Dana Structs Cookbook - Real-World Patterns¶
Practical recipes for common struct patterns and use cases
Table of Contents¶
Business Domain Models¶
Customer Relationship Management (CRM)¶
struct Contact:
id: str
first_name: str
last_name: str
email: str
phone: str
company_id: str
status: str # "active", "inactive", "prospect"
tags: list
created_at: str
struct Company:
id: str
name: str
industry: str
size: str # "startup", "small", "medium", "large", "enterprise"
contacts: list
revenue: float
location: str
struct Deal:
id: str
contact_id: str
company_id: str
amount: float
stage: str # "prospect", "proposal", "negotiation", "closed_won", "closed_lost"
probability: float
close_date: str
notes: str
# Business logic functions
def qualify_lead(contact: Contact, company: Company) -> str:
# AI-powered lead qualification
qualification = reason(
"Assess lead quality based on contact and company information",
context=[contact, company],
format="json"
)
return qualification
def predict_deal_success(deal: Deal, company: Company, contact: Contact) -> float:
# Predictive analysis
prediction = reason(
"Predict the probability of this deal closing successfully",
context=[deal, company, contact],
temperature=0.3 # More deterministic
)
# Extract probability from AI response
if "%" in prediction:
# Parse percentage
percentage_str = prediction.split("%")[0].split()[-1]
return float(percentage_str) / 100.0
return deal.probability
def generate_follow_up_strategy(deal: Deal, contact: Contact) -> str:
strategy = reason(
f"Generate a follow-up strategy for a {deal.stage} stage deal",
context=[deal, contact]
)
return strategy
# Usage patterns
def process_new_lead(contact_data: dict, company_data: dict) -> dict:
# Create domain objects
contact = Contact(
id=contact_data.get("id"),
first_name=contact_data.get("first_name"),
last_name=contact_data.get("last_name"),
email=contact_data.get("email"),
phone=contact_data.get("phone", ""),
company_id=company_data.get("id"),
status="prospect",
tags=[],
created_at=get_current_timestamp()
)
company = Company(
id=company_data.get("id"),
name=company_data.get("name"),
industry=company_data.get("industry"),
size=company_data.get("size"),
contacts=[],
revenue=company_data.get("revenue", 0),
location=company_data.get("location")
)
# AI-powered qualification
qualification = contact.qualify_lead(company)
# Create deal if qualified
if "qualified" in qualification.lower():
deal = Deal(
id=generate_id("DEAL"),
contact_id=contact.id,
company_id=company.id,
amount=estimate_deal_value(company),
stage="prospect",
probability=0.1,
close_date=estimate_close_date(),
notes=qualification
)
# Predict success probability
predicted_probability = deal.predict_deal_success(company, contact)
deal.probability = predicted_probability
# Generate follow-up strategy
strategy = deal.generate_follow_up_strategy(contact)
return {
"contact": contact,
"company": company,
"deal": deal,
"qualification": qualification,
"strategy": strategy,
"status": "qualified"
}
return {
"contact": contact,
"company": company,
"qualification": qualification,
"status": "not_qualified"
}
Financial Portfolio Management¶
struct Asset:
symbol: str
name: str
asset_type: str # "stock", "bond", "etf", "crypto"
current_price: float
currency: str
market_cap: float
sector: str
struct Position:
asset: Asset
quantity: float
average_cost: float
current_value: float
unrealized_gain_loss: float
weight: float # Portfolio weight percentage
struct Portfolio:
id: str
name: str
owner_id: str
positions: list
total_value: float
cash_balance: float
risk_tolerance: str # "conservative", "moderate", "aggressive"
target_allocation: dict # Sector/asset type targets
# Portfolio analysis functions
def calculate_portfolio_metrics(portfolio: Portfolio) -> dict:
# Calculate real-time metrics
total_value = portfolio.cash_balance
total_gain_loss = 0.0
for position in portfolio.positions:
