Copy-on-Write B+ Tree Validation for Faster Chunk Garbage Collection
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Solution Overview
Problem
Existing reference counting-based garbage collection techniques in storage systems suffer from low throughput and efficiency due to the need to iterate the entire indexing structure, leading to significant IO overhead and performance degradation, especially in large-scale deployments.
Innovation Solution
Implement an incremental reference counting validation process based on a copy-on-write indexing structure, which loads only changed parts of the indexing structure on demand to build and update an in-use chunk list, reducing IO overhead and speeding up the validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire indexing structure is iterated for reference counting validation, then validation completeness is ensured, but IO overhead increases and validation speed decreases
Solution Approach 1:
The patent divides the indexing structure into changed parts and unchanged parts. Only the changed parts (leaf pages modified since last validation) are iterated during reference counting validation, while unchanged parts are skipped. This segmentation approach maintains validation completeness for affected regions while dramatically reducing the overall validation scope and IO overhead.
Solution Approach 2:
The patent implements dynamic validation by tracking which parts of the indexing structure have changed since the last validation round. The validation process adapts its scope based on actual changes rather than iterating everything statically. This dynamic approach allows the system to adjust validation effort to match actual data changes, improving speed without sacrificing completeness.
2Reliability
If the entire indexing structure is iterated for reference counting validation, then validation thoroughness is ensured, but IO overhead increases
Solution Approach 1:
The patent segments the indexing structure into changed and unchanged portions. By identifying only the leaf pages that have been modified since the last validation, the system confines IO operations to only those segments, reducing total IO volume while maintaining thorough validation of affected areas.
Solution Approach 2:
The patent performs preliminary tracking of changes to the indexing structure during normal operation. By maintaining information about which pages have changed since last validation, the system prepares the validation process in advance, avoiding the need to scan the entire structure and reducing IO overhead during actual validation.
3Loss of energy
If incremental validation is implemented, then IO overhead is reduced, but validation accuracy may be compromised
Solution Approach 1:
The patent segments validation to only process changed leaf pages while using the mapping structure to accurately track reference counts for all pages. This ensures that even though we skip unchanged pages, we maintain accurate validation results by properly accounting for references in the changed segments and using the mapping to reconstruct complete reference count information.
Solution Approach 2:
The patent introduces a mapping structure as an intermediary between the indexing structure and the validation process. This mapping stores reference count information and allows the validation to accurately determine which chunks are actually in use, ensuring validation accuracy even when iterating only through changed parts of the indexing structure.
Data Source
AI summary
A system can maintain a copy-on-write b+ tree for a data storage system that is configured to store data as chunks, and a mapping between tree page identifiers of the b+ tree and first in-use chunk identifiers. The system can perform reference count validation, comprising iterating over the mapping, wherein the iterating over the mapping comprises, based on a first tree page identifier of the tree page identifiers being present in the mapping, loading second in-use chunk identifiers that correspond to the first tree page identifier directly from the mapping; and based on the first tree page identifier of the tree page identifiers being absent, loading corresponding content, extracting third in-use chunk identifiers from object metadata of the content, and updating the mapping based on the third in-use chunk identifiers, to produce an updated mapping. The system can perform garbage collection of the data storage system based on the validation.