Living Pattern Playbook: Rust, Laravel, Nuxt

A practical, evolving guide to design patterns, reliability tactics, and modeling techniques you can drop into real services. Code is organized in appendices so the core narrative stays clean.

Table of Contents {#table-of-contents}

Living Pattern Playbook — Rust, Laravel, Nuxt

Table of Contents

1. Purpose and scope

2. Principles that guide every service

3. Pattern index at a glance

4. How to choose patterns

5. Modeling with statecharts and contracts

6. Reliability toolkit

7. Observability plan

8. Delivery and rollout plan

9. Data and schema evolution

10. Security and trust as code

11. Team workflow and decision records

12. One month adoption plan

13. Appendices

Appendix A. Rust blueprints

Appendix B. Laravel blueprints

Appendix C. Nuxt blueprints

Appendix D. TLA+ and Alloy starter specs

Appendix E. ADR template

Appendix F. Property based testing recipes

Appendix G. CRDT and real time collaboration

Appendix H. Pure Reducer Patterns (Deep Dive)

1) What is a pure reducer?

2) Laws & invariants

3) Architecture: Functional core, imperative shell

4) Composition patterns

5) Testing reducers (fast + powerful)

6) Performance & ergonomics

7) Concurrency & Distribution

Aggregate boundaries

Serialization strategies

Cross-aggregate coordination

Distribution patterns

Invariants under concurrency

8) Anti-patterns

9) Blueprints

Rust — Pure reducer + effects pattern

Laravel (PHP) — Reducer-as-service and fold

Nuxt/TypeScript — Slice reducers + combine

Appendix I. Testing Strategies for Reducers

1) Property-based testing

2) Replay testing

3) Metamorphic testing

4) Simulation testing

5) Mutation testing

10. Migration & Interoperability

11. Checklist for shipping reducer-based features

12. Key Takeaways

13. Next Steps

1. Purpose and scope {#1.-purpose-and-scope}

Give teams a single source of truth for modern patterns that trade complexity for leverage. The goal is to make correctness and evolution natural.

Out of scope: exhaustive academic treatment. We target implementable slices with references to deeper sources when useful.

2. Principles that guide every service {#2.-principles-that-guide-every-service}

Information hiding and stable interfaces
High cohesion with low coupling
Explicit state and transitions
Idempotency by default
Time budget and deadlines propagate
Backpressure instead of buffers
Schemas are contracts with version rules
Security decisions are centralized and testable
Everything important is observable
Small controlled experiments behind a flag

3. Pattern index at a glance {#3.-pattern-index-at-a-glance}

Architecture

Hexagonal ports and adapters
Modular monolith with bounded contexts
Event sourcing and projections when audit or time travel is required
Orchestrated sagas for multi step money or entitlement flows
Workflow engines for durable timers and compensation

Concurrency and flow control

Actor model and CSP style channels
Reactive streams with backpressure
Bulkheads, circuit breakers, hedged requests

Data and evolution

Append only event logs
Data contracts with compatibility gates
Change data capture driven projections

Collaboration and offline

CRDT based sync for local first experiences
Causality aware UX

Testing and assurance

Design by contract
Property based and model based testing
Deterministic simulation for workflows
Mutation testing to harden test suites

4. How to choose patterns {#4.-how-to-choose-patterns}

Use a quick decision table at design time. Pick the dominant force and apply the smallest pattern that resolves it.

Force	Symptom	Minimal pattern	When to avoid
Temporal complexity	Many branches and retries	Statechart with guards and invariants	When flow is a single step
Multi service money flow	Needs compensation and audit	Saga with orchestrator plus outbox	When two steps and same database
Collaboration	Concurrent edits and offline	CRDT document plus event log	When strict linear history is required
Scale and latency	Queue storms and fan out	Backpressure with bounded queues	When throughput is tiny
Auditability	Who did what and when	Event sourcing with projections	When history is irrelevant

5. Modeling with statecharts and contracts {#5.-modeling-with-statecharts-and-contracts}

Model work as a hierarchical statechart with explicit events. Then state invariants as contracts. Keep the model text based and close to the code. See the code in Appendix A and B for Rust and Laravel implementations, and Appendix C for Nuxt UI state.

Checklist

All states listed with entry and exit actions
Events listed with guards and expected effects
Illegal states are unrepresentable in data types
Invariants captured as runtime contracts and property tests

6. Reliability toolkit {#6.-reliability-toolkit}

Use defaults that become shared code in every service.

