# Advanced CDC framework and streaming infrastructure
import asyncio
import logging
import json
import time
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Optional, Union, Callable, AsyncGenerator
from dataclasses import dataclass, field
from datetime import datetime, timezone
from enum import Enum
import uuid
import hashlib

class ChangeEventType(Enum):
    INSERT = "INSERT"
    UPDATE = "UPDATE"
    DELETE = "DELETE"
    TRUNCATE = "TRUNCATE"
    SCHEMA_CHANGE = "SCHEMA_CHANGE"

class CDCSourceType(Enum):
    DATABASE_LOG = "database_log"
    TRIGGER_BASED = "trigger_based"
    TIMESTAMP_BASED = "timestamp_based"
    SNAPSHOT_BASED = "snapshot_based"

@dataclass
class ChangeEvent:
    """Standardized change event structure"""
    event_id: str = field(default_factory=lambda: str(uuid.uuid4()))
    source_system: str = ""
    database: str = ""
    table: str = ""
    event_type: ChangeEventType = ChangeEventType.INSERT
    timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    transaction_id: Optional[str] = None
    lsn: Optional[str] = None  # Log Sequence Number
    before_image: Optional[Dict[str, Any]] = None
    after_image: Optional[Dict[str, Any]] = None
    primary_key: Dict[str, Any] = field(default_factory=dict)
    schema_version: str = "1.0"
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def get_partition_key(self) -> str:
        """Generate partition key for event distribution"""
        if self.primary_key:
            key_str = "_".join(str(v) for v in sorted(self.primary_key.values()))
            return hashlib.md5(key_str.encode()).hexdigest()
        return hashlib.md5(f"{self.database}_{self.table}".encode()).hexdigest()
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization"""
        return {
            "event_id": self.event_id,
            "source_system": self.source_system,
            "database": self.database,
            "table": self.table,
            "event_type": self.event_type.value,
            "timestamp": self.timestamp.isoformat(),
            "transaction_id": self.transaction_id,
            "lsn": self.lsn,
            "before_image": self.before_image,
            "after_image": self.after_image,
            "primary_key": self.primary_key,
            "schema_version": self.schema_version,
            "metadata": self.metadata
        }

class CDCSource(ABC):
    """Abstract base class for CDC sources"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.source_type = CDCSourceType(config.get("source_type", "database_log"))
        self.running = False
        
    @abstractmethod
    async def start(self) -> None:
        """Start CDC capture"""
        pass
    
    @abstractmethod
    async def stop(self) -> None:
        """Stop CDC capture"""
        pass
    
    @abstractmethod
    async def get_events(self) -> AsyncGenerator[ChangeEvent, None]:
        """Get stream of change events"""
        pass
    
    @abstractmethod
    async def get_current_position(self) -> str:
        """Get current position in the change stream"""
        pass
    
    @abstractmethod
    async def set_position(self, position: str) -> None:
        """Set position in the change stream"""
        pass

class PostgreSQLCDCSource(CDCSource):
    """PostgreSQL CDC source using logical replication"""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.connection_config = config["connection"]
        self.replication_slot = config.get("replication_slot", "cdc_slot")
        self.publication = config.get("publication", "cdc_publication")
        self.current_lsn = None
        
    async def start(self) -> None:
        """Start PostgreSQL CDC capture"""
        self.running = True
        
        # Initialize replication connection
        await self._setup_replication()
        
        logging.info(f"Started PostgreSQL CDC source for {self.connection_config['host']}")
    
    async def stop(self) -> None:
        """Stop PostgreSQL CDC capture"""
        self.running = False
        
        # Cleanup replication connection
        await self._cleanup_replication()
        
        logging.info("Stopped PostgreSQL CDC source")
    
    async def get_events(self) -> AsyncGenerator[ChangeEvent, None]:
        """Get PostgreSQL change events"""
        
        while self.running:
            try:
                # Simulate reading from PostgreSQL logical replication
                # In a real implementation, this would use psycopg2 or asyncpg
                events = await self._read_replication_stream()
                
                for event_data in events:
                    event = self._parse_postgres_event(event_data)
                    if event:
                        yield event
                
                await asyncio.sleep(0.01)  # Small delay to prevent busy waiting
                
            except Exception as e:
                logging.error(f"Error reading PostgreSQL events: {e}")
                await asyncio.sleep(1)  # Wait before retry
    
    async def _setup_replication(self):
        """Setup PostgreSQL logical replication"""
        # Implementation would:
        # 1. Create replication slot if not exists
        # 2. Create publication if not exists
        # 3. Establish replication connection
        pass
    
    async def _cleanup_replication(self):
        """Cleanup PostgreSQL replication resources"""
        # Implementation would cleanup replication connections
        pass
    
    async def _read_replication_stream(self) -> List[Dict[str, Any]]:
        """Read from PostgreSQL replication stream"""
        # Simulate replication stream data
        events = [
            {
                "lsn": "0/1234567",
                "transaction_id": "tx_001",
                "table": "customers",
                "action": "INSERT",
                "data": {"id": 123, "name": "John Doe", "email": "john@example.com"},
                "timestamp": datetime.now(timezone.utc)
            },
            {
                "lsn": "0/1234568",
                "transaction_id": "tx_002",
                "table": "orders",
                "action": "UPDATE",
                "data": {"id": 456, "status": "shipped", "updated_at": datetime.now(timezone.utc)},
                "old_data": {"id": 456, "status": "pending"},
                "timestamp": datetime.now(timezone.utc)
            }
        ]
        
        return events
    
    def _parse_postgres_event(self, event_data: Dict[str, Any]) -> Optional[ChangeEvent]:
        """Parse PostgreSQL replication event"""
        
        try:
            event_type_map = {
                "INSERT": ChangeEventType.INSERT,
                "UPDATE": ChangeEventType.UPDATE,
                "DELETE": ChangeEventType.DELETE
            }
            
            event_type = event_type_map.get(event_data["action"])
            if not event_type:
                return None
            
