distributed-patterns

name	distributed-patterns
department	architect
description	Use when designing distributed systems or evaluating distributed architecture patterns. Covers CAP theorem trade-offs, consensus protocols (Raft, Paxos), saga orchestration, CRDTs, event sourcing, partition handling, distributed transactions, and failure detectors. Do not use for general API design (use api-design) or database schema design (use schema-design).
version	1
triggers	["distributed","consensus","partition","saga","CRDT","event sourcing","microservices","distributed transactions","Raft","Paxos","CAP theorem"]

Distributed Patterns

Purpose

Evaluate distributed system design decisions and recommend appropriate patterns for consistency, availability, partition tolerance, and failure handling. Ensure designs account for the fundamental constraints of distributed computing.

Scope Constraints

Analyzes architecture documents, system diagrams, and code for distributed system patterns. Does not modify files or execute code. Does not benchmark or load-test distributed systems.

Inputs

• System architecture overview (services, data stores, communication patterns)
• Consistency requirements (strong, eventual, causal)
• Availability and latency SLAs
• Expected failure modes and partition scenarios
• Current distributed patterns in use, if any

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

• Step 1: Classify CAP trade-offs
• Step 2: Evaluate consistency patterns
• Step 3: Design failure handling
• Step 4: Review data synchronization
• Step 5: Assess transaction patterns
• Step 6: Validate operational readiness

Step 1: Classify CAP Trade-offs

• Identify each service's position on the CAP spectrum.
• Map which data requires strong consistency vs eventual consistency.
• Document partition behavior: does the system favor CP or AP per data domain?
• Flag services that implicitly assume no partitions (single-region, no failover).
• Verify the design explicitly acknowledges CAP trade-offs rather than ignoring them.

Step 2: Evaluate Consistency Patterns

• Identify consensus requirements (leader election, distributed locks, config agreement).
• Recommend consensus protocol fit: Raft for simplicity, Paxos/Multi-Paxos for flexibility, ZAB for ordered broadcast.
• Evaluate CRDT suitability for conflict-free replicated data (counters, sets, registers, maps).
• Check event sourcing applicability: append-only event log, projection rebuilds, event versioning.
• Verify causal ordering where needed (vector clocks, hybrid logical clocks).

Step 3: Design Failure Handling

• Enumerate failure detector strategy (heartbeats, phi-accrual, swim protocol).
• Define timeout budgets per service call (connect, read, total).
• Specify circuit breaker configuration (threshold, half-open behavior, fallback).
• Plan for partial failure: which operations can proceed with degraded service?
• Document split-brain prevention strategy for stateful services.

Step 4: Review Data Synchronization

• Map data replication topology (single-leader, multi-leader, leaderless).
• Identify read-after-write consistency requirements and implementation.
• Check for anti-entropy mechanisms (Merkle trees, read repair, hinted handoff).
• Verify conflict resolution strategy for concurrent writes (last-writer-wins, merge functions, manual).
• Assess replication lag tolerance per data domain.

Step 5: Assess Transaction Patterns

• Identify cross-service transaction requirements.
• Evaluate saga pattern fit: orchestration (central coordinator) vs choreography (event-driven).
• Define compensating transactions for each saga step.
• Check idempotency guarantees for all saga participants.
• Verify at-least-once delivery with deduplication for message-driven flows.
• Flag any use of distributed locks or two-phase commit — justify or replace.

Step 6: Validate Operational Readiness

• Verify distributed tracing spans cross service boundaries (trace ID propagation).
• Check that partition/failover scenarios have runbooks.
• Confirm chaos engineering tests cover: network partition, node failure, clock skew.
• Validate that the system degrades gracefully under partial failure.

Validation gate: Revisit this analysis after 5+ sessions to verify patterns held up under real-world usage and evolution.

Compaction resilience: If context was lost, re-read the Inputs section to identify the system under analysis, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Output Format

Pattern Selection Matrix

Concern	Pattern	Rationale	Trade-off
Consistency	Raft / CRDT / Event sourcing	...	...
Failure handling	Circuit breaker + phi-accrual	...	...
Transactions	Saga (orchestration)	...	...
Replication	Single-leader with read replicas	...	...

Failure Scenario Table

Scenario	Impact	Mitigation	Recovery Time
Network partition between A and B	...	...	...
Leader node failure	...	...	...

Handoff

• Hand off to api-design for service boundary API contracts.
• Hand off to operator/deployment-plan for multi-region deployment topology.
• Hand off to prover/formal-spec if consensus protocol correctness needs formal verification.

Quality Checks

• CAP trade-offs explicitly documented per data domain
• Consensus protocol selection justified with requirements
• Failure detectors and timeout budgets specified
• Saga compensating transactions defined for each step
• Idempotency and deduplication verified for message-driven flows
• Replication strategy and conflict resolution documented
• Distributed tracing confirmed across service boundaries
• Chaos engineering scenarios identified

Evolution Notes