Event-Based Data Intake System for Unstructured Machine Data
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Solution Overview
Problem
Modern data centers face challenges in analyzing and searching massive quantities of machine-generated data due to its unstructured nature and diverse formats, which complicates indexing and retrieval operations.
Innovation Solution
An event-based data intake and query system using a flexible schema, where data is processed and stored as events with timestamps, allowing for field-searchable and late-binding schema approaches to extract relevant information dynamically during search time, enabling efficient retrieval and analysis across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in unstructured format with diverse formats, then data volume and flexibility are improved, but indexing and searching operations become difficult and less efficient
Solution Approach 1:
The patent segments unstructured machine data into structured events with standardized fields (host, source, sourcetype, timestamp, etc.). Each event is divided into discrete, searchable components that can be independently indexed and queried, resolving the contradiction between maintaining data flexibility and enabling efficient searching.
Solution Approach 2:
The patent transforms unstructured data by changing its parameters into a standardized event format with specific fields and data types. This parameter transformation enables the data to maintain its diverse source characteristics while becoming searchable through standardized field names and structures.
2Productivity
If data is processed and stored as structured events with standardized fields, then indexing and searching efficiency are improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by structuring and indexing data as standardized events during the data ingestion phase, before any searching or analysis occurs. This upfront processing creates searchable metadata and field structures that enable rapid retrieval later, trading initial processing complexity for long-term retrieval efficiency.
Solution Approach 2:
The patent introduces an intermediary layer (the event structure with standardized fields) between the raw unstructured data and the search/analysis operations. This intermediary format serves as a universal interface that simplifies both data ingestion and querying, reducing overall system complexity despite the transformation step required.
3Adaptability or versatility
If flexible schema with late-binding approach is used, then adaptability to various data formats is improved, but data consistency and reliability may be compromised
Solution Approach 1:
The patent applies local quality by enforcing specific data type constraints and validation rules at each field level (host, source, sourcetype, timestamp) while maintaining overall schema flexibility. Each field has defined quality requirements that ensure consistency locally, while the system as a whole remains adaptable to diverse data sources.
Data Source
AI summary
Network connections are established between machines of an operating environment to be monitored and a server group of a data intake and query system (DIQS). Data reflecting machine and component operations of the environment is conveyed via the network to the DIQS where it is reflected as timestamped entries in a field-searchable datastore. Monitoring components may search the datastore and identify and record instances of notable events. Triaging models are selectively applied against the notable event instances to produce an enhanced notable event instance representation with modeled results effective to automatically perform or assist in triaging the notable events so they are dispatched in an optimal, effective, and efficient, manner.