Feature Flags & Idempotency

class FeatureFlags:
    def __init__(
        self,
        store: StoreProvider,
        logger: Logger = None,
    ):
        """
        Initialize feature flags with store provider.
        
        Parameters:
        - store: Store provider (usually AppConfigStore)
        - logger: Optional logger for feature flag events
        """

    def evaluate(
        self,
        name: str,
        context: Dict[str, Any] = None,
        default: Any = False,
    ) -> bool | Any:
        """
        Evaluate feature flag with optional context.
        
        Parameters:
        - name: Feature flag name
        - context: Evaluation context (user ID, region, etc.)
        - default: Default value if flag not found
        
        Returns:
        Feature flag value (boolean or complex value)
        """

    def get_enabled_features(self, context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Get all enabled features for given context.
        
        Parameters:
        - context: Evaluation context
        
        Returns:
        Dictionary of enabled feature names to values
        """

    def batch_evaluate(
        self,
        flags: List[str],
        context: Dict[str, Any] = None,
        default: Any = False,
    ) -> Dict[str, Any]:
        """
        Evaluate multiple feature flags in batch.
        
        Parameters:
        - flags: List of feature flag names
        - context: Evaluation context
        - default: Default value for missing flags
        
        Returns:
        Dictionary mapping flag names to values
        """

class AppConfigStore(StoreProvider):
    def __init__(
        self,
        environment: str,
        application: str,
        name: str,
        max_age: int = 5,
        sdk_config: Dict[str, Any] = None,
        envelope: str = None,
        jmespath_options: Dict[str, Any] = None,
        logger: Logger = None,
    ):
        """
        AWS AppConfig store provider for feature flags.
        
        Parameters:
        - environment: AppConfig environment name
        - application: AppConfig application name
        - name: AppConfig configuration profile name
        - max_age: Cache TTL in seconds
        - sdk_config: Boto3 client configuration
        - envelope: JMESPath envelope for data extraction
        - jmespath_options: JMESPath evaluation options
        - logger: Optional logger instance
        """

    def get_configuration(self) -> Dict[str, Any]:
        """
        Get configuration from AppConfig.
        
        Returns:
        Complete configuration dictionary
        """

    def _get_flag_value(
        self,
        name: str,
        context: Dict[str, Any] = None,
        default: Any = None,
    ) -> Any:
        """Get individual flag value with rule evaluation"""

class StoreProvider:
    """Base store provider interface for feature flags"""
    
    def get_configuration(self) -> Dict[str, Any]:
        """Get complete configuration"""
        raise NotImplementedError
    
    def get(
        self,
        name: str,
        context: Dict[str, Any] = None,
        default: Any = None,
    ) -> Any:
        """Get configuration value by name"""
        raise NotImplementedError

class RuleAction:
    ALLOW = "ALLOW"
    DENY = "DENY"

class SchemaValidator:
    def __init__(self, schema: Dict[str, Any]):
        """
        Initialize schema validator for feature flag configuration.
        
        Parameters:
        - schema: JSON schema for configuration validation
        """

    def validate(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate configuration data against schema.
        
        Parameters:
        - data: Configuration data to validate
        
        Returns:
        Validated configuration data
        
        Raises:
        SchemaValidationError: If validation fails
        """

def idempotent(
    persistence_store: BasePersistenceLayer,
    config: IdempotencyConfig = None,
) -> Callable:
    """
    Decorator for making Lambda handler idempotent.
    
    Parameters:
    - persistence_store: Storage layer for idempotency keys
    - config: Idempotency configuration options
    
    Returns:
    Decorated function that prevents duplicate processing
    """

def idempotent_function(
    data_keyword_argument: str,
    persistence_store: BasePersistenceLayer,
    config: IdempotencyConfig = None,
) -> Callable:
    """
    Decorator for making specific function calls idempotent.
    
