Multi-layered Histograms for Adaptive Data Pipeline Monitoring
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Solution Overview
Problem
Traditional data pipeline monitoring systems face challenges in accurately modeling data operations due to issues like schema changes, data quality problems, and software bugs, which are not effectively addressed by current histogram generation methods that lack sufficient resolution.
Innovation Solution
The implementation of multi-layered histograms, where data values exceeding a threshold are modeled as subsidiary histograms, enhancing resolution and allowing for adaptive density estimation and error detection in data pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histograms are used to model data pipeline operations, then the system complexity is low and computational resources are saved, but the measurement precision and ability to detect data quality issues are insufficient
Solution Approach 1:
The patent segments the histogram into multiple layers, where each layer focuses on specific data ranges or distributions. This segmentation allows high-resolution modeling of critical data regions while using coarser modeling for less critical regions, thereby improving measurement precision without proportionally increasing system complexity
Solution Approach 2:
The patent introduces a vertical dimension to traditional histograms by creating multi-layered structures. Each layer represents a different level of detail or focus, allowing the system to capture both macro-level data trends and micro-level anomalies simultaneously, thus enhancing measurement precision without linearly increasing complexity
2Reliability
If traditional histograms are used for data monitoring, then computational resources are conserved, but the ability to accurately model data operations and detect errors is compromised
Solution Approach 1:
The patent applies local quality by assigning different levels of modeling detail to different regions of the data distribution. High-density or critical data regions are modeled with higher precision using additional layers, while low-density regions use standard histogram buckets. This selective approach improves error detection reliability in critical areas without proportionally increasing computational resource consumption across the entire dataset
3Measurement precision
If multi-layered histograms are implemented to enhance resolution, then measurement precision and error detection improve, but computational resources and processing time increase
Solution Approach 1:
The patent implements partial action by applying multi-layered histogram modeling only to portions of the data that require enhanced resolution, rather than uniformly processing all data. This selective approach maintains measurement precision for critical data regions while minimizing the additional processing time required, as not all data buckets require the same level of detailed analysis
Data Source
AI summary
Various embodiments of the present technology relate to data monitoring systems to generate multi-layered histograms. In some examples, the data monitoring system comprises a computing device that stores an executable modeling component. The modeling component, in response to execution, reads a data record associated with a data pipeline and models the data record as a histogram. The histogram comprises histogram buckets that categorize data values of the data record. The modeling component scans the histogram buckets and determines when a proportion of the data values assigned to one of the histogram buckets exceeds a threshold value. When the threshold value is triggered, the modeling component models the data values assigned to the exceeding histogram bucket as a subsidiary histogram. The subsidiary histogram comprises subsidiary histogram buckets that categorize the data values assigned to that histogram bucket.