Pipeline Optimization Engine for Streaming Data Consolidation
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Solution Overview
Problem
Distributed stream data processing systems face inefficiencies due to increased resource consumption with the number of data processing pipelines, leading to limitations in the number of processes and threads that can be executed, as they consume significant memory and may exceed operating system limits.
Innovation Solution
A pipeline optimization engine that constructs graphs from metadata to consolidate data processing pipelines based on static and dynamic characteristics, such as latency and throughput, allowing for the combination of pipelines while ensuring non-functional criteria are met, and predicting future metrics to split or combine pipelines as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data processing pipelines are executed to handle diverse data sources and ETL operations, then the system's data processing capability and versatility are improved, but the resource consumption (memory, processes, threads) increases significantly
Solution Approach 1:
The patent combines multiple data processing pipelines into a single unified pipeline by merging their task sequences. The system identifies pipelines with compatible task characteristics and consolidates them, reducing the number of separate pipeline instances while maintaining the ability to process diverse data types through a shared infrastructure.
Solution Approach 2:
The patent creates a universal pipeline template that can handle multiple data sources and ETL operations through parameterization. Instead of creating dedicated pipelines for each data type, the system uses a single pipeline structure with configurable task parameters that adapt to different processing requirements, thereby reducing resource consumption while maintaining versatility.
2Adaptability or versatility
If the number of data processing pipelines is increased to handle more data sources, then the system's processing coverage is improved, but the number of processes and threads exceeds operating system limits
Solution Approach 1:
The patent merges multiple pipeline instances into a consolidated pipeline structure, reducing the total number of processes and threads required. By combining task sequences from multiple pipelines into a single unified execution flow, the system maintains comprehensive processing coverage while staying within operating system limits for concurrent processes.
3Manufacturing precision
If separate pipelines are maintained for different data types to ensure processing quality, then the manufacturing precision of data processing is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal pipeline template with parameterized task definitions that can adapt to different data types and processing requirements. This single unified structure replaces multiple specialized pipelines, reducing management complexity while maintaining processing quality through configurable task parameters that preserve data-type-specific processing logic.
Data Source
AI summary
A technique includes comparing a first sequence of tasks of a first data processing pipeline of a plurality of data processing pipelines for a data streaming system to a second data processing pipeline of the plurality of data processing pipelines. The technique includes, based on the comparing, combining the first data processing pipeline and the second data processing pipeline into a single data processing pipeline.