Incremental Data Replication for Cross-Version Cluster Migration
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Solution Overview
Problem
Data migration between clusters with different vendor versions is time-consuming and requires file-level migration, which can be inefficient due to the need to switch versions during the process.
Innovation Solution
A data replication system that performs file-level replication in two stages: full synchronization and incremental synchronization, using a set of rules to handle exceptions and optimize operator execution, allowing for version switching and decoupling file and metadata replication to improve throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If file-level migration is performed to switch cluster versions, then version unification is achieved, but migration time increases significantly
Solution Approach 1:
The patent segments the migration process into two distinct phases: an initial full synchronization phase that establishes baseline compatibility, and a subsequent incremental synchronization phase that performs rapid updates. This segmentation allows the system to achieve version unification without requiring complete file-level migration, thereby reducing overall migration time while maintaining adaptability across different cluster versions.
Solution Approach 2:
The patent performs preliminary full synchronization to establish a complete baseline copy of data before executing incremental synchronization operations. This preliminary action ensures that the destination cluster has all necessary data structures and metadata in place, enabling faster incremental updates while maintaining version compatibility, thus resolving the contradiction between thorough version unification and time efficiency.
2Reliability
If full synchronization is performed to ensure data consistency, then data completeness is achieved, but replication speed decreases
Solution Approach 1:
The patent implements periodic action by alternating between full synchronization (performed once initially) and incremental synchronization (performed repeatedly thereafter). The full synchronization ensures complete data consistency and establishes the baseline, while subsequent incremental synchronizations rapidly update only the changes. This periodic alternation maintains data reliability while significantly improving overall replication speed compared to repeated full synchronizations.
Solution Approach 2:
The full synchronization serves as a preliminary action that establishes a complete and consistent baseline data state on the destination cluster. Once this preliminary comprehensive synchronization is complete, the system can rely on faster incremental updates for subsequent synchronizations, thereby achieving both data consistency and improved replication speed for ongoing operations.
3Adaptability or versatility
If file-level replication is used to handle version differences, then version compatibility is maintained, but processing overhead increases
Solution Approach 1:
The patent segments the replication processing into two distinct operational modes: full synchronization that handles complete data sets with comprehensive version compatibility checks, and incremental synchronization that processes only changed data blocks with simplified version handling. This segmentation reduces processing overhead for routine operations while maintaining version compatibility through the initial comprehensive synchronization, effectively resolving the contradiction between adaptability and complexity.
4Productivity
If incremental synchronization is used to speed up replication, then replication efficiency improves, but handling exceptions becomes more complex
Solution Approach 1:
The full synchronization serves as a preliminary action that establishes a complete, consistent baseline data state on the destination cluster before incremental synchronization begins. This preliminary comprehensive setup ensures that all data structures, metadata, and version information are properly initialized, thereby simplifying exception handling during incremental operations since the baseline state is known to be valid and complete, reducing the complexity of managing partial or inconsistent states.
Data Source
AI summary
In some embodiments, during synchronizing of files in a source data set to a destination data set, a method receives a set of events that occurred at the source data set after replicating an image of the source data set to the destination data set. The method analyzes the set of events to determine if an exception to a first set of rules for performing a set of operators on the destination data set for the set of events occurs. A second set of rules for the exception is selected based on analyzing the set of events. The method processes the set of operators for the set of events according to the second set of rules to synchronize data from the first data set to the second data set based on the set of events. The processing of the set of operators uses the second set of rules.