We have set up a Datastream that replicates tables from a Cloud SQL MySQL instance to a BigQuery dataset. The backfill operations finished but several tables are missing rows in BigQuery.
When examining the data in BigQuery we see that several continous chunks of rows from the original dataset are just not there. For example there is an “account” table which has a “created_date” column and when looking at the number of accounts created on a per day basis, there are timeframes of several weeks sprinkled throughout the table which have no accounts at all.
To us it looks like the backfill process is splitting the source data when importing into chunks of rows that are processed in parallel and shuffled between processing nodes but some node is just dropping the data. There are no errors but out of 8.7M rows we end up with only 7.1M rows. The backfill was tried twice.
Navigating the complexities of missing rows after replicating data from a Cloud SQL MySQL instance to BigQuery using Google Cloud Datastream can be challenging. Below are some potential causes and a troubleshooting approach:
Potential Causes:
Change Data Capture (CDC) Limitations: CDC mechanisms rely on database activity logs to capture changes. Network disruptions or rapid database updates may exceed CDC’s tracking capabilities, leading to missed data.
Source Database Configuration: Optimal configuration of your Cloud SQL MySQL instance is crucial:
Binary Logging: Enable binary logging in ROW format to ensure all changes are captured.
Log Retention: Extend the binary log retention period beyond the backfill duration to prevent data loss.
Data Type Incompatibilities: Some MySQL data types, like BLOB/TEXT and specific DATETIME formats, may not directly translate to BigQuery, potentially causing data loss.
Parallel Processing Issues: Datastream’s parallel processing aims for faster replication but may lead to data loss due to challenges in maintaining data order and consistency.
Transient Network Issues: Network instability during the backfill process could disrupt data replication, resulting in missing rows.
Troubleshooting Approach:
Data Integrity Validation: Begin by comparing datasets between MySQL and BigQuery, focusing on row counts and identifying patterns in missing data.
Analyze Logs: Scrutinize Datastream and MySQL logs for errors or warnings. Enhancing MySQL logging can provide deeper insights into replication anomalies.
Investigate Data Transformation: Ensure that Datastream transformation rules are not inadvertently affecting data integrity before it reaches BigQuery.
Incremental Backfill: Segment the backfill into smaller intervals (e.g., daily, hourly) to pinpoint when and where data loss occurs.
Retry with Enhanced Monitoring: Restart the backfill under optimal conditions, closely monitoring Datastream metrics and BigQuery insertion errors.
Engage Google Cloud Support: If the issue persists, escalate to Google Cloud Support with a comprehensive account of your troubleshooting efforts and observations.
Important Reminders:
Backup First: Always back up your source database before making any changes.
Understand Error Handling: Familiarize yourself with Datastream’s error management and retry protocols.
Consult Documentation: Refer to the Google Cloud Datastream, Cloud SQL MySQL configuration, and BigQuery data type support documentation for in-depth guidance.