Extensible Data Model for Dynamic Event Processing
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Solution Overview
Problem
Existing systems face challenges in dynamically processing large volumes of user-generated data from online applications, such as social networks, for analytics and storage, particularly in handling increased data loads and ensuring fault tolerance and scalability.
Innovation Solution
The implementation of an extensible data model and a distributed system architecture with multiple masters and collectors, allowing dynamic configuration and fault-tolerant data processing, where nodes can interact to collect, aggregate, and perform analytics on datasets in real-time, with features like write-ahead logging and failover mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a distributed system architecture with multiple masters and collectors is implemented, then system reliability and fault tolerance are improved, but device complexity increases
Solution Approach 1:
The system is divided into multiple independent masters and collectors that can operate autonomously. Each master manages specific data sources and sinks, while collectors aggregate data from multiple masters. This segmentation allows the system to maintain functionality even when individual components fail, directly improving fault tolerance while managing complexity through modular design.
Solution Approach 2:
Collectors serve as intermediary components between masters and the central system. They aggregate data from multiple masters and perform preprocessing before forwarding to sinks, reducing the burden on individual masters and improving system reliability through distributed data handling. This intermediary layer manages complexity by providing a standardized interface between different system components.
2Productivity
If nodes are configured to collect and aggregate datasets in real-time, then productivity and data processing throughput are improved, but device complexity increases
Solution Approach 1:
The system employs dynamic configuration where masters and collectors can be added, removed, or reconfigured at runtime without shutting down the entire system. Data sources and sinks can be dynamically associated with masters, and collectors can adapt to changing data flow patterns. This dynamic approach enables real-time data processing while managing complexity through flexible, adaptable components rather than rigid static configurations.
Solution Approach 2:
Masters and collectors are designed as universal components that can handle multiple data sources and sinks simultaneously. A single master can manage multiple data sources, and a single collector can aggregate data from multiple masters, reducing the total number of components needed and simplifying configuration while maintaining high throughput through efficient resource utilization.
3Reliability
If write-ahead logging and failover mechanisms are implemented, then system reliability is improved, but loss of time and processing latency increase
Solution Approach 1:
Write-ahead logging implements preliminary action by recording data changes to a log before they are committed to the main storage system. This ensures that even if a failure occurs, the logged changes can be recovered and applied, improving reliability. The logging operates asynchronously, minimizing impact on processing latency by not blocking the main data flow path.
Solution Approach 2:
The system implements failover mechanisms that prepare backup masters and collectors in advance. When a master or collector fails, a pre-configured backup can immediately take over, cushioning the impact of the failure and maintaining system reliability. This beforehand preparation reduces recovery time and minimizes latency disruption compared to ad-hoc failure response.
Data Source
AI summary
Systems and methods of dynamically processing an event using an extensible data model are disclosed. One embodiment includes, specifying attributes of the event in a data model; the data model being extensible to add properties to the event as the dataset is streamed from the source to the sink.