    Parameters:
    - data_keyword_argument: Keyword argument name containing data for key generation
    - persistence_store: Storage layer for idempotency keys
    - config: Idempotency configuration options
    
    Returns:
    Decorated function with idempotency guarantees
    """

class IdempotencyConfig:
    def __init__(
        self,
        event_key_jmespath: str = None,
        payload_validation_jmespath: str = None,
        raise_on_no_idempotency_key: bool = False,
        expires_after_seconds: int = 3600,
        use_local_cache: bool = False,
        local_cache_max_items: int = 256,
        hash_function: str = "md5",
        lambda_context: LambdaContext = None,
    ):
        """
        Configuration for idempotency behavior.
        
        Parameters:
        - event_key_jmespath: JMESPath to extract idempotency key from event
        - payload_validation_jmespath: JMESPath to extract payload for validation
        - raise_on_no_idempotency_key: Whether to raise error if no key found
        - expires_after_seconds: TTL for idempotency records
        - use_local_cache: Whether to use in-memory cache
        - local_cache_max_items: Maximum items in local cache
        - hash_function: Hash function for key generation (md5, sha1, sha256)
        - lambda_context: Lambda context for additional key generation
        """

class BasePersistenceLayer:
    """Base persistence layer for idempotency records"""
    
    def __init__(
        self,
        table_name: str,
        key_attr: str = "id",
        expiry_attr: str = "expiration",
        status_attr: str = "status",
        data_attr: str = "data",
        validation_key_attr: str = "validation",
    ):
        """
        Initialize persistence layer.
        
        Parameters:
        - table_name: Storage table/collection name
        - key_attr: Attribute name for idempotency key
        - expiry_attr: Attribute name for expiration timestamp
        - status_attr: Attribute name for processing status
        - data_attr: Attribute name for stored result
        - validation_key_attr: Attribute name for payload validation
        """

    def save_inprogress(
        self,
        idempotency_key: str,
        remaining_time_in_millis: int = None,
    ) -> None:
        """
        Save in-progress idempotency record.
        
        Parameters:
        - idempotency_key: Unique idempotency key
        - remaining_time_in_millis: Remaining Lambda execution time
        """

    def save_success(
        self,
        idempotency_key: str,
        result: Any,
        remaining_time_in_millis: int = None,
    ) -> None:
        """
        Save successful processing result.
        
        Parameters:
        - idempotency_key: Unique idempotency key  
        - result: Function result to store
        - remaining_time_in_millis: Remaining Lambda execution time
        """

    def get_record(self, idempotency_key: str) -> Dict[str, Any] | None:
        """
        Retrieve idempotency record by key.
        
        Parameters:
        - idempotency_key: Idempotency key to lookup
        
        Returns:
        Stored record or None if not found
        """

    def delete_record(self, idempotency_key: str) -> None:
        """
        Delete idempotency record.
        
        Parameters:
        - idempotency_key: Key of record to delete
        """

class DynamoDBPersistenceLayer(BasePersistenceLayer):
    def __init__(
        self,
        table_name: str,
        key_attr: str = "id",
        expiry_attr: str = "expiration", 
        status_attr: str = "status",
        data_attr: str = "data",
        validation_key_attr: str = "validation",
        boto_config: Dict[str, Any] = None,
        boto3_session: boto3.Session = None,
    ):
        """
        DynamoDB persistence layer for idempotency.
        
        Parameters:
        - table_name: DynamoDB table name
        - key_attr: Primary key attribute name
        - expiry_attr: TTL attribute name
        - status_attr: Status attribute name
        - data_attr: Data attribute name
        - validation_key_attr: Validation key attribute name
        - boto_config: Boto3 client configuration
        - boto3_session: Boto3 session for authentication
        """

class IdempotentHookFunction:
    """Hook function interface for idempotency events"""
    
    def __call__(
        self,
        idempotency_key: str,
        result: Any = None,
        exception: Exception = None,
    ) -> None:
        """
        Hook function called during idempotency processing.
        
