Viewable Exchange Pipeline Architecture for Real-Time Data Processing
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Solution Overview
Problem
Real-time data processing systems face challenges in scaling while maintaining low latency, especially when handling thousands of simultaneous transactions and relying on data from multiple remote clients, requiring high-performance data storage and streaming architectures.
Innovation Solution
A data processing system comprising a transaction bus, a console application, and a view predictor subsystem that determines the likelihood of visual information being viewable within a user application's viewport, converting data into a common form of measurement, and selecting the appropriate source for delivery, incorporating message queueing and prediction models for historical viewability analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data processing systems handle thousands of simultaneous transactions from multiple remote clients, then the system capacity and data collection capability improve, but the latency increases and scaling becomes difficult
Solution Approach 1:
The system performs preliminary actions by predicting viewability metrics before data processing operations complete. The viewability prediction subsystem analyzes historical data and current context to pre-determine which data sources are likely to produce viewable results, allowing the system to prioritize and pre-process promising data streams before they are fully collected, thereby reducing subsequent processing latency
Solution Approach 2:
The architecture segments the data processing pipeline into independent modular components: transaction bus for event routing, viewability prediction subsystem for analytics, multiple data source interfaces, and result aggregation modules. This segmentation allows parallel processing of different data streams and enables the system to scale capacity by adding independent processing units without increasing overall latency
2Adaptability or versatility
If the system processes data from multiple remote clients with different measurement forms, then the data source versatility improves, but the processing complexity increases
Solution Approach 1:
The system applies parameter changes by dynamically transforming data from different measurement forms into a standardized internal representation. The transaction bus includes conversion capabilities that automatically adjust data parameters based on the source type, allowing seamless integration of diverse remote clients while maintaining consistent processing logic. Historical viewability data is similarly normalized to enable uniform analysis across different data sources
Data Source
AI summary
A data processing system includes a transaction bus, a console application in communication with the transaction bus, and a view predictor subsystem in communication with the transaction bus. A call for visual information is received at the transaction bus. Prior to returning a result to the user application in response to the call: a likelihood that the visual information will be viewable within a viewport of a user application is determined by the view predictor subsystem, the console application provides to the transaction bus a set of potential sources of the visual information, the transaction bus converts associated data from the potential sources into a common form of measurement using the likelihood, and the transaction bus selects one of the potential sources of the visual information to be the result based on the data converted into the common form of measurement. The result is returned to the user application.