Dynamic Search Guidance for Machine Data Indexing
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated in modern data centers and computing environments is challenging due to the vast types and formats of data, which existing tools often address by pre-processing and discarding non-essential data, limiting flexibility and insights.
Innovation Solution
A guidance technique using a pipelined search language (PSL) and machine-learning models to categorize search commands, generate operation flow models based on historical search data, and provide dynamic search guidance by recommending next operations and commands as users build queries, allowing for flexible analysis of minimally processed machine data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-processing is applied to extract only pre-specified data items, then retrieval efficiency is improved, but data flexibility and analysis capability are worsened
Solution Approach 1:
The system performs preliminary indexing of all machine data fields without pre-specifying which fields will be searched, creating an inverted index that captures all data. This preliminary action enables both efficient retrieval (through indexing) and full flexibility (by indexing everything rather than pre-selecting fields).
Solution Approach 2:
The inverted index structure serves multiple functions: it enables efficient full-text search across all fields, supports flexible query construction, and allows analysts to search any field without pre-specification. This universal data structure resolves the contradiction by providing both speed and adaptability simultaneously.
2Adaptability or versatility
If all machine data is stored for later retrieval, then analysis flexibility is improved, but search and analysis complexity increases
Solution Approach 1:
The inverted index acts as an intermediary data structure between the raw machine data and the search queries. It mediates the complexity by providing a standardized, efficient access path to all data fields, transforming the complex task of searching massive quantities of diverse machine data into simple keyword-based lookups.
3Loss of information
If massive quantities of machine data are stored, then insight discovery capability is improved, but storage and processing resources are worsened
Solution Approach 1:
The system extracts only the essential indexing information (field names and term frequencies) from the massive machine data during the indexing phase, storing this compressed representation in the inverted index. This extraction approach preserves the ability to discover insights from all data while using minimal storage resources compared to storing complete raw data.
Data Source
AI summary
Disclosed is a guidance technique that can be applied to guide search and analysis of stored data by a user. The technique can include inputting from a user a portion of a search query expressed in a pipelined search language, at a system for indexing and searching machine data. The system generates and outputs search guidance for the user as the user builds the search query, by applying the portion of the query to an operation flow model, where the operation flow model represents a plurality of searches performable by the system. The operation flow model has been generated based on multi-user historical search data and includes a plurality of states, each representing a different group of related commands of the pipelined search language.