        Parameters:
        - idempotency_key: The idempotency key
        - result: Function result (if successful)
        - exception: Exception (if failed)
        """

from aws_lambda_powertools.utilities.feature_flags import FeatureFlags, AppConfigStore
from aws_lambda_powertools.utilities.typing import LambdaContext
import os

# Initialize feature flags with AppConfig
store = AppConfigStore(
    environment=os.environ["ENVIRONMENT"],
    application=os.environ["APP_NAME"],
    name="feature-flags"
)

feature_flags = FeatureFlags(store=store)

def lambda_handler(event: dict, context: LambdaContext) -> dict:
    # Simple boolean flag
    if feature_flags.evaluate(name="enable_new_algorithm", default=False):
        result = new_algorithm_processing(event)
    else:
        result = legacy_algorithm_processing(event)
    
    # Feature flag with context
    user_id = event.get("user_id")
    user_context = {"userId": user_id, "region": event.get("region", "us-east-1")}
    
    if feature_flags.evaluate(name="enable_premium_features", context=user_context, default=False):
        result["premium_data"] = get_premium_data(user_id)
    
    # Complex feature flag with configuration
    recommendation_config = feature_flags.evaluate(
        name="recommendation_settings",
        context=user_context,
        default={"enabled": False, "algorithm": "basic", "limit": 5}
    )
    
    if recommendation_config.get("enabled", False):
        recommendations = get_recommendations(
            user_id=user_id,
            algorithm=recommendation_config.get("algorithm", "basic"),
            limit=recommendation_config.get("limit", 5)
        )
        result["recommendations"] = recommendations
    
    return {
        "statusCode": 200,
        "body": result
    }

def new_algorithm_processing(event: dict) -> dict:
    """New algorithm implementation"""
    return {"algorithm": "v2", "result": "processed_with_new_logic"}

def legacy_algorithm_processing(event: dict) -> dict:
    """Legacy algorithm implementation"""
    return {"algorithm": "v1", "result": "processed_with_legacy_logic"}

from aws_lambda_powertools.utilities.feature_flags import FeatureFlags, AppConfigStore
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger
import os

logger = Logger()

# AppConfig feature flag configuration example:
# {
#   "feature_flags": {
#     "enable_beta_features": {
#       "enabled": true,
#       "rules": {
#         "allow_beta_users": {
#           "when_match": [
#             {
#               "action": "ALLOW",
#               "conditions": [
#                 {
#                   "key": "userType",
#                   "value": "beta",
#                   "condition": "EQUALS"
#                 }
#               ]
#             }
#           ],
#           "when_no_match": {
#             "action": "DENY"
#           }
#         }
#       },
#       "default": false
#     },
#     "api_rate_limit": {
#       "enabled": true,
#       "rules": {
#         "premium_rate_limit": {
#           "when_match": [
#             {
#               "action": "ALLOW",
#               "conditions": [
#                 {
#                   "key": "userTier", 
#                   "value": "premium",
#                   "condition": "EQUALS"
#                 }
#               ]
#             }
#           ],
#           "when_no_match": {
#             "action": "DENY"
#           }
#         }
#       },
#       "default": {"requests_per_minute": 100}
#     }
#   }
# }

store = AppConfigStore(
    environment=os.environ["ENVIRONMENT"],
    application="my-app",
    name="feature-config"
)

feature_flags = FeatureFlags(store=store, logger=logger)

def lambda_handler(event: dict, context: LambdaContext) -> dict:
    # Extract user context from event
    user_context = {
        "userId": event.get("user_id"),
        "userType": event.get("user_type", "standard"),
        "userTier": event.get("user_tier", "basic"),
        "region": event.get("region", "us-east-1"),
        "deviceType": event.get("device_type", "web")
    }
    
    logger.append_keys(user_context=user_context)
    
