Streaming Data Visualization for Real-Time Log Pattern Analysis
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Solution Overview
Problem
Analyzing vast amounts of machine data in computing systems is time-consuming and cumbersome due to the textual format of logs, which hinders quick identification of issues and patterns, delaying necessary resolutions.
Innovation Solution
A data intake and query system that utilizes a flexible schema and late-binding schema to process and store machine data, enabling field-searchable events with extraction rules applied at search time, allowing for real-time analysis and visualization of data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data events are recorded in textual logs for individual machines, then complete data event information is preserved, but analysis time and operational complexity increase significantly
Solution Approach 1:
The patent transforms data from traditional textual log format into a multi-dimensional data model with hierarchical structure. Events are organized by machine, data path, and event type, creating additional dimensions for analysis. This dimensional transformation enables visual exploration and pattern recognition that would be impossible in flat textual format, resolving the contradiction between preserving complete information and enabling quick analysis.
Solution Approach 2:
The patent introduces an intermediary data model layer between raw logs and analysis tools. This intermediary structure processes and organizes raw log data into structured events with standardized fields and relationships, serving as a mediator that preserves all original information while presenting it in an analysis-friendly format that reduces operational complexity and analysis time.
2Measurement precision
If administrators review individual log files from multiple machines, then detailed event records are available, but the complexity and time required to identify patterns increase
Solution Approach 1:
The patent segments the monolithic log analysis task into hierarchical components: machines contain data paths, data paths contain events, and events contain specific fields. This segmentation allows administrators to analyze data at appropriate granularities, drilling down from high-level machine views to specific event details only when necessary, thereby maintaining measurement precision while reducing overall analysis complexity.
Solution Approach 2:
The patent creates a universal data model that standardizes event representation across different machines and data paths. By defining common event types, fields, and relationships that apply universally throughout the system, the patent enables pattern recognition and analysis techniques to be applied consistently across diverse data sources, reducing the complexity of analyzing heterogeneous log data while preserving detailed event information.
Data Source
AI summary
A device executes a visualization application program on a processor. Via the visualization application, a technique for visualizing data paths are performed. The technique includes receiving a data structure from a data intake and query system, where the data stream includes event stream data associated with the data path. The data path includes a set of entities, including an origin entity and a destination entity. The technique further includes generating visualizations of the origin entity, destination entity, and the event stream data. The visualization of the event stream data includes visualizations of events streaming between the visualization of the origin entity and visualization of the destination entity. The technique also includes causing the visualizations of the origin entity, destination entity, and the event stream data to be presented in an extended reality environment.