Mutable Immutable Data Stage Storage Architecture
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Solution Overview
Problem
As data storage systems grow to petabyte scales, retrieval efficiency declines due to increasing data amounts and coordination challenges, with existing systems struggling to manage access patterns between new and historical data effectively.
Innovation Solution
The data storage system is organized into mutable and immutable stages, where 'hot' data is stored in shared non-volatile memory and 'cold' data is cached in local memory, allowing faster access and reducing storage space requirements, with synchronization only needed for the mutable data stage to prevent coherence issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a single large storage system to manage petabyte-scale data, then storage capacity is sufficient, but retrieval speed slows exponentially and coordination complexity increases
Solution Approach 1:
The patent divides the storage system into two distinct segments: mutable storage for newly arrived data and immutable storage for historical data. This segmentation allows the system to maintain petabyte-scale capacity while improving retrieval speed by accessing only relevant data portions from the appropriate storage type, avoiding the exponential slowdown of searching through a single monolithic storage system.
Solution Approach 2:
The patent applies different storage characteristics to different data types: mutable storage provides fast write access for incoming data, while immutable storage provides efficient read access for historical data. This local quality differentiation ensures that each storage type is optimized for its specific access pattern, maintaining high retrieval speeds across the entire petabyte-scale system.
2Ease of operation
If all data is kept in mutable storage to allow frequent updates, then data accessibility is high, but storage space requirements and coordination overhead increase significantly
Solution Approach 1:
The patent extracts historical data from the mutable storage system and places it into immutable storage. This extraction reduces the amount of data that must be managed with full coordination overhead in mutable storage, thereby reducing storage space requirements and coordination complexity while maintaining data accessibility through the immutable storage interface.
3Productivity
If data is divided into mutable and immutable stages, then storage efficiency improves, but system complexity increases due to data transition management
Solution Approach 1:
The patent implements automatic data transition mechanisms where data moves from mutable to immutable storage based on predefined criteria such as age or access patterns. This self-service approach reduces the need for manual intervention and complex coordination management, thereby improving storage efficiency without proportionally increasing system complexity.
4Productivity
If multiple processing nodes access data simultaneously, then system throughput increases, but coordination issues and coherence management become more difficult
Solution Approach 1:
The patent inverts the typical approach by making historical data immutable rather than mutable. This inversion eliminates the need for complex coherence protocols when multiple processing nodes access historical data simultaneously, as immutable data by definition does not change. Meanwhile, mutable storage handles only newly arrived data with its own coordination mechanisms, thereby increasing system throughput without proportionally increasing coordination complexity.
Data Source
AI summary
A system includes processing nodes and shared memory. Each processing node includes a processor and local memory. The local memory of each processing node stores at least a partial copy of the immutable data stage of a dataset. The shared memory is accessible by each processing node and stores a sole copy of the mutable data stage of the dataset and a master copy of the immutable data stage of a dataset.