Late-Binding Schema for Machine Data Analysis
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated from diverse sources in IT environments is challenging due to its vastness and complexity, with existing tools often pre-processing and discarding data, limiting flexibility and insight.
Innovation Solution
A data intake and query system that uses a late-binding schema to store and process machine data as events, allowing flexible schema definition and extraction rules at search time, enabling field-searchability and efficient retrieval of insights across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is pre-processed and only specified data items are extracted for storage, then retrieval efficiency is improved, but data flexibility and analysis capability are reduced
Solution Approach 1:
The system performs preliminary indexing and metadata extraction during data ingestion, creating a searchable framework that enables efficient retrieval without pre-defining analysis queries. This allows the system to prepare data structures in advance while maintaining flexibility for future analysis needs.
Solution Approach 2:
The system employs dynamic schema evolution and adaptive query processing that can adjust to different analysis requirements. The data model allows fields and structures to be defined or modified at query time, enabling the system to optimize retrieval efficiency for specific queries while maintaining overall data flexibility.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval, then data analysis flexibility is improved, but search and analysis performance deteriorates
Solution Approach 1:
The system segments data into structured events with hierarchical organization (index, subindex, timestamp, field-value pairs). This segmentation allows the system to store comprehensive raw data while enabling efficient filtering and retrieval by breaking down search operations into manageable segments based on index structures and field hierarchies.
Solution Approach 2:
The system introduces an intermediary indexing layer that sits between raw data storage and query processing. This indexing structure acts as a mediator that enables fast search performance by providing pre-computed lookup paths, while the underlying raw data remains fully preserved for flexible analysis.
3Loss of information
If diverse data from multiple sources is collected and stored, then insight capability is improved, but data management complexity increases
Solution Approach 1:
The system employs a universal event schema that can represent diverse data types from multiple sources through a common structure. The event model with standardized fields (timestamp, host, source, sourcetype, field-value pairs) provides multi-functionality that handles various data formats uniformly, reducing management complexity while preserving insight capability.
Solution Approach 2:
The system uses parameter-based data representation where diverse data is standardized through configurable field parameters and extraction rules. This allows the system to maintain flexibility in handling diverse data sources while imposing a consistent structural framework that simplifies management and querying.
4Quantity of substance
If specified data items are extracted and stored during pre-processing, then storage efficiency is improved, but data utilization is reduced
Solution Approach 1:
The system creates indexed copies and metadata representations of raw data rather than storing only extracted items. These copies maintain the essential structure and searchability of the data while occupying less space than full raw data retention, enabling both storage efficiency and flexible utilization through the indexed representation.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for visualizing performance data of infrastructure components. The method includes receiving a query through an application for a metric for an infrastructure component, the metric comprising metric time series (MTS) data. The method also includes identifying sources for the metric. The method also includes querying the identified sources for the metric. The method also includes selecting from the identified sources best available data for the metric based on a selection algorithm. The method also includes enriching the best available data comprising linking dimensions and properties from the identified sources to the best available data. The method also includes causing display of the enriched best available data through a user interface of the application.