GCP Dataflow Pipeline scenarios with 'how to read property 'length' of undefined' scenarios During Execution
I'm confused about I'm testing a new approach and I've been struggling with this for a few days now and could really use some help. I am currently working on a GCP Dataflow pipeline using Apache Beam (version 2.34.0) to process streaming data from Pub/Sub. My pipeline is designed to read messages from a Pub/Sub topic, apply some transformations, and then write the results to BigQuery. However, when I run the pipeline, I encounter the following behavior: ``` TypeError: want to read property 'length' of undefined ``` This behavior occurs during the execution of the `ParDo` transformation where I am attempting to parse the incoming JSON messages. I suspect that some of the messages might not be in the expected format, but I'm not sure how to handle this gracefully. Here’s a snippet of the relevant code: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions class ParseJson(beam.DoFn): def process(self, element): import json try: data = json.loads(element) # Assuming 'items' is a key in the incoming JSON if 'items' in data: return [data['items']] # This could be the scenario if 'items' is missing else: yield beam.pvalue.TaggedOutput('missing_items', element) except json.JSONDecodeError: yield beam.pvalue.TaggedOutput('parse_errors', element) def run(): options = PipelineOptions() with beam.Pipeline(options=options) as p: (p | 'Read from PubSub' >> beam.io.ReadFromPubSub(topic='projects/my-project/topics/my-topic') | 'Parse JSON' >> beam.ParDo(ParseJson()).with_outputs('missing_items', 'parse_errors', main='parsed_items') | 'Write to BigQuery' >> beam.io.WriteToBigQuery(table='my_dataset.my_table')) if __name__ == '__main__': run() ``` I have already tried adding checks for the existence of keys within the incoming JSON, but the behavior still continues. Additionally, I have set up logging to capture malformed messages, but it seems I might be missing some edge cases where the data structure doesn't match my expectations. How can I avoid this behavior and ensure that my pipeline can handle unexpected input formats smoothly? Are there recommended best practices for behavior handling within Dataflow pipelines? For context: I'm using Python on Windows. This is part of a larger CLI tool I'm building. What am I doing wrong? I recently upgraded to Python LTS. Am I missing something obvious?