Writing streaming data to BigQuery via I/O connector using a Dataflow job

[dheerajpanyam]

I am using the `STORAGE_WRITE_API` method using BQ I/O connector to write data to BQ from a DataFlow job. I am seeing SSL connection related pipeline errors and unable to figure out the reason why it is happening. The more important question though is what happens to data in such scenarios are there any retries. From reading the documentation it appears that `STORAGE_WRITE_API` guarantees exactly once semantics does it mean data is lost?

[ms4446]

The BigQuery Storage Write API guarantees exactly-once semantics, which means that every record is written to BigQuery exactly once, even in the event of transient failures. This is achieved by using a commit protocol that ensures that all writes are applied to BigQuery in the correct order.

If a transient SSL connection error occurs during a Dataflow pipeline that is writing data to BigQuery using the Storage Write API, the pipeline will retry the write operation. The exactly-once semantics prevent data duplication during retries.

If all retries fail due to persistent errors, Dataflow typically provides mechanisms to handle these failures, such as redirecting problematic records to a separate location for further investigation.

In general, the combination of Dataflow and the BigQuery Storage Write API is designed to be robust and reliable. However, it is still important to monitor your pipelines and troubleshoot any errors that occur.

Troubleshooting SSL connection errors

If you are seeing SSL connection errors in your Dataflow pipeline, there are a few things you can do to troubleshoot the issue:

• Check the Dataflow pipeline logs to see if there are any specific errors related to SSL.
• Verify that the Dataflow workers have the correct SSL certificates installed.
• Check the network connectivity between the Dataflow workers and BigQuery.
• Try running the Dataflow job in a different region.

If you are still unable to resolve the SSL connection errors, you can contact Google Cloud support for assistance.

While it is important to be aware of the potential for data loss in any data processing system, the combination of Dataflow and the BigQuery Storage Write API is a robust and reliable solution for ingesting data into BigQuery. By following the best practices outlined above, you can minimize the risk of data loss and ensure that your data is reliably ingested into BigQuery.

[dheerajpanyam]

Thanks @ms4446 for the detailed explanation. Are there any metrics that can be setup to actually know how many records were lost if at all any occured?

[ms4446]

Hi @dheerajpanyam ,

The BigQuery Storage Write API guarantees exactly-once semantics, which means that every record is written to BigQuery exactly once, even in the event of transient failures. This is achieved by using a commit protocol that ensures that all writes are applied to BigQuery in the correct order.

If a transient SSL connection error occurs during a Dataflow pipeline that is writing data to BigQuery using the Storage Write API, the pipeline will retry the write operation. The exactly-once semantics prevent data duplication during retries.

If all retries fail due to persistent errors, Dataflow typically provides mechanisms to handle these failures, such as redirecting problematic records to a separate location for further investigation.

In general, the combination of Dataflow and the BigQuery Storage Write API is designed to be robust and reliable. However, it is still important to monitor your pipelines and troubleshoot any errors that occur.

Monitoring data loss

To monitor data loss in Dataflow pipelines using the BigQuery Storage Write API, you can use the following metrics:

• dataflow.googleapis.com/bigquery/write_count: This metric counts the number of records that were successfully written to BigQuery.
• dataflow.googleapis.com/bigquery/write_error_count: This metric counts the number of records that failed to be written to BigQuery.
• dataflow.googleapis.com/bigquery/write_error_rate: This metric calculates the percentage of records that failed to be written to BigQuery relative to the total number of records that were processed.

You can view these metrics in the Google Cloud Monitoring console or use the Dataflow REST API or the Cloud Monitoring API to get the values of these metrics programmatically.

In addition to the metrics listed above, you can also set up custom metrics to track specific aspects of data loss in your pipeline. For example, you could create a custom metric to track the number of records that were dropped due to a specific error code.

By tracking the appropriate metrics, you can gain a better understanding of data loss in your Dataflow pipelines using the BigQuery Storage Write API. This information can help you to identify and address the root causes of data loss, and to minimize the impact on your business.

While it is important to be aware of the potential for data loss in any data processing system, the combination of Dataflow and the BigQuery Storage Write API is a robust and reliable solution for ingesting data into BigQuery. By following the best practices outlined above, including monitoring the appropriate metrics, you can minimize the risk of data loss and ensure that your data is reliably ingested into BigQuery.

[renga3037]

Hi @ms4446 

We are using Storage_Write_Api for writing to bigquery from Pub/Sub through dataflow. But we are facing data loss in our pipeline ?

After reading avro messages from Pub/sub we are doing transformation as flattening then writing to bigquery through IO connector and Storage write API with exactly once semantic. Here we are using windowing. 

What could be the potential cause for data loss ? Please help here

[ms4446]

Here are a some potential causes and solutions that might help address the issue.

1. Windowing Logic Issues:

• Late Data: If late-arriving messages are not properly accounted for, they might be discarded if they arrive outside the predefined window bounds. To mitigate this, consider using withAllowedLateness() in your windowing strategy to capture these late messages. Unbounded windows can also be effective for ensuring all messages are processed.
• Incorrect Triggers: Check if the window triggers are firing prematurely, which could cause the pipeline to write incomplete data batches to BigQuery. Adjusting the trigger settings to delay firing until all data for a window is collected might be necessary.

2. Transformation Errors:

• Exceptions in Transformations: Errors during the flattening of Avro messages could lead to data exclusion. Implementing robust error handling and redirecting erroneous records to a dead-letter queue for further analysis can help. Using Cloud Logging for tracking exceptions is also advisable.
• Invalid Data: Ensure the transformed data matches the BigQuery table schema. Integrating schema validation checks into your development pipeline will help catch mismatches early.

3. Dataflow Pipeline Failures:

• Worker Crashes: Worker instability or crashes are critical issues to consider. Enabling autoscaling in Dataflow might help manage resource allocation more efficiently and mitigate these disruptions.
• Pipeline Termination: To prevent data loss during unexpected terminations, set up comprehensive monitoring and alerting. This will allow you to handle shutdowns more gracefully, preserving unprocessed data.

4. BigQuery Specific Issues:

• Quotas and Rate Limiting: Regular monitoring of BigQuery's usage and adjusting quotas according to your pipeline's needs can prevent write failures due to rate limiting.
• Schema Mismatches: Keep schema management flexible and continuously updated to accommodate changes during operations without disrupting data writes.

Additional Steps:

• Examine Dataflow Logs: Regularly review worker logs for any signs of issues that might lead to data loss.
• Monitor Pub/Sub Metrics: Ensure that the Pub/Sub system is delivering messages reliably by monitoring publish rates and success rates.
• Data Validation and Reconciliation: Use automated tools or scripts to verify that data written to BigQuery matches the source data in Pub/Sub.

It’s also good to remember that while the Storage Write API aims to offer exactly-once semantics, this doesn't inherently prevent all types of data loss. Continuous monitoring, error handling, and adjusting configurations as necessary are key to maintaining data integrity.