Logging Helpers

dataexcept.logging_helpers provides lightweight utilities that keep exception logging consistent across applications.

log_exception

from dataexcept.logging_helpers import log_exception

log_exception(exc, context={"job_id": job.id, "batch": batch_id})

The helper records the full traceback at the chosen logging level and attaches an optional context dictionary under the dataexcept_context attribute of the log record. This makes it easy to enrich structured logs with job or batch metadata without repeating boilerplate.

log_and_raise

Use the context manager when you need to log and re-raise a failure while preserving the original traceback:

from dataexcept.logging_helpers import log_and_raise

with log_and_raise(logger=my_logger, context={"job_id": job.id}):
    run_pipeline()

Any exception raised inside the block is logged (with exc_info=True) and then re-raised automatically.

log_then_raise

For code paths that still prefer a functional helper, log_then_raise mirrors the previous API:

from dataexcept.logging_helpers import log_then_raise

try:
    run_pipeline()
except Exception as exc:
    log_then_raise(exc, context={"job_id": job.id})

The helper logs the exception (with context) and re-raises the same instance. Whenever possible, prefer log_and_raise because it avoids tampering with the traceback.