Interactive Visualization of Isolated Execution Environment Relationships
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Solution Overview
Problem
Existing tools lack the ability to quickly and easily search and analyze large sets of raw machine data to visually identify data subsets of interest, particularly in IT environments with diverse data systems containing structured, semi-structured, and unstructured data.
Innovation Solution
A data intake and query system utilizing a late-binding schema that applies extraction rules to events during search time, enabling flexible schema development and allowing for field-searchable events, with components like indexing nodes and search nodes to process and store data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tools pre-process data based on anticipated analysis needs and extract specified data items, then retrieval and analysis efficiency is improved, but data flexibility and ability to analyze all generated data deteriorates
Solution Approach 1:
The system performs preliminary indexing of all raw machine data without discarding any information, creating a comprehensive foundation that enables both efficient retrieval and flexible analysis. The indexing process prepares data for future queries while maintaining the ability to adapt to unforeseen analysis needs.
Solution Approach 2:
The system changes the parameter of data retention from selective extraction to complete preservation, storing all raw machine data in its entirety. This parameter change enables analysts to query and analyze any aspect of the data without being constrained by pre-defined extraction schemas.
2Adaptability or versatility
If tools store massive quantities of raw machine data for later retrieval, then data analysis flexibility is improved, but data storage requirements and system complexity worsens
Solution Approach 1:
The system segments the data storage and processing architecture into distinct components: raw data storage, indexing structures, and query processing layers. This segmentation manages complexity by organizing massive data quantities into manageable, independently operable segments.
Solution Approach 2:
The system introduces an indexing layer as an intermediary between raw data storage and query processing. This intermediary structure enables efficient data retrieval and analysis without requiring direct manipulation of the entire raw data set, thereby managing system complexity while preserving data flexibility.
3Ease of operation
If analysts search data systems separately and collect results over a network, then data retrieval capability is improved, but analysis time and operational complexity worsens
Solution Approach 1:
The system merges multiple distributed data sources and search operations into a unified search interface. Analysts can query across all data systems simultaneously through a single operation, eliminating the need to separately search and aggregate results from multiple sources.
Solution Approach 2:
The system provides a universal search capability that functions across diverse data types and sources. A single query mechanism handles structured, semi-structured, and unstructured data from multiple systems, reducing operational steps and analysis time.
Data Source
AI summary
Systems and methods are described to determine relationships between one or more components of an isolated execution environment system based on data obtained from a data intake and query system. Based on the determined relationships, an interactive visualization is generated that indicates the hierarchical relationship of the components. In some cases, to illustrate the relationship between components of the isolated execution environment system, the visualization can include one or more display objects displayed in a subordinate or superior relationship to other display objects. In certain cases, based on an interaction with a display object, the system can generate a query and/or display additional information and/or visualizations based on the results of the query.