Data Flow Distributor for Parallel Buffer Partitioning
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Solution Overview
Problem
Existing ETL tools face challenges in scalability and efficiency, particularly when handling complex data transformations, as they require custom development and are not easily reusable, leading to resource-intensive processes and limited utility.
Innovation Solution
The implementation of a data transformation pipeline with a distributor and collector system that allows for implicit partitioning of buffers across multiple threads, enabling concurrent processing and efficient memory management, thereby improving scalability and performance by utilizing multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom development of complex ETL tools is performed to handle complex data transformations, then transformation functionality is achieved, but resource consumption increases and reusability decreases
Solution Approach 1:
The system segments complex data transformation tasks into reusable ETL tools that can be independently developed, stored in a toolbox, and combined through a graphical interface. This allows complex transformations to be built from modular components rather than custom-developed monolithic solutions, reducing resource consumption while maintaining functionality.
Solution Approach 2:
The system creates universal ETL tools with predefined functionality that can be reused across multiple data transformation scenarios. These tools are designed to handle common transformation patterns generically, eliminating the need for custom development in each case and improving both resource efficiency and reusability.
2Productivity
If simple ETL tools are used for widespread reuse, then reusability increases, but handling complex transformations becomes limited
Solution Approach 1:
The system merges multiple simple ETL tools into complex transformation pipelines through a graphical user interface that allows users to connect tools in sequence. This combination capability enables simple reusable tools to achieve complex transformation functionality when orchestrated together, maintaining reusability while expanding capability.
Solution Approach 2:
The system provides dynamic configuration of ETL tool pipelines through a graphical interface, allowing users to adapt simple reusable tools to different complex scenarios by changing the flow and connections rather than modifying the tools themselves. This maintains tool reusability while providing adaptability for complex transformations.
3Productivity
If data flow execution is distributed across multiple processors, then runtime performance increases, but system complexity increases
Solution Approach 1:
The system implements automatic workplan distribution that self-configures across available processors without requiring manual intervention. The distributor automatically partitions data flow execution and assigns tasks to multiple processors, achieving parallel performance benefits while keeping the system simple to use through automated management of the underlying complexity.
Data Source
AI summary
Various embodiments of the present invention are directed to the creation of multiple redundant chains of transforms, each on a separate processing thread, for a data flow execution (DFE) of a data transformation pipeline (DTP). For certain of these embodiments, a “distributor” receives a buffer as input and directs that buffer to one of several parallel identical threads to process that buffer. A scheduler would create each of these multiple threads, each thread having an identical (redundant) strings of transforms (chains) downstream from the distributor, and all of which would lead even further downstream to a collector that is responsible for collecting and, if necessary, ordering the buffers processed by the previous redundant chains. In this way, the distributors and collectors provide increased scalability for the pipeline by implicitly partitioning (distributing) individual buffers to one of many threads for at least a part of their execution/processing.