Advanced OSpec Patterns
Complex patterns and advanced techniques for sophisticated use cases
AdvancedAdvanced OSpec Patterns
This guide covers sophisticated patterns for complex use cases, enterprise environments, and advanced automation scenarios.
Multi-Service Orchestration
When building distributed systems with multiple interconnected services:
ospec_version: "1.0.0"
id: "microservices-ecommerce"
name: "E-commerce Microservices Platform"
description: "Distributed e-commerce system with multiple services"
outcome_type: "distributed-system"
services:
user_service:
outcome_type: "api"
stack:
runtime: "Node.js@18"
framework: "Express"
database: "PostgreSQL@14"
ports:
- "3001:3000"
dependencies: ["auth_service"]
product_service:
outcome_type: "api"
stack:
runtime: "Python@3.11"
framework: "FastAPI"
database: "MongoDB@6"
ports:
- "3002:8000"
dependencies: ["auth_service"]
auth_service:
outcome_type: "api"
stack:
runtime: "Go@1.21"
framework: "Gin"
database: "Redis@7"
ports:
- "3003:8080"
dependencies: []
deployment:
orchestrator: "Kubernetes"
namespace: "ecommerce"
service_mesh:
enabled: true
provider: "Istio"
features:
- "traffic_management"
- "security"
- "observability"
acceptance:
service_health:
- service: "user_service"
endpoint: "/health"
expected_status: 200
- service: "product_service"
endpoint: "/docs"
expected_status: 200
- service: "auth_service"
endpoint: "/ping"
expected_status: 200
integration_tests:
- name: "user_registration_flow"
steps:
- "POST /auth/register → 201"
- "POST /auth/login → 200"
- "GET /users/profile → 200"
- name: "product_catalog_flow"
steps:
- "GET /products → 200"
- "GET /products/123 → 200"
- "POST /products/123/reviews → 201"
Progressive Enhancement Pattern
Build systems that can be enhanced over time without breaking existing functionality:
ospec_version: "1.0.0"
id: "progressive-web-app"
name: "Progressive Web Application"
description: "Web app with progressive enhancement capabilities"
outcome_type: "web-app"
phases:
mvp:
priority: "critical"
features:
- "user_authentication"
- "basic_crud_operations"
- "responsive_design"
acceptance:
http_endpoints:
- path: "/login"
status: 200
- path: "/dashboard"
status: 200
auth_required: true
enhanced:
priority: "high"
depends_on: ["mvp"]
features:
- "offline_capability"
- "push_notifications"
- "advanced_search"
acceptance:
pwa_features:
- manifest_valid: true
- service_worker_registered: true
- offline_fallbacks: true
optimized:
priority: "medium"
depends_on: ["enhanced"]
features:
- "performance_optimization"
- "advanced_analytics"
- "ai_recommendations"
acceptance:
performance:
lighthouse_score: 90
core_web_vitals: "good"
bundle_size_mb: 2.0
# Feature flags for gradual rollout
feature_flags:
provider: "LaunchDarkly"
flags:
- name: "offline_mode"
default: false
rollout: "gradual"
- name: "ai_recommendations"
default: false
rollout: "a_b_test"
# Deployment strategy
deployment:
strategy: "blue_green"
canary:
enabled: true
traffic_split:
- version: "current"
percentage: 90
- version: "candidate"
percentage: 10
success_criteria:
error_rate_threshold: 0.01
latency_p99_threshold: 500
Multi-Environment Pipeline
Manage deployments across development, staging, and production environments:
ospec_version: "1.0.0"
id: "multi-env-pipeline"
name: "Multi-Environment CI/CD Pipeline"
description: "Production-ready deployment pipeline"
outcome_type: "infrastructure"
environments:
development:
auto_deploy: true
branch_trigger: "main"
stack:
compute: "AWS ECS Fargate"
database: "RDS PostgreSQL (dev)"
cdn: "CloudFront"
