Late-Binding Schema for Machine Data Intake and Query
Find Innovative SolutionsGenerate Solutions
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 challenging due to the vast amount of data and different formats, leading to inefficiencies in data retrieval and analysis.
Innovation Solution
An event-based data intake and query system with a late-binding schema that allows flexible schema definition and extraction rules, enabling the storage and search of raw machine data across disparate sources, facilitating the extraction of valuable insights at search time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If raw machine data from diverse sources is stored in massive quantities for later analysis, then data flexibility and insight potential are improved, but data retrieval and search efficiency deteriorate
Solution Approach 1:
The patent applies preliminary action by extracting and storing metadata, tags, and summary information about the raw data during the data ingestion phase, before actual analysis is needed. This pre-processing creates indexes and data structures that enable efficient searching and retrieval later, without requiring full analysis of the massive raw data sets at query time.
Solution Approach 2:
The patent introduces an intermediary layer between the raw data storage and the search/analysis functions. This intermediary includes extracted metadata, tags, summaries, and indexed structures that mediate between the vast raw data repository and the query system, enabling efficient retrieval without directly searching the entire raw data set.
2Loss of information
If diverse data formats from multiple sources are retained in original form, then data completeness and analysis potential are improved, but system complexity and processing difficulty worsen
Solution Approach 1:
The patent segments the data handling process into distinct components: raw data storage, metadata extraction, tagging, indexing, and query processing. Each component handles a specific aspect of data management, allowing the system to maintain complete raw data while processing and organizing it through specialized subsystems that reduce overall system complexity.
Solution Approach 2:
The patent implements a universal data model and standardized metadata schemas that can handle diverse data formats from multiple sources through a common framework. This multi-functional approach allows the same infrastructure to process various data types (logs, metrics, traces) without requiring separate specialized systems for each data source.
3Productivity
If data pre-processing extracts specified items for efficient retrieval, then data retrieval speed is improved, but data flexibility and analysis scope worsen
Solution Approach 1:
The patent implements dynamic extraction rules and metadata generation that can adapt to different query requirements. The system can adjust what metadata is extracted and how data is organized based on the specific analysis needs, allowing both efficient retrieval for common queries and flexibility for ad-hoc analysis without being locked into a fixed pre-processing schema.
Data Source
AI summary
Artifact life tracking storage techniques include performing an artifact request of an artifact at an artifact storage node. A current time to live (TTL) value is identified. A determination is made whether to increment a TTL flag of the artifact. Responsive to determining that the TTL tag should be incremented, the TTL flag is incremented to a subsequent value in a TTL extender list. Responsive to incrementing the TTL tag, the TTL modified tag value is set to the current time value.