Content Pack Management for Data Intake Systems
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Solution Overview
Problem
Analyzing and searching massive quantities of diverse machine data generated from various sources, such as system logs, network data, and sensor data, 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 with a flexible schema that allows for late-binding schema application, enabling the storage and search of raw machine data with field-searchability, using extraction rules and configuration files to process and index data on the fly, facilitating the use of a common information model across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If massive quantities of raw machine data are stored for later retrieval and analysis, then data flexibility and analytical opportunities are improved, but data retrieval and search efficiency deteriorate
Solution Approach 1:
The patent segments raw machine data into structured fields and values during ingestion, creating a hierarchical data model where data is divided into manageable components (fields, values, events) that can be efficiently indexed and retrieved while preserving the complete raw data for flexibility
Solution Approach 2:
The patent applies preliminary schema-based field extraction and indexing when data is ingested, organizing raw data into searchable fields and values before retrieval operations. This preliminary structuring enables efficient search without sacrificing the ability to analyze all raw data later
2Loss of information
If diverse data types and formats from numerous sources are retained, then analytical insights are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal schema-based data model that can accommodate diverse data types and formats from multiple sources through a common structure of fields, values, and events. This universal model handles varied data sources (logs, metrics, traces) uniformly, reducing system complexity while preserving information completeness
Solution Approach 2:
The patent uses configurable schema parameters to adapt the data model to different data sources and types. By changing schema parameters and field definitions, the system can handle diverse data formats without requiring fundamentally different processing paths, thereby managing complexity while retaining complete data
3Productivity
If pre-processing extracts specified data items for efficient retrieval, then data retrieval speed is improved, but data quantity and flexibility are reduced
Solution Approach 1:
The patent extracts specified data items (fields and values) from raw machine data during ingestion and stores them in an indexed structure for efficient retrieval. Simultaneously, it preserves the complete raw data in its original form, enabling both fast retrieval of specific items and access to the full data quantity for comprehensive analysis
Solution Approach 2:
The patent creates a nested data structure where indexed fields and values are embedded within the broader context of complete raw data events. This nesting allows the system to provide fast access to extracted fields while maintaining the ability to retrieve and analyze the complete nested event structure when needed
Data Source
AI summary
A method includes selecting, from content packs in a centralized content management system, a content pack to update in a data intake and query system. The content pack includes utility objects. For each utility object of at least a subset of the utility objects determining whether the utility object already exists in the data intake and query system, and loading the utility object to the data intake and query system when the utility object does not exist to obtain an updated utility object. The method further includes monitoring, by the data intake and query system, an endpoint of an endpoint type using the updated utility object.