src/datalake/query.py

"""
Query interface for data lake using AWS Athena SQL queries.

Provides methods to read and filter data from the Athena data lake
using SQL queries with support for device/message filtering and date ranges.
"""

from typing import List, Optional, Tuple
import pandas as pd
from .athena import AthenaQuery
from .catalog import DataLakeCatalog
from .config import DataLakeConfig
from .logger import setup_logger

logger = setup_logger(__name__)


class DataLakeQuery:
    """
    Query interface for Athena-based data lake.
    
    Provides efficient methods to read data using SQL queries,
    with support for filtering by device, message, date range, and time windows.
    """
    
    def __init__(self, athena_query: AthenaQuery, catalog: DataLakeCatalog):
        """
        Initialize query engine.
        
        Args:
            athena_query: AthenaQuery instance
            catalog: Data lake catalog
        """
        self.athena = athena_query
        self.catalog = catalog
        logger.info("Initialized DataLakeQuery")
    
    def read_device_message(
        self,
        device_id: str,
        message: str,
        date_range: Optional[Tuple[str, str]] = None,
        columns: Optional[List[str]] = None,
        limit: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Read all data for a device/message combination using SQL.
        
        Args:
            device_id: Device identifier
            message: Message/rule name
            date_range: Optional (start_date, end_date) tuple (YYYY-MM-DD format)
            columns: Optional column subset to read (improves performance)
            limit: Optional row limit
        
        Returns:
            DataFrame with query results
        """
        table_name = self.catalog.get_table_name(device_id, message)
        
        # Build SELECT clause
        if columns:
            # Validate columns exist
            schema = self.catalog.get_schema(device_id, message)
            if schema:
                valid_columns = [c for c in columns if c in schema]
                if not valid_columns:
                    logger.warning(f"None of requested columns found, using all columns")
                    select_clause = "*"
                else:
                    select_clause = ", ".join(valid_columns)
            else:
                select_clause = "*"
        else:
            select_clause = "*"
        
        # Build WHERE clause
        where_conditions = []
        
        if date_range:
            start_date, end_date = date_range
            # Parse dates and filter using $path column
            # Format: YYYY-MM-DD
            # Data structure: {device_id}/{message}/{year}/{month}/{day}/file.parquet
            start_parts = start_date.split('-')
            end_parts = end_date.split('-')
            
            if len(start_parts) == 3 and len(end_parts) == 3:
                start_year, start_month, start_day = start_parts
                end_year, end_month, end_day = end_parts
                
                # Extract year, month, day from path and filter
                # Path structure: .../year/month/day/file.parquet
                # Use element_at(split($path, '/'), -4) for year, -3 for month, -2 for day
                path_year = "try_cast(element_at(split(\"$path\", '/'), -4) AS INTEGER)"
                path_month = "try_cast(element_at(split(\"$path\", '/'), -3) AS INTEGER)"
                path_day = "try_cast(element_at(split(\"$path\", '/'), -2) AS INTEGER)"
                
                # Build partition filter using path-based extraction
                # This handles hierarchical partitioning: {device_id}/{message}/{year}/{month}/{day}/file.parquet
                where_conditions.append(
                    f"({path_year} > {start_year} OR "
                    f"({path_year} = {start_year} AND "
                    f"({path_month} > {start_month} OR "
                    f"({path_month} = {start_month} AND {path_day} >= {start_day}))))"
                )
                where_conditions.append(
                    f"({path_year} < {end_year} OR "
                    f"({path_year} = {end_year} AND "
                    f"({path_month} < {end_month} OR "
                    f"({path_month} = {end_month} AND {path_day} <= {end_day}))))"
                )
            else:
                # Fallback: try date column if it exists
                where_conditions.append(f"date >= '{start_date}' AND date <= '{end_date}'")
        
        where_clause = ""
        if where_conditions:
            where_clause = "WHERE " + " AND ".join(where_conditions)
        
        # Build LIMIT clause
        limit_clause = f"LIMIT {limit}" if limit else ""
        
        query = f"""
        SELECT {select_clause}
        FROM {self.catalog.config.database_name}.{table_name}
        {where_clause}
        {limit_clause}
        """
        
        logger.info(f"Executing query for {device_id}/{message}")
        return self.athena.query_to_dataframe(query)
    
    def read_date_range(
        self,
        device_id: str,
        message: str,
        start_date: str,
        end_date: str,
        columns: Optional[List[str]] = None,
    ) -> pd.DataFrame:
        """
        Read data for a specific date range.
        
