Late-Binding Schema for Machine Data Analysis
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated by modern data centers and computing environments is challenging due to the vast variety and volume of data types, formats, and sources, with existing tools often discarding non-preprocessed data and limiting analysis flexibility.
Innovation Solution
An event-based data intake and query system with a flexible schema that stores minimally processed machine data for later retrieval, allowing for real-time analysis and search using a late-binding schema, enabling the extraction of insights from all data, including disparate sources and formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine data is pre-processed and only pre-specified data items are extracted and stored, then data retrieval and analysis efficiency is improved, but data flexibility and analysis scope are reduced
Solution Approach 1:
The system performs preliminary actions by ingesting and storing all raw machine data without discarding any portions, while preparing the data infrastructure for efficient retrieval. The late-binding schema is prepared in advance to enable flexible analysis at query time without requiring pre-processing of specific data items.
Solution Approach 2:
The system employs a dynamic late-binding schema that allows the data structure and extraction rules to be determined at query time rather than at data ingestion time. This enables the system to adapt to different analysis needs dynamically, retrieving only the necessary data items when queries are executed, thus maintaining both efficiency and flexibility.
2Adaptability or versatility
If all machine data is stored for later retrieval and analysis, then data flexibility and analysis scope are improved, but data storage cost and processing complexity increase
Solution Approach 1:
The system segments the data handling process into distinct phases: data ingestion with minimal processing, data storage in raw form, and data extraction at query time. This segmentation allows the system to maintain raw data for flexibility while managing complexity through structured processing stages rather than attempting to pre-process all data comprehensively.
Solution Approach 2:
The late-binding schema acts as an intermediary layer between the stored raw machine data and the analysis queries. It provides a structured interface that enables flexible analysis without requiring the system to maintain complex pre-processed data structures, thus reducing overall system complexity while preserving adaptability.
3Loss of information
If minimally processed machine data is stored instead of pre-processed data, then data completeness and analysis flexibility are improved, but data retrieval and analysis time increase
Solution Approach 1:
The system uses dynamic late-binding schemas that are constructed at query time based on the specific analysis needs. This allows the system to retrieve and process only the relevant data items required for each query, rather than processing all stored data, thus maintaining data completeness while minimizing analysis time through selective extraction.
Solution Approach 2:
The system changes the binding time parameter of the schema from early-binding (at data ingestion) to late-binding (at query time). This parameter change enables the system to maintain complete raw data while optimizing retrieval time by applying extraction rules only when and where they are needed, based on specific query requirements.
Data Source
AI summary
Techniques are described for providing a machine learning (ML) data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” One such model is an outlier detection model to assist in the monitoring of computer network traffic and computer performance. For example, the ML data analytics application may generate an outlier detection model using user-identified data from a data source and parameter information. The generates outlier detection model can include distribution functions of distribution types selected from a plurality of distribution types by a distribution fitting algorithm.