Idempotency
- Carry an idempotency key in every external call and command
- Store attempt ledger with status and checksum
Retries
- Exponential backoff with jitter and a hard cap
- Retry only on safe conditions and idempotent operations
Timeouts and deadlines
- One default time budget, propagate deadline through calls
- Log remaining time in spans
Outbox and inbox tables
- Write business change and message in one transaction
- A background relay publishes from outbox
Circuit breaker and bulkheads
- Trip on error rate and latency budget breach
- Reserve capacity per consumer pool
Sagas
- Explicit steps with compensation and a durable state store

7. Observability plan {#7.-observability-plan}

Metrics
- RED for services and SLI per critical endpoint
Traces
- One span per request with business attributes and idempotency key
Logs
- Structured, sampled at the edge, correlated by trace id
SLOs and error budgets
- Publish targets and budget policy in repo

8. Delivery and rollout plan {#8.-delivery-and-rollout-plan}

Feature flags
Dark launch and shadow traffic
Canary then linear ramp
Rollback plan in the pull request template
Post release verification playbook

9. Data and schema evolution {#9.-data-and-schema-evolution}

Version schemas and review compatibility
Additive changes first, then migrations with dual write or backfill
CDC stream for projections and caches
Archive policy for append only stores

10. Security and trust as code {#10.-security-and-trust-as-code}

Threat model per feature with STRIDE
Policy engine for authorization rules
Secrets and keys rotated with short TTL
Signed events for audit with key provenance

11. Team workflow and decision records {#11.-team-workflow-and-decision-records}

One ADR per significant decision with alternatives and tradeoffs
Design review checklist in repo
Runbooks co located with services

12. One month adoption plan {#12.-one-month-adoption-plan}

Week 1

Pick one flow and model it as a statechart
Add property based tests for invariants

Week 2

Add outbox and idempotency middleware to one service
Add deadlines and a retry policy as a shared library

Week 3

Move a real saga into a workflow engine
Add standard metrics and traces with exemplars

Week 4

Pilot CRDT based sync or WASM plugin behind a flag
Shadow traffic and compare causally

13. Appendices {#13.-appendices}

Appendix A. Rust blueprints {#appendix-a.-rust-blueprints}

State machine with enums and pattern matching
Command handler with outbox pattern
Property based test with proptest
Tower layer for idempotency and deadlines
TLA+ sketch for a saga

See code in this appendix section.

Appendix B. Laravel blueprints {#appendix-b.-laravel-blueprints}

Module structure for bounded contexts
Domain events and outbox relay
Idempotency middleware
Horizon ready queue setup with retries and jitter
Saga coordinator options and Temporal interop
Pest tests with generators and mutation testing with Infection

Appendix C. Nuxt blueprints {#appendix-c.-nuxt-blueprints}

Statecharts with XState
Event modeled UI with projections
Offline first with Yjs and repair loops
Edge function adapter for ports and adapters style

Appendix D. TLA+ and Alloy starter specs {#appendix-d.-tla+-and-alloy-starter-specs}

Minimal specs to model order capture and payment saga

Appendix E. ADR template {#appendix-e.-adr-template}

Copy friendly template for quick decisions

Appendix F. Property based testing recipes {#appendix-f.-property-based-testing-recipes}

Patterns for generators and shrinking

Appendix G. CRDT and real time collaboration {#appendix-g.-crdt-and-real-time-collaboration}

Choose a document shape per feature: map for forms, array for ordered lists, text for editors
Store events in an append only log with causal metadata
Reconcile with a repair loop that compares projection to source of truth

Appendix H. Pure Reducer Patterns (Deep Dive) {#appendix-h.-pure-reducer-patterns-(deep-dive)}

1) What is a pure reducer? {#1)-what-is-a-pure-reducer?}

A reducer is a total, deterministic function of the form:

(State, Event) -> State

Pure: no IO, no time, no randomness, no hidden reads/writes.
Total: defined for all valid (state, event) pairs; if an event is illegal for a state, return the same state (no-op) or a well-defined error value (sum type).
Deterministic: same inputs, same output — this unlocks replay, time travel, and property-based testing.

Think: the reducer is the mathematical heart of your domain; everything else (IO, DB, queues) is the shell.

2) Laws & invariants {#2)-laws-&-invariants}

If you log events e1..en and fold them:

fold(State0, [e1, e2, ..., en]) = reduce(...reduce(reduce(State0, e1), e2)..., en)

Useful laws (context-dependent):

Idempotency (per event type): applying the same event twice should either be impossible (guarded by command-layer) or a no-op.
Monotonicity (for some domains): certain counters or sets only grow (append-only). Model as such.
Compositionality: global state can be a product of slices; reducers compose via product and sum types.
Illegal states unrepresentable: encode with ADTs/enums and typed fields.

