Binary Index Metadata Storage for Fast Metric Querying
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Solution Overview
Problem
Current systems face challenges in efficiently processing and querying large volumes of metric data due to limitations in metadata storage and retrieval methods, leading to slow query response times and high computational resource usage.
Innovation Solution
Implementing a SQL-based query engine with a hot metadata storage system that uses a binary index format, dynamic caching, and real-time search capabilities to optimize data ingestion, storage, and retrieval, allowing for near-real-time query handling and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional metadata storage methods are used, then data can be stored, but query response time becomes slow and computational resources are excessively consumed
Solution Approach 1:
The patent segments the monolithic metadata storage system into multiple specialized components: a hot metadata store for frequently accessed metadata, a cold metadata store for less frequently accessed metadata, and a binary index structure for rapid lookup. This segmentation allows queries to be directed to appropriate storage layers, reducing unnecessary computational overhead and improving query response time by avoiding full-scans of entire metadata repositories.
Solution Approach 2:
The patent implements preliminary action through pre-computed binary indexes and proactive caching strategies. Metadata is pre-processed and organized into binary index structures that enable O(log n) lookup times. Frequently accessed metadata is pre-loaded into the hot metadata store before queries occur, eliminating the need for computational heavy lifting during actual query execution and significantly reducing resource consumption.
2Reliability
If more computational resources are allocated for metadata processing, then query accuracy can be maintained, but system cost and complexity increase
Solution Approach 1:
The patent replaces traditional linear text-based metadata search mechanisms with a binary index structure that enables logarithmic-time lookup. This substitution transforms the computational approach from O(n) linear scanning to O(log n) binary search, maintaining query accuracy while dramatically reducing the computational resources and system complexity required to process metadata queries at scale.
Solution Approach 2:
The patent changes the fundamental parameter of metadata organization from flat text format to hierarchical binary index format. This parameter change enables the system to maintain high query accuracy through structured organization while reducing complexity by enabling efficient pruning of search spaces. The binary index structure allows the system to achieve the same query reliability with fraction of the computational resources compared to traditional approaches.
3Quantity of substance
If metadata is stored in external cloud storage, then storage capacity is increased, but retrieval speed decreases
Solution Approach 1:
The patent implements a nested storage architecture where a compact binary index structure (hot metadata) is nested within the larger cloud storage system. The binary index contains only the essential lookup information needed for rapid queries, while the full metadata resides in external cloud storage. This nested structure enables fast retrieval by first consulting the compact in-memory binary index to locate data, then fetching only the specific required metadata from cloud storage, rather than retrieving entire datasets.
Solution Approach 2:
The patent extracts the critical lookup information from the full metadata and places it in a separate binary index structure that can be rapidly accessed. This extraction separates the frequently accessed index information from the less frequently accessed full metadata, allowing the system to maintain high retrieval speeds for common operations while still providing access to the complete metadata set in external storage with increased capacity.
Data Source
AI summary
Described technologies generate a data structure corresponding to values sequenced based on a plurality of timestamps associated with the values. The data structure can include a first section identifying a first timestamp associated with the plurality of timestamps and a number representing how many timestamps are associated with the plurality of timestamps, and a second section including at least a value linked to the first timestamp, and an additional value representing an encoding type associated with the second section. The data structure can be stored in computer-implemented storage.