Agent Controller Late-Binding Schema for Machine Data
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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 packets, and sensors, is challenging due to the vast amount of data and its varied 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 extraction and search of data at search time, enabling the use of common field names across disparate data sources and facilitating the storage of minimally processed data for later analysis.
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
Solution Approach 1:
The system performs preliminary actions by storing all generated data in raw format without discarding any data during pre-processing. The data intake system captures and stores complete data events with all their attributes, enabling both efficient retrieval through indexing and flexible analysis of all stored data later.
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
Solution Approach 1:
The system segments data into structured events with defined schemas, organizing raw data into manageable units with consistent formats. This segmentation enables efficient indexing and retrieval operations while maintaining the ability to analyze all stored data, resolving the performance issue of searching massive raw data quantities.
Solution Approach 2:
The system changes the parameter of data organization from unstructured raw storage to structured event storage with schemas. By transforming data into standardized events with consistent fields and types, the system enables both flexible analysis and performant search operations on the stored data.
3Quantity of substance
If diverse data from various sources is collected and stored, then the amount of analyzable data is improved, but data management complexity increases
Solution Approach 1:
The system implements a universal data event schema that can accommodate diverse data from multiple sources (systems, applications, devices) through a common structure. This universal schema enables consistent management and analysis of heterogeneous data while maintaining organizational simplicity, allowing the system to handle various data types without increasing management complexity.
Data Source
AI summary
Embodiments described herein are directed to facilitating management of collection agents. In one embodiment, a control request is provided to an agent service manager from an agent controller that manages collection agents that collect data. The agent controller and the collection agents operate on a computing machine remote from the agent service manager. In response to the control request, a control directive is received, the control directive including an agent event indicator indicating an agent event to be executed in association with a set of collection agents of the collection agents. Thereafter, execution of the agent event is initiated in association with each collection agent of the set of collection agents.