Data Connector Module for Parallel Data Transmission
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Solution Overview
Problem
Current data processing systems face significant challenges in handling large data sets due to performance limitations of traditional drivers like ODBC and JDBC, which lead to bottlenecks and delays, especially when dealing with gigabytes, terabytes, or petabytes of data.
Innovation Solution
A data connector module that enables direct access to unstructured data sets, such as Hadoop big data repositories, bypassing traditional drivers by utilizing multiple processing nodes to process data in parallel, thereby providing results directly to the destination without aggregation, and optimizing data transfer through techniques like data locality and distributed file system management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional drivers like ODBC and JDBC are used for data access, then data can be accessed through standardized interfaces, but performance is limited due to bottlenecks and delays
Solution Approach 1:
The patent extracts the data access functionality from traditional driver-based interfaces (ODBC/JDBC) and implements a custom data connector that directly interfaces with the data source. This removes the intermediary layer that causes performance bottlenecks, allowing applications to access data without the overhead and limitations of standardized drivers, thereby improving throughput and reducing processing time.
Solution Approach 2:
The data connector is divided into multiple processing nodes that operate in parallel. Each processing node handles a portion of the data access requests independently, allowing simultaneous data retrieval and processing. This segmentation eliminates the single-point bottleneck inherent in traditional driver architectures and enables linear scaling of data processing capacity.
2Productivity
If multiple processing nodes are used to increase bandwidth, then data transfer speed improves, but system complexity increases
Solution Approach 1:
Multiple processing nodes are merged into a unified data connector architecture that presents a single interface to applications. The processing nodes work cooperatively under a common management structure, sharing resources and coordinating their operations. This merging approach maintains high output bandwidth while managing complexity through unified control and resource sharing.
Solution Approach 2:
The data connector is designed with universal interfaces and protocols that allow multiple processing nodes to operate through a standardized connection framework. This multi-functionality enables the system to handle various data access patterns and workloads while maintaining a consistent architectural interface, reducing the complexity burden of managing multiple nodes.
3Ease of operation
If data is processed through intermediary nodes for aggregation, then data can be centralized and managed, but performance bottlenecks occur at the intermediary layer
Solution Approach 1:
Instead of aggregating data at an intermediary node before delivery, the system inverts the traditional architecture by allowing processing nodes to deliver data directly to the application. This eliminates the intermediary aggregation step that creates bottlenecks, while maintaining data management capabilities through distributed coordination among processing nodes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for high-throughput parallel data transmission. In some implementations, a data connector module is configured to provide an application server with access to a data set of unstructured data. One or more computers receive a first request associated with a first application or service, and data identifying a second application or service different from the first application or service. A plurality of second requests are generated and distributed to processing nodes to generate results for the second requests. Result data derived from the results for the second requests to the second application or service.