Data Identifier Queues for Flexible External Data Search
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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, making it time-consuming and inefficient to derive insights from the vast amounts of data generated by IT environments.
Innovation Solution
An event-based data intake and query system with a late-binding schema that processes and stores machine data as events, allowing flexible schema application at search time, enabling field-searchability and common information modeling across disparate data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing data to extract specified data items is performed, then retrieval efficiency of extracted data is improved, but flexibility to analyze all generated data is reduced
Solution Approach 1:
The system segments data processing into two independent paths: a pre-processing path that extracts and indexes specified data items for efficient retrieval, and a raw data storage path that preserves all generated data for flexible analysis. This segmentation allows both optimized retrieval and comprehensive analysis capabilities to coexist without interfering with each other.
Solution Approach 2:
The system introduces an intermediary layer consisting of data identifiers (such as event IDs or row numbers) that link extracted data items to their corresponding raw data records. This intermediary enables the system to efficiently retrieve pre-processed data while maintaining the ability to access the complete original data through the same identifiers, thus resolving the contradiction between retrieval efficiency and analytical flexibility.
2Adaptability or versatility
If massive quantities of raw data are stored for later analysis, then flexibility to analyze all data is improved, but search and analysis performance deteriorates
Solution Approach 1:
The system performs preliminary actions by extracting and indexing specified data items before they are needed for analysis. This pre-processing creates optimized search structures (inverted indexes, full-text indexes) that enable rapid retrieval of relevant data, thus improving search performance while maintaining the option to analyze all raw data when necessary.
3Productivity
If data is extracted and stored in a structured manner, then retrieval efficiency is improved, but complexity of handling diverse data types increases
Solution Approach 1:
The system implements a universal data identification mechanism that works across multiple data types and formats. By using common identifiers (event IDs, row numbers, timestamps) that can be extracted from diverse data sources, the system creates a unified approach to retrieving and analyzing data regardless of its original format, thus reducing complexity while maintaining retrieval efficiency.
Data Source
AI summary
A computing device can receive a query that identifies a set of data to be processed and determine that a portion of the set of data resides in an external data system. The query system can request data identifiers associated with data objects of the set of data from the external data system and communicate the data identifiers to a data queue. The computing device can instruct one or more search nodes to retrieve the identifiers from the data queue. The search nodes can use the data identifiers to retrieve data objects from the external data system and process the data objects according to instructions received from the computing device. The search nodes can provide results of the processing to the computing device.