TB
    subgraph "Terraform Core"
        TC[Terraform CLI/Core]
        TS[Terraform State]
        TF[.tf Configuration Files]
    end

    subgraph "Plugin Protocol Layer"
        PP[gRPC Plugin Protocol v6]
        PB[Protocol Buffers]
    end

    subgraph "Pyvider Framework"
        direction TB
        PS[Protocol Service]
        PH[Protocol Handlers]
        CH[Component Hub]
        SM[Schema Manager]
        CV[Conversion Layer]
        ST[State Management]
    end

    subgraph "Your Provider Code"
        PC[Provider Class]
        RC[Resources]
        DS[Data Sources]
        FN[Functions]
        EP[Ephemerals]
    end

    TF --> TC
    TC <--> PP
    PP <--> PB
    PB <--> PS
    PS --> PH
    PH <--> CH
    CH <--> SM
    PH <--> CV
    PH <--> ST
    CH --> PC
    CH --> RC
    CH --> DS
    CH --> FN
    CH --> EP
    TS <--> TC

🔄 Request Flow

Understanding how a request flows through Pyvider is crucial for debugging and optimization:

1. Resource Creation Flow

sequenceDiagram
    participant T as Terraform
    participant G as gRPC Server
    participant H as Protocol Handler
    participant C as Component Hub
    participant R as Your Resource
    participant S as State Manager

    T->>G: ApplyResourceChange (create)
    G->>H: Handle request
    H->>C: Get resource by type
    C->>R: Instantiate resource
    H->>R: Call create() method
    R->>R: Execute business logic
    R-->>H: Return new state
    H->>S: Encrypt private state
    S-->>H: Encrypted blob
    H-->>G: Response with state
    G-->>T: Success with new state

2. Data Source Read Flow

sequenceDiagram
    participant T as Terraform
    participant G as gRPC Server
    participant H as Protocol Handler
    participant C as Component Hub
    participant D as Your Data Source

    T->>G: ReadDataSource
    G->>H: Handle request
    H->>C: Get data source by type
    C->>D: Instantiate data source
    H->>D: Call read() method
    D->>D: Fetch data
    D-->>H: Return state
    H-->>G: Response with data
    G-->>T: Success with data

🧩 Core Components

1. Component Hub (pyvider.hub)

The Component Hub is the central registry that manages all provider components:

# Internal structure
class ComponentHub:
    providers: dict[str, type[BaseProvider]]
    resources: dict[str, type[BaseResource]]
    data_sources: dict[str, type[BaseDataSource]]
    functions: dict[str, type[BaseFunction]]
    ephemerals: dict[str, type[BaseEphemeral]]

Responsibilities: - Automatic component discovery via decorators - Type validation and registration - Dependency injection for capabilities - Component lifecycle management

2. Protocol Service (pyvider.protocols.service)

The Protocol Service implements the Terraform Plugin Protocol v6 gRPC service:

class TerraformProviderServicer:
    async def GetProviderSchema(...)
    async def ValidateProviderConfig(...)
    async def ConfigureProvider(...)
    async def ValidateResourceConfig(...)
    async def PlanResourceChange(...)
    async def ApplyResourceChange(...)
    async def ReadResource(...)
    async def ImportResourceState(...)
    # ... and more

Key Features: - Full Protocol v6 implementation - Async/await support throughout - Automatic error handling and diagnostics - Request/response logging for debugging

3. Schema System (pyvider.schema)

The Schema System provides type-safe data modeling:

from pyvider.schema import s_resource, a_str, a_map, a_num

@attrs.define
class ResourceConfig:
    """Configuration attrs class"""
    name: str
    tags: dict[str, str]
    size: int

@register_resource("example")
class ExampleResource(BaseResource):
    config_class = ResourceConfig

    @classmethod
    def get_schema(cls):
        """Schema definition using factory functions"""
        return s_resource({
            "name": a_str(required=True, description="Resource name"),
            "tags": a_map(a_str(), default={}, description="Resource tags"),
            "size": a_num(
                validators=[lambda x: 1 <= x <= 100 or "Must be 1-100"],
                description="Resource size"
            ),
        })

Features: - Automatic schema generation from Python types - Built-in validators and constraints - Computed and sensitive attribute support - Nested blocks and complex types

