Runtime-Aware Data Flow Code Generation with Operator Insertion
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Solution Overview
Problem
Cloud-based data processing environments often lack runtime optimization, requiring manual code-level adjustments that users cannot perform due to lack of access to target data processing configurations, especially in low-code environments.
Innovation Solution
Systems and methods that optimize data integration tasks by evaluating input data flows and target runtimes, adding operators to improve performance while maintaining semantic equivalence, and generating human-readable code that is then compiled into machine-executable form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code-level optimization is performed to address data processing runtime configuration, then data processing efficiency is improved, but user accessibility is reduced because users do not have access to target runtime configuration
Solution Approach 1:
The system performs self-optimization by automatically analyzing the data flow and target runtime configuration to generate optimized code without requiring user intervention. The compiler service autonomously determines optimization opportunities and applies appropriate transformations, eliminating the need for users to manually access or modify runtime configuration settings.
Solution Approach 2:
The patent introduces an intermediary compiler service that acts as a bridge between the user's data flow definition and the target runtime execution. This intermediary automatically performs optimization transformations by analyzing both the data flow characteristics and target runtime configuration, then generates optimized code that users can execute without directly interacting with runtime configuration details.
2Productivity
If code-level manual optimization is required to address different cardinalities from input data and target cluster configuration, then processing performance is improved, but system complexity increases due to low-code environment limitations
Solution Approach 1:
The patent replaces manual mechanical code-editing processes with an automated compiler-based system. Instead of requiring users to manually analyze and rewrite code for optimization, the system uses a compiler service that automatically performs code transformations based on data flow analysis and target runtime characteristics, substituting human manual work with automated computational processes.
Solution Approach 2:
The system performs preliminary optimization analysis during the code generation phase, before the data flow is executed. The compiler service proactively identifies optimization opportunities by analyzing the data flow definition and target runtime configuration in advance, and applies optimizations during code generation rather than requiring post-deployment manual intervention.
3Productivity
If automated operator addition is implemented based on data characteristics and runtime configuration, then data processing efficiency is improved, but code complexity increases
Solution Approach 1:
The patent applies local quality by adding operators selectively at specific locations within the data flow where optimization is needed, rather than uniformly transforming the entire data flow. The compiler service analyzes the data flow to identify specific points where additional operators would benefit performance, and applies transformations only at those localized positions, maintaining simplicity elsewhere in the code.
Solution Approach 2:
The system changes parameters of the data flow representation by adding operators with specific characteristics based on data characteristics and runtime configuration. The compiler service modifies the data flow's structural parameters by inserting operators that have properties optimized for the target runtime environment, such as partitioning operators for distributed processing or caching operators for performance optimization.
Data Source
AI summary
Systems and methods for converting data flow to data processing code. One example system includes an electronic processor configured to receive a data flow for processing a set of source data on a target runtime, determine a characteristic associated with the set of source data, determine a target configuration of the target runtime, generate data processing code at least by adding an operator to the data flow at a point based at least on the characteristic associated with the set of source data and the target configuration of the target runtime, and output the data processing code to a compiler for generation of machine executable code.