Interim Processing Node Maps for Dynamic Data Reassignment
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Solution Overview
Problem
Managing and analyzing vast amounts of diverse data from various data systems, including structured, semi-structured, and unstructured data, poses challenges in terms of efficient storage and retrieval, especially when pre-processing discards significant portions of machine data, limiting analysis flexibility and efficiency.
Innovation Solution
Implementing an event-based data intake and query system with a late-binding schema that processes and stores minimally processed machine data, allowing for flexible schema development and field-searchable events, and utilizing ingestors and a message bus to improve throughput, resiliency, and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data preprocessing extracts specified data items and discards the remainder, then data retrieval efficiency is improved, but data analysis flexibility is reduced
Solution Approach 1:
The system performs preliminary data extraction and storage of specified data items during data intake, creating an optimized data structure in advance. This allows efficient retrieval for known analysis needs while the late-binding schema preserves the ability to adapt to new analysis requirements that emerge later.
Solution Approach 2:
The late-binding schema enables the data structure to be dynamically adapted based on actual analysis needs. The schema is not fixed in advance but is bound later when analysis requirements are known, allowing the system to flexibly adjust to diverse and evolving data analysis needs while maintaining efficient retrieval for predetermined data items.
2Adaptability or versatility
If vast amounts of diverse machine data are stored for later analysis, then data analysis flexibility is improved, but data storage and management complexity increases
Solution Approach 1:
The system segments data into two categories: specified data items that are extracted and stored in an optimized structure for efficient retrieval, and the remainder of the data that is discarded. This segmentation reduces storage complexity while preserving flexibility for analyzing the extracted data items across multiple analysis needs.
Solution Approach 2:
The extracted and stored data items serve multiple analysis functions and queries. By identifying and preserving key data items that are relevant to various potential analysis needs, the system creates a universal data structure that supports diverse analysis scenarios without requiring storage of all raw data.
3Quantity of substance
If pre-processing is performed to reduce data volume, then data storage requirements are reduced, but data analysis completeness is compromised
Solution Approach 1:
The system applies different quality levels to different portions of data based on their analytical value. Specified data items that are relevant to analysis needs are extracted and preserved with high quality, while the remainder of the data is discarded. This local quality approach reduces overall data volume while maintaining completeness for the specific analysis objectives.
Data Source
AI summary
A data intake and query system can manage the search of large amounts of data using one or more processing nodes. The data intake and query system can identify a first group of processing nodes and cause a first processing node of the group to download and search a particular data group based on a first node map. The data intake and query system may identify a second group of processing nodes that includes the first group of processing nodes and a second processing node. The data intake and query system can transmit commands to cancel one or more data group downloads at the first processing node and can reassign the corresponding data groups for download to the second processing node based on a second node map.