position.current_value = position.quantity * position.asset.current_price
position.unrealized_gain_loss = position.current_value - (position.quantity * position.average_cost)
total_value = total_value + position.current_value
total_gain_loss = total_gain_loss + position.unrealized_gain_loss
# Update portfolio total
portfolio.total_value = total_value
# Calculate weights
for position in portfolio.positions:
if total_value > 0:
position.weight = (position.current_value / total_value) * 100
return {
"total_value": total_value,
"total_gain_loss": total_gain_loss,
"total_return_pct": (total_gain_loss / (total_value - total_gain_loss)) * 100 if total_value > total_gain_loss else 0,
"cash_percentage": (portfolio.cash_balance / total_value) * 100 if total_value > 0 else 100
}
def analyze_risk_exposure(portfolio: Portfolio) -> dict:
# AI-powered risk analysis
sector_exposure = {}
asset_type_exposure = {}
for position in portfolio.positions:
# Sector exposure
sector = position.asset.sector
if sector not in sector_exposure:
sector_exposure[sector] = 0
sector_exposure[sector] = sector_exposure[sector] + position.weight
# Asset type exposure
asset_type = position.asset.asset_type
if asset_type not in asset_type_exposure:
asset_type_exposure[asset_type] = 0
asset_type_exposure[asset_type] = asset_type_exposure[asset_type] + position.weight
# AI risk assessment
risk_analysis = reason(
f"Analyze portfolio risk for {portfolio.risk_tolerance} investor",
context=[sector_exposure, asset_type_exposure, portfolio.target_allocation]
)
return {
"sector_exposure": sector_exposure,
"asset_type_exposure": asset_type_exposure,
"risk_analysis": risk_analysis,
"diversification_score": calculate_diversification_score(sector_exposure)
}
def generate_rebalancing_recommendations(portfolio: Portfolio) -> list:
# Calculate current vs target allocation
current_metrics = portfolio.calculate_portfolio_metrics()
risk_analysis = portfolio.analyze_risk_exposure()
recommendations = reason(
"Generate portfolio rebalancing recommendations",
context=[
portfolio.target_allocation,
risk_analysis["sector_exposure"],
portfolio.risk_tolerance,
current_metrics
]
)
return parse_recommendations(recommendations)
def optimize_tax_efficiency(portfolio: Portfolio, tax_year: str) -> dict:
# Tax loss harvesting and optimization
tax_optimization = reason(
"Suggest tax-efficient portfolio moves for the current tax year",
context=[portfolio.positions, tax_year]
)
return {
"tax_loss_harvesting": extract_tax_loss_opportunities(portfolio),
"optimization_suggestions": tax_optimization
}
# Helper functions
def calculate_diversification_score(sector_exposure: dict) -> float:
# Simple diversification score based on sector distribution
if not sector_exposure:
return 0.0
# Higher score for more even distribution
num_sectors = len(sector_exposure)
ideal_weight = 100.0 / num_sectors
variance_sum = 0.0
for weight in sector_exposure.values():
variance_sum = variance_sum + ((weight - ideal_weight) ** 2)
# Convert to 0-100 score (lower variance = higher score)
variance_penalty = variance_sum / (num_sectors * ideal_weight * ideal_weight)
return max(0, 100 - (variance_penalty * 100))
def extract_tax_loss_opportunities(portfolio: Portfolio) -> list:
opportunities = []
for position in portfolio.positions:
if position.unrealized_gain_loss < 0:
opportunities.append({
"symbol": position.asset.symbol,
"loss_amount": abs(position.unrealized_gain_loss),
"suggestion": f"Consider harvesting ${abs(position.unrealized_gain_loss):.2f} loss from {position.asset.symbol}"
})
return opportunities
# Usage example
def create_sample_portfolio() -> Portfolio:
# Create assets
apple = Asset(
symbol="AAPL",
name="Apple Inc.",
asset_type="stock",
current_price=175.50,
currency="USD",
market_cap=2800000000000,
sector="Technology"
)
sp500 = Asset(
symbol="SPY",
name="SPDR S&P 500 ETF",
asset_type="etf",
current_price=445.20,
currency="USD",
market_cap=400000000000,
sector="Diversified"