4. Conversion Layer (pyvider.conversion)

Handles bidirectional conversion between Python and Terraform types:

graph LR
    subgraph "Python Types"
        PT[str, int, bool, dict, list]
    end

    subgraph "CTY Types"
        CT[String, Number, Bool, Object, List]
    end

    subgraph "Protocol Buffers"
        PB[DynamicValue, Schema]
    end

    PT <--> CT
    CT <--> PB

Conversion Examples: - Python dict ↔ CTY Object ↔ Protocol Buffer DynamicValue - Python list[str] ↔ CTY List(String) ↔ Protocol Buffer DynamicValue - Python @attrs.define class ↔ CTY Object with schema

5. State Management (pyvider.resources.private_state)

Manages resource state with encryption for sensitive data:

class PrivateState:
    """Encrypted storage for sensitive provider data"""

    @classmethod
    def encrypt(cls, data: dict) -> bytes:
        # AES-256 encryption with key derivation

    @classmethod
    def decrypt(cls, encrypted: bytes) -> dict:
        # Secure decryption with validation

Security Features: - AES-256-GCM encryption - Key derivation with PBKDF2 - Automatic key rotation support - Tamper detection

🔌 Protocol Implementation

Terraform Plugin Protocol v6

Pyvider implements the complete Terraform Plugin Protocol v6 specification:

Supported RPCs

RPC Method	Purpose	Pyvider Support
GetProviderSchema	Returns provider schema	✅ Full
ValidateProviderConfig	Validates provider config	✅ Full
ConfigureProvider	Configures provider instance	✅ Full
ValidateResourceConfig	Validates resource config	✅ Full
ValidateDataResourceConfig	Validates data source config	✅ Full
UpgradeResourceState	Migrates resource state	✅ Full
ReadResource	Refreshes resource state	✅ Full
PlanResourceChange	Plans resource changes	✅ Full
ApplyResourceChange	Applies resource changes	✅ Full
ImportResourceState	Imports existing resources	✅ Full
MoveResourceState	Moves resources	✅ Full
ReadDataSource	Reads data source	✅ Full
GetFunctions	Returns function definitions	✅ Full
CallFunction	Executes functions	✅ Full
OpenEphemeralResource	Opens ephemeral resource	✅ Full
RenewEphemeralResource	Renews ephemeral resource	✅ Full
CloseEphemeralResource	Closes ephemeral resource	✅ Full
StopProvider	Graceful shutdown	✅ Full

Message Flow

graph TD
    subgraph "Terraform"
        T1[terraform plan]
        T2[terraform apply]
        T3[terraform destroy]
    end

    subgraph "Protocol Messages"
        M1[GetProviderSchema]
        M2[ValidateResourceConfig]
        M3[PlanResourceChange]
        M4[ApplyResourceChange]
        M5[ReadResource]
    end

    subgraph "Pyvider Handlers"
        H1[get_provider_schema.py]
        H2[validate_resource_config.py]
        H3[plan_resource_change.py]
        H4[apply_resource_change.py]
        H5[read_resource.py]
    end

    T1 --> M1 --> H1
    T1 --> M2 --> H2
    T1 --> M3 --> H3
    T2 --> M4 --> H4
    T2 --> M5 --> H5
    T3 --> M3 --> H3
    T3 --> M4 --> H4

🎯 Component Discovery

Pyvider uses a sophisticated discovery mechanism to find and register components:

Discovery Process

1. Entry Point Scanning: Looks for pyvider.components entry points
2. Package Traversal: Recursively scans packages for decorated classes
3. Decorator Detection: Identifies classes with registration decorators
4. Validation: Ensures components meet interface requirements
5. Registration: Adds valid components to the hub

Registration Flow

# Your code
@register_resource("my_resource")
class MyResource(BaseResource):
    pass

# Discovery process
1. Scanner finds @register_resource decorator
2. Validates MyResource extends BaseResource
3. Checks for required methods (create, read, update, delete)
4. Registers in hub.resources["my_resource"] = MyResource
5. Generates Terraform schema from class definition

🔧 Lifecycle Hooks

Pyvider provides lifecycle hooks for advanced customization:

Provider Lifecycle

stateDiagram-v2
    [*] --> Discovered: Component Discovery
    Discovered --> Initialized: Provider.__init__()
    Initialized --> Setup: Provider.setup()
    Setup --> Configured: Provider.configure()
    Configured --> Ready: Ready for requests
    Ready --> Processing: Handle requests
    Processing --> Ready: Request complete
    Ready --> Stopping: StopProvider RPC
    Stopping --> [*]: Cleanup complete

