In-Memory Metrics Catalog for IT Resource Analysis
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Solution Overview
Problem
Existing systems for processing and analyzing metrics data in IT environments are inadequate, failing to provide meaningful insights into the health and performance of computing resources, and are unable to efficiently handle the unique characteristics of metrics data, leading to a cognitive burden on analysts.
Innovation Solution
A data intake and query system that indexes and stores metrics data as timestamped events, allowing for efficient processing and querying, and integrates with non-metrics data to provide comprehensive insights into computing resource operations and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If metrics data is processed and analyzed using existing systems, then data processing capability is maintained, but meaningful insights into computing resource health and performance are not provided
Solution Approach 1:
The patent segments metrics data into distinct dimensional components (time, resource, metric type) and stores them in a normalized structure. This segmentation enables efficient querying and analysis by allowing the system to retrieve specific metric dimensions without processing entire datasets, thereby preserving meaningful insights while improving processing efficiency.
Solution Approach 2:
The patent introduces an intermediary layer (metrics catalog and normalization engine) between raw metrics data and analysis tools. This intermediary transforms raw metrics into a standardized format with explicit dimensional relationships, enabling analysts to efficiently explore meaningful insights without directly processing raw data, thus reducing information loss while maintaining productivity.
2Reliability
If separate systems are used to process metrics data and machine-generated data, then specialized processing is achieved, but cognitive burden on analysts increases
Solution Approach 1:
The patent merges metrics data and machine-generated data into a unified data model with consistent dimensional structure. By combining previously separate processing systems into a single normalized framework, the patent maintains specialized processing capabilities for each data type while presenting a unified interface to analysts, thereby reducing cognitive burden without sacrificing reliability.
Solution Approach 2:
The patent creates a universal data model that can handle both metrics data and machine-generated data through a common dimensional structure. This multi-functional framework allows the same querying and analysis tools to work with different data types, eliminating the need for analysts to switch between specialized systems and reducing operational complexity while maintaining specialized processing where needed.
3Quantity of substance
If traditional data storage methods are used for metrics data, then storage capability is maintained, but efficient querying and analysis are hindered
Solution Approach 1:
The patent transforms traditional flat data storage into a multi-dimensional structure where metrics are organized by time, resource, and metric type dimensions. This dimensional organization allows efficient querying by any dimension without requiring full data scans, enabling the system to maintain large storage capacity while dramatically improving query and analysis efficiency through targeted data retrieval.
Data Source
AI summary
The disclosed embodiments include a method performed by a data intake and query system. The method includes populating each metric including a measure value, cataloging metadata in an in-memory metrics catalog, where the metadata is related to the metrics. The method further includes receiving a search query including search criteria, evaluating the search query by applying the search criteria to the metadata of the metrics catalog to obtain results that satisfy the search criteria, and causing display, on a display device, of the results or data indicative of the results.