Utilities

def flasher(msg, severity=None):
    """
    Flash message utility for user notifications.
    Integrates with Flask's flash message system for UI feedback.
    
    Parameters:
    - msg: str, message text to display to user
    - severity: str, optional message severity ('info', 'warning', 'error', 'success')
    
    Usage:
    Used throughout the application to provide user feedback
    for operations, errors, and status updates.
    """

def parse_human_datetime(s):
    """
    Parse human-readable datetime strings.
    Supports natural language date expressions and ISO formats.
    
    Parameters:
    - s: str, datetime string in human-readable format
    
    Returns:
    datetime object parsed from input string
    
    Examples:
    - '2023-01-01' -> datetime(2023, 1, 1)
    - 'yesterday' -> datetime for previous day
    - '1 week ago' -> datetime for one week prior
    """

def datetime_f(dttm):
    """
    Format datetime objects for display.
    Standardized datetime formatting for UI consistency.
    
    Parameters:
    - dttm: datetime, datetime object to format
    
    Returns:
    str, formatted datetime string for display
    """

def base_json_conv(obj):
    """
    JSON serialization converter for complex objects.
    Handles datetime, Decimal, and other non-serializable types.
    
    Parameters:
    - obj: any, object to convert for JSON serialization
    
    Returns:
    JSON-serializable representation of object
    
    Usage:
    Used as default converter in json_dumps() for complex data types.
    """

def json_iso_dttm_ser(dttm, pessimistic=False):
    """
    ISO datetime serialization for JSON APIs.
    
    Parameters:
    - dttm: datetime, datetime object to serialize
    - pessimistic: bool, whether to use pessimistic timezone handling
    
    Returns:
    str, ISO 8601 formatted datetime string
    """

def json_int_dttm_ser(dttm):
    """
    Integer timestamp serialization for JavaScript compatibility.
    
    Parameters:
    - dttm: datetime, datetime object to serialize
    
    Returns:
    int, Unix timestamp in milliseconds for JavaScript Date()
    """

def json_dumps(obj, default=None, ignore_nan=False, encoding=None, sort_keys=False):
    """
    Enhanced JSON serialization with Superset-specific handling.
    
    Parameters:
    - obj: any, object to serialize to JSON
    - default: callable, custom serialization function for complex types
    - ignore_nan: bool, whether to ignore NaN values in numeric data
    - encoding: str, character encoding for string data
    - sort_keys: bool, whether to sort dictionary keys in output
    
    Returns:
    str, JSON string representation of object
    
    Features:
    - Handles pandas DataFrames and Series
    - Processes datetime objects with timezone awareness
    - Manages NaN and infinity values appropriately
    - Supports custom serialization handlers
    """

def pessimistic_connection_handling(engine):
    """
    Configure pessimistic disconnect handling for database connections.
    Improves connection reliability in unstable network environments.
    
    Parameters:
    - engine: SQLAlchemy Engine, database engine to configure
    
    Side Effects:
    Configures engine event listeners for connection validation
    and automatic reconnection on disconnect detection.
    """

def setup_cache(app, cache_config):
    """
    Initialize application cache configuration.
    Sets up Flask-Caching with specified backend and options.
    
    Parameters:
    - app: Flask application instance
    - cache_config: dict, cache configuration parameters
    
    Returns:
    Cache instance configured for the application
    
    Supported Backends:
    - Redis: High-performance distributed caching
    - Memcached: Memory-based caching system
    - Simple: In-memory Python dictionary cache
    - FileSystem: File-based cache storage
    """

def get_or_create_main_db():
    """
    Get or create main database connection instance.
    Ensures Superset has a configured main database for metadata storage.
    
    Returns:
    Database instance for Superset's main metadata database
    
    Usage:
    Called during application initialization to establish
    the primary database connection for application metadata.
    """

def get_main_database(session):
    """
    Retrieve main database instance from session.
    
    Parameters:
    - session: SQLAlchemy session for database operations
    
    Returns:
    Database instance representing the main Superset database
    """

def get_update_perms_flag():
    """
    Get permission update flag from configuration.
    Controls whether permissions are automatically updated during startup.
    
