Chunk Store Data Deduplication Locality Indexing
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Solution Overview
Problem
Data deduplication techniques face challenges such as latency in accessing files due to data fragmentation, resource overconsumption, and scalability issues as digital data grows rapidly, limiting their adoption in primary storage and efficiency in resource utilization.
Innovation Solution
A system and method for localizing, storing, and reorganizing data chunks by parsing data streams into sequences, generating metadata with offset and locality indicators, and using stream maps to efficiently store and retrieve data, while defragmenting and prioritizing data streams to reduce fragmentation and improve access latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If data is stored using traditional data deduplication techniques, then storage capacity is reduced, but access latency increases due to data fragmentation
Solution Approach 1:
The patent segments data into fixed-size chunks and organizes them in a chunk store with contiguous storage. Each chunk is assigned a unique identifier and stored in sequential locations, eliminating the fragmentation problem while maintaining deduplication benefits. The chunk store uses an index structure to map chunk IDs to physical locations, enabling fast random access without sequential scanning.
Solution Approach 2:
The patent introduces a chunk store as an intermediary layer between the deduplication engine and the file system. This chunk store acts as a mediator that handles all read/write operations, caching frequently accessed chunks and providing fast lookup through its index structure. The file system interacts with the chunk store through standardized interfaces, abstracting away the complexity of deduplication management.
2Loss of substance
If data deduplication is implemented on a file server, then data reduction is achieved, but device resources are overconsumed
Solution Approach 1:
The patent extracts the data deduplication function from the file server and implements it as a separate, dedicated chunk store service. This separation allows the file server to focus on file management operations while the chunk store handles deduplication, compression, and data lifecycle management. The chunk store is optimized specifically for these functions, reducing overall system resource consumption.
Solution Approach 2:
The chunk store implements self-service capabilities including automatic chunk identification, deduplication, compression, and lifecycle management. The system monitors its own performance metrics and automatically adjusts caching strategies, compression levels, and garbage collection schedules. This self-management reduces the operational overhead and resource consumption that would otherwise be required from the file server.
3Volume of stationary object
If storage devices are scaled to handle growing digital data, then storage capacity increases, but data deduplication techniques do not scale well
Solution Approach 1:
The patent transitions from traditional single-dimension storage organization to a multi-dimensional chunk store architecture. Data is organized by chunk ID, locality, and access frequency, creating multiple indexing dimensions. This allows the system to scale horizontally by adding more chunk store nodes while maintaining efficient access patterns. The distributed chunk store can partition data across multiple devices, enabling linear scalability with storage capacity.
Solution Approach 2:
The chunk store implements dynamic data structures and algorithms that adapt to changing workload patterns and storage conditions. The indexing structure dynamically adjusts to chunk access patterns, and the system automatically rebalances data distribution across storage devices. This dynamic behavior allows the deduplication system to maintain efficiency as storage capacity scales from GB to PB ranges.
Data Source
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AI summary
Data streams may be stored in a chunk store in the form of stream maps and data chunks. Data chunks corresponding to a data stream may be stored in a chunk container, and a stream map corresponding to the data stream may point to the data chunks in the chunk container. Multiple stream maps may be stored in a stream container, and may point to the data chunks in the chunk container in a manner that duplicate data chunks are not present. Techniques are provided herein for localizing the storage of related data chunks in such chunk containers, for locating data chunks stored in chunk containers, for storing data streams in chunk stores in localized manners that enhance locality and decrease defragmentation, and for reorganizing stored data streams in chunks stores.