World Wide Hadoop Framework for Distributed Data Processing
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Solution Overview
Problem
Current Big Data management systems face challenges in handling and analyzing large datasets distributed across multiple clusters and clouds due to limitations in data transfer speed, processing capacity, and the inability to process data across geographically dispersed locations, leading to bottlenecks in data-intensive computations.
Innovation Solution
The implementation of a World Wide Hadoop (WWH) framework that enables Massive Parallel Processing (MPP) across multiple clusters and clouds, using a distributed file system architecture that allows data to be split and processed closer to its location, reducing data transfer and leveraging a hierarchical structure for data access and management, with a World Wide Data model that facilitates data organization, indexing, and analysis across disparate data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is centralized in single data centers for processing, then data management is simplified, but data transfer time and network bandwidth consumption increase significantly
Solution Approach 1:
The system segments the centralized data management architecture into distributed data nodes organized in hierarchical clusters across multiple data centers. Each data node independently manages local data blocks, eliminating the need to centralize all data in single data centers while maintaining manageable operations through standardized interfaces and metadata coordination.
Solution Approach 2:
The patent introduces a new dimensional architecture by distributing data across geographical locations and organizing data nodes in hierarchical clusters. This multi-dimensional distribution allows data to be processed closer to its storage location while maintaining logical organization, thereby reducing data transfer time without sacrificing management simplicity.
2Loss of time
If data is distributed across multiple geographically dispersed data centers, then data transfer time is reduced, but system complexity and coordination overhead increase
Solution Approach 1:
The system implements a nested hierarchical structure where data nodes are organized into clusters, which are further organized into data center groups. This nested organization manages complexity by creating manageable layers of abstraction, where each layer handles specific coordination tasks locally before escalating to higher layers, thereby reducing overall system complexity despite geographical distribution.
Solution Approach 2:
The patent introduces intermediary components including name nodes that manage metadata and coordinate data access across distributed data centers. These intermediaries handle the complexity of cross-data center coordination, allowing individual data nodes to operate independently while maintaining system-wide consistency, thus reducing perceived system complexity for end users.
3Productivity
If data is processed at centralized locations, then processing resources can be consolidated, but network bandwidth consumption and processing time increase
Solution Approach 1:
Instead of bringing all data to centralized processing locations, the system inverts the traditional approach by bringing processing resources to where the data is stored. Data nodes execute computational tasks locally on stored data blocks, eliminating the need to transfer large volumes of data across the network while maintaining consolidated processing capabilities through distributed computation.
Solution Approach 2:
The patent enables data nodes to perform self-service processing by executing computational tasks directly on local data blocks without requiring data to be moved to centralized processing facilities. Each data node independently manages its own data processing operations, reducing network bandwidth consumption while maintaining high processing efficiency through localized computation.
Data Source
AI summary
A method, system, and apparatus for sending a request for data to a worldwide name node; wherein the worldwide name node is communicatively coupled to worldwide data nodes, sending from the worldwide name node to the worldwide data nodes the request for data; wherein each of the worldwide data nodes serves as an index for a cluster of data, processing at each of the worldwide data nodes the request for information; and returning the result to the world wide name node.