)
# Create positions
apple_position = Position(
asset=apple,
quantity=100,
average_cost=150.00,
current_value=0, # Will be calculated
unrealized_gain_loss=0, # Will be calculated
weight=0 # Will be calculated
)
spy_position = Position(
asset=sp500,
quantity=50,
average_cost=420.00,
current_value=0,
unrealized_gain_loss=0,
weight=0
)
# Create portfolio
portfolio = Portfolio(
id="PORT-001",
name="Growth Portfolio",
owner_id="USER-123",
positions=[apple_position, spy_position],
total_value=0, # Will be calculated
cash_balance=5000.00,
risk_tolerance="moderate",
target_allocation={
"Technology": 30,
"Diversified": 50,
"Cash": 20
}
)
return portfolio
# Complete workflow
def portfolio_analysis_workflow():
# Create portfolio
portfolio = create_sample_portfolio()
# Calculate metrics
metrics = portfolio.calculate_portfolio_metrics()
log(f"Portfolio value: ${metrics['total_value']:,.2f}")
log(f"Total return: {metrics['total_return_pct']:.2f}%")
# Analyze risk
risk_analysis = portfolio.analyze_risk_exposure()
log(f"Diversification score: {risk_analysis['diversification_score']:.1f}/100")
# Get recommendations
recommendations = portfolio.generate_rebalancing_recommendations()
for rec in recommendations:
log(f"Recommendation: {rec}")
# Tax optimization
tax_analysis = portfolio.optimize_tax_efficiency("2024")
log(f"Tax optimization: {tax_analysis['optimization_suggestions']}")
return {
"portfolio": portfolio,
"metrics": metrics,
"risk_analysis": risk_analysis,
"recommendations": recommendations,
"tax_analysis": tax_analysis
}
Data Processing Pipelines¶
ETL Pipeline for Analytics¶
struct DataSource:
id: str
name: str
type: str # "database", "api", "file", "stream"
connection_string: str
format: str # "json", "csv", "xml", "parquet"
schema: dict
last_updated: str
struct DataRecord:
source_id: str
record_id: str
data: dict
timestamp: str
quality_score: float
errors: list
struct ProcessingStage:
name: str
input_type: str
output_type: str
configuration: dict
metrics: dict
struct Pipeline:
id: str
name: str
sources: list
stages: list
destination: str
schedule: str
status: str # "running", "stopped", "failed", "completed"
last_run: str
error_count: int
# Data extraction functions
def extract_from_source(source: DataSource, batch_size: int = 1000) -> list:
log(f"Extracting data from {source.name} ({source.type})")
extracted_records = []
if source.type == "database":
records = query_database(source.connection_string, batch_size)
elif source.type == "api":
records = fetch_from_api(source.connection_string, source.format)
elif source.type == "file":
records = read_file(source.connection_string, source.format)
else:
log(f"Unsupported source type: {source.type}", level="error")
return []
# Convert to DataRecord objects
for record in records:
data_record = DataRecord(
source_id=source.id,
record_id=generate_record_id(),
data=record,
timestamp=get_current_timestamp(),
quality_score=0.0, # Will be calculated
errors=[]
)
extracted_records.append(data_record)
log(f"Extracted {len(extracted_records)} records from {source.name}")
return extracted_records
def validate_data_quality(record: DataRecord, source: DataSource) -> DataRecord:
# Schema validation
quality_issues = []
quality_score = 100.0
# Check required fields
required_fields = source.schema.get("required", [])
for field in required_fields:
if field not in record.data or record.data[field] is None:
quality_issues.append(f"Missing required field: {field}")
quality_score = quality_score - 20
# Data type validation
field_types = source.schema.get("types", {})
for field, expected_type in field_types.items():
if field in record.data:
actual_value = record.data[field]
if not validate_type(actual_value, expected_type):
quality_issues.append(f"Type mismatch for {field}: expected {expected_type}")
quality_score = quality_score - 10
# AI-powered quality assessment
ai_quality_check = reason(
"Assess data quality and identify potential issues",
