Self-Healing Data Synchronization for Missing Change Event Recovery
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Solution Overview
Problem
In multi-tenant computing systems, data synchronization often fails to identify all data change events due to issues with upstream data services, leading to data inconsistencies between different computing systems.
Innovation Solution
A self-healing synchronization engine identifies data records corresponding to previously undetected data change events using a scanning engine and sync discrepancy determination engine, initiating syncing of these records to maintain data consistency between systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data synchronization relies on upstream data services to provide data change events, then synchronization can be maintained with minimal processing overhead, but data inconsistencies occur when upstream services fail to report all changes accurately
Solution Approach 1:
The system performs preliminary actions by proactively scanning for missing data change events before they cause permanent inconsistencies. The scanning engine searches for events that occurred during time horizons when upstream services were unavailable or unresponsive, and the sync discrepancy determination engine identifies records that should have been synced but weren't, enabling corrective action before data divergence becomes critical.
Solution Approach 2:
The system implements feedback mechanisms where the scanning engine monitors upstream service availability and reports on missing events, the sync discrepancy determination engine compares expected vs. actual synced records, and the synchronization engine adjusts its behavior based on these feedback signals to ensure complete data synchronization even when upstream services fail.
2Reliability
If the system scans for missing data change events over extended time horizons, then data consistency can be restored, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by proactively scanning for missing data change events before they cause permanent inconsistencies. The scanning engine searches for events that occurred during time horizons when upstream services were unavailable or unresponsive, and the sync discrepancy determination engine identifies records that should have been synced but weren't, enabling corrective action before data divergence becomes critical.
Solution Approach 2:
The system uses periodic scanning over defined time horizons to check for missing data change events. Instead of continuous scanning which would be resource-intensive, the system performs scans at periodic intervals for specific time windows (e.g., past hour, past day), balancing thoroughness with efficiency by limiting the search scope to recent periods where inconsistencies are most likely to occur.
Data Source
AI summary
A self-healing data synchronization process includes an initial stage in which a collection of data change events is received, a set of data record(s) corresponding to the data change event(s) is identified, and a syncing of the set of data record(s) is initiated. Data that indicates which data record(s) successfully synced and which failed is stored. During a subsequent stage of the self-healing process, data change events that occurred during a preceding time horizon are identified, a corresponding first set of data record(s) are identified, a difference between the first set and a second set of data record(s) that successfully synced during the time horizon is determined as a third set of data record(s), and any data record that was attempted to be synced during the time horizon but failed is excluded from the third set. A sync of any data record remaining in the third set is then initiated.