Massive Data Ingestion With Erasure-Coded Slice Storage
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Solution Overview
Problem
Existing data ingestion systems in cloud storage face challenges in efficiently processing and storing large volumes of data across distributed networks, particularly in maintaining data integrity and availability in the face of storage unit failures.
Innovation Solution
A dispersed storage network (DSN) architecture that utilizes dispersed storage error encoding and decoding, managed by a DSN managing unit and integrity processing unit, to encode data into multiple encoded data slices, which are then stored across multiple geographically dispersed storage units, ensuring data integrity and availability even with storage unit failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in a distributed network across multiple storage units, then data availability and fault tolerance are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments data into multiple data slices and distributes them across different storage units in the network. Each storage unit stores only a portion of the data, which improves fault tolerance and availability while reducing the complexity burden on any single node. The segmentation principle directly addresses the contradiction by distributing data responsibility across multiple independent units.
Solution Approach 2:
The patent introduces a gateway as an intermediary component that coordinates data ingestion operations between external systems and the distributed storage network. The gateway handles data encoding, slicing, and distribution logic, thereby shielding individual storage units from complex coordination tasks and reducing overall system complexity while maintaining high availability.
2Reliability
If data is encoded and distributed across multiple storage units, then fault tolerance against storage unit failures is improved, but data processing time and computational overhead increase
Solution Approach 1:
The patent performs data encoding and slicing operations in advance during the data ingestion phase, before the data needs to be retrieved or processed. By pre-processing and distributing encoded data slices to multiple storage units upfront, the system avoids time-consuming encoding operations during data retrieval, thus improving fault tolerance while minimizing processing time delays.
Solution Approach 2:
The patent divides data into multiple independent slices that can be processed and retrieved in parallel from different storage units. This segmentation enables concurrent data processing operations, reducing overall data processing time while maintaining fault tolerance through the distributed nature of the slices.
3Reliability
If data slices are stored across geographically dispersed storage units, then data security and availability are improved, but network bandwidth consumption and synchronization overhead increase
Solution Approach 1:
The patent segments data into smaller slices and distributes them across geographically dispersed storage units. This segmentation reduces the amount of data that needs to be transmitted over the network at any given time, thereby reducing network bandwidth consumption while maintaining data security through geographic distribution.
Solution Approach 2:
The patent allows different storage units to store and manage data slices with locally optimized characteristics. Each storage unit can handle its local data slices independently, reducing the need for constant network synchronization and bandwidth consumption while maintaining overall data security through the distributed architecture.
Data Source
AI summary
A method for execution by one or more computing devices of a massive data ingestion system includes receiving data for storage in the massive data ingestion system. The method further includes storing a data segment of the data in a cache memory of the massive data ingestion system. The method further includes retrieving, based on one or more retrieval parameters, the data segment from the cache memory. The method further includes error encoding, in accordance with error encoding parameters, the data segment to produce a set of encoded data slices, wherein the error encoding parameters include a pillar width number and a decode threshold number, and wherein the decode threshold number is a minimum number of encoded data slices needed to reconstruct the data segment. The method further includes storing the set of encoded data slices in a set of storage units of the massive data ingestion system.