    # Evaluate feature flags with context
    results = {}
    
    # Beta features access
    beta_enabled = feature_flags.evaluate(
        name="enable_beta_features",
        context=user_context,
        default=False
    )
    
    if beta_enabled:
        logger.info("Beta features enabled for user")
        results["beta_features"] = get_beta_features(user_context)
    else:
        logger.info("Beta features not available for user")
    
    # Dynamic rate limiting
    rate_limit_config = feature_flags.evaluate(
        name="api_rate_limit",
        context=user_context,
        default={"requests_per_minute": 60}
    )
    
    # Apply rate limiting
    rate_limit = rate_limit_config.get("requests_per_minute", 60)
    if not check_rate_limit(user_context["userId"], rate_limit):
        return {
            "statusCode": 429,
            "body": {"error": "Rate limit exceeded"}
        }
    
    # Batch evaluate multiple flags
    flag_results = feature_flags.batch_evaluate(
        flags=["enable_analytics", "enable_notifications", "enable_cache"],
        context=user_context,
        default=False
    )
    
    # Process based on enabled features
    if flag_results.get("enable_analytics", False):
        track_user_event(event, user_context)
    
    if flag_results.get("enable_notifications", False):
        queue_notification(user_context["userId"], "feature_accessed")
    
    # Use caching if enabled
    cache_enabled = flag_results.get("enable_cache", False)
    if cache_enabled:
        results["cached_data"] = get_cached_data(user_context["userId"])
    else:
        results["fresh_data"] = get_fresh_data(user_context["userId"])
    
    return {
        "statusCode": 200,
        "body": results,
        "rate_limit": rate_limit
    }

def get_beta_features(user_context: dict) -> dict:
    """Get beta features for user"""
    return {
        "new_dashboard": True,
        "advanced_analytics": True,
        "ai_recommendations": True
    }

def check_rate_limit(user_id: str, limit: int) -> bool:
    """Check if user is within rate limit"""
    # Implementation would check against cache/database
    return True  # Simplified for example

def track_user_event(event: dict, user_context: dict):
    """Track analytics event"""
    logger.info("Tracking user event", extra={"event_type": "api_access"})

def queue_notification(user_id: str, notification_type: str):
    """Queue notification for user"""
    logger.info("Queuing notification", extra={
        "user_id": user_id,
        "type": notification_type
    })

from aws_lambda_powertools.utilities.idempotency import (
    idempotent, 
    DynamoDBPersistenceLayer,
    IdempotencyConfig
)
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger
import json

logger = Logger()

# Configure DynamoDB persistence layer
persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

# Configure idempotency behavior
config = IdempotencyConfig(
    event_key_jmespath="requestId",  # Extract requestId from event
    expires_after_seconds=3600,      # 1 hour TTL
    use_local_cache=True,           # Enable local caching
)

@idempotent(persistence_store=persistence_layer, config=config)
def lambda_handler(event: dict, context: LambdaContext) -> dict:
    """
    Idempotent Lambda handler - duplicate requests return cached result
    """
    
    request_id = event.get("requestId")
    logger.info(f"Processing request {request_id}")
    
    # This logic will only execute once per unique requestId
    user_id = event.get("userId")
    order_data = event.get("orderData", {})
    
    # Simulate expensive operation
    order_id = process_order(user_id, order_data)
    
    # Send confirmation (will only happen once)
    send_order_confirmation(user_id, order_id)
    
    result = {
        "orderId": order_id,
        "status": "processed",
        "timestamp": context.aws_request_id
    }
    
    logger.info(f"Order processed: {order_id}")
    
    return {
        "statusCode": 200,
        "body": json.dumps(result)
    }

def process_order(user_id: str, order_data: dict) -> str:
    """Process order (expensive operation)"""
    import uuid
    import time
    
