Data Access Gateway Optimizing Data Node Selection
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Solution Overview
Problem
Existing data cluster systems face inefficiencies in accessing data due to the lack of an effective method to identify and utilize available data nodes, leading to suboptimal resource utilization and computational overhead.
Innovation Solution
A data access gateway (DAG) system that obtains requests from hosts, gathers discovery metadata from data nodes, updates a metadata repository, identifies suitable data nodes, and directs requests to them, with mechanisms for retrying and utilizing nodes with higher computational, network, and storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data nodes are accessed directly without a gateway system, then system complexity is reduced, but resource utilization and access efficiency deteriorate
Solution Approach 1:
A data access gateway is introduced as an intermediary component between hosts and data nodes. The gateway receives data access requests from hosts, queries the metadata repository to identify suitable data nodes, and forwards requests to the appropriate nodes. This mediator architecture improves resource utilization and access efficiency while maintaining manageable system complexity through centralized coordination.
Solution Approach 2:
The system performs preliminary actions by maintaining a metadata repository that pre-stores information about data nodes, including their capabilities, resource availability, and data locations. Before processing actual data requests, the gateway queries this pre-prepared metadata to identify optimal data nodes, avoiding the need for real-time discovery and reducing access latency.
2Measurement precision
If discovery metadata is gathered from all data nodes, then data node identification accuracy is improved, but computational overhead increases
Solution Approach 1:
The gateway implements a balanced approach by gathering discovery metadata from data nodes selectively rather than continuously from all nodes. The system collects metadata including node identifiers, data types stored, and resource capabilities, maintaining sufficient information for accurate identification while avoiding excessive data collection that would increase computational overhead unnecessarily.
Solution Approach 2:
Discovery metadata is collected and stored in the metadata repository in advance, before actual data access requests are processed. This preliminary gathering of node information allows the gateway to quickly query and identify suitable data nodes without performing comprehensive node discovery at the time of each request, thereby reducing real-time computational overhead.
3Speed
If the DAG is executed on the host, then access speed is improved, but system reliability deteriorates due to host dependency
Solution Approach 1:
The data access gateway is segmented from the host system and implemented as an independent component, preferably as a separate microservice or container. This segmentation allows the gateway to maintain close proximity to data nodes for fast access while being independent of host system stability. The host sends requests to the gateway, which then communicates with data nodes, creating a reliable architecture where host dependency is minimized.
Data Source
AI summary
A method that is performed to access data nodes of a data cluster. The method includes obtaining, by a data access gateway (DAG), a request from a host, wherein the DAG is not executing on the host; in response to the request, obtaining discovery metadata from each data node of the data nodes in the data cluster; updating, based on the discovery metadata, a discovery metadata repository; identifying a first data node of the data nodes based on the discovery metadata; and sending the request to the first data node of the data nodes.