Kubernetes Cluster Map Navigator for Root Cause Analysis
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Solution Overview
Problem
Operating a Kubernetes environment at scale is challenging due to the complexity of layers of abstractions and constant churn, making it difficult to maintain a high fidelity view of what is happening across clusters, identify problems quickly, and understand root causes without getting lost in a sea of data.
Innovation Solution
A data structure navigator that provides a method for navigating Kubernetes clusters by gathering data from instrumented application software, identifying clusters, displaying a cluster map with health status for each node, pod, and container, and offering granular analysis to diagnose errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Kubernetes provides multiple layers of abstractions for container management, then flexibility and agility are improved, but device complexity increases making it difficult to maintain high fidelity view and identify problems
Solution Approach 1:
The patent segments the complex Kubernetes data structure into hierarchical levels (clusters, nodes, pods, containers) and presents them through a tree view interface. This segmentation allows users to navigate and understand the system at appropriate levels of detail without being overwhelmed by the entire structure at once, thus maintaining flexibility while reducing perceived complexity.
Solution Approach 2:
The patent introduces an intermediary data structure navigator that sits between the user and the complex Kubernetes data structure. This navigator provides a simplified interface with features like tree views, filtering, and health status indicators, acting as a mediator that translates complex underlying data into comprehensible visual representations without losing fidelity.
2Productivity
If Kubernetes implements constant churn for rapid deployment and updates, then productivity is improved, but difficulty of detecting and measuring problems increases
Solution Approach 1:
The patent implements feedback mechanisms through health status indicators that continuously monitor and display the state of Kubernetes components. The system provides real-time feedback about the health of clusters, nodes, pods, and containers, enabling users to quickly detect problems despite constant changes. The filtering capability allows users to focus feedback on specific components of interest.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and organizing data into the hierarchical data structure before users need to query it. The system maintains ready-to-display tree views with health status information already computed, so when users need to detect problems, the information is immediately available without requiring complex real-time analysis during troubleshooting.
3Measurement precision
If Kubernetes provides detailed data about each component, then measurement precision is improved, but loss of time increases due to getting lost in sea of data
Solution Approach 1:
The patent applies local quality by allowing users to view detailed information precisely where it is needed in the hierarchical structure. The tree view enables users to expand or collapse specific branches (clusters, nodes, pods, containers) to reveal detailed metrics only for the local region of interest, rather than presenting all data uniformly. This maintains measurement precision while reducing time loss through selective detail disclosure.
Solution Approach 2:
The patent implements partial action by providing filtering capabilities that allow users to selectively display only the portion of data they need at any given moment. Instead of overwhelming users with complete data about all components, the system enables partial views focused on specific clusters, nodes, or health statuses, reducing the time to find relevant information while maintaining the ability to access complete data when needed.
Data Source
AI summary
According to embodiments, a method for navigating clusters of a data structure includes gathering data from the data structure by instrumenting instances of application software executing on the data structure. The method also includes identifying clusters of the data structure based on the gathered data. The method also includes causing display of a cluster map of the data structure, the cluster map comprising a plurality of clusters, each cluster of the plurality of clusters comprising a plurality of nodes, each node of the plurality of nodes comprising a plurality of pods, each pod of the plurality of pods comprising a plurality of containers. The method also includes providing a status for each node, each pod, and each container of each cluster. The method also includes causing display of analysis of each cluster of the cluster map, the analysis comprising granular information for each cluster.