Cross-Stream Data Processor for Distributed File Updates
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Solution Overview
Problem
Traditional computing systems and server architectures are not well-suited for updating distributed data files in real-time, as they often require sequential processing of API calls and are limited by single-threaded designs, hindering scalability and efficiency in dynamic computing environments.
Innovation Solution
A cross-stream data processor is implemented to automatically update or modify distributed data files by correlating event data across multiple data streams, using a multi-stream event correlator, diffusivity index controller, and data compatibility controller to identify and integrate compatible data files, enabling real-time or near real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single-threaded server architectures are used to process API calls, then system stability is maintained, but scalability and real-time data update capability are hindered
Solution Approach 1:
The patent segments the monolithic server architecture into multiple independent microservices that can process API calls concurrently. Each microservice handles specific data update tasks, enabling parallel processing while maintaining system stability. This segmentation allows the system to scale horizontally by adding more microservice instances without increasing overall system complexity.
Solution Approach 2:
The patent introduces an event-driven intermediary layer that mediates between API calls and data update operations. This intermediary uses event streams to coordinate updates across distributed data files, enabling real-time processing without requiring complex direct communication between all system components. The intermediary abstracts the complexity of inter-service coordination.
2Speed
If sequential processing of API calls is implemented, then resource consumption is reduced, but processing speed and responsiveness to dynamic computing environments deteriorate
Solution Approach 1:
The patent implements periodic batch processing for non-critical data updates while maintaining real-time processing for high-priority API calls. This periodic action allows the system to consume computational resources in controlled bursts rather than continuously, reducing overall energy consumption while maintaining high processing speed for time-sensitive operations through asynchronous task queues.
Solution Approach 2:
The patent dynamically changes processing parameters such as thread pool size, batch processing intervals, and priority thresholds based on system load and resource availability. This allows the system to optimize the balance between processing speed and resource consumption in real-time, accelerating processing when resources are abundant and conserving energy when resources are constrained.
3Adaptability or versatility
If distributed data files are updated across multiple data streams, then data integration capability is improved, but system complexity and difficulty of maintaining data compatibility increase
Solution Approach 1:
The patent implements a universal event schema that can represent multiple types of data updates across different data streams using a common structure. This multi-functional event format allows the same processing logic to handle various data types and sources, improving data integration capability without requiring separate complex coordination mechanisms for each data stream type.
Solution Approach 2:
The patent uses event copying and publishing to the same event stream for multiple data update operations. Instead of coordinating complex multi-stream updates directly, the system creates copies of update events and publishes them to appropriate data streams independently. This copying approach simplifies coordination complexity while maintaining the ability to integrate data across multiple streams through the shared event publishing mechanism.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. In some examples, a method may include activating APIs to receive via a message throughput data pipe different data streams, extracting features from data using the APIs, identifying event-related data across data sources, correlating the event-related data to form data representing an even, classifying event-related data into a state classification, determining compatible data at data sources, identifying compatible data, and transmitting integration data to integrate with a data source.