Unified Search Platform for Multi-Source Data Integration
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Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing diverse data types from various data sources, including external systems, due to limited scope and unidirectional processing flows, which restricts the ability to route data to different destinations and integrate insights from multiple data systems.
Innovation Solution
A data intake and query system that extends search and analytics capabilities by employing a search process master and query coordinators, coupled with a scalable network of distributed nodes, allowing for data collection and processing from diverse data systems and presenting harmonized search results, thereby enabling scalable analytics across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and stored in a data system based on anticipated data analysis needs, then data retrieval and analysis efficiency is improved, but data flexibility and the ability to analyze all generated data is reduced
Solution Approach 1:
The system segments data processing into multiple stages: raw data collection from diverse sources, pre-processing/filtering based on query parameters, and final analysis. This allows the system to maintain raw data for flexibility while creating processed subsets for efficient retrieval, resolving the contradiction between efficiency and adaptability
Solution Approach 2:
The system performs preliminary actions by collecting and storing raw data from multiple data systems before analysis is needed. This preliminary data collection enables both efficient retrieval (data is already collected) and flexibility (raw data is preserved for various analysis needs), eliminating the need to choose between the two opposing requirements
2Adaptability or versatility
If tools are used to search data systems separately and collect results over a network, then data from multiple sources can be gathered, but the process is time-consuming and results are obtained in a piecemeal manner
Solution Approach 1:
The system merges multiple data systems and search operations into a unified search interface. The backend integrates connections to diverse data sources and coordinates searches across them simultaneously, combining results into a single comprehensive response. This eliminates the need for separate tool searches and network collection, providing both multi-source coverage and time efficiency
Solution Approach 2:
The system introduces an intermediary layer (the unified search platform) that mediates between the user and multiple data systems. This intermediary handles the complexity of multi-source searching, coordinates queries across different systems, and aggregates results, thereby reducing user time investment while maintaining comprehensive multi-source data access
3Device complexity
If a data intake and query system is designed with limited scope and unidirectional processing flow, then system complexity is reduced, but the ability to route data to different destinations and integrate insights from multiple data systems is restricted
Solution Approach 1:
The system implements dynamic data routing where the processing flow adapts based on query requirements. Data can be routed to different destinations (data systems, analysis modules, storage) depending on the specific query needs. This dynamic routing capability provides flexibility without requiring a completely complex static architecture, as the routing logic is flexible rather than structurally complicated
Data Source
AI summary
Systems and methods are disclosed for receiving and executing a query received from a data intake and query system and providing results to a first group of worker nodes in a distributed execution environment. The query identifies a set of data to be processed and a manner of processing the set of data. Based on the query, the system defines a query processing scheme, and generates instructions for a second group of worker nodes to obtain the set of data from one or more dataset sources and to process the set of data. The system communicates results of the query to the first group of worker nodes.