        Convenience method wrapping read_device_message with date range.
        
        Args:
            device_id: Device identifier
            message: Message name
            start_date: Start date (YYYY-MM-DD format)
            end_date: End date (YYYY-MM-DD format)
            columns: Optional column subset
        
        Returns:
            DataFrame with data for the date range
        """
        return self.read_device_message(
            device_id=device_id,
            message=message,
            date_range=(start_date, end_date),
            columns=columns,
        )
    
    def time_series_query(
        self,
        device_id: str,
        message: str,
        signal_name: str,
        start_time: Optional[int] = None,
        end_time: Optional[int] = None,
        limit: Optional[int] = None,
    ) -> pd.DataFrame:
        """
        Query single signal as time series.
        
        Args:
            device_id: Device identifier
            message: Message name
            signal_name: Signal column name
            start_time: Min timestamp (microseconds since epoch)
            end_time: Max timestamp (microseconds since epoch)
            limit: Optional row limit
        
        Returns:
            DataFrame with 't' (timestamp) and signal columns, sorted by time
        """
        table_name = self.catalog.get_table_name(device_id, message)
        
        # Build WHERE clause
        where_conditions = []
        
        if start_time is not None:
            where_conditions.append(f"t >= {start_time}")
        if end_time is not None:
            where_conditions.append(f"t <= {end_time}")
        
        where_clause = ""
        if where_conditions:
            where_clause = "WHERE " + " AND ".join(where_conditions)
        
        limit_clause = f"LIMIT {limit}" if limit else ""
        
        query = f"""
        SELECT t, {signal_name}
        FROM {self.catalog.config.database_name}.{table_name}
        {where_clause}
        ORDER BY t
        {limit_clause}
        """
        
        logger.info(f"Time series query for {device_id}/{message}/{signal_name}")
        return self.athena.query_to_dataframe(query)
    
    def execute_sql(self, sql: str) -> pd.DataFrame:
        """
        Execute custom SQL query.
        
        Args:
            sql: SQL query string
        
        Returns:
            DataFrame with query results
        
        Note:
            Query should reference tables in the format:
            {database_name}.{table_name}
        """
        logger.info("Executing custom SQL query")
        return self.athena.query_to_dataframe(sql)
    
    def aggregate(
        self,
        device_id: str,
        message: str,
        aggregation: str,
        group_by: Optional[List[str]] = None,
        where_clause: Optional[str] = None,
    ) -> pd.DataFrame:
        """
        Execute aggregation query.
        
        Args:
            device_id: Device identifier
            message: Message name
            aggregation: Aggregation expression (e.g., "COUNT(*), AVG(RPM)")
            group_by: Optional list of columns to group by
            where_clause: Optional WHERE clause (without WHERE keyword)
        
        Returns:
            DataFrame with aggregation results
        
        Example:
            df = query.aggregate(
                "device_001", "EngineData",
                "COUNT(*) as count, AVG(RPM) as avg_rpm, MIN(RPM) as min_rpm",
                group_by=["date"]
            )
        """
        table_name = self.catalog.get_table_name(device_id, message)
        
        group_by_clause = ""
        if group_by:
            group_by_clause = f"GROUP BY {', '.join(group_by)}"
        
        where_clause_sql = ""
        if where_clause:
            where_clause_sql = f"WHERE {where_clause}"
        
        query = f"""
        SELECT {aggregation}
        FROM {self.catalog.config.database_name}.{table_name}
        {where_clause_sql}
        {group_by_clause}
        """
        
        logger.info(f"Aggregation query for {device_id}/{message}")
        return self.athena.query_to_dataframe(query)