"""
Distributed Graph Operations (Optional/Future)

Provides foundation for distributed graph processing across multiple nodes.
This module defines interfaces and utilities for future distributed implementations.

Note: Current implementation focuses on single-node optimizations.
      Distributed features are placeholders for future scaling needs.
"""

import logging
from typing import Optional, List, Dict, Any
from enum import Enum
from dataclasses import dataclass

logger = logging.getLogger(__name__)


class PartitionStrategy(str, Enum):
    """Graph partitioning strategy"""

    HASH = "hash"  # Hash-based partitioning
    RANGE = "range"  # Range-based partitioning
    COMMUNITY = "community"  # Community detection-based
    CUSTOM = "custom"  # Custom partitioning function


@dataclass
class GraphPartition:
    """
    Graph partition metadata

    Describes a partition of the graph for distributed processing.
    """

    partition_id: int
    node_count: int
    edge_count: int
    node_ids: Optional[List[str]] = None

    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary"""
        return {
            "partition_id": self.partition_id,
            "node_count": self.node_count,
            "edge_count": self.edge_count,
            "has_node_list": self.node_ids is not None,
        }


class DistributedGraphMixin:
    """
    Mixin for distributed graph operations (Future Enhancement)

    Provides interfaces for partitioning and distributed query execution.
    Current implementation is a placeholder for future distributed features.

    Example (Future Use):
        ```python
        class DistributedGraphStore(PostgresGraphStore, DistributedGraphMixin):
            pass

        store = DistributedGraphStore()

        # Partition graph for distributed processing
        partitions = await store.partition_graph(num_partitions=4)

        # Execute distributed query
        result = await store.distributed_query(query, partitions)
        ```
    """

    async def partition_graph(
        self,
        num_partitions: int,
        strategy: PartitionStrategy = PartitionStrategy.HASH,
    ) -> List[GraphPartition]:
        """
        Partition graph for distributed processing

        Args:
            num_partitions: Number of partitions to create
            strategy: Partitioning strategy

        Returns:
            List of partition metadata

        Note:
            This is a placeholder for future implementation.
            Current version returns conceptual partitions.
        """
        logger.info("Graph partitioning requested but not yet implemented")
        logger.info("For production distributed graphs, consider:")
        logger.info("  - Neo4j Fabric for distributed queries")
        logger.info("  - TigerGraph for native distributed processing")
        logger.info("  - Amazon Neptune with read replicas")

        # Placeholder: Return empty partitions
        return [GraphPartition(partition_id=i, node_count=0, edge_count=0) for i in range(num_partitions)]

    async def get_partition_info(self, partition_id: int) -> Optional[GraphPartition]:
        """
        Get information about a specific partition

        Args:
            partition_id: Partition ID

        Returns:
            Partition metadata or None
        """
        # Placeholder
        logger.warning("Partition info not available in current implementation")
        return None

    async def distributed_query(self, query: str, partitions: Optional[List[int]] = None) -> Any:
        """
        Execute query across distributed partitions

        Args:
            query: Query to execute
            partitions: Specific partitions to query (None for all)

        Returns:
            Aggregated query results

        Note:
            This is a placeholder. Current implementation executes locally.
        """
        logger.warning("Distributed query not implemented, executing locally")
        # Fall back to local execution
        return None


# Utility functions for future distributed implementations


def hash_partition_key(entity_id: str, num_partitions: int) -> int:
    """
    Compute partition ID using hash function

    Args:
        entity_id: Entity ID to partition
        num_partitions: Total number of partitions

    Returns:
        Partition ID (0 to num_partitions-1)
    """
    return hash(entity_id) % num_partitions


def range_partition_key(entity_id: str, ranges: List[tuple[str, str]]) -> int:
    """
    Compute partition ID using range-based partitioning

    Args:
        entity_id: Entity ID to partition
        ranges: List of (start, end) ranges for each partition

    Returns:
        Partition ID
    """
    for i, (start, end) in enumerate(ranges):
        if start <= entity_id < end:
            return i
    return len(ranges) - 1  # Default to last partition


# Documentation for distributed graph deployment

DISTRIBUTED_DEPLOYMENT_NOTES = """
## Distributed Graph Deployment Options

For large-scale distributed graphs (>100M nodes), consider:

### 1. Neo4j Fabric
- Federated queries across multiple Neo4j databases
- Sharding support
- CYPHER-based distributed queries

### 2. TigerGraph
- Native distributed graph database
- Horizontal scaling
- GSQL for distributed queries

### 3. Amazon Neptune
- Managed graph database service
- Read replicas for scale-out reads
- Integration with AWS ecosystem

### 4. JanusGraph
- Distributed graph database
- Backend-agnostic (Cassandra, HBase, etc.)
- Gremlin query language

### 5. PostgreSQL with Citus
- Distributed PostgreSQL
- Can be used with current PostgresGraphStore
- Horizontal sharding of graph tables

### Current AIECS Architecture

For most use cases (< 100M nodes), single-node PostgreSQL is sufficient:
- Vertical scaling up to 1TB RAM
- Connection pooling for concurrent access
- Read replicas for read scaling
- Batch operations for bulk loading

When to consider distributed:
- > 100M nodes
- > 1B edges
- Multiple geographic regions
- Extreme write throughput requirements (>100K writes/sec)

### Migration Path

1. Start with single-node PostgreSQL (current)
2. Add read replicas for read scaling
3. Implement connection routing for replicas
4. If needed, migrate to distributed backend:
   - Implement custom GraphStore for chosen backend
   - Use GraphStorageMigrator for data migration
   - Test with compatibility suite
"""
