Runtime Custom Metrics for Schema-Validated Monitoring Platforms
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Solution Overview
Problem
Existing data monitoring platforms require predefined metrics, limiting the ability to dynamically add custom metrics during execution without recompiling or redeploying the system.
Innovation Solution
A system that generates custom metric types at run-time by using a custom schema and template, allowing dynamic definition and validation of custom metrics through a user interface, without recompiling or redeploying the data monitoring platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If metrics are defined prior to run-time with fixed schema, then system stability and reliability are maintained, but adaptability and versatility are limited
Solution Approach 1:
The patent implements dynamic schema evolution by allowing metric definitions to be modified at runtime through a schema registry. The system transitions from static, compile-time metric definitions to dynamic, runtime-configurable metrics using JSON schema validation. This enables the data monitoring platform to adapt to changing business needs without requiring system recompilation or redeployment, directly resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent changes the parameter of metric definition timing from compile-time to runtime. By introducing a schema registry that stores and validates custom metric schemas at runtime, the system allows metric parameters to be modified dynamically. This parameter change enables flexible adaptation to new monitoring requirements while maintaining system stability through validated schema evolution.
2Productivity
If custom metrics are added at run-time without recompilation, then productivity and responsiveness are improved, but reliability and stability may be compromised
Solution Approach 1:
The patent implements a schema validation feedback mechanism that ensures reliability before allowing runtime metric additions. The schema registry validates custom metric schemas against predefined rules and types, providing feedback on validity before the metrics are activated. This feedback loop enables rapid metric addition while maintaining system stability by preventing invalid or harmful metric definitions from compromising reliability.
Solution Approach 2:
The patent performs preliminary validation of custom metric schemas before they are executed. By requiring schema registration and validation in advance through the schema registry, the system ensures that only valid and safe metric definitions are allowed to run. This preliminary action prevents potential reliability issues from arising during runtime execution, enabling safe rapid metric addition.
3Adaptability or versatility
If predefined metrics are used, then ease of operation is maintained, but adaptability to custom needs is reduced
Solution Approach 1:
The patent uses schema templates and copies as a mechanism to simplify custom metric creation. The schema registry stores reusable metric schema templates that can be copied and adapted for custom needs. Users can base custom metrics on existing templates rather than creating schemas from scratch, maintaining ease of operation while enabling adaptability to custom monitoring requirements.
Data Source
AI summary
Techniques for creating a custom metric type to be added to a set of metrics generated by a data monitoring platform at run-time are disclosed. A system receives values defining properties of a custom metric type based on a custom metric template and a custom schema template. The system generates an instruction set, based on the values associated with the custom metric template, for generating the custom metric type on an executing data monitoring system. The system validates the instruction set and the custom schema to verify that the definitions for the custom metric type and the custom schema may be executed by the data monitoring system. The system adds the custom metric type, at run-time, to a set of metrics generated by the data monitoring system.