Kadaif Format for Hash-Based Metadata and Parallel Data Access
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Solution Overview
Problem
Existing data management systems face challenges in scalability, performance, and cost-effectiveness when handling large-scale datasets, particularly in managing metadata alongside files, due to inefficiencies in metadata search and retrieval, hierarchical limitations, and high infrastructure costs.
Innovation Solution
A data storage method that separates metadata and raw data into different files, using a hash function to calculate data location, allowing parallel streaming and minimizing I/O operations, with adaptable infrastructure support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional file systems with extended attributes or sidecar files are used for metadata storage, then the approach is simple and easy to implement, but severe performance bottlenecks occur when handling large datasets due to metadata search and retrieval inefficiencies
Solution Approach 1:
The patent segments the file system into two distinct components: data files for storing raw data and metadata files for storing metadata. This segmentation allows independent optimization of each component, enabling efficient metadata search and retrieval operations without being constrained by the limitations of conventional unified file systems.
Solution Approach 2:
The patent introduces a hash function as an intermediary mechanism that directly maps metadata keys to data file locations. This eliminates the need for linear search through metadata in conventional file systems, providing O(1) average time complexity for metadata lookup and resolving the performance bottleneck.
2Ease of operation
If database systems are used to store files and metadata in a structured format, then robust querying capabilities are achieved, but substantial I/O overhead is introduced and infrastructure costs become prohibitively high for large-scale applications
Solution Approach 1:
The patent extracts the file storage component from the database system, storing raw data in separate data files while the database only manages metadata. This extraction eliminates the I/O overhead of storing and retrieving large binary objects through the database, allowing the database to focus only on lightweight metadata operations.
Solution Approach 2:
The system segments data management into two parts: a database component for metadata management with querying capabilities, and a file-based component for raw data storage. This segmentation reduces I/O overhead by avoiding the translation and serialization overhead that occurs when storing files within database systems.
3Adaptability or versatility
If object storage systems are used to store files and metadata as key-value pairs, then horizontal scalability is achieved, but strong consistency across metadata updates in a distributed environment becomes challenging
Solution Approach 1:
The patent merges the benefits of object storage (horizontal scalability, key-value access) with the consistency guarantees of a database system. By using a distributed database to manage metadata while maintaining key-value pair structure, the system achieves both scalability and strong consistency across distributed nodes.
4Device complexity
If files are stored with both metadata and raw data together, then the structure is simple, but the amount of read and write operations (I/O) needed for random access or metadata modifications increases significantly
Solution Approach 1:
The patent segments each file into two separate storage units: a metadata file containing only metadata information and a data file containing the raw data. This segmentation allows independent access to metadata without reading the entire data file, significantly reducing I/O operations for metadata modifications and random access.
Solution Approach 2:
The patent introduces a hash function as an intermediary that directly computes the location of data based on metadata keys. This eliminates the need to scan through data files to find specific records, reducing I/O operations from O(n) to O(1) average time complexity.
Data Source
AI summary
Data structures and data storage methods for storing data in a storage system are disclosed. The methods include reading an input data file that includes metadata and raw data, generating a hash code based on an identifier associated with the input data file, generating one or more metadata records, each metadata record including the hash code and at least a portion of the metadata, generating one or more raw data records, each raw data record including the hash code and at least a portion of the raw data, storing the generated one or more metadata records into one or more metadata files, and storing the generated one or more raw data records into one or more raw data files.