            # Extract primary key
            primary_key = {}
            data = event_data.get("data", {})
            if "id" in data:
                primary_key["id"] = data["id"]
            
            event = ChangeEvent(
                source_system="postgresql",
                database=self.connection_config.get("database", ""),
                table=event_data["table"],
                event_type=event_type,
                timestamp=event_data["timestamp"],
                transaction_id=event_data.get("transaction_id"),
                lsn=event_data.get("lsn"),
                before_image=event_data.get("old_data"),
                after_image=event_data.get("data"),
                primary_key=primary_key
            )
            
            self.current_lsn = event_data.get("lsn")
            return event
            
        except Exception as e:
            logging.error(f"Error parsing PostgreSQL event: {e}")
            return None
    
    async def get_current_position(self) -> str:
        """Get current LSN position"""
        return self.current_lsn or "0/0"
    
    async def set_position(self, position: str) -> None:
        """Set LSN position"""
        self.current_lsn = position

class MySQLCDCSource(CDCSource):
    """MySQL CDC source using binlog"""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.connection_config = config["connection"]
        self.server_id = config.get("server_id", 1)
        self.current_position = None
        
    async def start(self) -> None:
        """Start MySQL CDC capture"""
        self.running = True
        
        # Initialize binlog reader
        await self._setup_binlog_reader()
        
        logging.info(f"Started MySQL CDC source for {self.connection_config['host']}")
    
    async def stop(self) -> None:
        """Stop MySQL CDC capture"""
        self.running = False
        
        # Cleanup binlog reader
        await self._cleanup_binlog_reader()
        
        logging.info("Stopped MySQL CDC source")
    
    async def get_events(self) -> AsyncGenerator[ChangeEvent, None]:
        """Get MySQL binlog events"""
        
        while self.running:
            try:
                # Simulate reading from MySQL binlog
                events = await self._read_binlog_stream()
                
                for event_data in events:
                    event = self._parse_mysql_event(event_data)
                    if event:
                        yield event
                
                await asyncio.sleep(0.01)
                
            except Exception as e:
                logging.error(f"Error reading MySQL events: {e}")
                await asyncio.sleep(1)
    
    async def _setup_binlog_reader(self):
        """Setup MySQL binlog reader"""
        # Implementation would setup python-mysql-replication or similar
        pass
    
    async def _cleanup_binlog_reader(self):
        """Cleanup MySQL binlog reader"""
        pass
    
    async def _read_binlog_stream(self) -> List[Dict[str, Any]]:
        """Read from MySQL binlog"""
        # Simulate binlog events
        events = [
            {
                "log_file": "mysql-bin.000001",
                "log_pos": 12345,
                "timestamp": datetime.now(timezone.utc),
                "event_type": "WRITE_ROWS_EVENT",
                "table": "products",
                "rows": [{"id": 789, "name": "Widget", "price": 29.99}]
            }
        ]
        
        return events
    
    def _parse_mysql_event(self, event_data: Dict[str, Any]) -> Optional[ChangeEvent]:
        """Parse MySQL binlog event"""
        
        try:
            event_type_map = {
                "WRITE_ROWS_EVENT": ChangeEventType.INSERT,
                "UPDATE_ROWS_EVENT": ChangeEventType.UPDATE,
                "DELETE_ROWS_EVENT": ChangeEventType.DELETE
            }
            
            event_type = event_type_map.get(event_data["event_type"])
            if not event_type:
                return None
            
            # Process each row in the event
            for row in event_data.get("rows", []):
                primary_key = {}
                if "id" in row:
                    primary_key["id"] = row["id"]
                
                event = ChangeEvent(
                    source_system="mysql",
                    database=self.connection_config.get("database", ""),
                    table=event_data["table"],
                    event_type=event_type,
                    timestamp=event_data["timestamp"],
                    after_image=row,
                    primary_key=primary_key,
                    metadata={
                        "log_file": event_data["log_file"],
                        "log_pos": event_data["log_pos"]
                    }
                )
                
                self.current_position = f"{event_data['log_file']}:{event_data['log_pos']}"
                return event
                
        except Exception as e:
            logging.error(f"Error parsing MySQL event: {e}")
            return None
    
    async def get_current_position(self) -> str:
        """Get current binlog position"""
        return self.current_position or "mysql-bin.000001:0"
    
    async def set_position(self, position: str) -> None:
        """Set binlog position"""
        self.current_position = position

class CDCProcessor:
    """Process and transform CDC events"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.transformations: List[Callable] = []
        self.filters: List[Callable] = []
        self.enrichers: List[Callable] = []
        
    def add_transformation(self, transformation: Callable[[ChangeEvent], ChangeEvent]):
        """Add event transformation function"""
        self.transformations.append(transformation)
    
    def add_filter(self, filter_func: Callable[[ChangeEvent], bool]):
        """Add event filter function"""
        self.filters.append(filter_func)
    
    def add_enricher(self, enricher: Callable[[ChangeEvent], ChangeEvent]):
        """Add event enrichment function"""
        self.enrichers.append(enricher)
    
    async def process_event(self, event: ChangeEvent) -> Optional[ChangeEvent]:
        """Process a single change event"""
        
        try:
            # Apply filters
            for filter_func in self.filters:
                if not filter_func(event):
                    return None  # Event filtered out
            
            # Apply transformations
            for transformation in self.transformations:
                event = transformation(event)
            
            # Apply enrichments
            for enricher in self.enrichers:
                event = enricher(event)
            
            return event
            
        except Exception as e:
            logging.error(f"Error processing event {event.event_id}: {e}")
            return None

class ConflictResolver:
    """Resolve conflicts in CDC events"""
    
    def __init__(self, strategy: str = "last_write_wins"):
        self.strategy = strategy
        self.event_cache: Dict[str, ChangeEvent] = {}
        
    async def resolve_conflict(self, events: List[ChangeEvent]) -> List[ChangeEvent]:
        """Resolve conflicts between multiple events"""
        
        if len(events) <= 1:
            return events
        
        if self.strategy == "last_write_wins":
            return self._last_write_wins(events)
        elif self.strategy == "merge":
            return self._merge_events(events)
        elif self.strategy == "version_vector":
            return self._version_vector_resolution(events)
        else:
            raise ValueError(f"Unknown conflict resolution strategy: {self.strategy}")
    
    def _last_write_wins(self, events: List[ChangeEvent]) -> List[ChangeEvent]:
        """Last write wins conflict resolution"""
        
        # Sort by timestamp and return the latest event
        sorted_events = sorted(events, key=lambda e: e.timestamp)
        return [sorted_events[-1]]
    
    def _merge_events(self, events: List[ChangeEvent]) -> List[ChangeEvent]:
        """Merge multiple events into a single event"""
        
        if not events:
            return []
        
