Late-Binding Schema for Real-Time Anomaly Detection in Data Intake Systems
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Solution Overview
Problem
Existing data systems struggle to efficiently search and analyze large volumes of raw machine data due to the lack of user-friendly tools for visually identifying data subsets of interest, particularly in big data ecosystems with diverse data types, leading to challenges in managing, understanding, and utilizing the data effectively.
Innovation Solution
A data intake and query system that utilizes a late-binding schema to process and store raw machine data, enabling flexible schema development and real-time analysis, with components like indexing and query systems for efficient data retrieval and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If massive quantities of raw data are stored for later retrieval and analysis, then data flexibility and analysis capability are improved, but data management complexity and search difficulty increase
Solution Approach 1:
The patent segments the massive raw data into discrete events with specific fields and types. Each event is structured with identifiable components (timestamp, host, source, event type, etc.), making the data manageable through systematic categorization while preserving flexibility for various analysis needs.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw data into structured events during ingestion. This intermediary step creates a standardized representation that simplifies subsequent search and analysis operations while maintaining the ability to handle diverse data sources and formats.
2Productivity
If pre-processing is applied to extract specified data items, then retrieval efficiency is improved, but data loss and analysis flexibility deteriorate
Solution Approach 1:
The patent performs preliminary actions during data ingestion by structuring and indexing data with a late-binding schema. This preliminary organization enables efficient retrieval through field-based searches while preserving all original data for later analysis, avoiding the need to discard information.
Solution Approach 2:
The patent implements a dynamic late-binding schema that adapts to different query requirements. The schema binds field names and types at query time rather than at data ingestion time, allowing the system to efficiently retrieve data in various formats while maintaining complete data preservation.
3Quantity of substance
If diverse data types from multiple sources are collected, then data comprehensiveness and insight potential are improved, but search and analysis difficulty increase
Solution Approach 1:
The patent creates a universal event structure that can represent diverse data types from multiple sources through a common schema. This multi-functional framework handles different event types, sources, and formats uniformly, enabling comprehensive data collection while simplifying search operations through consistent field-based access.
4Ease of operation
If user-friendly visualization tools are implemented, then ease of operation is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent creates simplified copies or representations of the complex data through structured events with standardized fields. This copying approach enables user-friendly visualization by presenting data in an accessible format while maintaining the complete original data for detailed analysis when needed.
Data Source
AI summary
Systems and methods are described for processing ingested data, detecting anomalies in the ingested data, and providing explanations of a possible cause of the detected anomalies as the data is being ingested. For example, a token or field in the ingested data may have an anomalous value. Tokens or fields from another portion of the ingested data can be extracted and analyzed to determine whether there is any correlation between the values of the extracted tokens or fields and the anomalous token or field having an anomalous value. If a correlation is detected, this information can be surfaced to a user.