"""Database saver for saving and updating data in the database."""

import pandas as pd
import logging

from dataclasses import dataclass
from pathlib import Path
from sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine
from sqlalchemy.pool import NullPool

from src.dataprocessor.domain.base_datasaver import BaseDataSaver

logger = logging.getLogger(__name__)


@dataclass
class SQLiteDataSaver(BaseDataSaver):
    """Class responsible for saving data to the SQLite database."""

    db_url: str
    engine: AsyncEngine

    @classmethod
    async def create(cls, db_path: str) -> "SQLiteDataSaver":
        """Create a new instance of DataSaver."""
        # Ensure the directory exists
        db_file = Path(db_path)
        db_file.parent.mkdir(parents=True, exist_ok=True)

        # Build the full db url with absolute path (4 slashes total)
        db_url = f"sqlite+aiosqlite:///{db_file.as_posix()}"

        # Use NullPool to avoid connection pooling issues with SQLite
        engine = create_async_engine(db_url, poolclass=NullPool, future=True)
        return cls(db_url=db_url, engine=engine)

    async def save_table(
        self, df: pd.DataFrame, table_name: str, overwrite: bool = True
    ):
        """Save the DataFrame to the specified table, overwriting existing data.

        Args:
            df: The DataFrame to save.
            table_name: The name of the table to save the data to.
            overwrite: If True, overwrite the existing table. If False, append to it.
        """
        async with self.engine.begin() as conn:
            operation = "replace" if overwrite else "append"

            # Sync to be compatible with Pandas:
            await conn.run_sync(
                lambda sync_conn: df.to_sql(
                    name=table_name,
                    con=sync_conn,  # Sync required
                    if_exists=operation,  # Overwrite
                    index=False,
                )
            )

        await self.engine.dispose()

        logger.info(
            f"Saved {len(df)} rows to table '{table_name}' in database '{self.db_url}'."
        )