"""
Spark Simplicity - Base Reader Utilities
========================================

Foundation utilities and common functions for all Spark DataFrame readers providing shared
validation, error handling, and data quality assurance capabilities. This module contains
core infrastructure used by all format-specific readers to ensure consistent behavior,
robust error handling, and comprehensive data validation across different file formats.

Key Features:
    - **Data Validation**: Comprehensive DataFrame structure and content validation
    - **Error Handling**: Standardized error detection and reporting mechanisms
    - **Quality Assurance**: Data integrity checks and corruption detection
    - **Performance Optimization**: Efficient validation with minimal overhead
    - **Production Safety**: Robust error recovery and detailed diagnostics
    - **Format Independence**: Validation logic applicable across all data formats

Core Functionality:
    **DataFrame Validation**:
    - Structure integrity verification for loaded DataFrames
    - Corrupt record detection and handling
    - Schema consistency validation
    - Data quality assessment and reporting

    **Error Management**:
    - Standardized exception handling across all readers
    - Comprehensive error categorization and reporting
    - Production-grade error recovery mechanisms
    - Detailed diagnostic information for troubleshooting

Validation Capabilities:
    **Structural Validation**:
    - Column presence and structure verification
    - Schema consistency checking across records
    - Data type validation and integrity assessment
    - Header and metadata validation

    **Content Validation**:
    - Corrupt record detection and isolation
    - Data completeness and quality assessment
    - Format compliance verification
    - Consistency checks across data sources

Enterprise Integration:
    - **Production Reliability**: Robust validation for enterprise data workflows
    - **Quality Assurance**: Comprehensive data quality validation and reporting
    - **Error Recovery**: Graceful handling of data corruption and format issues
    - **Performance Monitoring**: Efficient validation with minimal processing overhead
    - **Audit Compliance**: Detailed validation logging for regulatory requirements

Usage:
    This module provides shared utilities used by all format-specific readers
    and is not intended for direct use by end users. It ensures consistent
    validation and error handling across the entire reader ecosystem.

    from .base_reader import _validate_json_dataframe
"""

from pyspark.sql import DataFrame

from ...exceptions import CorruptDataError, DataFrameValidationError
from ...logger import get_logger

# Logger for readers
_reader_logger = get_logger("spark_simplicity.io.readers")


def _validate_json_dataframe(df: DataFrame) -> bool:
    """
    Validate DataFrame integrity with comprehensive corruption detection for JSON processing.

    Provides robust validation of DataFrame structure and content specifically designed
    for JSON data processing workflows. This function performs sophisticated analysis
    to detect corrupt records, validate schema integrity, and ensure data quality
    standards required for production data processing environments.

    The validation process is optimized to avoid triggering Spark's QUERY_ONLY_CORRUPT_RECORD_COLUMN
    error while providing comprehensive assessment of data quality and structural integrity.
    Essential for ensuring reliable data processing in enterprise JSON ingestion workflows.

    Args:
        df: Spark DataFrame to validate for structural integrity and data quality.
           Typically resulting from JSON parsing operations that may contain corrupt
           records due to malformed JSON, encoding issues, or format inconsistencies.
           The DataFrame may include Spark's special '_corrupt_record' column for
           tracking parsing failures.

    Returns:
        Boolean indicating overall DataFrame validity and usability:
        - True: DataFrame contains valid, processable data with acceptable structure
        - False: DataFrame is entirely corrupt or unusable for processing

        Validation considers both structural integrity and data content quality
        to provide comprehensive assessment for production data workflows.

    Validation Logic:
        **Complete Corruption Detection**:
        - Identifies DataFrames containing only corrupt records
        - Detects scenarios where all parsing attempts failed
        - Returns False for entirely unusable DataFrames

        **Mixed Data Assessment**:
        - Evaluates DataFrames with both valid and corrupt records
        - Considers overall data usability for processing workflows
        - Returns True if sufficient valid data exists for processing

        **Structure Validation**:
        - Verifies presence of processable columns
        - Validates basic DataFrame structure and schema
        - Ensures minimum requirements for data processing operations

    Error Handling Strategy:
        **DataFrame Validation Errors**: Wrapped in DataFrameValidationError with context
        **Corrupt Data Errors**: Re-raised as-is to preserve error semantics
        **Unexpected Errors**: Logged and wrapped with detailed diagnostic information
        **Spark Query Errors**: Avoided through intelligent validation approach

