Serverless Diagrams and Concept Maps

Use this appendix when a pattern is easier to remember as a shape than as a paragraph. The diagrams below collect the recurring flows from the guide so you can quickly review request paths, async processing, workflow coordination, retry behavior, state externalization, and the performance-cost trade-offs that appear across serverless systems.

Each diagram is intentionally short. The goal is to help you recognize the architecture quickly during design review or study, then remember what the main pressure points are.

Request Path with an API Gateway

    flowchart LR
	    A["Client"] --> B["API gateway"]
	    B --> C["Auth and validation"]
	    C --> D["Function"]
	    D --> E["Transactional store"]
	    D --> F["Optional async follow-up"]

What to notice:

• the request path should stay short and explicit
• authentication and validation happen before business logic runs
• optional slow work should usually leave the synchronous path

Event Flow and Async Reaction

    flowchart LR
	    A["Producer"] --> B["Event bus or topic"]
	    B --> C["Consumer A"]
	    B --> D["Consumer B"]
	    B --> E["Workflow starter"]

What to notice:

• one fact can drive several independent reactions
• producers and consumers are decoupled in time
• event contracts become part of the architecture

Queue-Triggered Background Processing

    flowchart LR
	    A["API or producer"] --> B["Queue"]
	    B --> C["Worker 1"]
	    B --> D["Worker 2"]
	    C --> E["Durable result"]
	    D --> E

What to notice:

• the queue buffers work and smooths bursts
• workers can scale independently of the producer
• durability and idempotency still matter even when the platform handles retries

Workflow Fan-Out and Fan-In

    flowchart TD
	    A["Start workflow"] --> B["Split work"]
	    B --> C["Task 1"]
	    B --> D["Task 2"]
	    B --> E["Task 3"]
	    C --> F["Aggregate results"]
	    D --> F
	    E --> F
	    F --> G["Complete or partial result"]

What to notice:

• the split and the aggregation rules are equally important
• total latency falls only if downstream dependencies can tolerate the parallelism
• fan-in must define what counts as success, timeout, or partial completion

Retry, Quarantine, and Replay

    flowchart LR
	    A["Event or job"] --> B["Consumer"]
	    B --> C{"Success?"}
	    C -->|Yes| D["Acknowledge"]
	    C -->|No| E["Retry with backoff"]
	    E --> B
	    E --> F["DLQ or quarantine"]
	    F --> G["Inspect and replay safely"]

What to notice:

• retry is an automatic recovery path
• quarantine is a separate operational path
• replay should be deliberate and duplicate-safe, not blind republishing

Function and State Store Pattern

    flowchart LR
	    A["Event or request"] --> B["Function"]
	    B --> C["State store"]
	    C --> D["Checkpoint or status"]
	    D --> E["Later invocation resumes safely"]

What to notice:

• the runtime is disposable, but the workflow state is not
• durable progress lives outside function memory
• this pattern is the basis for safe retries and long-running processes

Object-Triggered Processing

    flowchart LR
	    A["Client upload"] --> B["Object storage"]
	    B --> C["Object-created event"]
	    C --> D["Processing function"]
	    D --> E["Metadata store"]
	    D --> F["Derived artifact"]

What to notice:

• the object is the durable artifact boundary
• processing is event-driven rather than held on the upload request path
• metadata and queryable status usually belong in a separate store

Cost and Performance Pressure Model

    flowchart LR
	    A["One user action"] --> B["API request"]
	    B --> C["Function duration"]
	    C --> D["Downstream calls"]
	    D --> E["Queue or workflow steps"]
	    E --> F["Storage and transfer"]
	    F --> G["Total latency and total cost"]

What to notice:

• one business action can expand into several billable and latency-bearing steps
• architecture shape often drives cost more than one compute price does
• remote-call reduction usually improves both speed and spend

Concept Map

    flowchart TD
	    A["Serverless system"] --> B["Request path"]
	    A --> C["Async processing"]
	    A --> D["Workflow coordination"]
	    A --> E["State and storage"]
	    A --> F["Security and identity"]
	    A --> G["Observability and operations"]
	    C --> H["Retries and DLQ"]
	    D --> I["Compensation"]
	    E --> J["Object storage and databases"]

What to notice:

• the guide is not really about functions alone
• the core design areas connect through state, identity, and operational behavior
• most production issues appear where two of these areas interact, not where one is considered in isolation

How to Study with These Diagrams

Use these visuals in three ways:

• scan them before reviewing a chapter so you remember the system shape first
• compare a live architecture to the nearest diagram and note where it diverges
• use the “what to notice” notes as a short checklist during design review

If a system no longer matches any of these shapes cleanly, that usually means one of two things: either the workload needs a more advanced architecture, or the design has accumulated unnecessary complexity and should be simplified.