Azure Durable Functions is an extension of Azure Functions that enables you to write stateful functions in a serverless compute environment. Think of it as a way to orchestrate multiple Azure Functions into longer-running, reliable workflows.

Let me break this down into key concepts:

1 Core Components

a) Orchestrator Function

- This is like a conductor in an orchestra
- It defines the workflow and coordinates the execution of other functions
- Important: It must be deterministic (same input = same output every time)
- In your code, it's coordinating the processing of address data

[Function(nameof(WebHook))]
public static async Task<List<string>> RunOrchestrator(
    [OrchestrationTrigger] TaskOrchestrationContext context)

b) Activity Functions

- These are the actual work units
- They perform the operations (like database calls, HTTP requests, etc.)
- They can be non-deterministic and perform I/O operations
- In your example, it's saving an address to a database

[Function(nameof(NewAddress))]
public async Task<string> NewAddress([ActivityTrigger] Address address, FunctionContext executionContext)

c) Client Function (Starter)

- This is the entry point that kicks off an orchestration
- Often triggered by an HTTP request, timer, or other trigger
- Creates a new instance of the orchestration

[Function("WebHook_HttpStart")]
public async Task<HttpResponseData> HttpStart(
    [HttpTrigger(AuthorizationLevel.Anonymous, "get", "post")] HttpRequestData req,
    [DurableClient] DurableTaskClient client,
    FunctionContext executionContext)

2. Key Benefits

a. State Management

    - Unlike regular Azure Functions, Durable Functions maintain state between executions
    - They can pause and resume, waiting for external events or timeouts

b. Reliability

    - Built-in checkpointing and replay mechanisms
    - If a function fails, it can pick up where it left off
    - Automatic retry of failed operations

c. Patterns Support

    - Function Chaining (like in your example)
    - Fan-out/Fan-in (parallel processing)
    - Async HTTP APIs
    - Monitoring
    - Human interaction patterns

3. Real-world Analogy

Think of it like organizing a wedding:
- The Orchestrator is the wedding planner who coordinates everything
- Activity Functions are the vendors (caterer, photographer, etc.)
- The Client Function is like the initial meeting where you start planning the wedding

4. Common Use Cases

a. Process Automation

    - Order processing
    - File processing
    - Approval workflows

b. API Integration

    - Long-running operations with multiple steps
    - Systems that need to coordinate multiple services

c. Complex Data Processing

    - ETL operations
    - Data validation and transformation pipelines

5. Important Considerations

a. Orchestrator Constraints

    - Must be deterministic
    - Shouldn't perform I/O operations directly
    - Should delegate actual work to the activity functions

b. Monitoring

    - Built-in support for tracking orchestration status
    - Each instance has a unique ID for tracking

c. Error Handling

    - Can implement retry policies
    - Can handle exceptions at both orchestration and activity levels