Tenant-Aware Query Parameter Recommendation System
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Solution Overview
Problem
Current tools lack the ability to efficiently and flexibly search and analyze large sets of raw machine data, particularly in IT environments, where diverse data types and formats make it challenging to derive insights without pre-processing, leading to discarded data and limited analysis capabilities.
Innovation Solution
A data intake and query system that utilizes a flexible schema to process and store raw machine data, allowing for late-binding schema application during search time, enabling field-searchable events and efficient querying across various data sources, with features like indexing, pipelined search language, and metadata catalogs for enhanced data retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing data based on anticipated data analysis needs, then data retrieval and analysis efficiency is improved, but data flexibility and completeness deteriorate because only specified data items are stored and the rest is discarded
Solution Approach 1:
The system performs preliminary actions by pre-processing data to extract and store specified data items that are anticipated to be needed for analysis. This allows efficient retrieval of commonly queried data while maintaining the option to access raw data when flexibility is required.
Solution Approach 2:
The system dynamically adapts between two modes: using pre-processed data for efficient retrieval of anticipated queries, and using raw data for flexible analysis when needed. The data processing approach changes based on the specific analysis requirements, allowing the system to optimize between efficiency and flexibility.
2Adaptability or versatility
If storing raw data for later retrieval and analysis, then data flexibility and analysis completeness are improved, but system complexity and storage requirements worsen
Solution Approach 1:
The system segments data into two distinct types: pre-processed data for efficient retrieval and raw data for flexible analysis. This segmentation allows the system to manage different data types with different characteristics, reducing overall complexity by having dedicated handling for each data type rather than managing all data uniformly.
Solution Approach 2:
The system introduces an intermediary layer of pre-processed data that sits between raw data and analysis queries. This intermediary allows common queries to be answered efficiently from pre-processed data while raw data remains available as a fallback for flexible analysis, reducing the need to manage all raw data continuously.
3Reliability
If using traditional search tools to search data systems separately, then data retrieval capability is maintained, but analysis efficiency and insight derivation deteriorate due to piecemeal collection requirements
Solution Approach 1:
The system merges multiple data sources and search capabilities into a unified platform that can search and analyze data from diverse sources simultaneously. This consolidation eliminates the need for piecemeal collection from separate tools and enables efficient analysis by querying all relevant data sources through a single interface.
Solution Approach 2:
The system provides universal search and analysis capabilities that work across multiple data sources and formats. This multi-functional approach allows analysts to retrieve and analyze data from various sources using the same tools, improving efficiency by eliminating the need for specialized tools for different data types.
Data Source
AI summary
Systems and methods are disclosed for recommending query parameters to a user based on tenant information. The system can identify a token query parameter from a portion of a query entered in a user interface. The token query parameter can correspond to a system query parameter, such as a query command, a function, etc., or to a user query parameter. The system can identify a tenant of a distributed data intake and query system that is associated with the query. Based on the token query parameter, the system can identify at least one query parameter associated with the tenant. The at least one query parameter can include one or more query parameters previously entered by the user or other users of the tenant, etc. The system can cause the user interface to display one or more recommended query parameters for inclusion in the query.