flowfile-codegen-parity-campaign

To ensure API parity between a visual Flowfile flow and exported Python code, you run parity tests that verify the live DAG engine's execution results match the generated code output. This identifies discrepancies and guides necessary fixes.

Exported Python code returns different results than the live Flowfile engine due to known code generation divergences. Running parity testing checks the equivalence of the DAG engine's result with the exported code to inventory and fix these specific mismatches.

Code generation parity testing for data flows is the process of checking the equivalence of a live visual DAG engine's result with its exported Python code. It ensures that visual Flowfile flows and the FlowFrame API produce consistent execution results.

Yes, you need pytest and polars to run Flowfile parity tests. The testing framework requires these dependencies, alongside access to Flowfile core and testing environments with database connections, to verify the generated Python code matches the in-engine result.

The best way to inventory and fix FlowFrame API code generation discrepancies is to execute parity tests that compare the live DAG engine's output against the exported Python code. This identifies known divergences and provides guidance on fixing them for consistent results.