    # Simulate processing time
    time.sleep(1)
    
    order_id = str(uuid.uuid4())
    logger.info(f"Created order {order_id} for user {user_id}")
    
    return order_id

def send_order_confirmation(user_id: str, order_id: str):
    """Send order confirmation (side effect)"""
    logger.info(f"Sending confirmation for order {order_id} to user {user_id}")
    # Email/SMS sending logic here

from aws_lambda_powertools.utilities.idempotency import (
    idempotent,
    idempotent_function,
    DynamoDBPersistenceLayer,
    IdempotencyConfig
)
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger
import hashlib
import json

logger = Logger()

# Persistence layer with custom configuration
persistence_layer = DynamoDBPersistenceLayer(
    table_name="IdempotencyTable",
    key_attr="idempotency_key",
    expiry_attr="expires_at",
    status_attr="status",
    data_attr="result_data"
)

# Complex idempotency configuration
config = IdempotencyConfig(
    event_key_jmespath="headers.`Idempotency-Key` || body.transactionId",
    payload_validation_jmespath="body",  # Validate entire body for changes
    expires_after_seconds=86400,  # 24 hours
    use_local_cache=True,
    hash_function="sha256",
    raise_on_no_idempotency_key=True
)

@idempotent(persistence_store=persistence_layer, config=config)
def lambda_handler(event: dict, context: LambdaContext) -> dict:
    """Handler with custom idempotency key extraction"""
    
    # Process payment request
    payment_data = event.get("body", {})
    headers = event.get("headers", {})
    
    idempotency_key = headers.get("Idempotency-Key") or payment_data.get("transactionId")
    logger.append_keys(idempotency_key=idempotency_key)
    
    logger.info("Processing payment request")
    
    # This will only execute once per unique key
    result = process_payment_request(payment_data)
    
    return {
        "statusCode": 200,
        "body": json.dumps(result)
    }

# Separate persistence layer for user operations
user_persistence = DynamoDBPersistenceLayer(table_name="UserIdempotencyTable")

user_config = IdempotencyConfig(
    expires_after_seconds=3600,
    use_local_cache=True
)

@idempotent_function(
    data_keyword_argument="user_data",
    persistence_store=user_persistence,
    config=user_config
)
def create_user(user_data: dict) -> dict:
    """Idempotent user creation function"""
    
    logger.info("Creating new user", extra={"email": user_data.get("email")})
    
    # Check if user already exists
    if user_exists(user_data["email"]):
        raise ValueError("User already exists")
    
    # Create user (expensive operation)
    user_id = generate_user_id()
    save_user_to_database(user_id, user_data)
    send_welcome_email(user_data["email"])
    
    return {
        "user_id": user_id,
        "email": user_data["email"],
        "created_at": "2024-01-01T00:00:00Z"
    }

def process_payment_request(payment_data: dict) -> dict:
    """Process payment with external API"""
    
    amount = payment_data["amount"]
    currency = payment_data["currency"]
    payment_method = payment_data["payment_method"]
    
    # Call external payment processor
    transaction_id = call_payment_processor(amount, currency, payment_method)
    
    # Update internal records
    update_payment_records(transaction_id, payment_data)
    
    return {
        "transaction_id": transaction_id,
        "amount": amount,
        "currency": currency,
        "status": "completed"
    }

# Bulk operations with per-item idempotency
@idempotent_function(
    data_keyword_argument="item_data",
    persistence_store=persistence_layer,
    config=IdempotencyConfig(expires_after_seconds=1800)
)
def process_bulk_item(item_data: dict) -> dict:
    """Process individual item in bulk operation"""
    
    item_id = item_data["item_id"]
    logger.info(f"Processing item {item_id}")
    
