Data Object Sharding for Unique Instance Generation
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Solution Overview
Problem
In cloud-based data management systems, there is a challenge in efficiently generating and distributing unique instances of data objects while balancing storage overhead, performance, and uniqueness requirements, especially when handling large numbers of diverse data objects with varying popularity and geographical distribution of requests.
Innovation Solution
The system generates smaller data units (shards) from original data objects using coding algorithms like Reed-Solomon encoding, allowing for the reconstruction of unique instances from subsets of these shards, which are stored across multiple storage nodes, ensuring uniqueness and optimizing storage and performance by caching and dynamically generating shards based on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple distinct copies of data objects are stored to ensure uniqueness for each client, then uniqueness requirement is satisfied, but storage overhead increases significantly
Solution Approach 1:
The data object is divided into multiple shards (e.g., 10 shards for a 10GB video file). Instead of storing complete distinct copies for each client, the system stores these shards and reconstructs unique instances by selecting different combinations of shards for different clients, thereby reducing storage overhead while maintaining uniqueness
Solution Approach 2:
The patent implements a hierarchical structure where complete data objects are nested within shard combinations. Multiple unique instances are nested within the same set of stored shards by using different subset combinations, allowing the system to serve many clients from a compact stored representation
2Reliability
If complete distinct copies of data objects are stored for each client, then uniqueness is ensured, but system complexity and management overhead increase
Solution Approach 1:
By segmenting data into shards, the system simplifies management of unique instances. Instead of tracking and managing complete distinct copies for each client, the system manages shard combinations, which are fewer in number and easier to coordinate, thereby reducing system complexity
Solution Approach 2:
The patent uses a virtual copying mechanism where unique instances are reconstructed on-demand from stored shards rather than physically storing complete copies. This virtual reconstruction approach reduces the complexity of physical data management while ensuring uniqueness through controlled shard combination selection
3Quantity of substance
If data objects are subdivided into smaller units for storage, then storage efficiency improves, but reconstruction and retrieval complexity increases
Solution Approach 1:
The system performs preliminary organization of shards with metadata that enables efficient reconstruction. Shards are pre-labeled and indexed with information about their contents and relationships, allowing rapid reconstruction without complex real-time analysis, thus reducing retrieval complexity while maintaining storage efficiency
Data Source
AI summary
A set of data units associated with a data object is obtained, such that respective instances of the data object can be reconstructed from respective subsets of the set. Corresponding to a request for the data object, a first subset of the set is identified. The first subset meets a uniqueness criterion with respect to other subsets of the set that are used to respond to other requests for the data object. An instance of the data object is reconstructed from the first subset.