Machine-Learned Data Flow Control for Fresh, Low-Latency Replication
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Solution Overview
Problem
Conventional data replication systems face challenges in maintaining consistent data across multiple locations, leading to high storage and processor costs, and require significant engineering effort for setup and management, which can delay system deployment.
Innovation Solution
A computer-implemented method using machine-learned models to generate extraction and loading frameworks for data replication, optimizing data freshness and query latency by determining file interfaces, sharding data, and adjusting resource allocation based on client requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data replication is implemented to improve data availability and accessibility, then data freshness and query performance are improved, but storage costs and processor costs increase
Solution Approach 1:
The patent segments data replication into multiple independent data flows, each with its own extraction and loading frameworks. This allows selective replication of only the necessary data portions to specific locations, reducing unnecessary storage costs while maintaining data freshness where needed.
Solution Approach 2:
The system dynamically adjusts replication parameters including data freshness expectations, query latency requirements, and resource allocation based on real-time conditions. This enables optimal balancing of data freshness and storage costs by changing replication behavior according to actual system needs.
2Ease of operation
If manual setup and management of data replication is performed, then data flow configuration is precise, but engineering time and system deployment speed are reduced
Solution Approach 1:
The patent implements self-service through automated extraction and loading frameworks that automatically configure data flows based on machine-learned models. The system autonomously manages replication without requiring manual engineering setup, significantly reducing deployment time while maintaining operational precision.
Solution Approach 2:
The system performs preliminary actions by pre-configuring extraction and loading frameworks using machine learning models before actual data replication occurs. This preparation enables rapid deployment without manual setup time, as the frameworks are ready to execute immediately when needed.
3Speed
If resource allocation for data replication is increased, then data freshness and query latency performance are improved, but computing costs increase
Solution Approach 1:
The patent implements dynamic resource allocation where computing resources are adjusted in real-time based on actual data flow requirements, query patterns, and system conditions. This dynamic approach ensures optimal query latency performance while avoiding excessive resource consumption during low-demand periods.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on machine-learned predictions of data access patterns. By adjusting extraction and loading framework parameters according to actual needs, the system achieves good query performance with optimized computing resource usage rather than over-provisioning.
Data Source
AI summary
Techniques for generating replication data from a data source to be stored in a target location are described herein. A computing system can receive, from a client device, client requirements associated with a dataflow from the data source to the target location. The client requirements can include an expected data freshness value and an expected data query latency value. Additionally, the computing system can process the expected data freshness value with one or more machine-learned models to generate an extraction framework for extracting data from the data source. Moreover, the computing system can process the expected data query latency value with the one or more machine-learned models to generate a loading framework for loading data to the target location. Furthermore, the computing system can copy the replication data from the data source to the target location based on the extraction framework and the loading framework.