Google has added a logging feature for Python User-Defined Functions in BigQuery, sending structured logs from Python UDFs to Cloud Logging.

Developers can use Python's standard logging library inside BigQuery UDFs without external plugins or custom connectors. Google framed the addition around operational monitoring and debugging, including data pipeline checks that can continue running when individual records fail validation.

It illustrated the feature with an eCommerce event ingestion workflow in which a Python UDF parses incoming JSON payloads and checks for required fields. Valid records move to a destination table, while malformed or incomplete rows are logged and return null values so the wider query does not fail.

In the example, a function called safe_parse_json uses Python code within BigQuery to inspect each payload. If the payload is empty, syntactically invalid or missing a required key, the function writes a structured error entry through the logging library and returns no result for that row.

This allows a query to complete while excluding problematic records from the clean output table. In Google's sample dataset, valid events were written for records containing user identifiers and event names, while corrupted JSON and a payload missing the user_id field were sent to logging output.

Google described the workflow in three stages. First, the UDF performs inline validation. Second, Cloud Monitoring provides real-time alerting through a log-based metric that notifies teams when error levels pass a set threshold. Third, engineers use SQL-based triage and replay by querying logged records through BigQuery Log Analytics and reprocessing data after fixing the underlying issue.

Source link