context=record.data,
temperature=0.1 # Deterministic
)
if "issue" in ai_quality_check.lower() or "error" in ai_quality_check.lower():
quality_issues.append(f"AI detected: {ai_quality_check}")
quality_score = quality_score - 15
# Update record
record.quality_score = max(0, quality_score)
record.errors = quality_issues
return record
def transform_data(record: DataRecord, transformation_rules: dict) -> DataRecord:
# Apply transformation rules
transformed_data = record.data.copy()
# Field mapping
field_mappings = transformation_rules.get("field_mappings", {})
for old_field, new_field in field_mappings.items():
if old_field in transformed_data:
transformed_data[new_field] = transformed_data.pop(old_field)
# Data enrichment
enrichment_rules = transformation_rules.get("enrichment", {})
for field, rule in enrichment_rules.items():
if rule == "uppercase":
if field in transformed_data:
transformed_data[field] = str(transformed_data[field]).upper()
elif rule == "normalize_phone":
if field in transformed_data:
transformed_data[field] = normalize_phone_number(transformed_data[field])
elif rule.startswith("ai_"):
# AI-powered transformation
ai_result = reason(
f"Apply {rule} transformation to: {transformed_data.get(field)}",
context=transformed_data
)
transformed_data[field] = ai_result
# Create new record with transformed data
transformed_record = DataRecord(
source_id=record.source_id,
record_id=record.record_id,
data=transformed_data,
timestamp=get_current_timestamp(),
quality_score=record.quality_score,
errors=record.errors
)
return transformed_record
def execute_pipeline(pipeline: Pipeline) -> dict:
log(f"Starting pipeline execution: {pipeline.name}")
pipeline.status = "running"
pipeline.last_run = get_current_timestamp()
all_records = []
total_processed = 0
total_errors = 0
# Extract from all sources
for source in pipeline.sources:
try:
records = extract_from_source(source)
# Validate data quality
validated_records = []
for record in records:
validated_record = record.validate_data_quality(source)
if validated_record.errors:
total_errors = total_errors + len(validated_record.errors)
validated_records.append(validated_record)
all_records.extend(validated_records)
total_processed = total_processed + len(records)
except Exception as e:
log(f"Error extracting from {source.name}: {e}", level="error")
total_errors = total_errors + 1
# Process through pipeline stages
processed_records = all_records
for stage in pipeline.stages:
try:
stage_config = stage.configuration
if stage.name == "transform":
transformed_records = []
for record in processed_records:
transformed = record.transform_data(stage_config)
transformed_records.append(transformed)
processed_records = transformed_records
elif stage.name == "filter":
filtered_records = apply_filters(processed_records, stage_config)
processed_records = filtered_records
elif stage.name == "aggregate":
aggregated_records = apply_aggregations(processed_records, stage_config)
processed_records = aggregated_records
# Update stage metrics
stage.metrics = {
"records_in": len(all_records) if stage == pipeline.stages[0] else len(processed_records),
"records_out": len(processed_records),
"execution_time": measure_execution_time(),
"last_run": get_current_timestamp()
}
except Exception as e:
log(f"Error in stage {stage.name}: {e}", level="error")
total_errors = total_errors + 1
# Load to destination
if processed_records:
try:
load_to_destination(processed_records, pipeline.destination)
log(f"Loaded {len(processed_records)} records to {pipeline.destination}")
except Exception as e:
log(f"Error loading to destination: {e}", level="error")
total_errors = total_errors + 1
# Update pipeline status
pipeline.error_count = total_errors
if total_errors == 0:
pipeline.status = "completed"
else:
pipeline.status = "completed_with_errors"
results = {
"pipeline_id": pipeline.id,
"total_processed": total_processed,
