Distributed Data Collection System with Master-Node Coordination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently collecting and aggregating large volumes of user-generated datasets from various sources for analysis, particularly in handling increased data volumes and ensuring fault tolerance and scalability.
Innovation Solution
A system comprising client devices that generate datasets, interacting nodes, and a master configuration for data flow management, which enables dynamic configuration, fault-tolerant data aggregation, and scalable data processing through multiple masters and collectors, utilizing a distributed file system for storage and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a distributed system is used to collect and aggregate large volumes of data from multiple sources, then the system's scalability and fault tolerance are improved, but the system complexity increases
Solution Approach 1:
The system is divided into multiple independent nodes (collectors, aggregators, masters) that can operate autonomously. Each node handles a portion of the data collection and aggregation task, allowing the system to scale horizontally while maintaining fault tolerance through distributed architecture
Solution Approach 2:
Master nodes are introduced as intermediary components that coordinate between data sources and aggregation targets. The master manages node configurations, monitors system state, and handles failover logic, thereby simplifying the complexity of direct peer-to-peer coordination while maintaining system reliability
2Loss of time
If data is collected and aggregated in real-time from multiple sources, then the timeliness of analytics is improved, but the data processing load and system resource consumption increase
Solution Approach 1:
The system employs periodic batching mechanisms where data is collected and aggregated in time-based or size-based batches rather than continuously processing every individual data point. This reduces peak processing loads while maintaining acceptable timeliness for analytics
Solution Approach 2:
The system processes data at varying rates based on priority and resource availability. High-priority data streams receive immediate processing while lower-priority streams are buffered and processed during off-peak periods, optimizing resource utilization while maintaining timeliness for critical analytics
3Productivity
If multiple masters and collectors are deployed to handle increased data volumes, then the system's scalability is improved, but the coordination overhead and communication complexity increase
Solution Approach 1:
The system organizes nodes into a hierarchical structure with multiple levels (masters, aggregators, collectors) operating in different functional dimensions. This layered architecture allows parallel processing across multiple nodes while reducing coordination complexity by localizing management decisions at each hierarchical level
Data Source
AI summary
Systems and methods of facilitating collecting and aggregating datasets that are machine or user-generated for analysis are disclosed. One embodiment includes, collecting a dataset on a machine on which the dataset is received or generated, wherein, the dataset is collected from a data source on the machine, aggregating the dataset collected from the data source at a receiving location, performing analytics on the dataset upon collection or aggregation, and/or writing the dataset aggregated at the receiving location to a storage location.