Error in Dataflow pipeline - reading json and writing to BQ

[ammar_hanif]

Hi everyone,

I'm using a basic ETL pipeline to run in dataflow. Python script is in my default cloud shell VM from where I'm running it. I have defined the arguments as mentioned below,

python3 my_pipeline.py --project centon-data-pim-dev --region us-central1 --stagingLocation gs://sample_practice_hrp/staging --tempLocation gs://sample_practice_hrp/temp --runner DataflowRunner

link to the sample file used,
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/quests/dataflow_python/1_Ba...

while the other options are as below,

    input = 'gs://sample_practice_hrp/events.json'.format(opts.project)

    output = '{0}:logs.logs'.format(opts.project)

Unfortunately, I'm receiving some error, causing the pipeline to fail (picture attached)

[ms4446]

From the errors you're seeing, it's not immediately clear what's causing the issue, as the traceback isn't complete and there's a repeating error message. 

They are generic error messages that come from the Apache Beam Python SDK, and they indicate that an error occurred during the processing of elements by a DoFn function in the pipeline.

1. The first error, "An unknown error has occurred. No additional information was provided," is a very general error and could be caused by a wide range of issues. Without additional details, it's hard to diagnose the problem.

2. The second set of errors, which include the traceback "apache_beam/runners/common.py, line 1418, in apache_beam.runners.worker.operations.DoOperation.process", indicate that an error occurred during the processing of elements in a DoFn function. DoFn is a fundamental concept in Apache Beam representing a "Do Function", which is where element-wise operations are defined. However, without the full traceback or error message, it's hard to tell exactly what went wrong.

3. The final error message, "Workflow failed. Causes: S16:WriteToBQ/BigQueryBatchFileLoads/GroupFilesByTableDestinations/Read+WriteToBQ/BigQueryBatchFile", suggests that the error occurred during the step of writing to BigQuery. This could indicate a problem with the data being written, the schema of the BigQuery table, or the permissions of the service account used by Dataflow.

Given these error messages, here are a few things you could check:

• Verify that your data is correctly formatted and matches the expected schema.
• Check that the service account used by Dataflow has the necessary permissions to write to the BigQuery table.
• Look at the Cloud Logging logs for your Dataflow job to see if there are more detailed error messages that could help diagnose the problem.
• Test your pipeline locally using the DirectRunner to see if you can reproduce the error in a more controlled environment.

If you can  provide more information about the pipeline and the data you're processing. I can further diagnose the issue.

[ammar_hanif]

Hi,

Thanks for your detailed response, I wanted to share further details by sharing the error logs.json file (unfortunately, I wasn't able to upload it here).

However, with a random guess I diagnosed the issue was with the dataset in which the table in BigQuery was supposed to be created and that was not created. I initially thought the code would generate the dataset itself (like the table it created), which it didn't.

For the above scenario, what you suggest the most effective way would be to diagnose such issues in a most efficient manner. Since the logs are too lengthy to analyze and it is almost like finding a needle in a haystack.

[ms4446 · Accepted solution]

For diagnosing such issues, you might consider the following strategies:

1. Use a structured approach to error solving: Start with the error messages you receive. Even if they seem cryptic at first, they often provide a starting point for your investigation. Then, check the prerequisites for your pipeline (like whether the required datasets and tables exist, whether the data is in the expected format, etc.).

2. Check permissions: Ensure that the Dataflow service account has the necessary permissions to both the source (GCS) and the destination (BigQuery). It requires storage.objects.get and storage.objects.list for GCS, and bigquery.dataEditor and bigquery.jobUser for BigQuery.

3. Examine the logs: Cloud Logging is an invaluable tool for understanding what's happening in your pipeline. The logs for Dataflow jobs can be lengthy, but they're often the best place to find detailed error messages. You can filter the logs by the job ID or other parameters to narrow down the volume of logs you need to examine.

4. Validate your data: Make sure the input data is in the expected format and schema. Any discrepancies in data format or schema can cause your pipeline to fail.

5. Use error handling in your code: Consider adding try/except blocks around parts of your code where errors may occur. This can provide more detailed error messages and make it easier to identify the cause of the issue.