        # Start with the first event as base
        merged_event = events[0]
        
        # Merge data from subsequent events
        for event in events[1:]:
            if event.after_image:
                if merged_event.after_image:
                    merged_event.after_image.update(event.after_image)
                else:
                    merged_event.after_image = event.after_image.copy()
            
            # Update timestamp to latest
            if event.timestamp > merged_event.timestamp:
                merged_event.timestamp = event.timestamp
        
        return [merged_event]
    
    def _version_vector_resolution(self, events: List[ChangeEvent]) -> List[ChangeEvent]:
        """Version vector based conflict resolution"""
        
        # Simplified version vector implementation
        # In practice, this would use proper vector clocks
        
        latest_version = 0
        latest_event = None
        
        for event in events:
            version = event.metadata.get("version", 0)
            if version > latest_version:
                latest_version = version
                latest_event = event
        
        return [latest_event] if latest_event else []

# Advanced streaming infrastructure for CDC
class StreamingCDCPipeline:
    """High-performance streaming CDC pipeline"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.sources: Dict[str, CDCSource] = {}
        self.processor = CDCProcessor(config.get("processing", {}))
        self.conflict_resolver = ConflictResolver(config.get("conflict_resolution", "last_write_wins"))
        self.sinks: List[CDCSink] = []
        self.running = False
        self.metrics = CDCMetrics()
        
    def add_source(self, name: str, source: CDCSource):
        """Add CDC source"""
        self.sources[name] = source
        
    def add_sink(self, sink: 'CDCSink'):
        """Add CDC sink"""
        self.sinks.append(sink)
        
    async def start(self):
        """Start CDC pipeline"""
        self.running = True
        
        # Start all sources
        for name, source in self.sources.items():
            await source.start()
            logging.info(f"Started CDC source: {name}")
        
        # Start all sinks
        for sink in self.sinks:
            await sink.start()
        
        # Start processing tasks
        tasks = []
        for name, source in self.sources.items():
            task = asyncio.create_task(self._process_source(name, source))
            tasks.append(task)
        
        logging.info("Started CDC pipeline")
        
        # Wait for all tasks
        await asyncio.gather(*tasks)
    
    async def stop(self):
        """Stop CDC pipeline"""
        self.running = False
        
        # Stop all sources
        for name, source in self.sources.items():
            await source.stop()
            logging.info(f"Stopped CDC source: {name}")
        
        # Stop all sinks
        for sink in self.sinks:
            await sink.stop()
        
        logging.info("Stopped CDC pipeline")
    
    async def _process_source(self, source_name: str, source: CDCSource):
        """Process events from a specific source"""
        
        async for event in source.get_events():
            if not self.running:
                break
            
            try:
                start_time = time.time()
                
                # Process event
                processed_event = await self.processor.process_event(event)
                if not processed_event:
                    continue  # Event was filtered out
                
                # Send to all sinks
                for sink in self.sinks:
                    await sink.send_event(processed_event)
                
                # Record metrics
                processing_time = time.time() - start_time
                self.metrics.record_event_processed(source_name, processing_time)
                
            except Exception as e:
                logging.error(f"Error processing event from {source_name}: {e}")
                self.metrics.record_error(source_name, str(e))

class CDCSink(ABC):
    """Abstract base class for CDC sinks"""
    
    @abstractmethod
    async def start(self) -> None:
        """Start the sink"""
        pass
    
    @abstractmethod
    async def stop(self) -> None:
        """Stop the sink"""
        pass
    
    @abstractmethod
    async def send_event(self, event: ChangeEvent) -> None:
        """Send event to sink"""
        pass

class KafkaCDCSink(CDCSink):
    """Kafka CDC sink"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.topic_template = config.get("topic_template", "{database}.{table}")
        self.producer = None
        
    async def start(self) -> None:
        """Start Kafka producer"""
        # Initialize Kafka producer
        # In real implementation, would use aiokafka or similar
        self.producer = "kafka_producer"  # Placeholder
        logging.info("Started Kafka CDC sink")
    
    async def stop(self) -> None:
        """Stop Kafka producer"""
        if self.producer:
            # Close Kafka producer
            self.producer = None
        logging.info("Stopped Kafka CDC sink")
    
    async def send_event(self, event: ChangeEvent) -> None:
        """Send event to Kafka"""
        
        try:
            # Generate topic name
            topic = self.topic_template.format(
                database=event.database,
                table=event.table
            )
            
            # Serialize event
            message = json.dumps(event.to_dict())
            
            # Send to Kafka (simulated)
            await self._send_to_kafka(topic, event.get_partition_key(), message)
            
        except Exception as e:
            logging.error(f"Error sending event to Kafka: {e}")
            raise
    
    async def _send_to_kafka(self, topic: str, key: str, message: str):
        """Send message to Kafka (simulated)"""
        # In real implementation, would use producer.send()
        logging.debug(f"Sent to Kafka topic {topic}: {message[:100]}...")

class ElasticsearchCDCSink(CDCSink):
    """Elasticsearch CDC sink"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.index_template = config.get("index_template", "{database}_{table}")
        self.client = None
        
    async def start(self) -> None:
        """Start Elasticsearch client"""
        # Initialize Elasticsearch client
        self.client = "elasticsearch_client"  # Placeholder
        logging.info("Started Elasticsearch CDC sink")
    
    async def stop(self) -> None:
        """Stop Elasticsearch client"""
        if self.client:
            self.client = None
        logging.info("Stopped Elasticsearch CDC sink")
    
    async def send_event(self, event: ChangeEvent) -> None:
        """Send event to Elasticsearch"""
        
        try:
            # Generate index name
            index = self.index_template.format(
                database=event.database,
                table=event.table
            )
            
            # Prepare document
            doc = {
                "timestamp": event.timestamp.isoformat(),
                "event_type": event.event_type.value,
                "data": event.after_image or event.before_image,
                "primary_key": event.primary_key,
                "metadata": event.metadata
            }
            
            # Index document (simulated)
            await self._index_document(index, event.event_id, doc)
            
        except Exception as e:
            logging.error(f"Error sending event to Elasticsearch: {e}")
            raise
    
    async def _index_document(self, index: str, doc_id: str, document: Dict[str, Any]):
        """Index document in Elasticsearch (simulated)"""
        logging.debug(f"Indexed document in {index}: {doc_id}")

class CDCMetrics:
    """CDC pipeline metrics collection"""
    
    def __init__(self):
        self.events_processed = {}
        self.processing_times = {}
        self.errors = {}
        self.start_time = time.time()
        
    def record_event_processed(self, source: str, processing_time: float):
        """Record event processing metrics"""
        if source not in self.events_processed:
            self.events_processed[source] = 0
            self.processing_times[source] = []
        
        self.events_processed[source] += 1
        self.processing_times[source].append(processing_time)
        
