Persistent Queue for Machine Data Storage and Processing
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Solution Overview
Problem
The challenge lies in efficiently managing and analyzing vast amounts of diverse machine data generated by various components in IT environments, where existing systems often discard raw data during pre-processing, limiting the ability to investigate all data for insights and performance diagnostics.
Innovation Solution
A data intake and query system that stores minimally processed machine data as events with timestamps, using a late-binding schema for flexible extraction and search, allowing for accurate field-based searches and analysis without pre-defined formats, enabling the use of a common information model across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If raw data is discarded during pre-processing to reduce data volume, then data processing efficiency is improved, but data analysis flexibility and completeness deteriorate
Solution Approach 1:
The patent segments data processing into distinct stages: raw data collection, persistent queue storage, and on-demand processing. By separating the storage function from the processing function, the system can retain all raw data while processing only what is needed, thus maintaining both efficiency and flexibility.
Solution Approach 2:
The system performs preliminary action by storing all raw data in a persistent queue before any processing occurs. This preliminary storage action ensures data availability for future analysis without requiring premature processing decisions, resolving the contradiction between preserving data and processing efficiency.
2Adaptability or versatility
If all raw data is stored for later analysis, then data analysis flexibility is improved, but storage requirements and system complexity increase
Solution Approach 1:
The persistent queue acts as an intermediary component between data collection and data processing systems. It provides a standardized interface for storing and retrieving data, simplifying the overall system architecture while enabling flexible data analysis. The queue absorbs the complexity of data management, allowing other components to remain simple.
3Speed
If data is pre-processed with specific extraction rules, then retrieval efficiency is improved, but the ability to analyze all generated data deteriorates
Solution Approach 1:
The system changes the parameter of data processing timing from pre-processing to on-demand processing. By delaying processing until it is actually needed, the system can retrieve data quickly from the persistent queue and then apply processing only to the specific data required, maintaining both retrieval speed and data completeness.
Data Source
AI summary
A computerized method is disclosed for storing data using a persistent queue. The computerized method includes operations of obtaining machine data from a remote electronic device, providing the machine data to a persistent queue component, wherein the persistent queue component organizes the machine data for storage on a first data store and storing a copy of the organized machine data in the first data store, and responsive to completion of the storing of the copy of the organized machine data. Further operations include transmitting an acknowledgement communication to the remote electronic device indicating storage of the copy of the machine data in the first data store, processing the machine data for storage, and responsive to completion of the storing of the processed machine data in a second data store, deleting the copy of the machine data from the first data store. The persistent queue component may include a socket server.