Dynamic Resolution Estimation for Metric Time Series Detectors
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Solution Overview
Problem
Existing systems fail to dynamically adjust the output resolution of metric time series data in response to changes in data resolution, leading to missed alerts and inefficient resource utilization, as the initial static output resolution does not account for modifications in the metric time series data.
Innovation Solution
Implementing a dynamic resolution estimation system that processes metric time series data to detect changes in data resolution and dynamically update the output resolution, using a majority symbol algorithm to efficiently determine the dominant data resolution and recalculate the output resolution based on the modified resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static output resolution is used for metric time series data, then system simplicity is maintained, but the system cannot adapt to changes in data resolution leading to missed alerts and inefficient resource utilization
Solution Approach 1:
The system transitions from a static output resolution to a dynamic one by continuously monitoring data resolution changes and automatically adjusting the output resolution accordingly. The detector now adapts its behavior based on real-time data characteristics, allowing it to respond to changing conditions in the metric time series data without manual intervention.
Solution Approach 2:
The system implements feedback by monitoring the actual data resolution and using this information to adjust the output resolution. The detector evaluates data resolution changes and feeds this information back into the system to automatically modify processing parameters, ensuring the output resolution matches the input data characteristics.
2Productivity
If the output resolution is fixed at an initial value, then processing efficiency is maintained initially, but resource utilization becomes inefficient when data resolution changes
Solution Approach 1:
The system dynamically changes the output resolution parameter based on the actual data resolution. When data resolution changes, the detector modifies the output resolution parameter to match, ensuring optimal resource utilization. This prevents both over-processing (wasting resources) and under-processing (missing alerts) by aligning processing intensity with actual data characteristics.
3Reliability
If data resolution changes are not detected, then system simplicity is preserved, but alerts are missed and data insights are lost
Solution Approach 1:
The detector continuously monitors data resolution and uses feedback to adjust output resolution accordingly. This ensures that alerts are generated based on current data characteristics, improving reliability by preventing missed alerts while maintaining manageable system complexity through automated adaptive behavior.
Data Source
AI summary
Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and dynamically estimating a resolution of one or more streams of data points and updating an output resolution. Responsive to receiving a stream of data points, a data resolution can be derived and an output resolution can be set to a first value. When a change to the data resolution is detected, the output resolution can be changed, modifying a frequency at which output data points are generated and/or transmitted. In some instances, a detector can be implemented to trigger an alert responsive to ingested data points corresponding with triggering parameters. An output resolution for the detector can be dynamically modified based on dynamically detecting a change to the data resolution of the stream of data.