        # Keep only recent processing times
        if len(self.processing_times[source]) > 1000:
            self.processing_times[source] = self.processing_times[source][-1000:]
    
    def record_error(self, source: str, error: str):
        """Record error metrics"""
        if source not in self.errors:
            self.errors[source] = []
        
        self.errors[source].append({
            "timestamp": datetime.now(timezone.utc),
            "error": error
        })
        
        # Keep only recent errors
        if len(self.errors[source]) > 100:
            self.errors[source] = self.errors[source][-100:]
    
    def get_metrics_summary(self) -> Dict[str, Any]:
        """Get metrics summary"""
        
        summary = {
            "uptime_seconds": time.time() - self.start_time,
            "sources": {}
        }
        
        for source in self.events_processed:
            processing_times = self.processing_times.get(source, [])
            
            summary["sources"][source] = {
                "events_processed": self.events_processed[source],
                "error_count": len(self.errors.get(source, [])),
                "avg_processing_time_ms": (
                    sum(processing_times) / len(processing_times) * 1000 
                    if processing_times else 0
                ),
                "throughput_eps": (
                    self.events_processed[source] / (time.time() - self.start_time)
                    if (time.time() - self.start_time) > 0 else 0
                )
            }
        
        return summary

# Advanced schema evolution and compatibility management
from typing import Dict, List, Any, Optional, Tuple
import json
import logging
from enum import Enum
from dataclasses import dataclass

class SchemaCompatibilityType(Enum):
    BACKWARD = "backward"
    FORWARD = "forward"
    FULL = "full"
    NONE = "none"

class SchemaChangeType(Enum):
    ADD_FIELD = "add_field"
    REMOVE_FIELD = "remove_field"
    MODIFY_FIELD = "modify_field"
    RENAME_FIELD = "rename_field"
    CHANGE_TYPE = "change_type"

@dataclass
class SchemaChange:
    """Schema change description"""
    change_type: SchemaChangeType
    field_name: str
    old_definition: Optional[Dict[str, Any]] = None
    new_definition: Optional[Dict[str, Any]] = None
    compatibility_impact: Optional[str] = None

@dataclass
class SchemaVersion:
    """Schema version with metadata"""
    version: str
    schema: Dict[str, Any]
    timestamp: datetime
    compatibility_type: SchemaCompatibilityType
    changes: List[SchemaChange]
    metadata: Dict[str, Any]

class SchemaRegistry:
    """Centralized schema registry for CDC events"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.schemas: Dict[str, List[SchemaVersion]] = {}
        self.compatibility_checker = SchemaCompatibilityChecker()
        
    def register_schema(self, subject: str, schema: Dict[str, Any], 
                       compatibility_type: SchemaCompatibilityType = SchemaCompatibilityType.BACKWARD) -> str:
        """Register a new schema version"""
        
        # Generate version
        if subject not in self.schemas:
            self.schemas[subject] = []
            version = "1.0.0"
        else:
            latest_version = self.schemas[subject][-1].version
            version = self._increment_version(latest_version)
        
        # Check compatibility with previous version
        if self.schemas[subject]:
            previous_schema = self.schemas[subject][-1]
            changes = self.compatibility_checker.detect_changes(
                previous_schema.schema, schema
            )
            
            is_compatible = self.compatibility_checker.check_compatibility(
                previous_schema.schema, schema, compatibility_type
            )
            
            if not is_compatible:
                raise ValueError(f"Schema is not compatible with previous version")
        else:
            changes = []
        
        # Create schema version
        schema_version = SchemaVersion(
            version=version,
            schema=schema,
            timestamp=datetime.now(timezone.utc),
            compatibility_type=compatibility_type,
            changes=changes,
            metadata={}
        )
        
        self.schemas[subject].append(schema_version)
        
        logging.info(f"Registered schema version {version} for subject {subject}")
        return version
    
    def get_schema(self, subject: str, version: Optional[str] = None) -> Optional[SchemaVersion]:
        """Get schema by subject and version"""
        
        if subject not in self.schemas:
            return None
        
        if version is None:
            return self.schemas[subject][-1]  # Latest version
        
        for schema_version in self.schemas[subject]:
            if schema_version.version == version:
                return schema_version
        
        return None
    
    def get_latest_version(self, subject: str) -> Optional[str]:
        """Get latest schema version for subject"""
        
        if subject not in self.schemas or not self.schemas[subject]:
            return None
        
        return self.schemas[subject][-1].version
    
    def list_subjects(self) -> List[str]:
        """List all registered subjects"""
        return list(self.schemas.keys())
    
    def list_versions(self, subject: str) -> List[str]:
        """List all versions for a subject"""
        
        if subject not in self.schemas:
            return []
        
        return [sv.version for sv in self.schemas[subject]]
    
    def _increment_version(self, current_version: str) -> str:
        """Increment version number"""
        parts = current_version.split(".")
        major, minor, patch = int(parts[0]), int(parts[1]), int(parts[2])
        
        # Simple increment patch version
        patch += 1
        
        return f"{major}.{minor}.{patch}"

class SchemaCompatibilityChecker:
    """Check schema compatibility between versions"""
    
    def detect_changes(self, old_schema: Dict[str, Any], 
                      new_schema: Dict[str, Any]) -> List[SchemaChange]:
        """Detect changes between two schemas"""
        
        changes = []
        
        old_fields = old_schema.get("properties", {})
        new_fields = new_schema.get("properties", {})
        
        # Check for added fields
        for field_name, field_def in new_fields.items():
            if field_name not in old_fields:
                changes.append(SchemaChange(
                    change_type=SchemaChangeType.ADD_FIELD,
                    field_name=field_name,
                    new_definition=field_def
                ))
        
        # Check for removed fields
        for field_name, field_def in old_fields.items():
            if field_name not in new_fields:
                changes.append(SchemaChange(
                    change_type=SchemaChangeType.REMOVE_FIELD,
                    field_name=field_name,
                    old_definition=field_def
                ))
        
