intermittent error com.google.cloud.bigquery.BigQueryException: Connection reset

[dharma]

Hi Team,

We are getting intermittent  com.google.cloud.bigquery.BigQueryException: Connection reset error, when we are inserting/streaming to GCP Bigquery. We are using Java/Spring https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery#bigquery_table_insert_rows-java tabledata.insertAll() method to insert the data to Bigquery.

Thanks in advance.

Regards

Dharma

[ms4446]

The intermittent com.google.cloud.bigquery.BigQueryException: Connection reset error when streaming data into BigQuery using the tabledata.insertAll() method can arise from several causes, such as network instability, BigQuery service issues, or client configuration problems. To troubleshoot and mitigate this issue, consider the following steps:

• Ensure your network connection is stable with no disruptions or high latency periods. Utilize diagnostic tools like ping or traceroute to identify potential network issues. Additionally, implement retry logic with exponential backoff in your code to handle transient network problems, which is typically supported by BigQuery client libraries.
• Verify that you are not exceeding BigQuery API quotas or limits for streaming inserts by checking the BigQuery Quotas page in the Cloud Console. If streaming a high volume of data, ensure that the streaming buffer is not overloaded. To manage this, consider batching data before inserting or using tools like Cloud Pub/Sub to buffer data before streaming.
• Increase the timeout settings for the BigQuery client to accommodate longer processing times and help prevent connection resets due to timeouts. Ensure you are using the latest version of the BigQuery client library, as updates often include crucial bug fixes and performance improvements.
• Implement detailed logging around insert operations to capture more context when errors occur, such as request payloads and exact error messages, aiding in diagnosis. Use a robust retry strategy for handling exceptions, incorporating exponential backoff. For critical data, consider using dead-letter queues (e.g., Cloud Pub/Sub topics) to store failed inserts for later reprocessing.
• If real-time streaming isn't essential, consider batch loading for large volumes of data, which can be more reliable than continuous streaming. Alternatively, use Google Cloud Pub/Sub as a buffer between your application and BigQuery to absorb spikes in data volume and provide additional reliability.

Incorporate network diagnostic tools to ensure network stability. Introduce dead-letter queues to handle failed inserts effectively. Finally, set up alerts in Cloud Monitoring for proactive detection of issues related to BigQuery streaming.