Distributed Snapshot Sharding for Clustered Storage
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Solution Overview
Problem
Distributed data management and storage systems face inefficiencies in snapshotting large files, as existing methods fail to effectively utilize increased node numbers, leading to underutilization of resources and suboptimal performance when dealing with very large filesets.
Innovation Solution
The system dynamically adjusts file partitioning or sharding, determining whether to store large files as single files or shards across multiple nodes based on file size, available disk space, and estimated storage time, allowing for parallel fetching and storage across all nodes in the cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large files are stored as single files in distributed storage systems, then storage simplicity is maintained, but snapshotting performance cannot scale with increased node numbers
Solution Approach 1:
The patent applies segmentation by dividing large files into smaller shards that can be distributed across multiple storage nodes. The file is partitioned into N shards that can be stored in parallel across N different nodes, enabling the snapshotting performance to scale linearly with the number of nodes. This resolves the contradiction by transforming single large files into multiple manageable segments that leverage distributed storage capacity.
Solution Approach 2:
The patent implements dynamic file partitioning where the system automatically determines whether to store a file as a single file or partition it into shards based on file size, available disk space, and estimated storage time. This dynamic adjustment allows the system to adapt to varying conditions and optimize snapshotting performance without imposing fixed complexity on all storage operations.
2Loss of time
If all storage nodes are utilized for parallel fetching, then snapshotting time is reduced, but system complexity increases
Solution Approach 1:
By segmenting large files into N shards that can be fetched in parallel across N storage nodes, the system reduces snapshotting time proportionally to the number of nodes utilized. Each node independently fetches its assigned shard, eliminating sequential bottlenecks and achieving linear speedup while maintaining manageable coordination complexity through standardized shard distribution protocols.
3Productivity
If file partitioning is performed dynamically, then resource utilization is improved, but processing overhead increases
Solution Approach 1:
The system dynamically adjusts file partitioning decisions based on real-time conditions including file size, available disk space across nodes, and estimated storage time. This dynamic approach optimizes resource utilization by partitioning only when beneficial, avoiding unnecessary processing overhead for small files or scenarios where single-file storage is more efficient.
Data Source
AI summary
In some embodiments, there is provided a method for operating a data management system. The method comprising certain operations including determining a schedule for backing up a first virtual machine, configuring a job scheduler to implement the schedule for backing up the first virtual machine, initiating a snapshot process for acquiring a snapshot of the first virtual machine, determining a type of snapshot to be stored, and determining whether a full image of the first virtual machine is required to be stored in order to store the snapshot of the first virtual machine.