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easyfabric.fabric.logging_utils

contextlib​

json​

logging​

random​

re​

sys​

time​

uuid​

datetime​

timezone​

Path​

Optional​

notebookutils​

Row​

StringType​

StructField​

StructType​

TimestampType​

config​

get_spark​

spark​

MAX_MESSAGE_LEN​

LOG_PATTERN​

TIMESTAMP_PATTERN​

GUID_PATTERN​

EXCLUDED_LOGGERS​

LOG_SCHEMA​

is_top_level_notebook​

def is_top_level_notebook() -> bool

Identifies if the current execution is the top-most notebook.

save_log_file_to_table​

def save_log_file_to_table(end_log: bool = False) -> None

Reads a log file from OneLake, parses structured logs (including multiline), and bulk inserts into Meta.dbo.logging using Spark DataFrame.

Always logs an END entry with duration for the current notebook. If it's a top-level notebook or end_log is True, it also clears logging handlers, resets state, and persists log entries to the table.

save_historical_log_file_to_table​

def save_historical_log_file_to_table(abfs_path: str) -> None

Parses a specific log file by ABFS path and inserts missing logs into Meta.dbo.logging.

FabricLoggerAdapter Objects​

class FabricLoggerAdapter(logging.LoggerAdapter)

Adapter that automatically includes log_type and log_category in all log records.

__init__​

def __init__(logger: logging.Logger,
             log_type: Optional[str] = "IN PROCESS",
             log_category: Optional[str] = "Technical")

process​

def process(msg, kwargs)

OneLakeFileHandler Objects​

class OneLakeFileHandler(logging.Handler)

MAX_RETRIES​

BASE_DELAY​

__init__​

def __init__(path)

emit​

def emit(record)

SafeFormatter Objects​

class SafeFormatter(logging.Formatter)

Formatter that ensures custom fields exist to prevent KeyErrors from third-party libraries.

format​

def format(record)

formatTime​

def formatTime(record, datefmt=None)

Include milliseconds in the timestamp for better sorting.

to_snake_case​

def to_snake_case(string: str) -> str

Convert a string from camel case to snake case.

set_verbose_mode​

def set_verbose_mode(enabled=True)

Enable or disable verbose logging mode globally.

log_segment​

@contextlib.contextmanager
def log_segment(type: str, name: str)

Context manager to log the start and end of a logic segment. Usage: with log_segment("Data Load", "Bronze Loading"): ... logic ...

init_logging​

def init_logging(log_source: str = "Sys", log_object: str = None) -> str

Call once at the very top of the entry-point notebook / wheel. Returns the absolute OneLake path of the log file.

get_log_file_path​

def get_log_file_path() -> Optional[str]

Returns the path of the current log file. Checks singleton state, global config, and active handlers to ensure reliability even in nested notebooks or after module reloads.

extract_real_error​

def extract_real_error(log_text: str) -> str

Extracts the most relevant error message from a Spark stack trace.