        # Check for modified fields
        for field_name in old_fields:
            if field_name in new_fields:
                if old_fields[field_name] != new_fields[field_name]:
                    change_type = self._determine_change_type(
                        old_fields[field_name], new_fields[field_name]
                    )
                    changes.append(SchemaChange(
                        change_type=change_type,
                        field_name=field_name,
                        old_definition=old_fields[field_name],
                        new_definition=new_fields[field_name]
                    ))
        
        return changes
    
    def check_compatibility(self, old_schema: Dict[str, Any], 
                          new_schema: Dict[str, Any],
                          compatibility_type: SchemaCompatibilityType) -> bool:
        """Check if schemas are compatible"""
        
        changes = self.detect_changes(old_schema, new_schema)
        
        if compatibility_type == SchemaCompatibilityType.BACKWARD:
            return self._check_backward_compatibility(changes)
        elif compatibility_type == SchemaCompatibilityType.FORWARD:
            return self._check_forward_compatibility(changes)
        elif compatibility_type == SchemaCompatibilityType.FULL:
            return (self._check_backward_compatibility(changes) and 
                   self._check_forward_compatibility(changes))
        elif compatibility_type == SchemaCompatibilityType.NONE:
            return True
        
        return False
    
    def _check_backward_compatibility(self, changes: List[SchemaChange]) -> bool:
        """Check backward compatibility (new schema can read old data)"""
        
        for change in changes:
            if change.change_type == SchemaChangeType.REMOVE_FIELD:
                # Removing a field breaks backward compatibility
                # unless the field was optional
                old_def = change.old_definition or {}
                if old_def.get("required", True):
                    return False
            
            elif change.change_type == SchemaChangeType.CHANGE_TYPE:
                # Type changes generally break backward compatibility
                return False
        
        return True
    
    def _check_forward_compatibility(self, changes: List[SchemaChange]) -> bool:
        """Check forward compatibility (old schema can read new data)"""
        
        for change in changes:
            if change.change_type == SchemaChangeType.ADD_FIELD:
                # Adding a required field breaks forward compatibility
                new_def = change.new_definition or {}
                if new_def.get("required", True):
                    return False
            
            elif change.change_type == SchemaChangeType.CHANGE_TYPE:
                # Type changes generally break forward compatibility
                return False
        
        return True
    
    def _determine_change_type(self, old_def: Dict[str, Any], 
                              new_def: Dict[str, Any]) -> SchemaChangeType:
        """Determine the type of change between field definitions"""
        
        if old_def.get("type") != new_def.get("type"):
            return SchemaChangeType.CHANGE_TYPE
        
        return SchemaChangeType.MODIFY_FIELD

class SchemaEvolutionManager:
    """Manage schema evolution in CDC pipelines"""
    
    def __init__(self, schema_registry: SchemaRegistry):
        self.schema_registry = schema_registry
        self.converters: Dict[str, SchemaConverter] = {}
        
    def register_converter(self, from_version: str, to_version: str, 
                          converter: 'SchemaConverter'):
        """Register schema converter between versions"""
        key = f"{from_version}_to_{to_version}"
        self.converters[key] = converter
    
    async def handle_schema_change(self, event: ChangeEvent, 
                                  current_schema_version: str) -> ChangeEvent:
        """Handle schema changes in events"""
        
        event_schema_version = event.schema_version
        
        if event_schema_version == current_schema_version:
            return event  # No conversion needed
        
        # Find converter
        converter_key = f"{event_schema_version}_to_{current_schema_version}"
        
        if converter_key not in self.converters:
            # Try to find conversion path
            conversion_path = self._find_conversion_path(
                event_schema_version, current_schema_version
            )
            
            if not conversion_path:
                raise ValueError(
                    f"No conversion path from {event_schema_version} to {current_schema_version}"
                )
            
            # Apply multiple conversions
            converted_event = event
            for i in range(len(conversion_path) - 1):
                from_ver = conversion_path[i]
                to_ver = conversion_path[i + 1]
                converter_key = f"{from_ver}_to_{to_ver}"
                
                if converter_key in self.converters:
                    converted_event = await self.converters[converter_key].convert(converted_event)
            
            return converted_event
        
        # Direct conversion
        return await self.converters[converter_key].convert(event)
    
    def _find_conversion_path(self, from_version: str, to_version: str) -> Optional[List[str]]:
        """Find conversion path between schema versions"""
        
        # Simple implementation - in practice, you'd use graph algorithms
        # to find the shortest path through available converters
        
        available_conversions = set()
        for key in self.converters.keys():
            from_ver, to_ver = key.split("_to_")
            available_conversions.add((from_ver, to_ver))
        
        # For now, return direct path if available
        if (from_version, to_version) in available_conversions:
            return [from_version, to_version]
        
        return None

class SchemaConverter(ABC):
    """Abstract base class for schema converters"""
    
    @abstractmethod
    async def convert(self, event: ChangeEvent) -> ChangeEvent:
        """Convert event from one schema version to another"""
        pass

class CustomerSchemaV1ToV2Converter(SchemaConverter):
    """Convert customer schema from v1.0.0 to v2.0.0"""
    
    async def convert(self, event: ChangeEvent) -> ChangeEvent:
        """Convert customer event from v1 to v2"""
        
        if event.after_image:
            converted_data = event.after_image.copy()
            
            # v2 added 'full_name' field combining 'first_name' and 'last_name'
            if 'first_name' in converted_data and 'last_name' in converted_data:
                converted_data['full_name'] = f"{converted_data['first_name']} {converted_data['last_name']}"
            
            # v2 renamed 'phone' to 'phone_number'
            if 'phone' in converted_data:
                converted_data['phone_number'] = converted_data.pop('phone')
            
            event.after_image = converted_data
        
        if event.before_image:
            converted_data = event.before_image.copy()
            
            if 'first_name' in converted_data and 'last_name' in converted_data:
                converted_data['full_name'] = f"{converted_data['first_name']} {converted_data['last_name']}"
            
            if 'phone' in converted_data:
                converted_data['phone_number'] = converted_data.pop('phone')
            
            event.before_image = converted_data
        
        # Update schema version
        event.schema_version = "2.0.0"
        
        return event

# Advanced data transformation and enrichment
class DataTransformationEngine:
    """Advanced data transformation engine for CDC events"""
    
    def __init__(self):
        self.transformations: Dict[str, List[Callable]] = {}
        self.enrichment_sources: Dict[str, 'EnrichmentSource'] = {}
        
    def register_transformation(self, table: str, transformation: Callable[[Dict[str, Any]], Dict[str, Any]]):
        """Register transformation for specific table"""
        if table not in self.transformations:
            self.transformations[table] = []
        self.transformations[table].append(transformation)
    
    def register_enrichment_source(self, name: str, source: 'EnrichmentSource'):
        """Register enrichment data source"""
        self.enrichment_sources[name] = source
    
    async def transform_event(self, event: ChangeEvent) -> ChangeEvent:
        """Apply transformations to event"""
        
        table_key = f"{event.database}.{event.table}"
        
