Bidirectional replication consistency checking and repairing method based on record source identifier
By reserving a control area for records and configuring source identification rules in the in-memory database cluster, generating idempotent identifiers, and combining verification windows and shard comparison processes, the problem of data inconsistency in bidirectional replication is solved. This enables accurate identification and repair of data differences without interrupting business operations, thereby improving the reliability and traceability of the database cluster.
Patent Information
- Application Number
- CN202511989301.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-05-15
AI Technical Summary
In bidirectional replicated distributed in-memory database clusters, there is a lack of a consistency verification mechanism that can, without interrupting business operations, combine write source attributes and version evolution, and within a controlled verification window, finely identify, idempotently repair, and form an auditable chain of evidence for data differences between the two sides. This makes it difficult to discover and handle data inconsistencies in a timely, accurate, and traceable manner.
In the in-memory database cluster, a control area is reserved for records, which includes deletion markers, lock markers, and source markers. The primary key field and the source pseudo-column are specified as anchor points. Source identification rules are configured to generate idempotent identifiers. Table shards are divided by verification windows and primary key ranges. Row positioning arrays are used for comparison to generate repair decisions and construct a difference detail file as a consistency verification evidence chain.
It enables accurate identification of real differences under continuous bidirectional replication, avoids replication loops and false inconsistencies, maintains the consistency of business data between the two sides of the in-memory database cluster, and facilitates fault location, risk assessment and compliance auditing by the operation and maintenance side, thereby improving the long-term operational reliability of large-scale distributed in-memory database clusters.