Bucket Map Identifier for Dynamic Data Search Indexing
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated by various components in data centers and networks is challenging due to the vast types and formats of data, requiring efficient data processing and storage solutions to facilitate flexible analysis and retrieval.
Innovation Solution
A data intake and query system that uses a flexible schema to process and store machine data as events with timestamps, enabling field-searchable capabilities and late-binding schema for extraction rules, allowing for real-time operational intelligence and flexible data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If massive quantities of machine data are stored and processed, then data analysis capability is improved, but system complexity and processing difficulty increase
Solution Approach 1:
The patent segments machine data into discrete events with structured schemas, organizing vast quantities of data into manageable units that can be independently processed and queried without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary indexing layer that mediates between raw machine data and search queries, using extracted fields and metadata to bridge the gap between data volume and search efficiency
2Adaptability or versatility
If flexible data analysis is enabled through late-binding schema, then adaptability is improved, but processing time and complexity increase
Solution Approach 1:
The patent performs preliminary actions by extracting and indexing key fields from machine data during ingestion, preparing data in advance for future queries. This reduces processing time during actual searches while maintaining late-binding schema flexibility
Solution Approach 2:
The patent implements dynamic schema capabilities where extraction rules can be added, modified, or removed without reprocessing historical data. The system adapts to changing analysis requirements while maintaining performance through selective field extraction
3Loss of information
If all machine data is made searchably accessible, then information retrieval capability is improved, but storage and indexing resources increase
Solution Approach 1:
The patent extracts only the essential fields and metadata needed for searching from complete machine data events. This selective extraction makes data searchable while avoiding the overhead of indexing entire data sets, reducing storage requirements
Data Source
AI summary
Systems and methods are described for improving data availability and/or resiliency of indexers of a data intake and query system. Due to a lag between the time at which data is received and the time at which the data is available for searching, the data intake and query system may receive a query indicating that received (but unavailable for search) data is to be included as part of the query. A cluster master can dynamically track what data is available for searching by different indexers and map the data to filter criteria using a bucket map identifier. When a search head receives a query, it can request a bucket map identifier from the cluster master and send the bucket map identifier to the indexers that will be executing the query. The indexers can use the bucket map identifier to request the individual buckets that they are assigned to search.