Cloud-Noncloud Data Integration via Late-Binding Schema
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Solution Overview
Problem
Analyzing and searching massive quantities of diverse machine data generated from various sources, such as system logs, network packets, and sensors, is time-consuming due to the complexity and volume of data, with existing tools often discarding non-preprocessed data, limiting insights and flexibility.
Innovation Solution
An event-based data intake and query system with a late-binding schema that collects, indexes, and stores machine data as events, allowing for flexible schema definition and extraction rules application at search time, enabling field-searchability and efficient retrieval of minimally processed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 because most generated data is discarded
Solution Approach 1:
The system performs preliminary indexing of all generated data without discarding it, creating an inverted index structure that enables efficient retrieval while preserving complete data for future analysis. This preliminary action establishes a foundation that supports both fast search and flexible analysis.
Solution Approach 2:
Instead of permanently discarding non-preprocessed data, the system recovers and retains all generated data in the data store, making it available for later retrieval and analysis. This approach allows the system to discard data only temporarily during processing while ensuring its recovery for future use.
2Adaptability or versatility
If all generated raw data is stored for later analysis, then data flexibility and insight capability are improved, but search and analysis performance deteriorate due to the massive quantity and diversity of data
Solution Approach 1:
The system segments data into discrete events with structured fields, organizing massive quantities of diverse data into manageable units. Each event is parsed into specific fields that can be independently indexed and searched, making the overall data set more tractable while preserving complete information.
Solution Approach 2:
The system replaces traditional mechanical search methods with an inverted index data structure that enables efficient retrieval. Instead of scanning through all raw data linearly, the inverted index allows direct access to relevant data based on field values, dramatically improving search performance.
3Ease of operation
If pre-defined data structures are used to organize data, then data organization and retrieval are simplified, but the ability to analyze different aspects of data is limited
Solution Approach 1:
The system uses dynamic field extraction rules that can be modified at any time without requiring changes to the underlying data structure. Extraction rules are applied on-demand during search and analysis operations, allowing the system to adapt to different analysis needs while maintaining a consistent organizational framework.
Solution Approach 2:
The inverted index structure serves multiple functions simultaneously: it enables efficient search, supports flexible analysis, and accommodates diverse data types. The same data organization mechanism works for various query types and analysis scenarios without requiring separate structures for each use case.
4Adaptability or versatility
If cloud-based data systems are integrated with non-cloud data systems, then data accessibility and collaboration are improved, but system complexity and data management challenges increase
Solution Approach 1:
The system implements a universal data intake and query interface that works with both cloud-based and non-cloud data sources. The same event-based processing and inverted index mechanisms handle data from diverse sources uniformly, reducing the complexity that would otherwise arise from managing different integration approaches for different data locations.
Data Source
AI summary
A software module ingests data into a data intake and query system. At least a portion of the data is cloud data. The software module includes an event type definition that specifies a type of data to be ingested by the software module, a first tag that associates ingested data of the event type with a data model, and a second tag that designates ingested data of the event type as cloud data. The ingested data is stored in a data repository, and subsequently a search query that includes the first tag and the second tag is executed against the data repository, to identify ingested cloud data that satisfies the search query and a first search constraint specified in the data model. A display device is caused to display a visualization based on the identified ingested cloud data that satisfies the search query.