LoopyLuci Microservices Decomposition
Decomposing monolithic applications into microservices — from bounded context mapping through service boundaries, inter-service communication, data decomposition, and migration strategies.
When to Use
- Breaking a monolith into microservices
- Identifying service boundaries from domain analysis
- Designing inter-service communication patterns
- Migrating data from shared to per-service databases
- Managing the decomposition process safely
Decomposition Patterns
DECOMPOSITION_STRATEGIES = {
'business_capability': 'Split by business functions (orders, payments, shipping)',
'subdomain': 'Split by DDD subdomains (core, supporting, generic)',
'strangler_fig': 'Gradually replace monolith pieces with new services',
'event_storming': 'Identify aggregates and bounded contexts via workshops',
}
def identify_services(domain_events: List[str]) -> List[str]:
"""Identify candidate services from domain events."""
# Group events by noun/entity
services = set()
for event in domain_events:
words = event.split()
# First noun after the verb is likely a service candidate
for i, w in enumerate(words):
if w.lower() in ('created', 'updated', 'deleted', 'submitted', 'approved'):
if i + 1 < len(words):
services.add(words[i + 1].lower())
return list(services)
Common Pitfalls
- Distributed monolith — services that can't be deployed independently
- Shared database — services sharing a database = no real decomposition
- Too fine-grained — each method as a service creates overhead chaos
- Wrong boundaries — splitting along technical layers (not business domains)
- No data strategy — data is the hardest part; plan per-service databases
Verification Checklist
- Bounded contexts identified and mapped
- Services align with business capabilities
- Each service has its own data store
- Inter-service communication pattern chosen (sync/async/event)
- Strangler Fig migration plan in place
- Deployment independence verified