Data Model Summaries for Accelerated Search Performance
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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 and challenging due to the vast amounts of different types and formats of data, which existing systems often address by preprocessing and discarding most of the data, limiting analysis flexibility.
Innovation Solution
A data intake and query system that uses a flexible schema to process and store machine data as events with timestamps, allowing for late-binding schema application during search time, enabling field-searchable data storage and retrieval across disparate data sources using indexers, search heads, and a message bus for efficient data processing and querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If raw data is stored for later retrieval and analysis, then analysis flexibility is improved, but search and analysis performance deteriorates due to the vast amount of data
Solution Approach 1:
The patent segments data into two categories: processed data stored in traditional data structures for fast searching, and raw data stored in columnar data structures for flexible analysis. This segmentation allows the system to maintain both search performance and analysis flexibility by directing different types of queries to appropriate data storage formats.
Solution Approach 2:
The patent introduces a new dimension of data storage by implementing columnar data structures alongside traditional row-based structures. This dimensional change enables efficient storage and retrieval of raw data while maintaining the ability to perform flexible analysis across multiple data dimensions without compromising search performance.
2Quantity of substance
If massive quantities of diverse raw data are stored, then data diversity and completeness are improved, but data management complexity increases
Solution Approach 1:
The patent implements a universal data management architecture that handles both processed and raw data through a unified system. The system provides multi-functional capabilities including ingestion, processing, storage, and retrieval across different data formats and structures, reducing management complexity despite handling massive diverse data quantities.
Solution Approach 2:
The patent introduces data structures and indexing mechanisms as intermediaries between raw data and analysis operations. These intermediaries organize and structure raw data efficiently, enabling simplified management and access to massive quantities of diverse data without increasing operational complexity.
3Loss of information
If all generated data is retained for analysis, then insight potential is improved, but storage and processing resources increase
Solution Approach 1:
The patent changes the storage parameters of raw data by using columnar data structures that optimize space efficiency. This parameter change allows the system to retain all generated data for complete information availability while reducing the actual storage resources required through efficient data compression and organization.
Data Source
AI summary
Embodiments described herein facilitate enhancement of data model acceleration, including generating data model summaries and performing searches in an accelerated manner. In one implementation, obtaining a search query from a user device. A determination may be made to execute a search, in association with the search query, via an external computing service. As such, the search query, or a variant thereof, can be provided to the external computing service, wherein the external computing service executes the search using data model summaries stored in a remote data store that is separate from a set of events from which the data model summaries were generated. A set of search results are received from the external computing service, and such search results are provided to the user device.