Dashboard Property Update via Trusted Tunnel Bridge
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated from diverse sources poses challenges due to its vastness and complexity, as existing tools typically pre-process and discard data, limiting flexibility and insight derivation.
Innovation Solution
An event-based data intake and query system with a late-binding schema that collects, indexes, and stores machine data as events, allowing for flexible schema definition and extraction rules application at search time, enabling field-searchability and efficient retrieval of minimally processed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and only specified data items are extracted for storage, then retrieval and analysis efficiency is improved, but data flexibility and the ability to analyze all generated data is lost
Solution Approach 1:
The system performs preliminary indexing of all raw data fields during data intake, creating a searchable schema without discarding any data. This preliminary action enables both efficient retrieval (by having indexes ready) and full data flexibility (by maintaining all original data fields for future analysis needs).
Solution Approach 2:
The system changes the parameter of data storage from selective extraction to comprehensive retention with indexing. By maintaining all data fields in the raw data events with proper schema definition, the system allows analysts to access any data aspect later without being constrained by pre-defined extraction rules.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data analysis flexibility is improved, but search and analysis performance deteriorates
Solution Approach 1:
The system segments the raw data into structured events with defined schemas, where each event contains specific fields that can be independently indexed and searched. This segmentation allows the system to manage massive data quantities efficiently while maintaining flexibility to analyze any aspect of the data.
Solution Approach 2:
The system replaces brute-force search through raw data with an event-based indexing mechanism. By substituting the mechanical approach of searching all data with an organized event structure and schema-based retrieval, the system achieves both high performance and full data accessibility.
3Loss of time
If data is pre-processed based on anticipated analysis needs, then retrieval efficiency is improved, but the ability to derive new insights from unexpected data aspects is lost
Solution Approach 1:
The system creates a universal data structure where all raw data fields are preserved and indexed with a flexible schema. This multi-functional approach allows the same data structure to serve both efficient retrieval (for known analysis needs) and discovery of new insights (for unexpected analysis needs) without compromising either capability.
Data Source
AI summary
In various embodiments, a computer-implemented method comprises determining that a first property associated with a dashboard is modified at a first device, determining that the dashboard is accessible at a second device, where the first device and the second device are coupled via a trusted tunnel bridge, and in a real-time response to determining that the first property was modified, transmitting, to the second device via the trusted tunnel bridge, an update that causes the second device modify the dashboard based on the modified first property.