        # Apply table-specific transformations
        if table_key in self.transformations:
            for transformation in self.transformations[table_key]:
                if event.after_image:
                    event.after_image = transformation(event.after_image)
                if event.before_image:
                    event.before_image = transformation(event.before_image)
        
        # Apply enrichments
        event = await self._enrich_event(event)
        
        return event
    
    async def _enrich_event(self, event: ChangeEvent) -> ChangeEvent:
        """Enrich event with additional data"""
        
        # Customer enrichment example
        if event.table == "customers" and event.after_image:
            customer_id = event.after_image.get("id")
            if customer_id:
                # Enrich with customer segment
                if "customer_segments" in self.enrichment_sources:
                    segment_source = self.enrichment_sources["customer_segments"]
                    segment_data = await segment_source.get_enrichment_data(customer_id)
                    if segment_data:
                        event.after_image["customer_segment"] = segment_data.get("segment")
                        event.after_image["segment_score"] = segment_data.get("score")
        
        return event

class EnrichmentSource(ABC):
    """Abstract base class for enrichment data sources"""
    
    @abstractmethod
    async def get_enrichment_data(self, key: Any) -> Optional[Dict[str, Any]]:
        """Get enrichment data for given key"""
        pass

class RedisEnrichmentSource(EnrichmentSource):
    """Redis-based enrichment source"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.redis_client = None  # Would initialize Redis client
        
    async def get_enrichment_data(self, key: Any) -> Optional[Dict[str, Any]]:
        """Get enrichment data from Redis"""
        
        # Simulate Redis lookup
        enrichment_data = {
            "segment": "premium",
            "score": 0.85,
            "last_updated": datetime.now(timezone.utc).isoformat()
        }
        
        return enrichment_data

class DatabaseEnrichmentSource(EnrichmentSource):
    """Database-based enrichment source"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.connection_pool = None  # Would initialize database connection pool
        
    async def get_enrichment_data(self, key: Any) -> Optional[Dict[str, Any]]:
        """Get enrichment data from database"""
        
        # Simulate database lookup
        enrichment_data = {
            "segment": "gold",
            "score": 0.75,
            "preferences": {"newsletter": True, "sms": False}
        }
        
        return enrichment_data

# Production deployment and monitoring for CDC systems
import asyncio
import logging
import time
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from datetime import datetime, timezone
import json

class CDCDeploymentManager:
    """Manage CDC system deployment and lifecycle"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.health_checker = CDCHealthChecker()
        self.performance_monitor = CDCPerformanceMonitor()
        self.backup_manager = CDCBackupManager()
        
    async def deploy_cdc_system(self, deployment_config: Dict[str, Any]) -> bool:
        """Deploy CDC system with zero downtime"""
        
        try:
            # Pre-deployment checks
            await self._pre_deployment_checks(deployment_config)
            
            # Create deployment snapshot
            snapshot_id = await self.backup_manager.create_snapshot()
            
            # Deploy new version
            await self._deploy_new_version(deployment_config)
            
            # Perform health checks
            health_status = await self.health_checker.comprehensive_health_check()
            
            if not health_status["healthy"]:
                # Rollback on health check failure
                await self._rollback_deployment(snapshot_id)
                return False
            
            # Performance validation
            performance_ok = await self.performance_monitor.validate_performance()
            
            if not performance_ok:
                await self._rollback_deployment(snapshot_id)
                return False
            
            # Cleanup old version
            await self._cleanup_old_version()
            
            logging.info("CDC system deployment completed successfully")
            return True
            
        except Exception as e:
            logging.error(f"CDC deployment failed: {e}")
            return False
    
    async def _pre_deployment_checks(self, config: Dict[str, Any]):
        """Run pre-deployment validation checks"""
        
        # Check source connectivity
        for source_config in config.get("sources", []):
            if not await self._test_source_connectivity(source_config):
                raise ValueError(f"Source connectivity check failed: {source_config['name']}")
        
        # Check sink connectivity
        for sink_config in config.get("sinks", []):
            if not await self._test_sink_connectivity(sink_config):
                raise ValueError(f"Sink connectivity check failed: {sink_config['name']}")
        
        # Validate schema registry
        if not await self._validate_schema_registry():
            raise ValueError("Schema registry validation failed")
        
        logging.info("Pre-deployment checks passed")
    
    async def _test_source_connectivity(self, source_config: Dict[str, Any]) -> bool:
        """Test connectivity to data source"""
        # Implementation would test actual connectivity
        return True
    
    async def _test_sink_connectivity(self, sink_config: Dict[str, Any]) -> bool:
        """Test connectivity to data sink"""
        # Implementation would test actual connectivity
        return True
    
    async def _validate_schema_registry(self) -> bool:
        """Validate schema registry connectivity and health"""
        # Implementation would validate schema registry
        return True
    
    async def _deploy_new_version(self, config: Dict[str, Any]):
        """Deploy new version of CDC system"""
        
        # Blue-green deployment strategy
        logging.info("Starting blue-green deployment")
        
        # Deploy to staging environment first
        await self._deploy_to_staging(config)
        
        # Run integration tests
        test_results = await self._run_integration_tests()
        if not test_results["passed"]:
            raise ValueError("Integration tests failed")
        
