Late-Binding Event Indexing for Flexible Machine Data Retrieval
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Solution Overview
Problem
Managing and analyzing vast amounts of minimally processed machine data generated by IT environments is challenging due to the diverse types and formats of data, which are time-consuming to process and analyze, and existing systems often discard most of this data during pre-processing.
Innovation Solution
An event-based data intake and query system with a late-binding schema that collects, indexes, and stores machine data as events, allowing flexible schema definition and field-searchability, enabling efficient retrieval and analysis at search time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-processing is performed to extract specified data items and store them in a data system, then retrieval efficiency is improved, but data flexibility and analysis capability are reduced because most generated data is discarded
Solution Approach 1:
The system performs preliminary indexing of all generated data without discarding any information. Field names are extracted and indexed in advance, enabling both efficient retrieval and flexible analysis of the complete dataset later.
Solution Approach 2:
The system changes the parameter of data retention from selective extraction to complete retention. By storing all generated data with indexed field names, the system transforms the approach from pre-processing with discarding to comprehensive storage with on-demand analysis.
2Adaptability or versatility
If massive quantities of raw data are stored for later retrieval and analysis, then data flexibility and analysis capability are improved, but processing time and computational resources increase
Solution Approach 1:
Field names are extracted and indexed in advance during data generation. This preliminary action enables the system to store all raw data while maintaining fast retrieval capability through pre-computed indexes of field names and their locations.
Solution Approach 2:
The system introduces an intermediary indexing layer that sits between raw data storage and analysis operations. This index structure mediates between complete data retention and efficient retrieval by providing a searchable interface to the stored data without requiring full data processing during queries.
3Loss of information
If all generated data is retained instead of discarding most of it during pre-processing, then analytical insights are improved, but storage and processing complexity increase
Solution Approach 1:
The system performs preliminary extraction and indexing of field names from all generated data. This preliminary action simplifies subsequent operations by organizing data with structured field name indexes, reducing the complexity of analyzing complete datasets later.
Data Source
AI summary
Embodiments described herein are directed to facilitating efficient management of data storage and retrieval. In one embodiment, filter data associated with a bucket of data is obtained at a local data store from a remote data store. Based on analysis of the filter data, it is determined that the bucket of data is a candidate to contain data relevant to a search query. Based on such a determination, the index data associated with the bucket of data is obtained at the local data store from the remote data store. Thereafter, it may be determined that the bucket of data includes data relevant to the search query based on analysis of the index data. Based on the determination that the bucket of data includes data relevant to the search query, the journal data associated with the bucket of data is obtained at the local data store from the remote data store.