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 diverse data types across various data sources, as their capabilities are often limited to internal data stores, and processing flows are unidirectional, lacking the ability to route data to different destinations for comprehensive analysis.
Innovation Solution
A data intake and query system is developed that extends search and analytics capabilities by employing a search process master and query coordinators combined with a scalable network of distributed nodes, enabling processing and analysis of large volumes of diverse data from various data systems, including external sources like MySQL, PostgreSQL, and cloud storage, through a big data pipeline that integrates with open stack technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data systems store and process only internal data stores, then system complexity is reduced, but search and analysis capabilities are limited to internal data only
Solution Approach 1:
The system is divided into distinct components: a master process that coordinates search operations and worker nodes that execute actual search queries across distributed data sources. This segmentation allows the system to handle external data sources while maintaining manageable complexity through clear separation of coordination and execution functions.
Solution Approach 2:
The master process acts as an intermediary between the user interface and distributed worker nodes, coordinating search requests and aggregating results. This intermediary layer enables the system to manage multiple external data sources without requiring direct complex connections between all components.
2Adaptability or versatility
If data processing flows are unidirectional, then system design is simplified, but the ability to route data to different destinations for comprehensive analysis is lost
Solution Approach 1:
The processing flow is made dynamic by allowing the master process to route search requests to multiple worker nodes based on the specific query requirements and data source types. This dynamic routing enables comprehensive analysis across diverse data sources while maintaining simplified individual processing paths through adaptive task distribution.
3Productivity
If tools search data systems separately and collect results over a network, then each data system can be searched independently, but the analysis is performed in a piecemeal manner without comprehensive insights
Solution Approach 1:
The system merges search operations across multiple distributed data sources by having the master process coordinate queries and aggregate results from worker nodes. This combining approach maintains the efficiency of separate searches while preventing information loss through comprehensive result aggregation and unified analysis.
Solution Approach 2:
The master process serves multiple functions: it manages worker nodes, coordinates search operations, aggregates results from diverse data sources, and provides unified analysis. This multi-functionality enables efficient separate searches while ensuring comprehensive insights through centralized result processing.
Data Source
AI summary
Systems and methods are disclosed for generating one or more journey instances from events having raw machine data associated with a timestamp. The system generates a user interface that includes field identifiers associated with the plurality events for selection as one or more pivot identifiers and one or more step identifiers. Based on the one or more pivot identifiers, the system identifies related events that share a common field value, and based on the one or more step identifiers, the system groups the related events into a subset of events. Using the subset of events the system builds a journey instance.