        # Switch traffic to new version
        await self._switch_traffic()
        
        logging.info("Blue-green deployment completed")
    
    async def _deploy_to_staging(self, config: Dict[str, Any]):
        """Deploy to staging environment"""
        # Implementation would deploy to staging
        await asyncio.sleep(1)  # Simulate deployment time
    
    async def _run_integration_tests(self) -> Dict[str, Any]:
        """Run integration tests"""
        # Implementation would run actual tests
        return {"passed": True, "test_count": 25, "duration_seconds": 30}
    
    async def _switch_traffic(self):
        """Switch traffic to new version"""
        # Implementation would switch load balancer or service mesh routing
        await asyncio.sleep(0.5)
    
    async def _rollback_deployment(self, snapshot_id: str):
        """Rollback to previous version"""
        logging.warning(f"Rolling back deployment to snapshot {snapshot_id}")
        await self.backup_manager.restore_snapshot(snapshot_id)
    
    async def _cleanup_old_version(self):
        """Cleanup old version resources"""
        # Implementation would cleanup old containers, services, etc.
        await asyncio.sleep(0.5)

class CDCHealthChecker:
    """Comprehensive health checking for CDC systems"""
    
    def __init__(self):
        self.health_checks = {
            "source_connectivity": self._check_source_connectivity,
            "sink_connectivity": self._check_sink_connectivity,
            "processing_pipeline": self._check_processing_pipeline,
            "schema_registry": self._check_schema_registry,
            "conflict_resolution": self._check_conflict_resolution,
            "memory_usage": self._check_memory_usage,
            "lag_metrics": self._check_lag_metrics
        }
    
    async def comprehensive_health_check(self) -> Dict[str, Any]:
        """Run comprehensive health check"""
        
        health_status = {
            "healthy": True,
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "checks": {}
        }
        
        for check_name, check_func in self.health_checks.items():
            try:
                check_result = await check_func()
                health_status["checks"][check_name] = check_result
                
                if not check_result.get("passed", False):
                    health_status["healthy"] = False
                    
            except Exception as e:
                health_status["checks"][check_name] = {
                    "passed": False,
                    "error": str(e)
                }
                health_status["healthy"] = False
        
        return health_status
    
    async def _check_source_connectivity(self) -> Dict[str, Any]:
        """Check connectivity to all data sources"""
        # Simulate source connectivity check
        return {
            "passed": True,
            "sources_checked": 3,
            "all_connected": True,
            "details": {
                "postgresql": {"connected": True, "latency_ms": 5},
                "mysql": {"connected": True, "latency_ms": 8},
                "mongodb": {"connected": True, "latency_ms": 12}
            }
        }
    
    async def _check_sink_connectivity(self) -> Dict[str, Any]:
        """Check connectivity to all data sinks"""
        return {
            "passed": True,
            "sinks_checked": 2,
            "all_connected": True,
            "details": {
                "kafka": {"connected": True, "latency_ms": 3},
                "elasticsearch": {"connected": True, "latency_ms": 15}
            }
        }
    
    async def _check_processing_pipeline(self) -> Dict[str, Any]:
        """Check processing pipeline health"""
        return {
            "passed": True,
            "pipeline_status": "running",
            "active_processors": 4,
            "queue_depth": 125,
            "processing_rate_eps": 1250
        }
    
    async def _check_schema_registry(self) -> Dict[str, Any]:
        """Check schema registry health"""
        return {
            "passed": True,
            "registry_status": "healthy",
            "schemas_registered": 45,
            "compatibility_checks_passed": True
        }
    
    async def _check_conflict_resolution(self) -> Dict[str, Any]:
        """Check conflict resolution system"""
        return {
            "passed": True,
            "conflicts_detected": 12,
            "conflicts_resolved": 12,
            "resolution_rate": 100.0
        }
    
    async def _check_memory_usage(self) -> Dict[str, Any]:
        """Check memory usage"""
        return {
            "passed": True,
            "memory_usage_percent": 68,
            "memory_threshold_percent": 80,
            "gc_frequency": "normal"
        }
    
    async def _check_lag_metrics(self) -> Dict[str, Any]:
        """Check processing lag metrics"""
        return {
            "passed": True,
            "avg_lag_seconds": 2.5,
            "max_lag_seconds": 8.2,
            "lag_threshold_seconds": 30
        }

class CDCPerformanceMonitor:
    """Monitor CDC system performance"""
    
    def __init__(self):
        self.performance_metrics = {}
        self.alert_thresholds = {
            "throughput_eps": {"min": 100, "max": 10000},
            "latency_p95_ms": {"max": 1000},
            "error_rate_percent": {"max": 1.0},
            "memory_usage_percent": {"max": 85},
            "cpu_usage_percent": {"max": 80}
        }
    
    async def collect_performance_metrics(self) -> Dict[str, Any]:
        """Collect comprehensive performance metrics"""
        
        metrics = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "throughput": await self._collect_throughput_metrics(),
            "latency": await self._collect_latency_metrics(),
            "errors": await self._collect_error_metrics(),
            "resources": await self._collect_resource_metrics(),
            "business": await self._collect_business_metrics()
        }
        
        return metrics
    
    async def _collect_throughput_metrics(self) -> Dict[str, Any]:
        """Collect throughput metrics"""
        return {
            "events_per_second": 1847,
            "bytes_per_second": 2456789,
            "transactions_per_second": 312,
            "peak_throughput_eps": 2156
        }
    
    async def _collect_latency_metrics(self) -> Dict[str, Any]:
        """Collect latency metrics"""
        return {
            "end_to_end_p50_ms": 45,
            "end_to_end_p95_ms": 185,
            "end_to_end_p99_ms": 425,
            "processing_p95_ms": 12,
            "sink_delivery_p95_ms": 8
        }
    
    async def _collect_error_metrics(self) -> Dict[str, Any]:
        """Collect error metrics"""
        return {
            "error_rate_percent": 0.12,
            "retries_per_minute": 5,
            "failed_events_per_minute": 2,
            "dead_letter_queue_size": 8
        }
    
    async def _collect_resource_metrics(self) -> Dict[str, Any]:
        """Collect resource utilization metrics"""
        return {
            "cpu_usage_percent": 65,
            "memory_usage_percent": 72,
            "disk_usage_percent": 45,
            "network_io_mbps": 125,
            "open_connections": 234
        }
    
    async def _collect_business_metrics(self) -> Dict[str, Any]:
        """Collect business-relevant metrics"""
        return {
            "data_freshness_minutes": 1.8,
            "schema_evolution_events": 0,
            "duplicate_events_detected": 3,
            "out_of_order_events": 12
        }
    
    async def validate_performance(self) -> bool:
        """Validate performance against thresholds"""
        
        metrics = await self.collect_performance_metrics()
        
        # Check throughput
        throughput = metrics["throughput"]["events_per_second"]
        if (throughput < self.alert_thresholds["throughput_eps"]["min"] or 
            throughput > self.alert_thresholds["throughput_eps"]["max"]):
            logging.warning(f"Throughput outside acceptable range: {throughput} EPS")
            return False
        
