Late-Binding Schema for Machine Data Search
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated from diverse sources poses challenges due to its vastness and complexity, as existing tools typically pre-process and discard data, limiting flexibility and insight derivation.
Innovation Solution
An event-based data intake and query system with a late-binding schema that allows flexible schema definition and extraction rules application at search time, enabling the storage and analysis of minimally processed machine data across disparate sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is pre-processed to extract specified data items for efficient retrieval, then retrieval efficiency is improved, but data flexibility and analysis capability are reduced
Solution Approach 1:
The system performs preliminary indexing of all raw data fields without pre-defining extraction rules. The index structure is prepared in advance to accommodate any field, but actual data extraction and transformation occur only when queries are executed, allowing both efficient retrieval and flexible analysis of any data item.
Solution Approach 2:
The system dynamically determines data extraction rules at query time rather than statically pre-defining them. The schema binding occurs dynamically based on the specific query requirements, enabling the system to adapt to different analysis needs while maintaining efficient retrieval through the pre-built index structure.
2Adaptability or versatility
If all raw data is stored for later analysis, then data flexibility and insight derivation are improved, but data volume and storage complexity increase
Solution Approach 1:
The system extracts only the specific data items needed for each query from the raw data, rather than pre-extracting all possible data items. This on-demand extraction approach maintains data flexibility while minimizing the quantity of processed data that needs to be stored and managed.
Solution Approach 2:
The system creates virtual copies of raw data through dynamic schema application at query time. Instead of physically storing multiple processed versions of data, the system generates derived data structures on-demand from the original raw data, reducing storage requirements while maintaining analytical flexibility.
3Quantity of substance
If pre-processing extracts only fraction of data for storage, then storage efficiency is improved, but analysis completeness is reduced
Solution Approach 1:
The system performs preliminary indexing of complete raw data without discarding any information. All raw data is retained in the index structure with full field information preserved, enabling complete analysis while maintaining storage efficiency through compact index representations.
4Productivity
If schema is defined early for data processing, then processing efficiency is improved, but flexibility for new data types is reduced
Solution Approach 1:
The system dynamically binds schemas to data at query time rather than using static early-defined schemas. This allows the system to efficiently process data using the appropriate schema for each specific query while simultaneously adapting to new data types and structures as they are encountered.
Solution Approach 2:
The system changes the schema parameters dynamically based on the query requirements and data characteristics. Instead of fixing schema parameters in advance, the system adjusts them at processing time to optimize both efficiency and adaptability for different data types and analysis needs.
Data Source
AI summary
Described are techniques for evaluating technology add-ons used with a data intake and query system to identify errors that may be present in or associated with configuration files defining the functions of the technology add-on or with operation of the technology add-on. For example, the technology add-on may be used to provide searching of event data stored by the data intake and query system using a late-binding schema, where the technology add-on provides for formalized interpretation of non-standard event data according to a user- or vendor-defined scheme. The disclosed techniques can identify errors and determine if the technology add-on is compliant with a schema definition for a common information model.