Integrated Key-Value Store Architecture for Low Write Amplification
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Solution Overview
Problem
Existing key-value stores face issues with write amplification, resource contention, and poor read performance, particularly in cloud computing environments dealing with large data values, leading to inefficiencies in data ingestion and storage.
Innovation Solution
Implementing a key-value separation mechanism with a log engine for concurrent writing, a hash engine for hashtable-like read performance, and a sorting engine for range scans, along with a file system using log-structured append-only writing and a collaboration layer to optimize data management, reducing write amplification and resource contention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If log structured merge trees are used in key-value stores, then rapid data ingestion is achieved, but write amplification increases and resource contention occurs
Solution Approach 1:
The patent segments the key-value store into two separate engines: a log engine for handling write operations and a hash engine for handling read operations. This segmentation allows write operations to be performed without the overhead of traditional LSM tree merging, reducing write amplification while maintaining rapid data ingestion capability.
Solution Approach 2:
The patent extracts the hash table functionality from the traditional LSM tree structure, creating a dedicated hash engine that operates independently. This extraction eliminates the need for complex merge operations during writes, thereby reducing write amplification and resource contention while preserving fast ingestion rates.
2Productivity
If log structured merge trees are used in key-value stores, then rapid data ingestion is achieved, but read performance deteriorates
Solution Approach 1:
The patent segments the key-value store into separate log engine and hash engine components. The hash engine is specifically optimized for read operations using hash table data structures, providing O(1) read performance while the log engine handles writes independently, thus resolving the trade-off between ingestion rate and read performance.
Solution Approach 2:
The patent introduces a collaboration layer as an intermediary between the log engine and hash engine. This mediator coordinates operations between the two engines, allowing read operations to be efficiently routed to the hash engine while maintaining the rapid data ingestion capability of the log engine.
3Reliability
If traditional key-value store architecture is used, then data persistence is achieved, but CPU and storage consumption increases
Solution Approach 1:
The patent extracts the indexing functionality from the traditional key-value store architecture, implementing a separate hash engine that provides O(1) lookup without requiring the complex B-tree or LSM tree structures. This extraction reduces CPU consumption while maintaining data persistence through the log engine's append-only write model.
Solution Approach 2:
The patent changes the data structure parameters by using hash tables instead of tree structures for the read path. This parameter change from O(log n) to O(1) time complexity significantly reduces CPU consumption while the log engine maintains data persistence through its immutable append-only design.
4Reliability
If traditional key-value store architecture is used, then data persistence is achieved, but storage efficiency deteriorates
Solution Approach 1:
The patent extracts redundant metadata and index structures from the traditional architecture. The hash engine stores only essential key-value mappings with minimal overhead, while the log engine stores data in an append-only format without repeated metadata, significantly improving storage efficiency while maintaining data persistence.
Solution Approach 2:
The patent discards redundant metadata that is traditionally stored with each key-value pair in LSM trees. The log engine recovers storage efficiency by using an append-only model where each write is stored once without repeated metadata overhead, while the hash engine provides efficient lookup without requiring duplicate data copies.
Data Source
AI summary
A key-value store and a file system are integrated together to provide improved operations. The key-value store can include a log engine, a hash engine, a sorting engine, and a garbage collection manager. The features of the key-value store can be configured to reduce the number of I/O operations involving the file system, thereby improving read efficiency, reducing write latency, and reducing write amplification issues inherent in the combined key-value store and file system.