Containerized Hadoop Cluster Assembly via Image Cloning
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Solution Overview
Problem
Creating Hadoop clusters in a cloud environment is time-consuming, often taking hours for small clusters and weeks for large ones, and requires frequent maintenance and patch management to ensure availability and reliability.
Innovation Solution
The mechanism involves separating installed binaries from the cluster state by building them into virtual machine images and externalizing the cluster state into volume templates, allowing pre-built virtual machines with required binaries and pre-populated states to assemble clusters quickly, using containerization (like Docker) to create clusters on demand with reduced failure risks and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Hadoop clusters are created using traditional installation methods, then the clusters can be set up with proper configurations and binaries, but the creation process takes hours for small clusters and weeks for large clusters
Solution Approach 1:
The patent applies preliminary action by pre-installing all necessary Hadoop binaries, configurations, and dependencies into container images before deployment. This allows clusters to be created rapidly by simply instantiating pre-configured containers rather than performing lengthy installations during cluster creation, directly resolving the contradiction between reliable cluster setup and creation time
Solution Approach 2:
The patent uses copying by creating standardized container images that can be replicated and deployed across multiple nodes. Instead of installing Hadoop from scratch on each node, the same pre-configured container image is copied and instantiated, ensuring consistency and reliability while dramatically reducing setup time
2Reliability
If traditional Hadoop cluster installation is used, then proper cluster configurations can be achieved, but frequent maintenance and patch management are required to ensure availability
Solution Approach 1:
The patent implements self-service through automated container orchestration that handles updates, patches, and maintenance tasks. The containerized architecture allows for automated rolling updates where new versions can be deployed without manual intervention on each node, reducing maintenance complexity while maintaining high availability through automated failover and health checks
Solution Approach 2:
The patent applies universality by creating standardized container images that can serve multiple purposes across different cluster nodes and environments. The same container image can be deployed universally across all nodes, simplifying maintenance as updates need to be applied to a single image template rather than individually configuring each node
3Productivity
If Hadoop binaries and cluster state are integrated in traditional installations, then the system can function, but the creation process is time-consuming and prone to failures
Solution Approach 1:
The patent applies segmentation by separating Hadoop binaries, configurations, and cluster state into distinct containerized components. Each Hadoop node runs as an isolated container with its own filesystem and processes, allowing independent deployment and management. This segmentation enables parallel container instantiation, dramatically improving assembly speed while reducing overall system complexity through modular architecture
Solution Approach 2:
The patent introduces an intermediary layer in the form of container runtime environments that mediate between the host system and Hadoop binaries. This intermediary abstracts the complexity of Hadoop installation and configuration, providing a standardized interface for deploying and managing clusters while handling the complexity of binary integration, dependency management, and configuration files
Data Source
AI summary
A mechanism is provided in a data processing system for creating clusters on demand. The mechanism installs a cluster on the data processing system. The cluster comprises a master node and a managed node having distributed software installed thereon. The mechanism stores state of cluster on external volumes and removes references specific to the cluster from the state of the cluster stored on the external volumes. The mechanism takes a snapshot of the state of the cluster stored on the external volumes to form a set of volume templates and takes a snapshot of images of the master node and the managed node to form a set of node type images. Responsive to receiving a request to create a specified cluster, the mechanism creates a new set of nodes on the data processing system based on the set of node type images, clones the set of volume templates to form a new set of volumes in the data processing system, mounts the new set of volumes to the new set of nodes, and recreates configurations based on details in the request.