Multi-layer Cache System for Query Performance and Storage Cost Reduction
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Solution Overview
Problem
Existing data processing solutions struggle to efficiently query large amounts of already indexed data, as they typically abstract files at the filesystem level, leading to high costs and inefficiencies, especially when dealing with large files stored remotely.
Innovation Solution
The implementation of a multi-layer caching system that decouples storage from compute, allowing for efficient execution of search queries by using a custom information retrieval library and a multi-layer cache mechanism, which enables on-demand access to data stored remotely without the need for local storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored locally to enable fast query execution, then query performance is improved, but storage costs increase
Solution Approach 1:
The patent segments data storage into multiple cache layers (L1, L2, L3) with different storage speeds and costs. Frequently accessed data is stored in faster, more expensive local cache (L1), while less frequently accessed data is stored in slower, cheaper remote storage (L3), resolving the contradiction between query speed and storage cost through hierarchical segmentation
Solution Approach 2:
The patent adds a temporal dimension to data access by implementing a multi-layer cache system that dynamically moves data between storage layers based on access frequency and recency. This temporal caching strategy allows the system to optimize between immediate query speed (keeping hot data local) and long-term storage cost (moving cold data remote)
2Productivity
If all data is indexed locally for efficient searching, then search performance is improved, but system complexity increases
Solution Approach 1:
The patent extracts the indexing function from the storage system by implementing a separate, distributed indexing layer that operates independently from the multi-layer cache. This allows efficient local searching of indexed data while keeping the complex indexing logic separate from the storage management complexity
Solution Approach 2:
The patent introduces an intermediary indexing layer that sits between the query interface and the multi-layer cache. This intermediary maintains local indexes for frequently accessed data, enabling efficient searches without requiring the entire dataset to be locally indexed, thus reducing system complexity while maintaining search performance
3Quantity of substance
If remote storage is used to reduce costs, then storage cost is reduced, but query performance deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-loading frequently accessed data into local cache layers before it is actually needed for queries. The system proactively caches data based on access patterns, prediction algorithms, and query workloads, ensuring that hot data is already in fast storage when needed, thus eliminating the performance penalty of remote storage access
Solution Approach 2:
The patent implements a dynamic multi-layer cache system where data automatically moves between storage layers (L1, L2, L3) based on real-time access patterns, cache hit rates, and workload characteristics. This dynamic adaptation allows the system to optimize data access speed for frequently queried data while maintaining cost efficiency for less frequently accessed data
Data Source
AI summary
Provided are systems and methods for multi-layer caching of data. An example method commences with creating a data structure on top of an information retrieval library. The data structure is designed to access data associated with the information retrieval library through a local index. The method includes creating a plurality of ordered cache layers for the data accessed via the local index. The plurality of ordered cache layers are ordered based on frequency of access and a last layer is used to access the data over a network. The method then continues with receiving a search query. The method further includes determining that the search query is for the data associated with the last layer. The method further continues with executing, using the local index, the search query locally to retrieve a matching document from the data associated with the last layer remotely.