Hierarchical Filesystem Metric Aggregation via Inode Storage
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Solution Overview
Problem
Conventional filesystems incur significant latency and performance imbalances due to the need for exhaustive traversal of tree structures to aggregate metadata attributes, leading to slow metric value retrieval and potential inaccuracies in reflecting concurrent modifications.
Innovation Solution
A facility that persistently maintains hierarchical aggregates of file attributes at various levels within a filesystem tree, allowing for rapid access to metric values without exhaustive traversal, by storing aggregated metrics in each directory and updating them frequently to reflect changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional filesystems traverse the entire tree structure to aggregate metadata attributes, then measurement precision of filesystem metrics is improved, but loss of time and system performance deteriorate
Solution Approach 1:
The patent pre-calculates and stores aggregate metadata attributes (such as total size, file count, and other metrics) at each directory node in the filesystem tree. When a metric query is received, the system retrieves these pre-computed values directly from the directory metadata instead of traversing the entire subtree, thereby eliminating the time-consuming traversal operation while maintaining measurement accuracy.
Solution Approach 2:
The patent divides the filesystem tree into hierarchical segments (directories and subdirectories), with each segment maintaining its own aggregate metadata attributes. This segmentation allows the system to compute and store metrics locally at each directory level, enabling rapid retrieval without needing to traverse the entire filesystem tree, thus resolving the contradiction between measurement precision and time loss.
2Reliability
If conventional filesystems perform exhaustive tree traversal to aggregate metadata, then reliability of metric data is improved, but device complexity and processing overhead worsen
Solution Approach 1:
The system performs preliminary aggregation of metadata attributes when files or directories are created, modified, or deleted. Each directory node maintains pre-computed aggregate values (total size, file count, etc.) that are updated incrementally as the filesystem changes. This approach ensures reliable metric data while avoiding the complexity of exhaustive traversal at query time.
Solution Approach 2:
Each directory node in the filesystem tree maintains its own aggregate metadata attributes and automatically updates them when contents change. This self-service mechanism eliminates the need for complex centralized aggregation processes, reducing device complexity while maintaining data reliability through local, incremental updates.
3Measurement precision
If filesystems retrieve metric values through synchronous API calls and tree traversal, then measurement accuracy is improved, but productivity and system responsiveness worsen
Solution Approach 1:
The patent pre-computes and stores aggregate metadata attributes at each directory node, transforming the metric retrieval process from a synchronous tree traversal operation into a simple data lookup. This preliminary action maintains measurement accuracy by preserving exact aggregate values while dramatically improving productivity by eliminating the time-consuming traversal and aggregation steps during query execution.
Data Source
AI summary
A software and hardware facility persistently maintains metrics on directories at different levels within a tree of a filesystem. The facility hierarchically aggregates attributes of files contained by directories and stores them as metric values in inode date of each directory within a tree. The stored metric values represent summed or otherwise aggregated data from the descendant directories and files. The metric values are utilized to provide representative compositional samplings of data in the filesystem tree structure. Based on the metric value in its inode data, each directory is weighted in a manner that determines the likelihood that each file sample is taken from that directory.