Flexible Schema Data Intake System for Late-Binding Analysis
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Solution Overview
Problem
Analyzing and searching massive quantities of diverse machine data generated by various components in IT environments is challenging due to the vast amount of data types and formats, with existing tools often pre-processing and discarding data, limiting flexibility and insight.
Innovation Solution
A data intake and query system that utilizes a flexible schema for data processing and storage, allowing for late-binding schema application during search time, enabling field-searchable events and efficient data retrieval across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing and extracting specified data items from generated data, then efficient retrieval and analysis of extracted data is improved, but flexibility to analyze all generated data is reduced and data loss occurs
Solution Approach 1:
The system segments data processing into two independent paths: a pre-processing path for extracting and indexing specified data items for efficient retrieval, and a raw data storage path for maintaining complete generated data for flexible analysis. This segmentation allows both efficiency and flexibility to coexist without compromise.
Solution Approach 2:
Instead of permanently discarding unprocessed data, the system recovers and preserves it in raw data storage. The pre-processing extracts useful information while the remaining data is retained in its original form, allowing analysts to recover and analyze any portion of the complete data set when needed.
2Adaptability or versatility
If storing massive quantities of raw data for later retrieval, then flexibility to analyze all generated data is improved, but data management complexity increases
Solution Approach 1:
The data management system is segmented into distinct components: pre-processing modules for extracting and indexing specified data, raw data storage for maintaining complete data, and query processing modules for retrieving data based on user needs. This segmentation organizes complexity into manageable, independent functions.
Solution Approach 2:
The system introduces an intermediary indexing structure that bridges raw data storage and query processing. This intermediary layer provides efficient retrieval mechanisms without requiring direct manipulation of the entire raw data set, reducing operational complexity while maintaining flexibility.
3Reliability
If using existing tools to search data systems separately, then data analysis capability is provided, but ease of operation and quick analysis is reduced
Solution Approach 1:
The system merges multiple data sources and processing functions into a unified platform that provides both pre-processed data analysis and raw data access through a single interface. This consolidation eliminates the need to switch between separate tools and simplifies the analysis workflow.
Solution Approach 2:
The system creates a universal data analysis platform that handles multiple data types, provides both pre-processed and raw data access, and supports various analysis operations through a single unified interface. This multi-functionality eliminates the need for multiple specialized tools and improves ease of operation.
Data Source
AI summary
Systems and methods are described for graphical user interfaces that enable users to graphically design nodes and interconnections of pipelines for preprocessing data later ingested into an indexing system. The preprocessing can include receiving messages published to a first publish-subscribe messaging system, the messages containing raw machine data generated by one or more components in an information technology environment, performing one or more processing operations on at least some of the messages to generate preprocessed messages, republishing the preprocessed messages to a second publish-subscribe messaging system, and providing to the indexing system, a subset of the messages from the second publish-subscribe messaging system. Nodes in the pipeline can specify the preprocessing operations, and interconnections in the pipeline can represent data flow through the nodes of the pipeline.