Data Stream Management System for Rate Decoupling
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Solution Overview
Problem
Capturing and storing large volumes of diverse data from multiple sources, such as social media and sensor data, across various data retention platforms is challenging due to varying data reception rates and the need for efficient management systems.
Innovation Solution
A data stream management system that includes an ingest module to parse and authenticate data, a buffer module for horizontal elasticity, and a router module to route data independently to data streams, allowing data to be written to data stores at rates independent of the source rates, thereby managing backpressure and optimizing storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is captured from multiple independent data sources at their respective rates, then data completeness is improved, but system complexity increases due to rate variability
Solution Approach 1:
The patent introduces an intermediary data stream management system that sits between multiple independent data sources and data stores. This intermediary decouples the variable data production rates from the data storage operations, absorbing rate variability and presenting a unified, controlled data flow to downstream systems. The mediator manages buffering, rate adjustment, and coordination without requiring complex integration at each data source or data store.
2Loss of time
If data is written to data stores at rates matching source rates, then data freshness is improved, but data store reliability deteriorates due to load variability
Solution Approach 1:
The system dynamically adjusts data writing rates based on data store capacity, current load conditions, and prioritization rules. Rather than writing at fixed source rates, the data stream management system continuously adapts the write rate to match data store承受能力, preventing overload while maintaining data freshness through intelligent rate modulation and backpressure management.
Solution Approach 2:
The system implements feedback mechanisms where data store status (capacity, load, health) is continuously monitored and fed back to the data stream management system. This feedback enables real-time adjustment of data ingestion and writing rates, allowing the system to maintain reliability by throttling writes when stores are near capacity while preserving data freshness when stores have available capacity.
3Reliability
If data buffering is implemented to manage rate variability, then data loss is reduced, but memory usage increases
Solution Approach 1:
The system implements selective buffering strategies where not all data streams are buffered to the same extent. High-priority or time-sensitive data streams may use smaller buffers or stream directly, while lower-priority streams use larger buffers. This partial buffering approach reduces overall memory consumption while still protecting against data loss for critical streams, avoiding the need to buffer everything at maximum capacity.
Data Source
AI summary
A system may include at least one processor. The at least one processor may receive data from a plurality of independent data sources. The data from each respective data source is received at a rate determined by the respective data source. The at least one processor may further write the received data to at least one data store at a rate independent of the respective rates at which data from the plurality of independent data sources is received. A method and computer-readable medium are also disclosed.