Big Data Cluster Deployment Interface Automation
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Solution Overview
Problem
The deployment of big data clusters is complex and inefficient, requiring technical personnel to manually download and install software, modify configuration files, and having high deployment costs due to high requirements for technical expertise.
Innovation Solution
A method involving a visualized deployment interface where nodes are created and dragged into a temporary resource pool, then deployed to a physical pool, with containers being created on servers to provide big data cluster services, simplifying the process and reducing costs through automated configuration and plugin utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual deployment methods are used through distributed computing platforms, then deployment flexibility and control are maintained, but deployment complexity increases and deployment efficiency decreases
Solution Approach 1:
The patent introduces a deployment platform as an intermediary between users and the distributed computing platform. This platform provides automated deployment services, including one-click deployment, automatic resource allocation, and intelligent scheduling, thereby reducing deployment complexity while maintaining the underlying flexibility of the distributed computing platform.
Solution Approach 2:
The deployment platform performs preliminary actions by pre-configuring deployment packages, pre-allocating resource templates, and pre-establishing deployment workflows. This allows users to deploy big data clusters through simple operations without needing to manually perform complex configuration tasks in advance.
2Ease of operation
If automated container deployment is implemented, then deployment efficiency and ease of operation improve, but deployment system complexity increases
Solution Approach 1:
The deployment platform implements self-service mechanisms where the system automatically performs deployment tasks without requiring user intervention in complex procedures. The platform autonomously handles container creation, resource allocation, configuration generation, and service registration, making the deployment process as easy as a single click while managing the underlying complexity automatically.
Solution Approach 2:
The patent utilizes parameter changes by transforming complex deployment parameters into simplified user inputs. The platform accepts high-level deployment specifications from users and automatically translates them into detailed configuration parameters for containers and services, thereby improving ease of operation while managing system complexity through automated parameter transformation.
Data Source
AI summary
The present disclosure relates to a method of deploying a big data cluster. In the present disclosure, a deployment interface is provided, to provide a big data cluster deployment function through the deployment interface. The method includes: in response to a node creation operation in the deployment interface, displaying a to-be-deployed node in a temporary resource pool region in the deployment interface; in response to a drag-and-drop operation on the to-be-deployed node in the temporary resource pool region, displaying the to-be-deployed node in a physical pool in the deployment resource pool region in the deployment interface; and in response to a start deployment operation in the deployment interface, according to the physical pool where the to-be-deployed node is located, creating a container corresponding to the to-be-deployed node on a server corresponding to the physical pool, where the container is configured to provide a big data cluster service.