Distributed Data Intake System for Cross-Source Search
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing large volumes of diverse data across various data sources, as their capabilities are often limited to internal data stores, and they lack the ability to route data to different destinations, restricting the scope of search and analytics operations.
Innovation Solution
A data intake and query system that employs a search process master and query coordinators combined with a scalable network of distributed nodes to collect and process data from diverse data systems, enabling extended search and analytics capabilities beyond internal data stores to include external data sources, such as MySQL, PostgreSQL, and cloud storage systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data systems store massive quantities of raw data for later retrieval and analysis, then data analysis flexibility and completeness are improved, but data storage requirements and system complexity increase
Solution Approach 1:
The system segments data storage and processing across multiple distributed nodes rather than centralizing it. Each node can independently store and process portions of raw data, allowing the system to scale horizontally while maintaining flexibility for various analysis scenarios without proportionally increasing overall system complexity.
Solution Approach 2:
The distributed query system implements universal interfaces and protocols that allow diverse data sources to be accessed through a common framework. This multi-functionality enables the system to handle various data types and storage formats without requiring separate specialized systems, thereby improving adaptability while managing complexity through standardization.
2Productivity
If data systems pre-process data based on anticipated analysis needs, then data retrieval efficiency is improved, but data analysis flexibility deteriorates
Solution Approach 1:
The system performs preliminary indexing and metadata extraction on raw data during ingestion, organizing it into a structured format that enables efficient querying. This preliminary action prepares data for fast retrieval while preserving the original raw data, allowing both efficient access and flexible analysis of unprocessed data when needed.
Solution Approach 2:
The system creates indexed copies of raw data with extracted metadata and field values, storing these copies in an optimized format for efficient querying. The original raw data remains preserved and unmodified, allowing analysts to retrieve data efficiently through indexes while maintaining the ability to perform flexible analysis on the complete original datasets.
3Adaptability or versatility
If search tools are designed to handle diverse data types from multiple sources, then search capability comprehensiveness is improved, but tool complexity and difficulty of operation increase
Solution Approach 1:
The system introduces an intermediary layer consisting of standardized query interfaces and abstraction mechanisms that sit between the user and diverse data sources. This intermediary translates various query types into unified search operations, allowing users to perform comprehensive searches across diverse data types without needing to understand the underlying complexity of each data source.
Solution Approach 2:
The search system implements universal query protocols and standardized field representations that work across different data types and sources. This universality allows a single tool interface to handle diverse data comprehensively while maintaining ease of operation through consistent interaction patterns regardless of the underlying data complexity.
Data Source
AI summary
Systems and methods are disclosed for processing events having raw machine data associated with a timestamp using one or more pivot identifiers and one or more step identifiers to generate one or more journey instances. Based on the one or more pivot identifier field, the system can relate events that have a common field value for the pivot identifier field. Based on the one or more step identifiers, the system can group the related events into a subset of events. Using the subset of events, the system can build a journey instance.