    # Expensive per-item processing
    result = expensive_item_processing(item_data)
    
    return {
        "item_id": item_id,
        "processed": True,
        "result": result
    }

def bulk_handler(event: dict, context: LambdaContext) -> dict:
    """Handle bulk operations with per-item idempotency"""
    
    items = event.get("items", [])
    results = []
    
    for item in items:
        try:
            # Each item is processed idempotently
            result = process_bulk_item(item_data=item)
            results.append(result)
            
        except Exception as e:
            logger.exception(f"Failed to process item {item.get('item_id')}")
            results.append({
                "item_id": item.get("item_id"),
                "error": str(e)
            })
    
    return {
        "statusCode": 200,
        "body": json.dumps({
            "processed_count": len([r for r in results if not r.get("error")]),
            "failed_count": len([r for r in results if r.get("error")]),
            "results": results
        })
    }

from aws_lambda_powertools.utilities.feature_flags import (
    FeatureFlags, 
    AppConfigStore,
    ConfigurationStoreError
)
from aws_lambda_powertools.utilities.idempotency import (
    idempotent,
    DynamoDBPersistenceLayer,
    IdempotencyConfig,
    IdempotencyAlreadyInProgressError,
    IdempotencyInconsistentStateError,
    IdempotencyKeyError
)
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger, Metrics
from aws_lambda_powertools.metrics import MetricUnit

logger = Logger()
metrics = Metrics()

# Feature flags with error handling
try:
    store = AppConfigStore(
        environment="production",
        application="my-app", 
        name="feature-flags",
        max_age=30  # Refresh every 30 seconds
    )
    feature_flags = FeatureFlags(store=store, logger=logger)
except ConfigurationStoreError as e:
    logger.error("Failed to initialize feature flags", extra={"error": str(e)})
    # Fallback to default configuration
    feature_flags = None

# Idempotency with error handling
persistence_layer = DynamoDBPersistenceLayer(table_name="IdempotencyTable")

config = IdempotencyConfig(
    event_key_jmespath="requestId", 
    expires_after_seconds=3600,
    use_local_cache=True
)

@metrics.log_metrics(capture_cold_start_metric=True)
@idempotent(persistence_store=persistence_layer, config=config)
def lambda_handler(event: dict, context: LambdaContext) -> dict:
    """Handler with comprehensive error handling"""
    
    request_id = event.get("requestId")
    logger.append_keys(request_id=request_id)
    
    try:
        # Feature flag evaluation with fallbacks
        features = get_enabled_features(event)
        
        # Record feature usage metrics
        for feature_name, enabled in features.items():
            metrics.add_metric(
                name=f"FeatureFlag_{feature_name}",
                unit=MetricUnit.Count,
                value=1 if enabled else 0
            )
        
        # Process request based on enabled features
        result = process_request(event, features)
        
        # Record success metrics
        metrics.add_metric(name="RequestProcessed", unit=MetricUnit.Count, value=1)
        
        return {
            "statusCode": 200,
            "body": result
        }
        
    except IdempotencyAlreadyInProgressError:
        # Request is already being processed by another instance
        logger.warning("Request already in progress")
        metrics.add_metric(name="IdempotencyInProgress", unit=MetricUnit.Count, value=1)
        
        return {
            "statusCode": 409,
            "body": {"error": "Request already in progress"}
        }
        
    except IdempotencyKeyError as e:
        # Missing or invalid idempotency key
        logger.error("Invalid idempotency key", extra={"error": str(e)})
        metrics.add_metric(name="IdempotencyKeyError", unit=MetricUnit.Count, value=1)
        
        return {
            "statusCode": 400,
            "body": {"error": "Invalid or missing idempotency key"}
        }
        
    except Exception as e:
        # Unexpected error
        logger.exception("Request processing failed")
        metrics.add_metric(name="RequestFailed", unit=MetricUnit.Count, value=1)
        
        return {
            "statusCode": 500,
            "body": {"error": "Internal server error"}
        }

def get_enabled_features(event: dict) -> dict:
    """Get enabled features with fallback logic"""
    
    features = {
        "enhanced_processing": False,
        "premium_features": False,
        "beta_algorithms": False
    }
    
    if feature_flags is None:
        logger.warning("Feature flags unavailable, using defaults")
        return features
    
    try:
        user_context = {
            "userId": event.get("userId"),
            "userTier": event.get("userTier", "basic"),
            "region": event.get("region", "us-east-1")
        }
        
