Live Origin Storage for Mutable-Immutable Data Consistency
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Solution Overview
Problem
Conventional live origin servers are not optimized for live streaming, leading to slow read and write processing due to lack of strong read after write consistency, throttling of write requests, high latency from single cloud region operation, and slow deletion of small media content item segments.
Innovation Solution
Implement a distributed datastore with selective consistency support for mutable and immutable data, caching immutable data, and using multi-cloud region awareness to handle network jitter and optimize storage utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional live origin servers implement strong read after write consistency, then data accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The patent segments data into mutable and immutable categories, applying different consistency strategies to each type. This allows optimized paths for each category rather than a single conservative approach for all data.
Solution Approach 2:
The system implements selective consistency where only mutable data requires version checking and consistency verification, while immutable data bypasses these checks. This local differentiation of quality requirements improves overall system performance.
2Stability of the object's composition
If conventional live origin servers throttle write requests, then system stability is improved, but publishing speed deteriorates
Solution Approach 1:
The system changes the parameter of write request handling by removing throttling mechanisms and instead relying on the immutable/mutable data classification to manage system load, allowing high-speed publishing without compromising stability.
3Device complexity
If conventional live origin servers operate in a single cloud region, then system complexity is reduced, but latency increases
Solution Approach 1:
The patent extends the system from a single cloud region to multiple cloud regions, adding a geographic dimension to data distribution. This allows the system to serve users from the nearest region, reducing latency without significantly increasing complexity.
4Loss of information
If conventional live origin servers store all data versions, then data completeness is improved, but storage efficiency deteriorates
Solution Approach 1:
The system extracts and separates immutable data from the version control mechanism, storing it once without maintaining multiple versions. Only mutable data retains version tracking, eliminating redundant storage while preserving necessary data completeness.
Data Source
AI summary
One embodiment of a method for storing data includes determining whether first data to be stored is mutable data that can be modified or immutable data that is not modified, in response to determining that the first data is mutable data, storing the first data in a datastore, and in response to determining that the first data is immutable data, storing the first data in the datastore and in a cache.