Resource Lifecycle

stateDiagram-v2
    [*] --> Creating: terraform apply (new)
    Creating --> Created: create() returns State
    Created --> Reading: terraform refresh
    Reading --> Read: read() returns State
    Read --> Updating: terraform apply (change)
    Updating --> Updated: update() returns State
    Updated --> Reading: Continue management
    Read --> Deleting: terraform destroy
    Deleting --> [*]: delete() completes

⚡ Performance Optimizations

1. Async Everything

All I/O operations use async/await for maximum concurrency:

async def _create_apply(self, ctx: ResourceContext) -> tuple[State | None, None]:
    # Parallel API calls
    results = await asyncio.gather(
        self.create_network(),
        self.allocate_storage(),
        self.configure_security()
    )
    return State(...), None

2. Connection Pooling

gRPC connections are pooled and reused:

# Automatic connection management
channel_pool = grpc.aio.insecure_channel(
    target='localhost:50051',
    options=[
        ('grpc.max_connection_idle_ms', 30000),
        ('grpc.keepalive_time_ms', 10000),
    ]
)

3. Schema Caching

Schemas are computed once and cached:

@cached_property
def schema(self) -> PvsSchema:
    return self._generate_schema()

4. Lazy Loading

Components are loaded only when needed:

def get_resource(self, name: str) -> type[BaseResource]:
    if name not in self._loaded:
        self._loaded[name] = self._load_resource(name)
    return self._loaded[name]

🔍 Debugging Architecture

Debug Logging

Enable comprehensive debug logging:

export PYVIDER_LOG_LEVEL=DEBUG
export FOUNDATION_LOG_LEVEL=DEBUG

Request Tracing

Every request includes trace IDs for correlation:

[2024-01-15 10:23:45] [INFO] [trace_id=abc123] ApplyResourceChange started
[2024-01-15 10:23:45] [DEBUG] [trace_id=abc123] Resource type: my_resource
[2024-01-15 10:23:46] [INFO] [trace_id=abc123] ApplyResourceChange completed

Performance Profiling

Built-in profiling for optimization:

from pyvider.resources.context import ResourceContext

with timed_block(logger, "resource_creation"):
    state, _ = await resource._create_apply(ResourceContext(config=config))
# Logs: [⏱️] resource_creation duration_ms=234.56

🛡️ Security Architecture

1. Input Validation

All inputs are validated before processing:

from pyvider.schema import s_provider, a_str

@attrs.define
class ProviderConfig:
    """Provider configuration attrs class"""
    api_key: str

@register_provider("example")
class ExampleProvider(BaseProvider):
    @classmethod
    def _build_schema(cls):
        """Schema with validators"""
        return s_provider({
            "api_key": a_str(
                required=True,
                validators=[
                    lambda x: 32 <= len(x) <= 64 or "Must be 32-64 chars",
                    lambda x: x.isalnum() or "Must be alphanumeric"
                ]
            )
        })

2. Secret Management

Sensitive data never logged or exposed:

from pyvider.schema import s_provider, a_str

@classmethod
def _build_schema(cls):
    return s_provider({
        "password": a_str(
            required=True,
            sensitive=True,  # Never logged or shown in output
            description="Database password"
        )
    })

3. Secure Communication

gRPC with TLS support:

credentials = grpc.ssl_channel_credentials()
channel = grpc.aio.secure_channel('localhost:50051', credentials)

🎓 Architecture Best Practices

1. Separation of Concerns

• Provider: Configuration and authentication
• Resources: CRUD operations for infrastructure
• Data Sources: Read-only data fetching
• Functions: Pure transformations
• Capabilities: Reusable functionality

2. Error Handling

async def _create_apply(self, ctx: ResourceContext) -> tuple[State | None, None]:
    try:
        result = await self.api_call()
    except ApiError as e:
        raise ResourceError(f"Failed to create: {e}")
    return State(...), None

3. Resource Design

• Keep resources focused on a single concern
• Use composition via capabilities for shared functionality
• Implement proper error handling and rollback
• Always validate inputs

4. State Management

• Store only essential data in state
• Use private state for sensitive information
• Implement proper read() to detect drift
• Handle missing resources gracefully

⚠️ Alpha Considerations

Pyvider's architecture is stable, but as an alpha project:

• Internal APIs may change before 1.0
• Performance characteristics are still being optimized
• Some edge cases may not be fully handled

Report architectural issues or suggestions in GitHub Discussions.

📚 Further Reading

• Component Model - Deep dive into component system
• Schema System - Advanced schema features
• Schema System - Schema documentation

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