secrets: "AWS Secrets Manager"
acceptance:
deployment_time_max_minutes: 10
health_check_timeout_seconds: 60
staging:
auto_deploy: true
branch_trigger: "release/*"
requires_approval: false
stack:
compute: "AWS ECS Fargate"
database: "RDS PostgreSQL (staging)"
cdn: "CloudFront"
secrets: "AWS Secrets Manager"
acceptance:
deployment_time_max_minutes: 15
integration_tests_pass: true
security_scan_pass: true
performance_test_pass: true
production:
auto_deploy: false
requires_approval: true
approvers: ["tech-lead", "product-owner"]
stack:
compute: "AWS ECS Fargate (Multi-AZ)"
database: "RDS PostgreSQL (prod, Multi-AZ)"
cdn: "CloudFront + WAF"
secrets: "AWS Secrets Manager"
monitoring: "DataDog"
acceptance:
deployment_time_max_minutes: 30
zero_downtime: true
rollback_capability: true
monitoring_alerts_configured: true
# Cross-environment configurations
shared_config:
terraform:
backend:
type: "s3"
bucket: "company-terraform-state"
dynamodb_table: "terraform-locks"
observability:
logging: "CloudWatch Logs"
metrics: "CloudWatch Metrics"
tracing: "AWS X-Ray"
alerting: "PagerDuty"
guardrails:
environment_protection:
production:
- "require_pull_request_reviews"
- "require_status_checks"
- "require_signed_commits"
- "restrict_force_push"
security_scans:
- type: "dependency_check"
fail_on: "critical"
- type: "container_scan"
fail_on: "high"
- type: "infrastructure_scan"
fail_on: "medium"
# Disaster recovery
disaster_recovery:
rpo_hours: 4
rto_hours: 2
backup_strategy:
- type: "database"
frequency: "daily"
retention_days: 30
- type: "application_data"
frequency: "daily"
retention_days: 7
Plugin Architecture Pattern
Design extensible systems with plugin capabilities:
ospec_version: "1.0.0"
id: "extensible-platform"
name: "Extensible Development Platform"
description: "Platform with plugin architecture for extensibility"
outcome_type: "platform"
core_system:
stack:
backend: "Node.js@18 + Express"
frontend: "React@18 + TypeScript"
database: "PostgreSQL@14"
cache: "Redis@7"
plugin_interface:
api_version: "v1"
contract: |
interface Plugin {
name: string;
version: string;
init(context: PluginContext): Promise<void>;
execute(input: any): Promise<any>;
cleanup(): Promise<void>;
}
plugin_ecosystem:
registry:
type: "npm_registry"
namespace: "@platform/plugins"
plugin_types:
- name: "data_source"
interface: "IDataSourcePlugin"
examples: ["@platform/plugins-mysql", "@platform/plugins-mongodb"]
- name: "auth_provider"
interface: "IAuthPlugin"
examples: ["@platform/plugins-oauth", "@platform/plugins-saml"]
- name: "notification"
interface: "INotificationPlugin"
examples: ["@platform/plugins-email", "@platform/plugins-slack"]
# Plugin security and sandboxing
plugin_security:
sandboxing:
enabled: true
runtime: "isolated_vm"
memory_limit_mb: 256
cpu_limit_percent: 10
network_access: "restricted"
permissions:
default: "minimal"
escalation_required: true
audit_log: true
vetting:
static_analysis: true
dependency_scan: true
manual_review_required: true
acceptance:
plugin_system:
- plugin_discovery_works: true
- plugin_installation_secure: true
- plugin_isolation_enforced: true
- plugin_api_stable: true
extensibility:
- custom_plugin_loads: true
- plugin_hot_reload: true
- plugin_dependency_resolution: true
- plugin_error_isolation: true
# Example plugin configuration
plugin_config:
enabled_plugins:
- name: "@platform/plugins-mysql"
version: "^1.0.0"
config:
connection_pool_size: 10
timeout_ms: 5000
- name: "@platform/plugins-slack"
version: "^2.0.0"
config:
webhook_url: ""
default_channel: "#notifications"
