Cluster File System Telemetry for Dynamic Short-Term Metrics
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Solution Overview
Problem
Existing telemetry architectures in distributed systems lack flexibility to dynamically define and process different types of telemetry data, particularly failing to accommodate short-term metrics efficiently.
Innovation Solution
A subscription-based telemetry architecture with dynamic registration of metrics, allowing producers to add new datasets and consumers to subscribe dynamically, utilizing Kubernetes orchestration and Open Telemetry for flexible data collection and sharing, with features like RBAC, automatic compliance checks, and optimized data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If present telemetry architectures use fixed data collection mechanisms, then system stability is maintained, but adaptability to new metric types and durations is insufficient
Solution Approach 1:
The patent implements dynamic telemetry architecture where metric definitions, collection frequencies, and retention policies can be changed at runtime without system restart. The system allows dynamic registration of new metric types with different durations (short-term vs long-term) and automatically adjusts data handling behavior accordingly, resolving the contradiction between adaptability and complexity through controlled dynamic behavior.
Solution Approach 2:
The system changes key parameters such as metric retention duration, collection interval, and data format dynamically based on metric type. Short-term metrics are automatically configured with shorter retention periods and different processing paths compared to long-term metrics, allowing the system to adapt to new metric types while managing complexity through parameterization.
2Quantity of substance
If the system collects all telemetry data indefinitely, then data availability is maximized, but storage requirements and processing overhead increase significantly
Solution Approach 1:
The patent applies different quality characteristics to different telemetry data based on metric type and duration. Short-term metrics are automatically purged after their retention period expires, while long-term metrics are retained indefinitely. This local differentiation of data handling quality optimizes storage efficiency and processing load while maintaining necessary data availability for each metric type.
Solution Approach 2:
The system continuously monitors metric definitions and automatically enforces retention policies without interruption. Background processes continuously clean up expired short-term metrics and maintain long-term data availability, ensuring continuous efficient processing while maximizing useful data retention based on metric characteristics.
3Productivity
If manual configuration of telemetry metrics is used, then system control is maintained, but operational speed and responsiveness decrease
Solution Approach 1:
The patent implements self-service telemetry configuration where the system automatically detects, registers, and configures new metric types without manual intervention. When new metrics are introduced, the system automatically determines appropriate retention durations, collection frequencies, and processing paths, eliminating manual configuration steps while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system incorporates feedback mechanisms where metric definitions automatically trigger configuration changes in data collection and storage infrastructure. The system monitors metric characteristics and automatically adjusts processing parameters, creating a feedback loop that accelerates metric registration speed while maintaining ease of operation through automated responses.
Data Source
AI summary
A telemetry processing system in a cluster network generates telemetry data from a plurality of telemetry producers and formats it into a structured format for storage in a datastore. Producers can define new metrics for use in a running network. Schema for a new metric is validated by a schema validator and stored in a telemetry catalog. For a metric that is to be enabled for only a short duration, the producer registers the metric with a duration and condition for triggering collection of metric datasets. A telemetry collector detects the condition and collects the datasets from the producer and stops collection at the end of the specified duration. Any short-term metric that is no longer needed or requires updating can be unregistered to clean up the telemetry catalog.