Timeline Compression in Data Store Backup Systems
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Solution Overview
Problem
Conventional data storage systems face challenges in maintaining an extended timeline for data recovery due to storage capacity limitations, leading to the need for frequent data discarding and increased costs, which restricts data recovery capabilities to a finite past period.
Innovation Solution
The technique of timeline compression reduces the granularity level of backup data as they age, allowing older data to be coalesced into coarser levels (e.g., hourly, daily, weekly), enabling a longer timeline maintenance without significant storage increases, through a user-configurable Timeline Lifecycle Profile that specifies rollup conditions and storage usage thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If storage capacity is increased to maintain extended timeline, then data recovery capability is improved, but cost and system complexity increase
Solution Approach 1:
The patent segments the backup data into different retention levels: recent data is kept at full granularity in the current store, while older data is progressively compressed into coarser granularity in the time store. This segmentation allows the system to maintain detailed recovery capability for recent data while reducing storage requirements for historical data, thereby extending the maintainable timeline without proportionally increasing storage capacity.
Solution Approach 2:
The patent changes the granularity parameter of backup data over time. Recent backups are stored at fine granularity (individual write commands), while older backups are stored at coarser granularity (aggregated time intervals). This parameter change allows the system to extend the timeline by reducing the storage footprint of historical data while maintaining adequate recovery capability for most practical purposes.
2Duration of action of stationary object
If storage capacity is increased to maintain extended timeline, then data recovery capability is improved, but system performance deteriorates
Solution Approach 1:
The patent segments the time store into compressed data sets with different granularity levels. When performing data recovery or analysis, the system can selectively parse only the relevant granularity level rather than processing all raw backup data from the entire timeline. This segmentation significantly improves performance by reducing the amount of data that needs to be parsed while still maintaining the ability to recover data across the extended timeline.
3Quantity of substance
If backup data are discarded after a few days, then storage space is freed, but data recovery capability is limited
Solution Approach 1:
The patent applies parameter changes by transitioning backup data from fine granularity to coarse granularity over time. This allows the system to retain historical data for extended periods (months or years) in the time store with significantly reduced storage requirements compared to maintaining full-granularity data throughout the entire timeline. The coarse-granularity storage still enables data recovery capability while occupying minimal storage space.
Data Source
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AI summary
A technique for timeline compression in a data store is disclosed. In one particular exemplary embodiment, the technique may be realized as a method for timeline compression in a storage system, wherein digital content of the storage system is backed up to enable restoration of the digital content to one or more points in a timeline. The method may comprise selecting a time interval in the timeline, one or more sets of backup data recorded for the selected time interval. The method may further comprise discarding other backup data recorded for the selected time interval, thereby reducing a granularity level of the timeline in the selected time interval.