AI/ML Pipeline Pattern
Sophisticated machine learning workflows with training, evaluation, and deployment:
ospec_version: "1.0.0"
id: "ml-recommendation-system"
name: "ML-Powered Recommendation System"
description: "End-to-end ML pipeline for product recommendations"
outcome_type: "ml-pipeline"
data_pipeline:
ingestion:
sources:
- type: "database"
connection: ""
tables: ["users", "products", "interactions"]
- type: "streaming"
source: "Kafka"
topics: ["user-events", "product-updates"]
processing:
framework: "Apache Spark"
features:
- "data_cleaning"
- "feature_engineering"
- "data_validation"
storage:
feature_store: "Feast"
data_warehouse: "BigQuery"
model_development:
experiments:
tracking: "MLflow"
framework: "PyTorch"
models:
- name: "collaborative_filtering"
algorithm: "Matrix Factorization"
hyperparameters:
embedding_dim: [64, 128, 256]
learning_rate: [0.001, 0.01, 0.1]
- name: "content_based"
algorithm: "TF-IDF + Cosine Similarity"
hyperparameters:
max_features: [10000, 50000, 100000]
ngram_range: [(1,1), (1,2), (1,3)]
training:
schedule: "weekly"
compute: "Kubernetes + GPU"
validation:
method: "time_series_split"
metrics:
- "precision@k"
- "recall@k"
- "ndcg@k"
thresholds:
precision_at_10: 0.15
recall_at_10: 0.25
deployment:
serving:
platform: "Kubernetes + KServe"
scaling:
min_replicas: 2
max_replicas: 10
target_cpu_percent: 70
model_registry: "MLflow Model Registry"
inference_api:
endpoint: "/recommend"
max_latency_ms: 100
throughput_rps: 1000
monitoring:
model_performance:
- metric: "precision_at_10"
threshold: 0.12
action: "alert"
- metric: "inference_latency"
threshold: 150
action: "scale_up"
data_drift:
detector: "Evidently"
schedule: "daily"
threshold: 0.1
acceptance:
model_quality:
- precision_at_10_min: 0.15
- recall_at_10_min: 0.25
- inference_latency_max_ms: 100
infrastructure:
- model_endpoint_available: true
- monitoring_dashboards_configured: true
- automated_retraining_works: true
- rollback_capability_tested: true
Zero-Trust Security Pattern
Implement comprehensive security with zero-trust principles:
ospec_version: "1.0.0"
id: "zero-trust-application"
name: "Zero-Trust Secure Application"
description: "Application built with zero-trust security principles"
outcome_type: "web-app"
security_model:
principles:
- "never_trust_always_verify"
- "least_privilege_access"
- "assume_breach"
- "verify_explicitly"
identity_management:
authentication:
multi_factor: "required"
providers:
- "Azure AD"
- "Okta"
session_management:
timeout_minutes: 30
refresh_required: true
authorization:
model: "RBAC + ABAC"
policy_engine: "Open Policy Agent"
permissions:
granular: true
time_bound: true
context_aware: true
network_security:
micro_segmentation:
enabled: true
default_deny: true
service_mesh: "Istio"
encryption:
in_transit: "TLS 1.3"
at_rest: "AES-256"
key_management: "AWS KMS"
api_gateway:
provider: "Kong"
features:
- "rate_limiting"
- "request_validation"
- "threat_detection"
application_security:
secure_coding:
static_analysis: "SonarQube"
dependency_scan: "Snyk"
secret_detection: "GitGuardian"
runtime_protection:
waf: "AWS WAF"
ddos_protection: "CloudFlare"
bot_detection: "PerimeterX"
monitoring_security:
siem: "Splunk"
threat_detection: "AWS GuardDuty"
incident_response:
automation: "Phantom/SOAR"
playbooks: "defined"
escalation: "PagerDuty"
compliance:
frameworks:
- "SOC 2 Type II"
- "ISO 27001"
- "GDPR"
audit_logging:
all_access: true
immutable: true
retention_years: 7
data_governance:
classification: "automatic"
data_loss_prevention: "Microsoft Purview"
