KVS Tree Garbage Metrics for SSD Wear Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LSM trees face issues with significant write amplification, inefficient search performance, and limited write throughput due to their constant merging and sorted nature, leading to increased wear on SSDs and resource consumption.
Innovation Solution
The KVS tree employs temporally ordered sequences of immutable key-value sets with determinative mapping and separate key-value storage, reducing write amplification and improving search efficiency through bloom filters and metrics, while managing SSD wear by grouping data with similar lifetimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LSM trees use constant merging and sorted structure, then search performance is improved, but write amplification increases and SSD wear increases
Solution Approach 1:
The patent segments the key-value data structure into separate key tree and value tree structures. Keys are stored in a key-tree with efficient search paths, while values are stored separately in value-blocks. This segmentation allows the key structure to remain sorted for efficient search while reducing the overhead of merging entire key-value pairs, thereby reducing write amplification.
Solution Approach 2:
The patent extracts the key storage and value storage into separate structures. The key-tree contains only keys and metadata necessary for search, while values are extracted and stored in value-blocks referenced by keys. This extraction allows the search structure to be optimized for speed without carrying the burden of value data, reducing write amplification during merges.
2Measurement precision
If LSM trees use constant merging and sorted structure, then search performance is improved, but write throughput is limited
Solution Approach 1:
By segmenting keys and values into separate structures, the patent enables parallel write operations. Multiple key-value pairs can be inserted by updating the key-tree and value-blocks independently, allowing higher write throughput without compromising the sorted structure needed for search performance.
Solution Approach 2:
The patent introduces dynamic structures where the key-tree can be updated incrementally with new keys without requiring complete re-sorting or merging of the entire structure. This dynamic approach allows continuous writes at high throughput while maintaining search efficiency through selective updates to the sorted key structure.
3Device complexity
If LSM trees store key-value pairs together, then storage is simplified, but storage efficiency decreases and garbage collection is inefficient
Solution Approach 1:
The patent segments storage into key-tree structures and value-block structures. This segmentation allows independent management of keys and values, enabling efficient garbage collection of obsolete values without affecting the key structure, and improving overall storage efficiency by eliminating the need to store and manage entire key-value pairs together.
Solution Approach 2:
The patent enables efficient discarding of obsolete values through separate value-block management. When keys are updated or deleted, the corresponding old values can be marked for garbage collection and recovered space can be reused, improving storage efficiency without requiring complex operations on combined key-value structures.
Data Source
AI summary
Systems and techniques for collecting and using merge tree garbage metrics are described herein. A kvset is created for a node in a KVS tree. Here, a set of kvset metrics for the kvset are computed as part of the node creation. The kvset is added to the node. The node is selected for a compaction operation based on a metric in the set of kvset metrics. The compaction operation is performed on the node.