Dictionary Bucketing for Managed Environment Data Retrieval
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Solution Overview
Problem
Managed environments face performance reductions and inefficient data storage due to excessive data volumes, leading to high resource usage and long latency in garbage collection and retrieval processes, as existing systems struggle to efficiently manage large dictionaries with millions of objects.
Innovation Solution
Implementing sequential bucketing techniques to optimize key-value datasets, reducing the number of objects and organizing data in a structured manner, allowing for fast and accurate data retrieval with minimized disk seeks and memory costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in traditional dictionary structures with millions of objects, then data storage capacity is achieved, but system performance deteriorates due to excessive garbage collection overhead and long retrieval latency
Solution Approach 1:
The patent segments the dictionary data into multiple buckets organized in a tree structure, where each level of the tree represents a segmentation of the key space. This segmentation reduces the number of objects that need to be processed together during garbage collection and enables parallel processing of different segments, thereby improving system performance while maintaining data storage capacity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional flat dictionary structure by organizing buckets into a tree with multiple levels. This dimensional transformation allows the system to manage millions of objects through a multi-level hierarchy rather than a single flat structure, reducing the overhead of garbage collection and improving retrieval performance through targeted access paths.
2Reliability
If garbage collection processes all stored objects, then memory management is performed, but system resources are excessively consumed and latency increases
Solution Approach 1:
The patent segments the dictionary into multiple buckets that can be independently managed. Garbage collection can target specific buckets or segments rather than processing all objects uniformly, allowing the system to perform memory management more efficiently with reduced latency by focusing only on relevant segments.
Solution Approach 2:
The patent enables partial garbage collection where only necessary buckets or segments are processed based on actual memory pressure and usage patterns. This partial action approach avoids the excessive processing of all objects, reducing garbage collection latency while maintaining reliable memory management for the critical portions of the data structure.
3Quantity of substance
If millions of objects are stored in the dictionary, then data coverage is comprehensive, but memory costs and space requirements increase significantly
Solution Approach 1:
The patent merges multiple dictionary entries that share common prefixes or patterns into shared bucket structures. By combining common elements at higher levels of the hierarchical tree and storing only unique variations at lower levels, the system achieves comprehensive data coverage while significantly reducing the total number of objects that need to be stored in memory.
Solution Approach 2:
The patent implements a nested hierarchical structure where buckets contain sub-buckets in a tree arrangement. This nesting allows the system to store comprehensive data by organizing it in nested containers, where outer containers share space and inner containers hold only the specific variations, thereby reducing overall memory requirements while maintaining complete data coverage.
4Ease of operation
If traditional dictionary retrieval processes are used, then data access is performed, but disk seek operations and read latency are excessive
Solution Approach 1:
The patent performs preliminary organization of data into a hierarchical bucket structure during the data loading phase. This preliminary action creates optimized access paths and positions data in advance according to the hierarchical structure, enabling faster retrieval operations without requiring extensive disk seeking during actual data access, thereby reducing read latency.
Solution Approach 2:
The patent segments the data access process into hierarchical levels, where each level narrows down the search space. This segmentation allows retrieval operations to quickly eliminate large portions of the data structure at each level, reducing the number of disk seek operations required and minimizing read latency while maintaining ease of data access.
Data Source
AI summary
Systems and methods are provided for optimizing data structures to improve the data retrieval through the use of bucketing techniques. A number of objects within an environment is drastically reduced utilizing bucketing techniques. Within the buckets, items are sequentially organized such that location is quicker. Items, or keys, are aligned with the same hash value together in a bucket and a mapping of the hash value to the offset of the first key occurrence in that bucket. This guarantees each lookup operation is only two random read accesses. Systems and methods provided herein control the pressures on a system for garbage collection and minimize memory usage with minimal impacts on performance.