Unicode Data Structure for High-Volume Performance Metric Search
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Solution Overview
Problem
Current methods for managing and analyzing large amounts of performance data, such as those from server virtualization and sensor systems, face challenges including high bandwidth requirements, scalability issues, and inefficiencies in data storage and search processes, particularly when using relational databases and SQL queries.
Innovation Solution
A system that stores performance data in a file system with a special directory structure, using regular expressions to search and analyze data, eliminating the need for relational databases and reducing bandwidth requirements by converting numeric data to Unicode characters for text-based search patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote monitoring is implemented using virtual private network or agent programs, then performance data can be gathered and analyzed, but high bandwidth requirements increase costs and limit scalability
Solution Approach 1:
The patent extracts only the essential performance metric values from monitored systems, converting them to compact Unicode character representations. This extraction approach sends only necessary data over the network rather than complete data sets, dramatically reducing bandwidth consumption while maintaining monitoring capability.
Solution Approach 2:
The patent changes the representation parameter of performance data by converting numeric metric values into Unicode character codes. This parameter transformation compresses the data format, reducing the bandwidth required for transmission while preserving the ability to retrieve and analyze performance metrics.
2Ease of operation
If relational databases with SQL queries are used to store and search performance data, then data can be organized and queried, but storage overhead and search complexity increase with large data volumes
Solution Approach 1:
Instead of storing complete numeric performance data and searching through it, the patent inverts the approach by storing Unicode character representations of metric values and using regex pattern matching on these compact representations. This inversion reduces storage requirements while maintaining search functionality through text-based pattern matching.
Solution Approach 2:
The patent substitutes the mechanical SQL database query system with a text-based regex pattern matching system operating on Unicode character sequences. This substitution replaces complex database join operations and SQL parsing with simpler string matching operations, reducing both storage overhead and computational complexity for large data sets.
3Adaptability or versatility
If custom stored procedures are written to search for patterns in performance data, then complex pattern analysis can be performed, but programming complexity and maintenance burden increase
Solution Approach 1:
The patent creates a universal regex-based search interface that handles multiple pattern matching scenarios through a single unified mechanism. Instead of requiring separate custom procedures for different pattern types, the system uses versatile regex patterns that can match various performance data patterns, time ranges, and metric types through one flexible framework.
Solution Approach 2:
The patent changes the search mechanism from procedural programming to declarative pattern matching. By transforming performance data into Unicode character sequences and using regex patterns, the system allows complex pattern analysis to be expressed through pattern syntax rather than programming logic, reducing the complexity barrier for performing sophisticated searches.
Data Source
AI summary
A system to collect and analyze performance metric data recorded in time-series measurements, converted into unicode, and arranged into a special data structure. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is also organized into a special data structure. The data structure at the server where analysis is done has a directory for every day of performance metric data collected with a subdirectory for every resource type. Each subdirectory contain text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.