    Examples:
        Validate JSON DataFrame after parsing:

         json_df = spark.read.json("data.json")
         is_valid = _validate_json_dataframe(json_df)

         if is_valid:
        ...     print("DataFrame is valid for processing")
        ...     # Proceed with data processing
        ... else:
        ...     print("DataFrame contains only corrupt data")
        ...     # Handle corrupt data scenario

        Integration with JSON reader error handling:

         try:
        ...     df = spark.read.json("complex.json")
        ...     if _validate_json_dataframe(df):
        ...         return df  # Valid DataFrame
        ...     else:
        ...         return None  # Try alternative parsing strategy
        ... except DataFrameValidationError as e:
        ...     logger.warning(f"Validation failed: {e}")
        ...     return None

    Corruption Detection Scenarios:
        **All Records Corrupt**:
        - DataFrame contains only '_corrupt_record' column
        - All JSON parsing attempts failed due to format issues
        - No processable data available for analysis
        - Returns False to indicate unusable state

        **Mixed Valid/Corrupt Records**:
        - DataFrame contains both '_corrupt_record' and valid columns
        - Some JSON records parsed successfully
        - Sufficient data available for processing despite some corruption
        - Returns True to indicate partial usability

        **Clean Data**:
        - No '_corrupt_record' column present
        - All JSON records parsed successfully
        - Full data integrity maintained throughout parsing
        - Returns True for optimal processing conditions

    Performance Considerations:
        **Validation Overhead**: Minimal - only metadata inspection, no data scanning
        **Spark Query Avoidance**: Intelligent approach prevents expensive query operations
        **Error Prevention**: Avoids triggering Spark's corrupt record column errors
        **Production Efficiency**: Optimized for high-throughput validation scenarios

    Production Usage Patterns:
        **JSON Reader Integration**:
        - Called by JSON readers to validate parsing results
        - Used in cascading format detection strategies
        - Enables intelligent fallback between parsing approaches

        **Data Quality Pipelines**:
        - Integrated into data quality validation workflows
        - Used for automated data integrity checking
        - Supports data processing decision automation

        **Error Recovery Workflows**:
        - Enables graceful handling of partially corrupt data
        - Supports automatic retry with alternative parsing strategies
        - Facilitates comprehensive error reporting and diagnostics

    Diagnostic Information:
        **Validation Context**: Detailed error information in DataFrameValidationError
        **Function Traceability**: Error context includes validation function identification
        **Debugging Support**: Comprehensive logging for troubleshooting validation issues
        **Audit Trail**: Validation results logged for operational monitoring

    See Also:
        - ``load_json()``: Primary JSON reader using this validation
        - ``DataFrameValidationError``: Exception type for validation failures
        - ``CorruptDataError``: Exception type for data corruption scenarios

    Note:
        This function is designed for internal use by reader modules and implements
        sophisticated logic to handle Spark's corrupt record column behavior while
        providing reliable validation results for production data processing workflows.
        The validation approach balances comprehensive checking with performance
        optimization for high-throughput scenarios.
    """
    try:
        # Check if all records are corrupt (contains only _corrupt_record column)
        if "_corrupt_record" in df.columns and len(df.columns) == 1:
            return False

        # Check if there are corrupt records mixed with valid data
        if "_corrupt_record" in df.columns:
            # If we have other columns besides _corrupt_record, we likely have valid data
            # Avoid the QUERY_ONLY_CORRUPT_RECORD_COLUMN error by not doing any operations
            return len(df.columns) > 1

        # No corrupt record column means the DataFrame is likely valid
        # Just check if it has any columns (structure validation)
        return len(df.columns) > 0

    except (AttributeError, ValueError) as e:
        _reader_logger.warning(f"Failed to validate DataFrame structure: {e}")
        raise DataFrameValidationError(
            "DataFrame structure validation failed",
            details={"error": str(e), "function": "_validate_json_dataframe"},
        )
    except CorruptDataError:
        # Re-raise CorruptDataError as-is
        raise
    except Exception as e:
        _reader_logger.error(f"Unexpected error during DataFrame validation: {e}")
        raise DataFrameValidationError(
            "Unexpected error during DataFrame validation",
            details={"error": str(e), "function": "_validate_json_dataframe"},
        )