    Returns:
    bool, True if permissions should be updated automatically
    """

def merge_extra_filters(form_data, extra_filters):
    """
    Merge additional filters into form data.
    Combines dashboard-level filters with chart-specific filters.
    
    Parameters:
    - form_data: dict, chart configuration and existing filters
    - extra_filters: list, additional filters to apply
    
    Returns:
    dict, updated form data with merged filters
    
    Usage:
    Used when dashboard filters need to be applied to individual charts
    for consistent filtering across dashboard components.
    """

def merge_request_params(form_data, params):
    """
    Merge HTTP request parameters into form data.
    Incorporates URL parameters and form submissions into chart configuration.
    
    Parameters:
    - form_data: dict, existing chart configuration
    - params: dict, HTTP request parameters to merge
    
    Returns:
    dict, updated form data with request parameters
    """

def get_since_until(time_range=None, since=None, until=None, time_shift=None, relative_start=None, relative_end=None):
    """
    Parse and process time range parameters for queries.
    Handles various time range specifications and converts to absolute timestamps.
    
    Parameters:
    - time_range: str, natural language time range ('Last week', '30 days ago', etc.)
    - since: str, start time specification
    - until: str, end time specification
    - time_shift: str, time shift offset for comparisons
    - relative_start: str, relative start time specification
    - relative_end: str, relative end time specification
    
    Returns:
    tuple, (since_datetime, until_datetime) with processed time boundaries
    
    Features:
    - Natural language time range parsing
    - Relative time calculations
    - Time zone handling and conversion
    - Support for rolling time windows
    """

def add_ago_to_kwargs(kwargs, time_ago):
    """
    Add time offset to query parameters for temporal comparisons.
    
    Parameters:
    - kwargs: dict, query parameters to modify
    - time_ago: str, time offset specification ('1 week ago', '30 days', etc.)
    
    Returns:
    dict, modified parameters with time offset applied
    
    Usage:
    Used for period-over-period comparisons and trend analysis
    where historical data needs to be queried with time shifts.
    """

def zlib_compress(data):
    """
    Compress data using zlib compression algorithm.
    
    Parameters:
    - data: bytes or str, data to compress
    
    Returns:
    bytes, compressed data suitable for storage or transmission
    
    Usage:
    Used for compressing large query results and cached data
    to reduce storage requirements and network bandwidth.
    """

def zlib_decompress(data):
    """
    Decompress zlib-compressed data.
    
    Parameters:
    - data: bytes, compressed data to decompress
    
    Returns:
    bytes, original uncompressed data
    
    Usage:
    Companion function to zlib_compress() for retrieving
    compressed cached data and query results.
    """

def validate_json(obj):
    """
    Validate JSON structure and content.
    
    Parameters:
    - obj: any, object to validate for JSON compliance
    
    Returns:
    bool, True if object is valid JSON, False otherwise
    
    Raises:
    ValueError for invalid JSON structures
    
    Usage:
    Used throughout the application to validate configuration
    parameters, API inputs, and stored JSON data.
    """

class memoized:
    """
    Memoization decorator for function result caching.
    
    Properties:
    - watch: list, instance variables to monitor for cache invalidation
    
    Usage:
    Decorator that caches function results based on arguments.
    Automatically invalidates cache when watched instance variables change.
    
    Example:
    @memoized
    def expensive_calculation(self, param1, param2):
        return complex_computation(param1, param2)
    
    @memoized(watch=('config', 'settings'))  
    def config_dependent_function(self):
        return process_configuration(self.config)
    """

def now_as_float():
    """
    Get current timestamp as floating point number.
    
    Returns:
    float, current time as Unix timestamp with millisecond precision
    
    Usage:
    Used for performance timing, cache key generation,
    and high-precision timestamp requirements.
    """

DTTM_ALIAS: str = '__timestamp'
"""
Standard alias for datetime columns in queries.
Consistent column name used across visualizations for time-based data.
"""

EPOCH: datetime
"""
Unix epoch datetime object (1970-01-01 00:00:00 UTC).
Reference point for timestamp calculations and conversions.
"""

JS_MAX_INTEGER: int = 9007199254740991  # 2^53-1
"""
Maximum safe integer value for JavaScript compatibility.
Used to prevent precision loss when sending large integers to frontend.
"""

class MediumText:
    """
    Extended text column type for MySQL databases.
    Provides larger text storage capacity than standard TEXT type.
    
