Query Processing Using Resource Utilization Data
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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, as their capabilities are often isolated to internal data stores, lacking the ability to route data to different destinations and extending search and analytics operations beyond their scope.
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 search and analytics operations across internal and external data sources, including MySQL, PostgreSQL, NoSQL data stores, cloud storage, and common storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data systems store and process data separately in isolated internal data stores, then data management is simpler and more controlled, but search and analytics capabilities are limited to internal data only
Solution Approach 1:
The data fabric service implements a universal interface layer that enables the data intake and query system to perform search and analytics operations across multiple types of data sources (internal data stores, external databases, cloud storage, NoSQL stores) through a single unified system, rather than requiring separate processing for each data type
2Adaptability or versatility
If the system extends search operations to external data sources, then data analysis flexibility increases, but query processing time and resource consumption increase
Solution Approach 1:
The data fabric service acts as an intermediary layer between the query coordinator and diverse external data sources, handling protocol translation, connection management, and data format standardization. This mediator approach abstracts the complexity of accessing different data sources and optimizes query routing to reduce processing time
3Loss of information
If diverse data types from multiple sources are integrated, then analytical insights improve, but data processing complexity and resource usage increase
Solution Approach 1:
The system segments the data integration process into distinct functional layers: the data fabric service handles connection and protocol diversity, the query coordinator manages query routing and coordination, and individual data sources maintain their own data processing logic. This segmentation isolates complexity to specific components rather than requiring the entire system to handle all diversity simultaneously
Data Source
AI summary
Systems and methods are disclosed for processing queries against one or more dataset sources. The system tracks query resource data and resource utilization data. The query-resource usage data can indicate resources used to execute queries. The node resource utilization data can indicate current utilization of nodes in the system. Upon receipt of a query that identifies a set of data to be processed and a manner of processing the set of data, the system can use the query-resource usage data and the resource utilization data to define a query processing scheme. The query can then be executed using the query processing scheme. In some cases, the query coordinator can dynamically allocate partitions operating on worker nodes to execute the query.