"total_loaded": len(processed_records),
"total_errors": total_errors,
"execution_time": measure_total_execution_time(),
"status": pipeline.status
}
log(f"Pipeline {pipeline.name} completed: {results}")
return results
# Helper functions
def validate_type(value, expected_type: str) -> bool:
if expected_type == "string":
return isinstance(value, str)
elif expected_type == "number":
return isinstance(value, (int, float))
elif expected_type == "boolean":
return isinstance(value, bool)
elif expected_type == "array":
return isinstance(value, list)
elif expected_type == "object":
return isinstance(value, dict)
return true
def normalize_phone_number(phone: str) -> str:
# Simple phone normalization
if not phone:
return ""
# Remove non-digits
digits_only = "".join(c for c in phone if c.isdigit())
# Format as (XXX) XXX-XXXX for US numbers
if len(digits_only) == 10:
return f"({digits_only[:3]}) {digits_only[3:6]}-{digits_only[6:]}"
elif len(digits_only) == 11 and digits_only.startswith("1"):
return f"({digits_only[1:4]}) {digits_only[4:7]}-{digits_only[7:]}"
return phone # Return original if can't normalize
# Usage example
def create_analytics_pipeline() -> Pipeline:
# Define data sources
customer_db = DataSource(
id="SRC-001",
name="Customer Database",
type="database",
connection_string="postgresql://user:pass@localhost/customers",
format="sql",
schema={
"required": ["customer_id", "email", "created_at"],
"types": {
"customer_id": "string",
"email": "string",
"created_at": "string",
"age": "number"
}
},
last_updated=""
)
sales_api = DataSource(
id="SRC-002",
name="Sales API",
type="api",
connection_string="https://api.company.com/sales",
format="json",
schema={
"required": ["sale_id", "customer_id", "amount"],
"types": {
"sale_id": "string",
"customer_id": "string",
"amount": "number"
}
},
last_updated=""
)
# Define processing stages
transform_stage = ProcessingStage(
name="transform",
input_type="raw",
output_type="normalized",
configuration={
"field_mappings": {
"customer_id": "cust_id",
"created_at": "registration_date"
},
"enrichment": {
"email": "ai_classify_domain",
"phone": "normalize_phone"
}
},
metrics={}
)
filter_stage = ProcessingStage(
name="filter",
input_type="normalized",
output_type="filtered",
configuration={
"quality_threshold": 80.0,
"date_range": {
"start": "2024-01-01",
"end": "2024-12-31"
}
},
metrics={}
)
# Create pipeline
pipeline = Pipeline(
id="PIPE-001",
name="Customer Analytics Pipeline",
sources=[customer_db, sales_api],
stages=[transform_stage, filter_stage],
destination="data_warehouse.analytics.customer_360",
schedule="daily",
status="stopped",
last_run="",
error_count=0
)
return pipeline
def run_analytics_pipeline():
pipeline = create_analytics_pipeline()
results = pipeline.execute_pipeline()
# Generate summary report
summary = reason(
"Generate a summary report of the pipeline execution",
context=results
)
log(f"Pipeline Summary: {summary}")
return results
Configuration Management¶
Application Configuration System¶
struct DatabaseConfig:
host: str
port: int
database: str
username: str
password: str
ssl_enabled: bool
connection_pool_size: int
timeout_seconds: int
struct APIConfig:
base_url: str
api_key: str
rate_limit_per_minute: int
timeout_seconds: int
retry_attempts: int
enable_logging: bool
struct FeatureFlags:
use_new_algorithm: bool
enable_caching: bool
debug_mode: bool
beta_features: bool
ai_suggestions: bool
struct AppConfig:
environment: str # "development", "staging", "production"
debug: bool
log_level: str
database: DatabaseConfig
api: APIConfig
features: FeatureFlags
version: str
deployment_time: str
# Configuration management functions
def load_config_from_environment() -> AppConfig:
# Load from environment variables
env = get_environment()
database_config = DatabaseConfig(
host=env.get("DB_HOST", "localhost"),
port=int(env.get("DB_PORT", "5432")),
database=env.get("DB_NAME", "app_db"),
username=env.get("DB_USER", "app_user"),
