Discrete Data Replication via Block-Level Snapshot Comparison
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Solution Overview
Problem
Current data replication methods require significant processing power and network bandwidth, especially for large databases, and can impact the performance of the source system, as they often involve copying entire volumes or using time-consuming backup techniques like differential and incremental backups.
Innovation Solution
The system performs discrete data replication by identifying and replicating only the changed blocks of data between a source and a destination, using a snapshot to capture a known good state of the application data, thereby reducing the amount of data transferred and processing resources required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire volume data is replicated to destination storage, then data protection reliability is improved, but network bandwidth consumption increases and processing time is extended
Solution Approach 1:
The patent extracts and replicates only the changed blocks of data from the source volume to the destination storage, rather than replicating entire volumes. This is achieved by identifying modified files through journal tracking and copying only the specific data blocks that have changed, thereby reducing network bandwidth consumption while maintaining data protection reliability.
Solution Approach 2:
The patent segments the data replication process into block-level operations, dividing the volume into individual data blocks that can be independently tracked and replicated. By segmenting the replication task at the block level rather than volume level, the system reduces the quantity of data transmitted over the network while ensuring complete data protection.
2Productivity
If differential backup operations are used to copy changed files, then data protection efficiency is improved, but processing power consumption increases
Solution Approach 1:
The patent extracts only the essential change information from modified files using journal tracking, which records file system changes in a lightweight manner. This approach maintains high data protection efficiency by identifying changed files quickly while minimizing processing power consumption compared to full file analysis methods.
3Measurement precision
If data replication is performed frequently to maintain current data, then data accuracy is improved, but network load increases and processing resources are consumed
Solution Approach 1:
The patent extracts and replicates only the changed blocks of data at each replication interval, rather than replicating entire volumes. This maintains data accuracy by ensuring changed data is captured while significantly reducing network load compared to frequent full volume replication.
Solution Approach 2:
The patent implements periodic replication operations that occur at scheduled intervals, combining this with change tracking to determine what data needs replication. This periodic approach maintains data accuracy over time while minimizing network load by only replicating changed blocks during each periodic operation.
Data Source
AI summary
Systems and methods for performing intelligent replication of production data on a source storage device. In certain examples, a full backup of the production data is first performed and stored on a destination system. A journal module on the storage device (e.g., a shared storage device), or on at least one source system, tracks files on the storage device that change during a particular period of time. Following this period, a snapshot is taken of the production data. Upon mounting the snapshot, the replication system uses the journal information to identify modified files, conduct a block-level comparison of only the modified files with corresponding files on the destination system, and identify the particular data blocks that changed. The replication system then replicates only the changed blocks to the destination system. Certain systems further store information indicating a source system time of a known good state of the production data.