How to write failed records while writing to bigquery using Apache python operator writetobigquery.

[Hemantk]

• How to write failed records while writing to bigquery using Apache beam python operator.

beam.io.WriteToBigQuery.

@gcp12 

[ms4446]

To write failed records while using the WriteToBigQuery transform in Apache Beam with Python, you can implement a custom dead-letter strategy. This strategy involves capturing errors during the write operation and storing the failed records in another location, such as another BigQuery table, Cloud Storage, or Pub/Sub for further analysis.

Below are some steps you can take:

1. Set Up the Pipeline First, you need to set up your Beam pipeline, which reads from your source and writes to BigQuery.

2. Implement Dead-Letter Handling You can use the withFailures method provided by beam.io.WriteToBigQuery to handle records that fail to be written.

   import apache_beam as beam
   from apache_beam.options.pipeline_options import PipelineOptions
   from apache_beam.io.gcp.bigquery import   
   WriteToBigQuery
   
   # Define your pipeline options
   options = PipelineOptions()
   
   # Define your dead-letter table schema (adjust if needed)
   dead_letter_schema = 'field1:STRING, field2:STRING, error_message:STRING' 
   
   # Define a function to handle failures
   def handle_failed_record(failure):
       record = failure.element
       error_message = failure.error_message
       # You can append the error message to the record or create a new one
       record['error_message'] = error_message
       return record
   
   with beam.Pipeline(options=options) as p:
       records = p | 'ReadFromSource' >> beam.io.ReadFromSource('your_source')
   
       (records 
        | 'WriteToBigQuery' >> beam.io.WriteToBigQuery(
               'your_project:your_dataset.your_table',
               schema='field1:STRING, field2:STRING',
               write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
               # Configure retries for transient errors
               insert_retry_strategy='RETRY_ON_TRANSIENT_ERROR',
               insert_retry_attempts=3 
           ).withFailures()
           | 'HandleFailedRecords' >> beam.Map(handle_failed_record)
           | 'WriteFailedRecordsToBigQuery' >> beam.io.WriteToBigQuery(
               'your_project:your_dataset.dead_letter_table',
               schema=dead_letter_schema,
               write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND
           )
       )
   

   1. stackoverflow.com

   stackoverflow.com

3. Explanation of Key Components

   • withFailures(): This method returns a PCollection of beam.io.gcp.bigquery.WriteToBigQuery.FailedInsert objects, which contains the failed records and the error message.
   • handle_failed_record: This function processes the failed records, potentially adding an error message or other relevant information.
   • WriteToBigQuery: The first WriteToBigQuery writes the main records, while the second one writes the failed records to the dead-letter table.
   • insert_retry_strategy and insert_retry_attempts: These configure retries to handle transient errors during the write operation.

4. Customize the Dead-Letter Storage You can customize where you want to store the failed records.

       (records 
        | 'WriteToBigQuery' >> beam.io.WriteToBigQuery(...)  # ... same as above
           .withFailures()
           | 'HandleFailedRecords' >> beam.Map(handle_failed_record)
           | 'WriteFailedRecordsToGCS' >> beam.io.WriteToText(
               'gs://your_bucket/failed_records.txt',
               file_name_suffix='.json'
           )
       )
   

5. Schema Enforcement

   • Ensure that the dead_letter_schema accurately reflects the structure of your failed records, especially if your handle_failed_record function modifies them.
   • Schema mismatches between your data and the BigQuery table can lead to additional failures.

6. Monitoring and Alerting

   • Set up monitoring and alerting to track:
     • The number of failed inserts.
     • Any potential errors or issues within the dead-letter handling process itself.
     • This proactive approach helps you identify and address problems early on.

[Jbtl]

This solution didn’t work for me. I’m getting an error that says the 'WriteToBigQuery' object has no attribute 'withFailures'. I've already updated the library, but it still doesn't work.

[ms4446]

The withFailures() method was introduced in Apache Beam 2.43.0. If you're using an older version, this method won't be available, and you'll encounter the "AttributeError: 'WriteToBigQuery' object has no attribute 'withFailures'" error.

Here's how to resolve this:

1. Upgrade Apache Beam:

   The most straightforward solution is to upgrade your Apache Beam library to version 2.43.0 or later. Here's how you can do this:

   pip install --upgrade apache-beam>=2.43.0
   

2. Alternative Approach (for older Beam versions):

   If upgrading is not immediately feasible, you can use an alternative approach to capture failed inserts in older Beam versions:Using a Custom Sink:

   • Create a custom Sink class that extends beam.io.iobase.Sink.
   • In your Sink class, override the write method to handle write operations to BigQuery.
   • Implement error handling and dead-letter logic within your custom Sink.
   • Use beam.io.Write with your custom Sink to write data to BigQuery.

   This approach requires more code, but it gives you fine-grained control over error handling and dead-letter processing.

   Example:

   import apache_beam as beam
   from apache_beam.io import iobase
   from google.cloud import bigquery
   import logging
   
   class BigQueryDeadLetterSink(iobase.Sink):
       def __init__(self, table, schema, dead_letter_table):
           self.table = table
           self.schema = schema
           self.dead_letter_table = dead_letter_table
           self.client = bigquery.Client()
   
       def write(self, record):
           try:
               # Write record to BigQuery
               table_ref = self.client.dataset(self.table.split(':')[1]).table(self.table.split('.')[1])
               errors = self.client.insert_rows_json(table_ref, [record]) 
               if errors:
                   logging.error(f"BigQuery insert errors: {errors}")
                   self.write_to_dead_letter(record, errors)
           except Exception as e:
               logging.error(f"Error writing to BigQuery: {e}")
               self.write_to_dead_letter(record, str(e))
   
       def write_to_dead_letter(self, record, error_message):
           try:
               # Write failed record to dead-letter table
               record['error_message'] = str(error_message)
               dead_letter_table_ref = self.client.dataset(self.dead_letter_table.split(':')[1]).table(self.dead_letter_table.split('.')[1])
               self.client.insert_rows_json(dead_letter_table_ref, [record])
           except Exception as e:
               logging.error(f"Error writing to dead-letter table: {e}")
   
   # Define pipeline options
   options = PipelineOptions()
   with beam.Pipeline(options=options) as p:
       records = p | 'Create Records' >> beam.Create([
           {'field1': 'value1', 'field2': 'value2'},  # Replace with your actual records
       ])
   
       records | 'WriteToBigQueryWithDeadLetter' >> beam.io.Write(
           BigQueryDeadLetterSink(
               table='your-project:your_dataset.your_table',
               schema='field1:STRING, field2:STRING',
               dead_letter_table='your-project:your_dataset.dead_letter_table'
           )
       )
   

I recommend upgrading to the latest Apache Beam version if possible, as it provides the most convenient way to handle dead letters with withFailures(). If that's not an option, the custom Sink approach provides a reliable workaround for older versions.

[caryna]

Hi @Hemantk ,

Welcome to the Google Cloud Community!

In addition to what @ms4446  mentioned, you might find these resources helpful for managing records that fail to be written to BigQuery during data processing:

• BigQueryIO deadletter pattern 
• Dead letter queue for errors with Beam, Asgarde, Dataflow and alerting in real time

I hope the above information is helpful.