password=env.get("DB_PASSWORD", ""),
ssl_enabled=env.get("DB_SSL", "false").lower() == "true",
connection_pool_size=int(env.get("DB_POOL_SIZE", "10")),
timeout_seconds=int(env.get("DB_TIMEOUT", "30"))
)
api_config = APIConfig(
base_url=env.get("API_BASE_URL", "https://api.example.com"),
api_key=env.get("API_KEY", ""),
rate_limit_per_minute=int(env.get("API_RATE_LIMIT", "100")),
timeout_seconds=int(env.get("API_TIMEOUT", "30")),
retry_attempts=int(env.get("API_RETRIES", "3")),
enable_logging=env.get("API_LOGGING", "true").lower() == "true"
)
feature_flags = FeatureFlags(
use_new_algorithm=env.get("FEATURE_NEW_ALGO", "false").lower() == "true",
enable_caching=env.get("FEATURE_CACHE", "true").lower() == "true",
debug_mode=env.get("FEATURE_DEBUG", "false").lower() == "true",
beta_features=env.get("FEATURE_BETA", "false").lower() == "true",
ai_suggestions=env.get("FEATURE_AI", "true").lower() == "true"
)
config = AppConfig(
environment=env.get("ENVIRONMENT", "development"),
debug=env.get("DEBUG", "false").lower() == "true",
log_level=env.get("LOG_LEVEL", "INFO"),
database=database_config,
api=api_config,
features=feature_flags,
version=env.get("APP_VERSION", "1.0.0"),
deployment_time=get_current_timestamp()
)
return config
def validate_config(config: AppConfig) -> dict:
validation_results = {
"valid": true,
"errors": [],
"warnings": []
}
# Database validation
if not config.database.host:
validation_results["errors"].append("Database host is required")
validation_results["valid"] = false
if config.database.port < 1 or config.database.port > 65535:
validation_results["errors"].append("Database port must be between 1 and 65535")
validation_results["valid"] = false
if not config.database.password and config.environment == "production":
validation_results["errors"].append("Database password is required in production")
validation_results["valid"] = false
# API validation
if not config.api.api_key and config.environment == "production":
validation_results["errors"].append("API key is required in production")
validation_results["valid"] = false
if not config.api.base_url.startswith("https") and config.environment == "production":
validation_results["warnings"].append("HTTPS is recommended for production API endpoints")
# Environment-specific validation
if config.environment == "production":
if config.debug:
validation_results["warnings"].append("Debug mode should be disabled in production")
if config.features.debug_mode:
validation_results["warnings"].append("Debug features should be disabled in production")
# Performance validation
if config.database.connection_pool_size > 50:
validation_results["warnings"].append("Large connection pools may impact performance")
if config.api.rate_limit_per_minute < 10:
validation_results["warnings"].append("Very low rate limits may cause application issues")
# AI-powered validation
ai_validation = reason(
"Review this configuration for potential security or performance issues",
context=config,
temperature=0.1
)
if "issue" in ai_validation.lower() or "warning" in ai_validation.lower():
validation_results["warnings"].append(f"AI analysis: {ai_validation}")
return validation_results
def apply_environment_overrides(config: AppConfig, environment: str) -> AppConfig:
# Apply environment-specific overrides
if environment == "production":
config.debug = false
config.features.debug_mode = false
config.log_level = "WARN"
config.database.ssl_enabled = true
elif environment == "staging":
config.debug = false
config.features.beta_features = true
config.log_level = "INFO"
elif environment == "development":
config.debug = true
config.features.debug_mode = true
config.features.beta_features = true
config.log_level = "DEBUG"
return config
def generate_config_documentation(config: AppConfig) -> str:
# AI-generated configuration documentation
documentation = reason(
"Generate comprehensive documentation for this application configuration",
