Introduce data consistency checking and repair toolkit. #308
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added 2 commits
January 7, 2026 01:54
This commit introduces a comprehensive toolkit for data consistency checking and repair operations in Spock multi-master replication environments. The toolkit provides essential primitives for detecting data divergence between nodes, monitoring replication health, and generating precise SQL repair statements to restore consistency across the cluster. The toolkit delivers powerful building blocks for consistency workflows. It enables deep inspection of table metadata, precise comparison of row data between nodes, pinpoint identification of divergent columns, and intelligent generation of targeted repair SQL. Health monitoring capabilities assess subscription status and table integrity to identify replication issues before they propagate into widespread data inconsistencies. These production-ready functions form the foundation for building sophisticated repair workflows and operational tools. They handle edge cases gracefully, provide detailed error diagnostics, and integrate naturally into monitoring pipelines for mission-critical multi-master deployments.
- Implement UPSERT logic using INSERT ... ON CONFLICT DO UPDATE SET - Add Phase 3b for DELETE operations with p_delete_extra_rows parameter - Enhance crash scenario to test all three operation types - Update reporting with rows_inserted and rows_deleted statistics - Treat source node as authoritative source of truth
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This commit introduces a comprehensive toolkit for data consistency checking and repair operations in Spock multi-master replication environments. The toolkit provides essential primitives for detecting data divergence between nodes, monitoring replication health, and generating precise SQL repair statements to restore consistency across the cluster.
The toolkit delivers powerful building blocks for consistency workflows. It enables deep inspection of table metadata, precise comparison of row data between nodes, pinpoint identification of divergent columns, and intelligent generation of targeted repair SQL. Health monitoring capabilities assess subscription status and table integrity to identify replication issues before they propagate into widespread data inconsistencies.
These production-ready functions form the foundation for building sophisticated repair workflows and operational tools. They handle edge cases gracefully, provide detailed error diagnostics, and integrate naturally into monitoring pipelines for mission-critical multi-master deployments.