3) Architecture: Functional core, imperative shell {#3)-architecture:-functional-core,-imperative-shell}

Core: reducers + pure decision logic (validation, pricing, transitions). No framework/IO.
Shell: adapters that translate commands to events, persist events, call reducers, publish side effects.
Boundary: the shell never mutates domain state directly — it feeds events to the reducer.

Pattern: handle(command) -> [events, effects] where the reducer stays (state, event) -> state. The command handler is allowed to do IO to validate and then emit events; the reducer folds them.

4) Composition patterns {#4)-composition-patterns}

Slice reducers (product types): split state into slices, each with its reducer, then combine.
Layered reducers: base reducer + decorators that enforce cross-cutting invariants (e.g., quotas, limits).
Event upcasting: support schema evolution by mapping old events to new ones before reducing.
Lens-based updates: use lenses/paths to focus updates inside nested state without mutation.

5) Testing reducers (fast + powerful) {#5)-testing-reducers-(fast-+-powerful)}

Example tests: given state + event => expected state.
Property-based: generate arbitrary event sequences; assert invariants always hold and no panics occur.
Replay tests: a production event log must reproduce production projections bit-for-bit.
Metamorphic tests: inserting a no-op event, or reordering commutative events, does not change final state.

6) Performance & ergonomics {#6)-performance-&-ergonomics}

Prefer persistent data structures or structural sharing (Rust: Arc, smallvec, custom; JS: Immer or immutable libs) to keep copying cheap.
Normalize collections (ids -> maps) to keep reducer operations O(1) or O(log n).
Derive views with selectors and memoization; reducers stay ignorant of derived data.

7) Concurrency & Distribution {#7)-concurrency-&-distribution}

Reducers shine when scoped correctly — especially under concurrency and distributed conditions.

Aggregate boundaries {#aggregate-boundaries}

Scope each reducer to a single aggregate key (e.g., order_id, account_id).
Within that boundary, events are processed serially, guaranteeing consistency.
Concurrency across aggregates is safe because each reducer instance is isolated.

Serialization strategies {#serialization-strategies}

Actor mailbox: each aggregate reducer runs inside an actor, processing one event at a time.
DB row lock: lock per aggregate row while applying events, ensuring serial consistency.
Partition key: use Kafka/SQS/stream partitions keyed by aggregate ID to guarantee event ordering.

Cross-aggregate coordination {#cross-aggregate-coordination}

Use a saga or workflow orchestrator to coordinate reducers across multiple aggregates.
Reducers themselves stay pure and local; the saga emits events to each aggregate’s reducer.

Distribution patterns {#distribution-patterns}

Event sourcing with sharded logs: reducers replay from partitions assigned per aggregate.
Snapshotting: store snapshots alongside events to reduce replay cost.
Leader election: for reducers with global scope (rare), ensure only one active instance processes events.

Invariants under concurrency {#invariants-under-concurrency}

Keep invariants scoped to the aggregate whenever possible.
For global invariants (e.g., unique usernames), enforce at command level with locks or consensus, not inside reducers.

8) Anti-patterns {#8)-anti-patterns}

Things to avoid when designing reducers:

Impure reducers: calling time, random, DB, or network APIs.
Hidden mutations: modifying global singletons, static caches, or shared state.
Transport leakage: mixing HTTP, headers, or gRPC details into domain events.
Side effects inside reducer: reducers must only return new state, not perform I/O.
Overloaded reducers: too many responsibilities; split into slices or layers.

9) Blueprints {#9)-blueprints}

Rust — Pure reducer + effects pattern {#rust-—-pure-reducer-+-effects-pattern}

#[derive(Clone, Debug)]
pub enum OrderState { Draft, Pending { total: u64 }, Paid { txn: String, total: u64 }, Cancelled { reason: String } }

#[derive(Clone, Debug)]
pub enum OrderEvent { Submitted { total: u64 }, PaymentReceived { txn: String }, Cancelled { reason: String } }

pub fn reduce(state: &OrderState, ev: &OrderEvent) -> OrderState {
    use OrderEvent::*; use OrderState::*;
    match (state, ev) {
        (Draft, Submitted { total }) if *total > 0 => Pending { total: *total },
        (Pending { total }, PaymentReceived { txn }) => Paid { txn: txn.clone(), total: *total },
        (Draft, Cancelled { reason }) | (Pending { .. }, Cancelled { reason }) => Cancelled { reason: reason.clone() },
        (s, _) => s.clone(), // no-op for illegal transitions
    }
}

pub fn fold(mut s: OrderState, events: &[OrderEvent]) -> OrderState {
    for e in events { s = reduce(&s, e); }
    s
}

