ddd-microservice-simulator

---
name: ddd-microservice-simulator
description: >
  Domain-Driven Design simulator for business logic-rich microservice systems.
  Isolates core business logic from communication and transactional infrastructure,
  evaluates identical application code under varying consistency guarantees and
  network constraints, and supports Sagas and Transactional Causal Consistency (TCC)
  transactional models. Activation: DDD microservices, saga pattern, TCC,
  transactional consistency, microservice simulation, aggregate modeling,
  distributed consistency validation, shift-left testing.
---

# DDD Microservice Simulator

> Simulator that isolates core business logic from communication and transactional
> infrastructure in DDD microservice architectures, enabling deterministic shift-left
> validation of consistency trade-offs, coordination overhead, and resilience across
> deployment topologies.

## Metadata

- **arXiv ID:** 2605.01159v1 | **Category:** cs.SE, cs.DC
- **Title:** A Domain-Driven Design Simulator for Business Logic-Rich Microservice Systems
- **Authors:** Daniel da Palma Pereira, António Rito Silva
- **Published:** 2026-05-01
- **Keywords:** domain-driven design, microservices, sagas, transactional causal
  consistency, aggregate, distributed consistency, simulation

## Core Problem

Developing business-logic-rich microservices requires navigating complex trade-offs
between data consistency and distributed coordination. Patterns like Sagas and
Transactional Causal Consistency (TCC) are difficult to validate before production
deployment. Engineers lack a deterministic environment to evaluate how identical
application code behaves under different consistency guarantees, network
constraints, and deployment topologies.

## Key Innovation

A DDD microservice simulator that models systems around **aggregates** and
**isolates business logic from infrastructure**, allowing developers to:

1. **Evaluate identical code under varying consistency models** — compare Sagas,
   TCC, and strong consistency without rewriting application logic
2. **Transition seamlessly across deployment topologies** — from centralized
   monolith to fully distributed, with the same codebase
3. **Run deterministic concurrency testing** — reproduce race conditions and
   consistency violations in a controlled environment
4. **Quantify coordination overhead** — benchmark performance and resilience
   of different transactional models

## Technical Framework

### Architecture Layers

```
┌─────────────────────────────────────────────────┐
│              Application Code (DDD)              │
│  Aggregates │ Domain Services │ Value Objects   │
├─────────────────────────────────────────────────┤
│          Simulator Infrastructure Layer          │
│  ┌──────────┬──────────┬──────────┬──────────┐  │
│  │ Consist. │ Network  │ Deploy-  │ Concur-  │  │
│  │ Model    │ Faults   │ Topology │ rency    │  │
│  │ Config   │ Injector │ Selector │ Engine   │  │
│  └──────────┴──────────┴──────────┴──────────┘  │
├─────────────────────────────────────────────────┤
│           Execution / Storage Backend            │
│  In-memory │ Embedded DB │ Distributed Storage  │
└─────────────────────────────────────────────────┘
```

### Transactional Models Supported

| Model | Consistency | Coordination | Use Case |
|-------|-------------|--------------|----------|
| **Strong** | Linearizable | Blocking (2PC) | Single aggregate, ACID required |
| **Sagas** | Eventual | Non-blocking compensation | Long-running cross-aggregate workflows |
| **TCC** | Causal | Optimistic + causal tracking | Multi-aggregate with causal ordering |

### Aggregate Modeling

The simulator models microservice systems as collections of **aggregates** —
consistent units of domain state. Each aggregate encapsulates:

- **State**: The current data held by the aggregate
- **Domain logic**: Business rules and invariants
- **Commands**: Operations that modify state
- **Events**: Outcomes emitted after successful state changes

```
Aggregate
├── state: Dict[str, Any]
├── handle(command: Command) → Result[Event, Error]
├── apply(event: Event) → NewState
└── invariants: List[Predicate]
```

### Deployment Topology Spectrum

The simulator allows evaluation across a continuum:

```
Centralized ←────────────────────→ Fully Distributed
  (single process)                 (network-isolated nodes)
       │                                  │
       ▼                                  ▼
  - Shared memory                    - Message passing
  - ACID transactions                - Eventual consistency
  - No network latency               - Configurable delays
  - Single failure domain            - Independent failure domains
```

