Data Intake System Late-Binding Schema for Tenant Data
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Solution Overview
Problem
Current tools lack the capability to efficiently and flexibly search and analyze large sets of raw machine data from diverse sources, particularly in IT environments, as they often require pre-processing that discards significant amounts of data and do not provide intuitive visual interfaces for identifying data subsets of interest.
Innovation Solution
A data intake and query system that utilizes a late-binding schema and metadata catalog to process and store raw machine data, allowing for flexible schema development and extraction of insights at search time, with features like field-searchability and automatic generation of extraction rules, enabling users to refine queries dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-processing is performed to reduce data volume, then data retrieval efficiency is improved, but data loss increases
Solution Approach 1:
The system performs preliminary indexing on specific fields of raw data without fully processing or discarding the rest of the data. This allows for efficient query initiation while preserving the complete raw data for later analysis, thus achieving both retrieval efficiency and data preservation
Solution Approach 2:
The system extracts only the essential fields needed for indexing and querying from the raw data, while maintaining the complete raw data intact. This extraction approach enables efficient search capabilities without sacrificing the完整性 of the original data
2Adaptability or versatility
If all raw data is stored for later analysis, then data analysis flexibility is improved, but storage requirements increase
Solution Approach 1:
The system segments data storage into two parts: indexed data for efficient querying and complete raw data for comprehensive analysis. This segmentation allows the system to maintain flexibility for various analysis needs while optimizing storage utilization through selective indexing
3Productivity
If tools search data systems separately, then data retrieval is possible, but analysis completeness deteriorates
Solution Approach 1:
The system merges separate data systems into a unified search environment where data from multiple sources can be searched and analyzed together. This unification enables comprehensive analysis across all data while maintaining the productivity benefits of efficient search capabilities
Data Source
AI summary
Systems and methods are described for processing incoming data. The system can receive, from a first partition manager of a data intake and query system, first data that is associated with a first identifier, and can receive, from a second partition manager of the data intake and query system, second data that is associated with a second identifier. The system can process the first data and store first results of said processing the first data in one or more first buckets associated with the first tenant identifier. The system can process the second data and store second results of said processing the second data in one or more second buckets associated with the second tenant identifier.