Query-as-a-Service System for Real-Time Web Analytics
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Solution Overview
Problem
Current web analytics services face limitations in providing real-time data analysis due to significant lag times in producing reports and analyses, which hinders dynamic feedback and efficient web site optimization for e-commerce and electronic services.
Innovation Solution
A Query-as-a-Service (QAAS) system that continuously collects and processes vast amounts of data from network-connected entities, enabling real-time data streaming and query execution, allowing for immediate analysis and dynamic feedback through a distributed-query-execution engine and persistent storage of both raw and query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional web analytics services are used to collect and analyze user interaction data, then data collection capability is provided, but significant lag times occur in producing reports and analyses
Solution Approach 1:
The system pre-processes and stores user interaction data in a persistent data store as it is collected, preparing it for immediate query execution. This preliminary action eliminates the need for time-consuming batch processing later, enabling real-time analytics while maintaining continuous data collection operations.
Solution Approach 2:
A query processing engine acts as an intermediary between the persistent data store and client applications. This engine continuously executes queries against the stored data and delivers results to clients in real-time, decoupling the data collection process from the analysis process and enabling immediate insights without lag.
2Quantity of substance
If continuous data collection from enormous numbers of network-connected entities is implemented, then data volume and coverage increase, but system complexity and processing requirements increase
Solution Approach 1:
The system architecture is segmented into distinct functional components: data collection subsystems that gather data from network entities, a persistent data store that stores raw and processed data, and a query processing engine that executes analytics queries. This segmentation allows each component to be optimized independently and scaled according to specific requirements, managing overall system complexity.
Solution Approach 2:
The persistent data store serves multiple functions: storing raw collected data, storing processed data, and providing data for both real-time querying and historical analysis. This multi-functionality reduces the need for separate specialized storage systems, simplifying the overall architecture while handling enormous data volumes from numerous network-connected entities.
3Speed
If real-time query execution is implemented on collected data, then immediate analysis and dynamic feedback are enabled, but processing power and computational resources increase
Solution Approach 1:
Data is pre-processed and stored in an optimized format in the persistent data store as it is collected, with relevant fields and aggregations prepared in advance. This preliminary processing reduces the computational burden during real-time query execution, enabling fast query responses while managing computational resource requirements.
Solution Approach 2:
The query processing engine continuously executes queries against the persistent data store in real-time, maintaining a steady state of data analysis without periodic batch processing interruptions. This continuous operation optimizes resource utilization by keeping processing pipelines full and efficient, reducing the peak computational demands that would occur with periodic batch processing of enormous data volumes.
Data Source
AI summary
The current document is directed to a query-as-a-service system (“QAAS system”) that collects enormous volumes of data from network-connected entities, referred to as “Things” in the phrase “Internet of Things,” persistently stores the collected data and provides a distributed-query-execution engine that allows remote clients to continuously execute queries against the collected data. In a described implementation, both the raw data and query results are persistently stored in the QAAS system, with the raw data stored for significantly longer periods of time. Query results generated by the query-processing engine are securely transmitted to QAAS remote clients for distribution to file systems, storage.