Flexible Schema Data Intake System for Distributed Computing
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Solution Overview
Problem
Current systems lack efficient tools for quickly searching and analyzing large sets of raw machine data from distributed computing systems to identify data subsets of interest, particularly in complex IT environments with diverse data types and formats.
Innovation Solution
A data intake and query system that utilizes a flexible schema to process and store machine data as events, allowing for late-binding schema application during search time, enabling field-searchable data storage and analysis across disparate data sources using a pipelined search language and extraction rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tools search data systems separately and collect results over a network, then data can be retrieved from multiple sources, but the analysis process becomes piecemeal and inefficient
Solution Approach 1:
The patent combines multiple separate data system searches into a single unified search operation. The system executes one search query across multiple data systems simultaneously, collects results from all sources, and presents them in a unified interface, eliminating the need for separate searches and piecemeal analysis.
Solution Approach 2:
The search system is designed to work universally across diverse data systems with different formats and structures. It handles various data types (structured, semi-structured, unstructured) and presents them through a common interface, making the tool applicable to multiple data sources without requiring separate specialized tools.
2Productivity
If pre-processing extracts specified data items for efficient retrieval, then analysis speed improves, but flexibility to analyze all generated data is reduced
Solution Approach 1:
The system dynamically adapts its processing approach based on the search query and data characteristics. It can handle both pre-processed structured data and raw unstructured data in the same search operation, adjusting the retrieval and processing method according to the specific needs of each query rather than following a fixed pre-processing path.
Solution Approach 2:
The system changes processing parameters on-the-fly during search execution. It can switch between different data formats, processing depths, and analysis levels based on the query requirements, allowing efficient retrieval of pre-processed data when needed while maintaining the capability to analyze all raw data when comprehensive analysis is required.
3Productivity
If distributed computing systems become more complex to process and store vast amounts of data, then data processing capability increases, but monitoring fault conditions becomes more difficult
Solution Approach 1:
The system implements comprehensive monitoring that provides feedback on the status of all computing devices in the distributed system. It tracks performance metrics, resource utilization, and fault conditions across the entire system, presenting this information in a unified manner that makes it easier to detect and respond to issues despite the underlying complexity.
Data Source
AI summary
Systems and methods are disclosed for monitoring features of a computing device of a distributed computing system using a self-monitoring module. The self-monitoring module can include multiple feature-specific monitoring modules and one or more parent nodes for the feature-specific monitoring modules. A feature-specific monitoring module can identify or detect a fault status change, such as a fault condition or fault resolution, for one or more features. Based on the identified fault conditions or fault resolutions, the feature-specific monitoring module can determine an internal status and communicate an updated status to a parent node.