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).

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.

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.

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.

# 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

# 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