context=config
)
return documentation
def monitor_config_changes(old_config: AppConfig, new_config: AppConfig) -> dict:
changes = {
"changed_fields": [],
"security_impact": false,
"performance_impact": false,
"restart_required": false
}
# Database changes
if old_config.database.host != new_config.database.host:
changes["changed_fields"].append("database.host")
changes["restart_required"] = true
if old_config.database.connection_pool_size != new_config.database.connection_pool_size:
changes["changed_fields"].append("database.connection_pool_size")
changes["performance_impact"] = true
# Security-sensitive changes
if old_config.database.ssl_enabled != new_config.database.ssl_enabled:
changes["changed_fields"].append("database.ssl_enabled")
changes["security_impact"] = true
if old_config.api.api_key != new_config.api.api_key:
changes["changed_fields"].append("api.api_key")
changes["security_impact"] = true
# Feature flag changes
if old_config.features.use_new_algorithm != new_config.features.use_new_algorithm:
changes["changed_fields"].append("features.use_new_algorithm")
changes["performance_impact"] = true
# AI analysis of changes
if changes["changed_fields"]:
impact_analysis = reason(
"Analyze the impact of these configuration changes",
context=changes["changed_fields"]
)
changes["impact_analysis"] = impact_analysis
return changes
# Usage patterns
def config_management_workflow():
# Load configuration
config = load_config_from_environment()
log(f"Loaded configuration for {config.environment} environment")
# Validate configuration
validation = config.validate_config()
if not validation["valid"]:
log("Configuration validation failed:", level="error")
for error in validation["errors"]:
log(f" Error: {error}", level="error")
return false
if validation["warnings"]:
log("Configuration warnings:")
for warning in validation["warnings"]:
log(f" Warning: {warning}", level="warn")
# Apply environment-specific settings
config = config.apply_environment_overrides(config.environment)
# Generate documentation
docs = config.generate_config_documentation()
save_documentation("config_docs.md", docs)
# Set up monitoring for future changes
save_config_snapshot(config)
log("Configuration management completed successfully")
return config
def hot_reload_config(current_config: AppConfig) -> AppConfig:
# Load new configuration
new_config = load_config_from_environment()
# Validate new configuration
validation = new_config.validate_config()
if not validation["valid"]:
log("New configuration is invalid, keeping current config", level="error")
return current_config
# Analyze changes
changes = monitor_config_changes(current_config, new_config)
if changes["restart_required"]:
log("Configuration changes require application restart", level="warn")
schedule_restart()
if changes["security_impact"]:
log("Configuration changes have security implications", level="warn")
notify_security_team(changes)
log(f"Configuration reloaded with {len(changes['changed_fields'])} changes")
return new_config
Summary¶
These patterns demonstrate Dana's strengths in building maintainable, AI-integrated business applications:
- Clear Domain Modeling: Structs represent business concepts naturally
- Flexible Function Dispatch: Same function names work with different types
- AI Integration: Natural reasoning capabilities for complex business logic
- Type Safety: Type hints guide both humans and AI
- Composability: Small, focused functions combine into powerful workflows
Key principles from these patterns:
- Separate Data from Behavior: Structs hold state, functions provide operations
- Leverage AI Reasoning: Use
reason()for complex analysis and decision-making - Design for Maintainability: Clear naming and type hints make code self-documenting
- Handle Errors Gracefully: Validate inputs and provide meaningful error messages
- Performance Awareness: Understand overhead and optimize hot paths when needed