        # Check latency
        latency_p95 = metrics["latency"]["end_to_end_p95_ms"]
        if latency_p95 > self.alert_thresholds["latency_p95_ms"]["max"]:
            logging.warning(f"High latency detected: {latency_p95}ms")
            return False
        
        # Check error rate
        error_rate = metrics["errors"]["error_rate_percent"]
        if error_rate > self.alert_thresholds["error_rate_percent"]["max"]:
            logging.warning(f"High error rate detected: {error_rate}%")
            return False
        
        # Check resource usage
        memory_usage = metrics["resources"]["memory_usage_percent"]
        if memory_usage > self.alert_thresholds["memory_usage_percent"]["max"]:
            logging.warning(f"High memory usage detected: {memory_usage}%")
            return False
        
        cpu_usage = metrics["resources"]["cpu_usage_percent"]
        if cpu_usage > self.alert_thresholds["cpu_usage_percent"]["max"]:
            logging.warning(f"High CPU usage detected: {cpu_usage}%")
            return False
        
        return True
    
    async def generate_performance_report(self) -> Dict[str, Any]:
        """Generate comprehensive performance report"""
        
        metrics = await self.collect_performance_metrics()
        
        report = {
            "report_id": str(uuid.uuid4()),
            "generated_at": datetime.now(timezone.utc).isoformat(),
            "metrics": metrics,
            "alerts": self._generate_alerts(metrics),
            "recommendations": self._generate_recommendations(metrics),
            "trends": await self._analyze_trends()
        }
        
        return report
    
    def _generate_alerts(self, metrics: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate alerts based on metrics"""
        
        alerts = []
        
        # Throughput alerts
        throughput = metrics["throughput"]["events_per_second"]
        if throughput < self.alert_thresholds["throughput_eps"]["min"]:
            alerts.append({
                "type": "low_throughput",
                "severity": "warning",
                "message": f"Throughput below minimum: {throughput} EPS",
                "threshold": self.alert_thresholds["throughput_eps"]["min"]
            })
        
        # Latency alerts
        latency_p95 = metrics["latency"]["end_to_end_p95_ms"]
        if latency_p95 > self.alert_thresholds["latency_p95_ms"]["max"]:
            alerts.append({
                "type": "high_latency",
                "severity": "critical",
                "message": f"High latency detected: {latency_p95}ms",
                "threshold": self.alert_thresholds["latency_p95_ms"]["max"]
            })
        
        return alerts
    
    def _generate_recommendations(self, metrics: Dict[str, Any]) -> List[str]:
        """Generate performance recommendations"""
        
        recommendations = []
        
        # Resource-based recommendations
        memory_usage = metrics["resources"]["memory_usage_percent"]
        if memory_usage > 75:
            recommendations.append("Consider increasing memory allocation or optimizing memory usage")
        
        cpu_usage = metrics["resources"]["cpu_usage_percent"]
        if cpu_usage > 75:
            recommendations.append("Consider scaling horizontally or optimizing CPU-intensive operations")
        
        # Throughput recommendations
        throughput = metrics["throughput"]["events_per_second"]
        if throughput < 500:
            recommendations.append("Consider optimizing processing pipeline or increasing parallelism")
        
        return recommendations
    
    async def _analyze_trends(self) -> Dict[str, Any]:
        """Analyze performance trends"""
        
        # Simulate trend analysis
        return {
            "throughput_trend": "stable",
            "latency_trend": "improving",
            "error_rate_trend": "stable",
            "resource_usage_trend": "increasing"
        }

class CDCBackupManager:
    """Manage CDC system backups and recovery"""
    
    def __init__(self):
        self.snapshots: Dict[str, Dict[str, Any]] = {}
        
    async def create_snapshot(self) -> str:
        """Create system snapshot"""
        
        snapshot_id = str(uuid.uuid4())
        timestamp = datetime.now(timezone.utc)
        
        snapshot = {
            "id": snapshot_id,
            "timestamp": timestamp.isoformat(),
            "configuration": await self._backup_configuration(),
            "schema_registry": await self._backup_schema_registry(),
            "processing_state": await self._backup_processing_state(),
            "metadata": {
                "version": "1.0",
                "created_by": "cdc_backup_manager"
            }
        }
        
        self.snapshots[snapshot_id] = snapshot
        
        logging.info(f"Created snapshot {snapshot_id}")
        return snapshot_id
    
    async def restore_snapshot(self, snapshot_id: str) -> bool:
        """Restore from snapshot"""
        
        if snapshot_id not in self.snapshots:
            logging.error(f"Snapshot {snapshot_id} not found")
            return False
        
        snapshot = self.snapshots[snapshot_id]
        
        try:
            # Restore configuration
            await self._restore_configuration(snapshot["configuration"])
            
            # Restore schema registry
            await self._restore_schema_registry(snapshot["schema_registry"])
            
            # Restore processing state
            await self._restore_processing_state(snapshot["processing_state"])
            
            logging.info(f"Restored snapshot {snapshot_id}")
            return True
            
        except Exception as e:
            logging.error(f"Failed to restore snapshot {snapshot_id}: {e}")
            return False
    
    async def _backup_configuration(self) -> Dict[str, Any]:
        """Backup system configuration"""
        return {
            "sources": ["postgresql_config", "mysql_config"],
            "sinks": ["kafka_config", "elasticsearch_config"],
            "processing": "processing_config"
        }
    
    async def _backup_schema_registry(self) -> Dict[str, Any]:
        """Backup schema registry state"""
        return {
            "schemas": "schema_backup_data",
            "versions": "version_backup_data"
        }
    
    async def _backup_processing_state(self) -> Dict[str, Any]:
        """Backup processing pipeline state"""
        return {
            "positions": "position_backup_data",
            "offsets": "offset_backup_data",
            "checkpoints": "checkpoint_backup_data"
        }
    
    async def _restore_configuration(self, config_data: Dict[str, Any]):
        """Restore system configuration"""
        # Implementation would restore actual configuration
        await asyncio.sleep(0.1)
    
    async def _restore_schema_registry(self, schema_data: Dict[str, Any]):
        """Restore schema registry state"""
        # Implementation would restore schema registry
        await asyncio.sleep(0.1)
    
    async def _restore_processing_state(self, state_data: Dict[str, Any]):
        """Restore processing pipeline state"""
        # Implementation would restore processing state
        await asyncio.sleep(0.1)