Query Coordinator for Multi-Source Data Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data intake and query systems face challenges in seamlessly searching and analyzing large sets of diverse data from various sources, including structured, semi-structured, and unstructured data, due to limited scope and unidirectional processing flows that restrict the ability to route data to different destinations for comprehensive insights.
Innovation Solution
A data intake and query system with a search process master and query coordinators, combined with a scalable network of distributed nodes, enables the collection and processing of data from diverse data systems, extending search and analytics capabilities beyond internal data stores to include external data systems, common storage, and ingested data buffers, facilitating scalable analytics across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and stored in internal data stores only, then search and analysis can be performed efficiently, but the scope of data sources is limited and flexibility is reduced
Solution Approach 1:
The query coordinator enables the data intake and query system to perform multiple functions: it coordinates queries across internal data stores, external data systems, and ingested data buffers through a single interface, making the system universally adaptable to diverse data sources without requiring separate processing paths for each source type
Solution Approach 2:
The query coordinator acts as an intermediary component that mediates between the search process master and diverse data sources (external data systems, common storage, ingested data buffers). It translates queries into appropriate formats and routes them to the correct data sources, enabling seamless integration without direct coupling between components
2Device complexity
If unidirectional processing flow is used, then system complexity is reduced, but the ability to route data to different destinations for comprehensive insights is restricted
Solution Approach 1:
The system implements dynamic data routing where the query coordinator can adaptively route queries to different data sources (external data systems, common storage, ingested data buffers) based on the query requirements and data availability, allowing the processing flow to change dynamically rather than following a fixed unidirectional path
Solution Approach 2:
The system segments the data processing architecture into independent components (search process master, query coordinator, worker nodes, external data systems, common storage, ingested data buffers) that can operate and be routed independently, enabling flexible data flow paths while maintaining manageable complexity through modular design
3Reliability
If search capabilities are limited to internal data stores, then system reliability is maintained, but comprehensive search and analysis across diverse data sources cannot be achieved
Solution Approach 1:
The query coordinator serves as a reliable intermediary that manages interactions with external data systems and diverse data sources, maintaining system stability by handling connection management, error handling, and data consistency while enabling expanded search capabilities across multiple data sources
Data Source
AI summary
Systems and methods are described for determining a quantity of records generated by a processing task of a query executed in a data intake and query. The system receives a query and identifies a processing task of the query and a quantity of records to be processed according to the query. The system determines the number of records generated by the processing task based on the number of records to be processed and a record generation estimate. The system can allocate compute resources or determine a query execution time for at least a portion of the query based on the determined quantity of records generated.