Observability Pipeline Search Using System State Context Filters
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Solution Overview
Problem
Existing observability pipeline systems face inefficiencies in searching event data without prior knowledge of data characteristics, leading to unnecessary data collection and increased computational resources, especially when targeting specific system state contexts.
Innovation Solution
The implementation of a search functionality in observability pipelines that utilizes system state context as a filter, allowing targeted searches by reordering search operators to prioritize system state checks, thereby reducing unnecessary data collection and enhancing search efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If observability pipelines search event data without prior knowledge of data characteristics, then the system can handle various data types and locations, but unnecessary data collection and increased computational resources occur
Solution Approach 1:
The system performs preliminary actions by collecting and storing system state context information (process states, hardware states, network states, etc.) before actual search operations. This preliminary collection enables the search engine to use system state context as a filter during search, avoiding unnecessary data collection and reducing computational resources while maintaining the ability to handle various data types and locations.
2Loss of information
If observability pipelines collect all data without filtering by system state context, then complete data is available for analysis, but search efficiency and accuracy are reduced
Solution Approach 1:
The system preliminarily collects and stores system state context information about processes, hardware, networks, etc., before search operations. During search, this pre-collected context is used as a filter to identify relevant data, maintaining data completeness for analysis while significantly improving search efficiency by avoiding unnecessary data collection.
Solution Approach 2:
System state context acts as an intermediary between the search query and the actual event data. Instead of directly searching all event data, the search engine first filters through system state context to identify relevant processes or hardware states, then targets only the event data associated with those states, improving search efficiency while maintaining completeness.
3Measurement precision
If search queries target specific system state contexts, then search relevance is improved, but the system complexity increases
Solution Approach 1:
The system collects system state context information for multiple purposes: as a filter during search operations, as metadata for data organization, and as context for analysis. This multi-functionality allows the same collected information to improve search relevance without proportionally increasing system complexity, as the context serves multiple roles within the existing architecture.
Data Source
AI summary
In some aspects, search functionality is provided in an observability pipeline system. A search query is received at a computer node from a leader role of an observability pipeline system. The search query represents a request to search event data and includes a first search operator that specifies a system state context criterion associated with hardware on the computer node, and a second search operator that specifies an event criterion. An observability pipeline process is configured according to the search query. Search results are generated by applying the observability pipeline process, which includes determining if a current system state of the hardware matches the system state context criterion. In response to the determination, a subset of event data on the computer node that matches the event criterion is identified by searching the event data. The search results, which include the subset of event data, are sent to the leader role.