Query Result Caching in Data Intake Panels
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Solution Overview
Problem
Current data intake and query systems face challenges in efficiently searching and analyzing large sets of raw machine data, as existing tools lack intuitive and efficient visual interfaces for identifying data subsets of interest, leading to time-consuming analysis processes.
Innovation Solution
A data intake and query system architecture that includes a flexible schema for event processing, late-binding schema for field extraction, and a graphical user interface for query formation and execution, enabling users to run queries on raw machine data and generate insights across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If analysts use existing tools to search data systems separately and collect results over a network, then data can be retrieved from multiple sources, but the analysis process becomes piecemeal and time-consuming
Solution Approach 1:
The patent combines multiple separate data system searches into a single unified search interface. The system allows analysts to query across multiple data systems simultaneously through one graphical user interface, consolidating what was previously piecemeal separate searches into a unified operation that reduces time loss while maintaining versatility.
Solution Approach 2:
The search system is designed with universal functionality to handle multiple types of data systems through a single interface. The graphical user interface provides multi-functional capabilities that enable analysts to search diverse data sources without needing separate tools for each system, thereby improving efficiency while maintaining adaptability.
2Productivity
If tools provide comprehensive search functionality across large datasets, then more data can be analyzed, but the user interface becomes complex and difficult to use
Solution Approach 1:
The system segments the complex data analysis task into manageable visual components displayed in the graphical user interface. Instead of presenting overwhelming complexity, the interface divides data exploration into visual panels and structured query building blocks, making comprehensive data analysis accessible while maintaining ease of operation.
Solution Approach 2:
The graphical user interface acts as an intermediary between the analyst and the complex data search functionality. It provides a simplified visual layer that mediates between the user's simple search intent and the complex backend operations required to analyze large datasets, thereby maintaining both productivity and ease of operation.
3Speed
If pre-processing extracts specified data items for efficient retrieval, then query speed improves, but flexibility to analyze all generated data is reduced
Solution Approach 1:
The system dynamically adjusts its processing approach based on user needs. The graphical user interface allows analysts to choose between pre-processed data for quick retrieval and raw data for comprehensive analysis. This dynamic capability enables the system to optimize for speed when appropriate while maintaining flexibility to analyze all generated data when needed.
Solution Approach 2:
The system changes processing parameters based on query requirements. Analysts can modify parameters such as data processing depth and filtering criteria through the graphical interface, allowing the system to switch between efficient pre-processed retrieval and comprehensive raw data analysis, thereby maintaining both speed and adaptability.
Data Source
AI summary
Systems and methods are disclosed for allowing a user to view query results associated with a time range that is different from a time range indicated by a query. For example, a user interface (UI) data manager can receive a request for a panel of a workbook with a query that identifies the first time range. The UI data manager can obtain the panel, including the query and query results, and cause display of a panel view corresponding to the panel. The displayed query results may not correspond to the same time range as the literal time range indicated by the query. Rather, the query results may be the query results generated during an earlier time range that corresponds to the last time the query was run.