Distributed Replica Management for Data Reliability
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Solution Overview
Problem
In data processing systems, maintaining an optimal number of replicas to prevent data loss due to hardware or software failures is challenging, as too few replicas lead to data loss and too many result in redundancy, with traditional systems being limited by a single master node that can cause a single point of failure and restrict capacity.
Innovation Solution
A method and system that determines the number of available nodes storing replicas based on a hash value, adjusts storage distribution by adding or removing replicas as needed, and performs replica maintenance without a master node, utilizing a distributed hash table to ensure the correct number of replicas across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of data replicas is increased to prevent data loss, then data reliability is improved, but data redundancy increases
Solution Approach 1:
The system dynamically adjusts the number of replicas based on real-time node availability status. When nodes fail or join the network, the system automatically recalculates and modifies replica distribution to maintain the optimal replica count, preventing both data loss and excessive redundancy
Solution Approach 2:
The system implements a feedback mechanism where node status changes (failures, joins, or comebacks) trigger automatic replica maintenance operations. The system continuously monitors node availability and adjusts replica distribution accordingly, ensuring the replica count remains optimal without manual intervention
2Device complexity
If a single master node is used to manage replicas, then system complexity is reduced, but system reliability deteriorates due to single point of failure
Solution Approach 1:
The system divides the centralized master node functionality into distributed peer nodes. Each node can independently perform replica maintenance operations, eliminating the single point of failure while maintaining manageable complexity through standardized node behavior and protocols
Solution Approach 2:
Each node in the network is empowered to autonomously perform replica maintenance operations without requiring master node intervention. Nodes independently monitor their own replica status and execute maintenance actions, reducing system complexity while improving reliability through distributed decision-making
3Measurement precision
If manual monitoring and adjustment of replicas is performed, then replica accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements automated replica maintenance where nodes autonomously monitor their own replica status and execute maintenance operations without manual intervention. This eliminates time-consuming manual monitoring while maintaining accurate replica distribution through algorithmic decision-making
Solution Approach 2:
The system continuously monitors node status and automatically triggers replica maintenance operations based on real-time feedback. When nodes fail or join, the system immediately recalculates and adjusts replica distribution, maintaining accuracy without manual intervention and reducing time consumption through automated response
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device and a computer program product for data processing. In one embodiment, the method includes determining, at a first node in a network, one or more nodes in the network storing replicas of target data based on a hash value associated with the target data in the network. The method further includes executing replica maintenance for the target data, which includes determining the number of available nodes in the one or more nodes comparing a number of the available nodes with the predetermined number of replicas of the target data in the network and in response to the number of the available nodes being different than the predetermined number, changing storage distribution of replicas of the target data in the network.