Tiered Update Management for Distributed Data Stores
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Solution Overview
Problem
Managing large distributed databases is challenging due to complex update and management processes, which can result in significant downtime and potential errors, especially when updates are applied across thousands or millions of individual data stores.
Innovation Solution
The approach involves dividing a distributed database into logical tiers based on business logic or allocation criteria, allowing updates to be applied sequentially to each tier, enabling simplified error detection and rollback, and controlling the update rate to minimize downtime and impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If updates are applied across all data stores simultaneously, then update speed is improved, but risk of system-wide failure and downtime increases
Solution Approach 1:
The patent divides the distributed data store into multiple logical tiers (e.g., hot, warm, cold tiers) and applies updates sequentially to each tier rather than simultaneously across all data stores. This segmentation allows the system to maintain higher availability by limiting the scope of downtime to individual tiers while improving overall reliability through controlled rollbacks if failures occur.
2Reliability
If updates are applied sequentially to individual data stores, then system reliability is improved, but total update time increases
Solution Approach 1:
The patent introduces a temporal dimension to the update process by organizing data stores into time-based tiers (hot, warm, cold) with different update priorities. This allows the system to apply updates in a controlled sequence across tiers while maintaining parallel access to different tiers, effectively reducing total update time compared to strictly sequential single-data-store updates.
3Loss of time
If all data stores are updated at once, then downtime is minimized, but error detection and rollback complexity increases
Solution Approach 1:
By segmenting data stores into logical tiers, the patent simplifies error detection and rollback procedures. When an error occurs during an update, the system can easily identify which specific tier is affected and rollback only that tier's updates, rather than managing complex rollbacks across all data stores simultaneously. This segmentation reduces the complexity of error management while maintaining acceptable downtime levels.
Data Source
AI summary
The updating of a definition layer or schema for a large distributed database can be accomplished using a plurality of data store tiers. A distributed database can be made up of many individual data stores, and these data stores can be allocated across a set of tiers based on business logic or other allocation criteria. The update can be applied sequentially to the individual tiers, such that only data stores for a single tier are being updated at any given time. This can help to minimize downtime for the database as a whole, and can help to minimize problems that may result from an unsuccessful update. Such an approach can also allow for simplified error detection and rollback, as well as providing control over a rate at which the update is applied to the various data stores of the distributed database.