Metadata Catalog Query Generation for Machine Data
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Solution Overview
Problem
Current tools lack the ability to efficiently and intuitively search and analyze large sets of raw machine data across diverse data systems, particularly in IT environments, due to the complexity and volume of data generated from various sources.
Innovation Solution
A data intake and query system that utilizes a metadata catalog to facilitate the processing, indexing, and querying of machine data, allowing for flexible schema definition and late-binding schema application, enabling efficient storage and retrieval of raw data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tools search data systems separately and collect results over a network, then data can be retrieved from multiple sources, but the analysis process becomes piecemeal and inefficient
Solution Approach 1:
The patent combines multiple separate data system searches into a single unified search operation. The system integrates results from multiple data systems simultaneously, allowing analysts to search across diverse data sources (databases, cloud services, machine data systems) in one operation rather than separately querying each system and manually consolidating results.
2Speed
If pre-processing extracts specified data items for efficient retrieval, then analysis speed improves, but flexibility to analyze all generated data is reduced
Solution Approach 1:
The system performs preliminary indexing and metadata extraction on all incoming data without discarding raw information. Configuration parameters and data characteristics are pre-processed and stored in metadata catalogs, enabling both fast retrieval through pre-computed indexes and flexible analysis of the complete raw data set when needed.
Solution Approach 2:
The patent introduces metadata catalogs as intermediary structures that bridge pre-processed data and raw data. These catalogs store configuration parameters, data type information, and indexing structures that enable efficient query execution while maintaining access to the complete raw data set for flexible analysis.
3Adaptability or versatility
If massive quantities of raw data are stored for later analysis, then data flexibility and completeness improve, but storage costs and data management complexity increase
Solution Approach 1:
The patent segments raw data into organized collections with associated metadata catalogs. Each data collection is described by configuration parameters stored in separate metadata structures, allowing the system to manage large volumes of raw data through modular, independently queryable units rather than as a monolithic data mass.
Solution Approach 2:
The system creates metadata copies and indexes of raw data characteristics without duplicating the entire raw data set. Configuration parameters, data type definitions, and indexing structures are stored as lightweight metadata representations that enable efficient data management and query planning while maintaining access to the complete raw data.
Data Source
AI summary
Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives a first query identifying a set of data to be processed and a manner of processing the set of data. The data intake and query system parses the first query and uses a dataset association records of a metadata catalog to dynamically identify one or more datasets associated with the query and generate a second query. The data intake and query system executes the second query.