""" 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)