Late-Binding Schema for Machine Data Indexing
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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, leading to inefficiencies in data retrieval and analysis.
Innovation Solution
An event-based data intake and query system that uses a late-binding schema to process, index, and store machine data, allowing for flexible schema development and extraction rules application at search time, enabling field-searchability and efficient data retrieval across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and only specified data items are extracted and stored, then data retrieval and analysis efficiency is improved, but the flexibility to analyze all generated data is lost and大量data is discarded
Solution Approach 1:
The system performs preliminary indexing of all raw data without discarding any generated data. Event data is ingested and indexed in its entirety at data intake time, creating a comprehensive index that enables both efficient retrieval and flexible analysis of all available data later.
Solution Approach 2:
The system creates indexes as copies of the essential elements of raw event data, allowing the original data to be preserved in its entirety while providing efficient access paths. The index contains sufficient information for field searches without requiring pre-extraction and storage of specific data items.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data analysis flexibility is improved, but data search and analysis performance deteriorates due to the vast amount of diverse data
Solution Approach 1:
The system segments the vast amount of raw event data into discrete, indexable events with structured fields. Each event is parsed into standardized field-name/value pairs, creating a segmented representation that enables efficient field-based searches across the entire dataset without sacrificing flexibility.
Solution Approach 2:
The system changes the parameter representation of raw data by transforming unstructured or semi-structured event data into standardized fields with consistent naming conventions. This parameter transformation enables performant field searches while preserving all original data for flexible analysis.
3Productivity
If a rigid schema is applied during data ingestion, then data processing efficiency is improved, but the ability to handle diverse data types and formats deteriorates
Solution Approach 1:
The system employs a dynamic schema approach where the data model adapts to diverse event types rather than forcing all data into a rigid structure. The schema evolves to accommodate different data types and formats while maintaining consistent field naming conventions that enable efficient processing and searching.
Solution Approach 2:
The system creates a universal data model that can handle multiple data types and formats through standardized field representations. The event schema is designed to be multi-functional, accommodating various event sources and data structures while enabling consistent processing and search operations across all data types.
Data Source
AI summary
Techniques are described for providing on-premises action execution agents used to execute orchestration, automation, and response (OAR) actions in users' IT environments. An on-premises action execution agent can be used to execute actions involving computing resources located in users' on-premises IT environments, where such resources may be located behind a firewall and thus not directly accessible to an IT and security operations application running in a cloud-based environment or elsewhere. An intermediary secure tunnel service is used to establish secure connections between an IT and security operations application and on-premises action execution agents, thereby enabling the encrypted transfer of credentials, API tokens, and other sensitive information used by an on-premises action execution agent to execute actions. The executed actions can include on-demand actions initiated by a user and automated actions included, e.g., as part of a playbook that is executed responsive to the identification of certain types of incidents.