Cipher-Coded Datetime Tracing for End-to-End Data Pipelines
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Solution Overview
Problem
Traditional timestamping methods in end-to-end data processing pipelines face challenges with validation errors and compatibility, necessitating a robust mechanism for systematic logging of timestamped events and real-time analytics to ensure optimal performance and informed decision-making.
Innovation Solution
A cipher-coding technique is applied to convert numerical date-time stamps into alpha codes that are compatible with data processing pipelines, allowing for the injection of tracer data packets that are not rejected, with latency measurement and aggregation to generate graphs for user dashboards and alerting systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional numeric timestamping methods are used in data processing pipelines, then timestamping is simple and direct, but validation errors and compatibility issues occur
Solution Approach 1:
The patent transforms timestamp parameters from numeric format to cipher-coded format (e.g., base64 encoding). This parameter change allows timestamps to pass through data processing pipelines without triggering validation errors, as the cipher-coded format is treated as opaque data rather than structured numeric values that require validation.
Solution Approach 2:
The patent introduces cipher-coding as an intermediary layer between the timestamp generation and the data processing pipeline. The cipher code acts as a mediator that preserves the timestamp information while preventing validation errors, as the pipeline processes the encoded format without attempting to validate it as a numeric timestamp.
2Adaptability or versatility
If cipher-coding technique is applied to convert numerical date-time stamps, then compatibility with data processing pipelines is improved, but system complexity increases
Solution Approach 1:
The patent applies cipher-coding to timestamps before injecting them into the data processing pipeline. This preliminary action ensures that timestamps are already in a compatible format, preventing validation errors downstream and eliminating the need for complex runtime validation or conversion mechanisms within the pipeline itself.
Solution Approach 2:
The patent creates cipher-coded copies of the original numeric timestamps. These encoded copies serve as proxies that carry the timestamp information through the pipeline without requiring the pipeline to understand or validate the actual timestamp format, thus maintaining simplicity in the core processing logic while improving compatibility.
3Measurement precision
If tracer data packets are injected into the pipeline, then latency measurement is enabled, but additional processing overhead is introduced
Solution Approach 1:
The patent uses tracer data packets as disposable objects that are injected into the pipeline solely for latency measurement purposes. These packets are simple, lightweight structures that carry timestamp information and are quickly processed through the pipeline. After serving their measurement function, they are discarded, minimizing their impact on overall system productivity while enabling precise latency measurement.
Solution Approach 2:
The patent implements latency measurement by injecting tracer packets at specific points in the pipeline and measuring their passage time. This partial action approach measures latency at critical junctures without requiring comprehensive monitoring of every data element, thus enabling measurement precision while limiting the overhead to only the necessary measurement points rather than excessive continuous monitoring.
Data Source
AI summary
An instrumentation and measurement tool that provides a technological solution for precise data flow latency tracing within end-to-end data processing pipelines from ingestion of raw data through to consuming product systems. Cipher-coded date-time stamps for data field tagging replace numeric timestamps to eliminate data validation errors that can be triggered if numeric timestamps were used in data fields where character data is expected. The resulting latency metrics are systematically aggregated and presented through a dashboard, providing comprehensive insights into the performance of end-to-end data processing pipelines with adaptive monitoring and alerting capabilities.