        # Evaluate each feature flag with error handling
        for feature_name in features:
            try:
                features[feature_name] = feature_flags.evaluate(
                    name=feature_name,
                    context=user_context,
                    default=features[feature_name]
                )
            except Exception as e:
                logger.warning(
                    f"Failed to evaluate feature flag {feature_name}",
                    extra={"error": str(e)}
                )
                # Keep default value
        
        return features
        
    except ConfigurationStoreError as e:
        logger.warning("Feature flag evaluation failed, using defaults", extra={"error": str(e)})
        metrics.add_metric(name="FeatureFlagError", unit=MetricUnit.Count, value=1)
        return features

def process_request(event: dict, features: dict) -> dict:
    """Process request based on enabled features"""
    
    result = {"processed_features": []}
    
    if features.get("enhanced_processing", False):
        logger.info("Using enhanced processing")
        result["enhanced_data"] = enhanced_processing(event)
        result["processed_features"].append("enhanced_processing")
    
    if features.get("premium_features", False):
        logger.info("Including premium features")
        result["premium_data"] = get_premium_data(event)
        result["processed_features"].append("premium_features")
    
    if features.get("beta_algorithms", False):
        logger.info("Using beta algorithms")
        result["beta_results"] = beta_algorithm_processing(event)
        result["processed_features"].append("beta_algorithms")
    
    # Base processing
    result["base_data"] = base_processing(event)
    
    return result

def enhanced_processing(event: dict) -> dict:
    """Enhanced processing implementation"""
    return {"type": "enhanced", "quality": "high"}

def get_premium_data(event: dict) -> dict:
    """Premium feature data"""
    return {"premium_insights": True, "advanced_analytics": True}

def beta_algorithm_processing(event: dict) -> dict:
    """Beta algorithm processing"""
    return {"algorithm": "beta_v2", "accuracy": "experimental"}

def base_processing(event: dict) -> dict:
    """Base processing implementation"""
    return {"type": "standard", "quality": "normal"}

from typing import Dict, Any, List, Union, Optional, Callable
from aws_lambda_powertools.utilities.typing import LambdaContext

# Feature flag types
FeatureFlagValue = Union[bool, str, int, float, Dict[str, Any], List[Any]]
FeatureFlagContext = Dict[str, Any]
FeatureFlagEvaluation = Dict[str, FeatureFlagValue]

# Rule action constants
RuleAction = Literal["ALLOW", "DENY"]

# Store provider types
class StoreProvider:
    """Base interface for feature flag store providers"""
    pass

# Configuration store error
class ConfigurationStoreError(Exception):
    """Raised when configuration store operations fail"""
    pass

# Schema validation error  
class SchemaValidationError(Exception):
    """Raised when feature flag schema validation fails"""
    pass

# Idempotency types
IdempotencyKey = str
IdempotencyRecord = Dict[str, Any]

# Idempotency exceptions
class IdempotencyError(Exception):
    """Base idempotency error"""
    pass

class IdempotencyKeyError(IdempotencyError):
    """Raised when idempotency key is missing or invalid"""
    pass

class IdempotencyAlreadyInProgressError(IdempotencyError):
    """Raised when request is already being processed"""
    pass

class IdempotencyInconsistentStateError(IdempotencyError):
    """Raised when idempotency state is inconsistent"""
    pass

class IdempotencyValidationError(IdempotencyError):
    """Raised when payload validation fails"""
    pass

# Persistence layer types
PersistenceRecord = Dict[str, Any]

# Hook function type
IdempotencyHook = Callable[[str, Any, Optional[Exception]], None]

# Hash function type
HashFunction = Literal["md5", "sha1", "sha256", "sha512"]

# JMESPath expression type
JMESPathExpression = str

# Boto3 configuration types
Boto3Config = Dict[str, Any]
Boto3Session = Any  # boto3.Session