    Features:
    - Supports up to 16MB of text data
    - MySQL-specific optimization
    - Automatic fallback for other database engines
    """

# Custom SQLAlchemy Types
"""
Various custom column types for specialized data storage:
- JSON columns for configuration data
- Encrypted columns for sensitive information
- Compressed columns for large text data
- Custom numeric types for specialized calculations
"""

def get_celery_app(config):
    """
    Get or create Celery application instance.
    
    Parameters:
    - config: dict or object, Celery configuration parameters
    
    Returns:
    Celery application instance configured for Superset tasks
    
    Features:
    - Automatic configuration from Superset settings
    - Task routing and queue management
    - Result backend configuration
    - Worker process management
    
    Usage:
    Used to initialize Celery for asynchronous query processing,
    email notifications, and background task execution.
    """

class QueryStatus:
    """
    Query execution status enumeration.
    Defines standardized status values for tracking query lifecycle.
    """
    
    STOPPED = 'stopped'
    """Query execution was manually stopped or cancelled."""
    
    FAILED = 'failed'
    """Query execution failed due to error or exception."""
    
    PENDING = 'pending'
    """Query is queued and waiting for execution."""
    
    RUNNING = 'running'
    """Query is currently executing on database."""
    
    SCHEDULED = 'scheduled'
    """Query is scheduled for future execution."""
    
    SUCCESS = 'success'
    """Query completed successfully with results available."""
    
    TIMED_OUT = 'timed_out'
    """Query exceeded maximum allowed execution time."""

ADHOC_METRIC_EXPRESSION_TYPES = {
    'SIMPLE': 'SIMPLE',
    'SQL': 'SQL'
}
"""
Adhoc metric expression type constants.

- SIMPLE: Basic aggregation functions (SUM, AVG, COUNT, etc.)
- SQL: Custom SQL expressions for complex calculations
"""

def to_adhoc(fds, metric, label=None):
    """
    Convert metric definition to adhoc metric format.
    
    Parameters:
    - fds: dict, form data structure containing metric context
    - metric: str or dict, metric name or definition to convert
    - label: str, optional custom label for the metric
    
    Returns:
    dict, adhoc metric definition suitable for query processing
    
    Usage:
    Used to standardize metric definitions from various sources
    into a consistent format for query generation and visualization.
    """

# Parse natural language dates
start_date = parse_human_datetime('30 days ago')
end_date = parse_human_datetime('today')

# Format for display
formatted_date = datetime_f(start_date)

# JSON serialization with complex types
data = {
    'timestamp': datetime.now(),
    'values': [1.5, 2.7, float('nan')],
    'metadata': {'source': 'database'}
}
json_string = json_dumps(data, ignore_nan=True)

class DataProcessor:
    def __init__(self):
        self.config = {}
    
    @memoized(watch=['config'])
    def process_data(self, dataset_id):
        """Expensive data processing with configuration dependency."""
        return expensive_calculation(dataset_id, self.config)
    
    @memoized
    def get_metadata(self, table_name):
        """Cached metadata retrieval."""
        return fetch_table_metadata(table_name)

# Merge dashboard filters with chart filters
chart_data = merge_extra_filters(
    form_data={'metrics': ['count'], 'groupby': ['category']},
    extra_filters=[{'col': 'status', 'op': '==', 'val': 'active'}]
)

# Process time range parameters
since, until = get_since_until(
    time_range='Last 30 days',
    time_shift='1 week ago'
)

# Setup application cache
app = Flask(__name__)
cache = setup_cache(app, {
    'CACHE_TYPE': 'redis',
    'CACHE_REDIS_URL': 'redis://localhost:6379/0'
})

# Configure database connection
engine = create_engine(database_url)
pessimistic_connection_handling(engine)

# Initialize Celery application
celery_config = {
    'broker_url': 'redis://localhost:6379/0',
    'result_backend': 'redis://localhost:6379/0'
}
celery_app = get_celery_app(celery_config)

# Define async task
@celery_app.task
def process_large_query(query_id):
    return execute_sql_query(query_id)