"""
Spark Simplicity - I/O Validation Package
==========================================

Enterprise-grade validation utilities for secure and reliable Spark I/O operations.
This package provides comprehensive validation capabilities essential for production
Spark deployments using shared storage, distributed filesystems, and complex cluster
environments. Ensures robust file system operations with proactive validation.

**Core Validation Capabilities:**
    - **Mount Point Validation**: Cluster-wide shared storage accessibility verification
    - **Path Utilities**: Cross-platform path handling and permission validation
    - **Cluster Coordination**: Distributed validation across all Spark executor nodes
    - **Storage Integration**: Native support for NFS, HDFS, SMB, and cloud storage
    - **Security Validation**: Comprehensive permission and access control checking
    - **Error Prevention**: Proactive validation to prevent runtime I/O failures

**Validation Architecture:**
    The validation system follows a layered approach ensuring comprehensive coverage:

    - **Path Analysis Layer**: Intelligent mount point detection and path validation
    - **Local Validation Layer**: Driver node accessibility and permission checking
    - **Distributed Validation Layer**: Cluster-wide executor node validation
    - **Result Analysis Layer**: Comprehensive diagnostics and error reporting

**Enterprise Features:**
    - **Production Safety**: Fail-fast validation prevents partial processing failures
    - **Comprehensive Logging**: Detailed audit trails for troubleshooting and monitoring
    - **Performance Optimization**: Parallel validation leveraging cluster capabilities
    - **Cross-Platform Support**: Windows, Linux, and macOS compatibility
    - **Network Storage**: Full support for enterprise storage systems
    - **Cluster Integration**: Native Spark cluster topology awareness

**Validation Workflow:**
    1. **Path Analysis**: Extract and analyze mount points from file paths
    2. **Driver Validation**: Validate accessibility on Spark driver node
    3. **Cluster Distribution**: Create validation tasks for all executor nodes
    4. **Parallel Execution**: Simultaneously validate across entire cluster
    5. **Result Aggregation**: Collect and analyze validation results
    6. **Diagnostic Reporting**: Generate detailed error reports and remediation guidance

**Storage System Compatibility:**
    **Network File Systems:**
    - **NFS (Network File System)**: Full validation support for Unix/Linux NFS mounts
    - **SMB/CIFS**: Windows and Samba share validation with permission checking
    - **AFP**: Apple File Protocol support for mixed-platform environments

    **Distributed File Systems:**
    - **HDFS**: Hadoop Distributed File System validation and accessibility testing
    - **GlusterFS**: Red Hat distributed storage system support
    - **Ceph**: Distributed object storage validation capabilities

    **Cloud Storage:**
    - **Amazon S3**: S3-compatible storage mount validation
    - **Azure Blob**: Azure storage mount accessibility verification
    - **Google Cloud Storage**: GCS mount point validation support
    - **MinIO**: Self-hosted S3-compatible storage validation

**Security and Access Control:**
    - **Permission Validation**: Comprehensive read/write permission checking
    - **Access Control Lists**: Support for complex ACL-based permission systems
    - **Authentication**: Validation of storage system authentication credentials
    - **Network Security**: Firewall and network connectivity validation
    - **Privilege Escalation**: Detection of insufficient privilege scenarios

**Performance Considerations:**
    **Validation Strategies:**
    - **Parallel Execution**: Leverages cluster parallelism for rapid validation
    - **Early Termination**: Fail-fast on driver validation to avoid cluster overhead
    - **Caching**: Validation result caching for repeated operations
    - **Resource Efficiency**: Minimal cluster resource usage during validation

    **Scalability Characteristics:**
    - **Small Clusters (2-10 nodes)**: Sub-second validation completion
    - **Medium Clusters (10-50 nodes)**: Network latency becomes primary factor
    - **Large Clusters (50+ nodes)**: Parallel validation prevents linear scaling issues
    - **Enterprise Scale (100+ nodes)**: Optimized for production-grade deployments

**Integration Patterns:**
    **ETL Pipeline Integration:**
    - Pre-flight validation before data processing operations
    - Input/output path validation for reliable pipeline execution
    - Storage health checks integrated into pipeline monitoring

    **CI/CD Integration:**
    - Automated validation in deployment pipelines
    - Infrastructure validation for new cluster deployments
    - Storage configuration verification in testing environments

    **Monitoring Integration:**
    - Scheduled validation for proactive storage monitoring
    - Integration with enterprise monitoring and alerting systems
    - Storage accessibility metrics for cluster health dashboards

**Common Use Cases:**
    **Production Data Pipelines:**
    - Validate data lake accessibility before ETL processing
    - Ensure output storage availability for critical business reports
    - Verify backup storage accessibility for disaster recovery

    **Cluster Management:**
    - New node validation during cluster expansion
    - Storage system health monitoring and alerting
    - Infrastructure validation after maintenance windows

    **Multi-Tenant Environments:**
    - Tenant-specific storage accessibility validation
    - Cross-tenant security boundary verification
    - Resource isolation and access control validation

**Usage Examples:**
    Basic mount point validation:

     from spark_simplicity.io.validation import _validate_mount_point

     if _validate_mount_point(spark, "/nfs/shared/data/input.parquet"):
    ...     print("Storage accessible across entire cluster")
    ... else:
    ...     print("Storage validation failed - check logs")

    Path configuration with validation:

     from spark_simplicity.io.validation import configure_spark_path

     spark_path = configure_spark_path(
    ...     Path("/hdfs/warehouse/table"),
    ...     shared_mount=True,
    ...     spark=spark_session
    ... )

    Advanced path analysis:

     from spark_simplicity.io.validation import _extract_mount_point, _check_path_access

     mount_point = _extract_mount_point("/mnt/storage/data/file.csv")
     success, error = _check_path_access(mount_point, "mount point")

**Error Handling Strategy:**
    The validation system uses comprehensive logging-based error handling:
    - **Detailed Diagnostics**: Specific error messages for each failure type
    - **Actionable Guidance**: Error messages include resolution steps
    - **Cluster Context**: Node-specific error reporting for distributed failures
    - **Production Safety**: Graceful degradation without cluster disruption

**Troubleshooting Integration:**
    - **Structured Logging**: Machine-parseable error messages for automation
    - **Diagnostic Categories**: Organized error types for systematic troubleshooting
    - **Remediation Guidance**: Specific steps for resolving common storage issues
    - **Escalation Paths**: Clear guidance for complex infrastructure problems

See Also:
    - Main I/O package: ``spark_simplicity.io`` for data reading/writing operations
    - Writers package: ``spark_simplicity.io.writers`` for format-specific output
    - Session management: ``spark_simplicity.session`` for cluster configuration
    - Logging system: ``spark_simplicity.logger`` for comprehensive audit trails

Note:
    This validation package is essential for production Spark deployments using
    shared storage. It prevents common I/O failures through proactive validation
    and provides comprehensive diagnostics for troubleshooting storage issues
    in complex distributed environments.
"""

from .mount_validator import _validate_mount_point
from .path_utils import _check_path_access, _extract_mount_point, configure_spark_path

__all__ = [
    "_validate_mount_point",
    "_extract_mount_point",
    "_check_path_access",
    "configure_spark_path",
]