## Implementation Guide

### Step 1: Define Aggregates

Isolate domain logic from infrastructure. Each aggregate should be a pure
function from (state, command) to (new state, events).

```python
from dataclasses import dataclass
from typing import List, Union

@dataclass
class OrderCreated:
    order_id: str
    customer_id: str
    total: float

@dataclass
class Order:
    order_id: str
    status: str = "draft"
    total: float = 0.0

    def handle_create(self, cmd: CreateOrder) -> List[OrderCreated]:
        assert self.status == "draft"
        assert cmd.total > 0
        return [OrderCreated(
            order_id=self.order_id,
            customer_id=cmd.customer_id,
            total=cmd.total
        )]

    def apply(self, event: OrderCreated) -> "Order":
        return Order(
            order_id=self.order_id,
            status="created",
            total=event.total
        )
```

### Step 2: Configure Simulator

Select transactional model, network constraints, and deployment topology.

```python
simulator = DDDBasedSimulator(
    aggregates=[OrderAggregate, PaymentAggregate, ShippingAggregate],
    consistency_model="saga",      # or "tcc", "strong"
    topology="distributed",        # or "centralized", "partitioned"
    network_config=NetworkConfig(
        latency_ms=(10, 50),       # min-max latency range
        failure_rate=0.01,         # message drop probability
        partition_probability=0.001,
    ),
    concurrency_config=ConcurrencyConfig(
        max_threads=8,
        scheduling="random",       # deterministic interleaving
        seed=42,                   # reproducible runs
    ),
)
```

### Step 3: Define Sagas / TCC Workflows

Model cross-aggregate business processes with compensation logic.

```python
class OrderFulfillmentSaga:
    """Saga for order → payment → shipping workflow."""

    def steps(self):
        return [
            SagaStep(
                name="reserve_inventory",
                action=InventoryAggregate.reserve,
                compensating=InventoryAggregate.release,
            ),
            SagaStep(
                name="process_payment",
                action=PaymentAggregate.charge,
                compensating=PaymentAggregate.refund,
            ),
            SagaStep(
                name="create_shipment",
                action=ShippingAggregate.create,
                compensating=ShippingAggregate.cancel,
            ),
        ]
```

### Step 4: Run Simulation Scenarios

Execute the same business logic under different configurations.

```python
# Scenario A: Strong consistency, centralized
results_a = simulator.run(
    scenario=order_scenarios,
    consistency_model="strong",
    topology="centralized",
)

# Scenario B: Sagas, distributed with network faults
results_b = simulator.run(
    scenario=order_scenarios,       # same scenarios!
    consistency_model="saga",
    topology="distributed",
    network_config=faulty_network,
)

# Compare: correctness, latency, compensation rates
compare(results_a, results_b)
```

### Step 5: Analyze Results

```python
# Key metrics the simulator reports:
# - Consistency violations: count and types
# - Compensation rate: % of sagas that needed rollback
# - Latency distribution: p50, p95, p99
# - Throughput: operations/second
# - Coordination overhead: % time spent in consensus/coordination
# - Anomaly detection: lost updates, dirty reads, causal violations

report = simulator.analyze(results)
report.print_summary()
```

## Applications

- **Shift-left validation**: Test distributed consistency behaviors before
  deploying to production environments
- **Architecture comparison**: Quantify trade-offs between Sagas, TCC, and
  strong consistency for a given domain
- **Resilience testing**: Evaluate system behavior under network partitions,
  message delays, and node failures
- **Performance benchmarking**: Measure coordination overhead of different
  transactional models
- **Team communication**: Use simulation results to align engineering teams
  on consistency requirements
- **DDD refactoring**: Validate that extracted aggregates maintain correctness
  under distributed execution

## Pitfalls

- **Business logic must be pure**: Aggregates must not contain I/O, database
  access, or network calls — the simulator intercepts infrastructure. Mixed
  logic defeats isolation.
- **Saga compensations are not undo**: Compensating actions are new business
  transactions, not database rollbacks. Design compensations to handle partial
  completion of prior steps.
- **TCC requires causal tracking**: Transactional Causal Consistency depends
  on correct causal metadata propagation. Missing causal vectors can cause
  false positives in anomaly detection.
- **Determinism vs. realism**: Fixed-seed random scheduling gives reproducible
  results but may miss rare interleavings. Run with multiple seeds.
- **Topology mismatch**: Centralized simulation results do not directly
  translate to distributed performance — network overhead is modeled, not
  measured.
- **Aggregate boundary errors**: Incorrect aggregate boundaries (too large or
  too small) skew consistency analysis. Validate boundaries against domain
  invariants before simulation.

## Related Skills

- distributed-agent-orchestration
- knowledge-graph-ops
- distributed-systems-design
- event-sourcing-patterns
- cqrs-microservice-architecture