Partitioned Query Modification for Flexible Machine Data Retrieval
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data from diverse sources is challenging due to varying data types and formats, leading to inefficiencies in data retrieval and analysis.
Innovation Solution
Implementing a data intake and query system that uses a late-binding schema to process and store machine data, enabling flexible schema development and extraction rules applied at search time, along with partitioned datasets for efficient data management and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If raw data from diverse sources is stored and analyzed, then data flexibility and insight potential are improved, but data retrieval efficiency and analysis performance deteriorate
Solution Approach 1:
The patent divides the large-scale heterogeneous data into multiple partitions based on different data sources, types, or time periods. Each partition can be independently managed and queried, allowing the system to handle diverse data while maintaining retrieval efficiency by searching only relevant partitions rather than scanning all data.
2Productivity
If pre-processing is applied to reduce data volume, then data retrieval efficiency is improved, but data analysis flexibility and completeness deteriorate
Solution Approach 1:
The patent implements dynamic schema binding where the data processing and analysis approach adapts based on the specific query requirements. Rather than applying fixed pre-processing, the system can dynamically select appropriate processing strategies for different analysis needs, maintaining both efficiency and flexibility.
3Quantity of substance
If massive quantities of diverse data are stored, then insight potential is improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent organizes massive diverse data into structured partitions with defined schemas and metadata. This segmentation approach allows the system to manage large volumes of data by treating them as organized units rather than unmanageable bulk, reducing system complexity through structured organization.
Data Source
AI summary
Systems and methods are disclosed for modifying a query using partitioned datasets. A query system may receive a query that includes a data field identifier and identifies a set of data to be processed. The system may use the query to identify a partitioned dataset that is associated with the query and identify partitions of the partitioned dataset that include a data field that satisfies the data field identifier. The query system may use the identified partitions to modified the query to include filter criteria that includes a partition identifier for the identified partitions.