Laravel (PHP) — Reducer-as-service and fold {#laravel-(php)-—-reducer-as-service-and-fold}

final class OrderReducer {
  public function reduce(OrderState $s, array $e): OrderState {
    return match([$s, $e['type'] ?? null]) {
      [OrderState::Draft, 'Submitted'] => $e['total'] > 0 ? OrderState::Pending : $s,
      [OrderState::Pending, 'PaymentReceived'] => OrderState::Paid,
      [OrderState::Draft, 'Cancelled'], [OrderState::Pending, 'Cancelled'] => OrderState::Cancelled,
      default => $s,
    };
  }

  public function fold(OrderState $s, array $events): OrderState {
    foreach ($events as $e) { $s = $this->reduce($s, $e); }
    return $s;
  }
}

Nuxt/TypeScript — Slice reducers + combine {#nuxt/typescript-—-slice-reducers-+-combine}

export type OrderState =
  | { tag: 'Draft' }
  | { tag: 'Pending'; total: number }
  | { tag: 'Paid'; total: number; txn: string }
  | { tag: 'Cancelled'; reason: string };

export type OrderEvent =
  | { type: 'Submitted'; total: number }
  | { type: 'PaymentReceived'; txn: string }
  | { type: 'Cancelled'; reason?: string };

export function reduce(s: OrderState, e: OrderEvent): OrderState {
  switch (s.tag) {
    case 'Draft':
      if (e.type === 'Submitted' && e.total > 0) return { tag: 'Pending', total: e.total };
      if (e.type === 'Cancelled') return { tag: 'Cancelled', reason: e.reason || 'unknown' };
      return s;
    case 'Pending':
      if (e.type === 'PaymentReceived') return { tag: 'Paid', total: s.total, txn: e.txn };
      if (e.type === 'Cancelled') return { tag: 'Cancelled', reason: e.reason || 'unknown' };
      return s;
    default:
      return s; // terminal states are idempotent
  }
}

export const fold = (s0: OrderState, events: OrderEvent[]) => events.reduce(reduce, s0);

10) Migration & Interoperability {#10)-migration-&-interoperability}

Wrapping legacy systems: translate DB mutations into domain events, fold them with reducers to cross-check state.
Event upcasting: evolve event schemas by mapping old events into new forms before reduction.
Time travel debugging: reducers + event logs allow replaying history step by step.
Golden log tests: keep canonical logs to ensure replay results remain consistent after refactors.

11) Checklist for shipping reducer-based features {#11)-checklist-for-shipping-reducer-based-features}

12) Key Takeaways {#12)-key-takeaways}

Reducers are the simplest truth-preserving core for domain logic.
By keeping reducers pure, you enable replay, testing, and deterministic debugging.
Reducers compose naturally across slices and aggregates.
Paired with event sourcing, reducers provide infinite observability into past states.
Anti-patterns usually involve breaking purity or hiding side effects.

13) Next Steps {#13)-next-steps}

Apply reducers to a production flow (orders, payments, certificates).
Establish golden event logs for replay.
Extend reducer approach with property-based testing.
Pilot reducer + event sourcing in a small bounded context before rolling wider.

Appendix I. Testing Strategies for Reducers {#appendix-i.-testing-strategies-for-reducers}

1) Property-based testing {#1)-property-based-testing}

Generate random sequences of domain events.
Fold them through the reducer.
Assert invariants (e.g., totals never negative, illegal states unreachable).
Use shrinking to isolate minimal counterexamples.

2) Replay testing {#2)-replay-testing}

Record actual production event logs.
Replay them through the reducer to reconstruct state.
Compare against the persisted state (DB snapshot) for consistency.
Golden logs: preserve a canonical set of event logs to ensure deterministic replay across refactors.

3) Metamorphic testing {#3)-metamorphic-testing}

Insert no-op events and verify final state remains unchanged.
Reorder commutative events (e.g., adding items in a cart) and assert same final state.
Append redundant cancellations and ensure idempotency.

4) Simulation testing {#4)-simulation-testing}

Use deterministic schedulers to interleave events from concurrent aggregates.
Validate that per-aggregate invariants hold regardless of scheduling order.

5) Mutation testing {#5)-mutation-testing}

Apply mutations to reducer logic (invert conditions, alter comparisons).
Run property and replay tests to ensure they catch regressions.
Ensures your test suite is strong enough to detect subtle bugs.