privacy_controls: "OneTrust"
acceptance:
security_posture:
- vulnerability_scan_pass: true
- penetration_test_pass: true
- compliance_audit_pass: true
- zero_critical_findings: true
operational_security:
- incident_response_tested: true
- backup_recovery_verified: true
- security_training_completed: true
- monitoring_alerts_verified: true
Event-Driven Architecture
Build scalable event-driven systems:
ospec_version: "1.0.0"
id: "event-driven-ecommerce"
name: "Event-Driven E-commerce System"
description: "Scalable event-driven architecture for e-commerce"
outcome_type: "distributed-system"
event_infrastructure:
message_broker: "Apache Kafka"
event_store: "EventStore"
stream_processing: "Apache Flink"
schema_registry: "Confluent Schema Registry"
event_patterns:
event_sourcing:
enabled: true
aggregates:
- "Order"
- "Customer"
- "Inventory"
- "Payment"
cqrs:
enabled: true
read_models:
- "OrderSummary"
- "CustomerProfile"
- "InventoryView"
projections:
strategy: "eventually_consistent"
rebuild_capability: true
services:
order_service:
events_published:
- "OrderCreated"
- "OrderUpdated"
- "OrderCancelled"
events_consumed:
- "PaymentProcessed"
- "InventoryReserved"
payment_service:
events_published:
- "PaymentProcessed"
- "PaymentFailed"
- "RefundProcessed"
events_consumed:
- "OrderCreated"
- "OrderCancelled"
inventory_service:
events_published:
- "InventoryReserved"
- "InventoryReleased"
- "StockUpdated"
events_consumed:
- "OrderCreated"
- "OrderCancelled"
# Event schema definitions
event_schemas:
OrderCreated:
version: "1.0.0"
schema: |
{
"type": "object",
"properties": {
"orderId": {"type": "string"},
"customerId": {"type": "string"},
"items": {"type": "array"},
"totalAmount": {"type": "number"},
"timestamp": {"type": "string", "format": "date-time"}
},
"required": ["orderId", "customerId", "items", "totalAmount", "timestamp"]
}
resilience_patterns:
retry_policy:
max_attempts: 3
backoff: "exponential"
jitter: true
circuit_breaker:
failure_threshold: 5
timeout_seconds: 60
half_open_max_calls: 3
bulkhead:
thread_pools: "isolated"
resource_isolation: true
acceptance:
event_flow:
- event_publishing_reliable: true
- event_ordering_preserved: true
- duplicate_detection_works: true
- dead_letter_handling: true
scalability:
- horizontal_scaling_verified: true
- partition_rebalancing_works: true
- backpressure_handling: true
- throughput_target_met: true
Best Practices Summary
Pattern Selection Guidelines
- Complexity Assessment: Choose patterns appropriate for your system’s complexity
- Team Expertise: Consider team familiarity with patterns and technologies
- Scalability Requirements: Select patterns that match your scale needs
- Maintenance Burden: Balance sophistication with long-term maintainability
- Business Context: Align technical patterns with business requirements
Common Anti-Patterns to Avoid
- Over-engineering: Using complex patterns for simple problems
- Inconsistent patterns: Mixing incompatible architectural styles
- Vendor lock-in: Tying patterns too tightly to specific technologies
- Premature optimization: Implementing advanced patterns before they’re needed
- Pattern proliferation: Using too many different patterns in one system
Testing Advanced Patterns
- Component testing: Test individual services in isolation
- Integration testing: Verify interactions between components
- Contract testing: Ensure API compatibility across services
- Chaos engineering: Test resilience under failure conditions
- Performance testing: Validate patterns meet scale requirements