> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Testing a flow with pytest

You can test Metaflow flows with pytest in two ways: test the logic inside steps, or test the flow end to end.

> [tab] Testing logic in steps
>
> A helpful design pattern is to move non-orchestration logic out of your flows and into separate modules, then write unit tests for those functions. For example, say your flow file has logic embedded directly in a step:
>
> ```py title="my_flow.py" theme={null}
> from metaflow import FlowSpec, step
>
> class MyFlow(FlowSpec):
>
>     @step
>     def start(self):
>         # load the input data
>         # transform and validate it
>         # write the result
>         self.next(self.next_step)
>
>     # rest of flow
>     ...
> ```
>
> You cannot import and test that logic on its own, because it only exists inside the flow. Move it into a separate module file:
>
> ```py title="my_module.py" theme={null}
> def do_logic():
>     # load the input data
>     # transform and validate it
>     # write the result
> ```
>
> Then, update the flow to import the module instead:
>
> ```py title="my_flow.py" theme={null}
> from metaflow import FlowSpec, step
>
> class MyFlow(FlowSpec):
>
>     @step
>     def start(self):
>         from my_module import do_logic
>         do_logic()
>         self.next(self.next_step)
>
>     # rest of flow
> ```
>
> The logic is identical in both versions of the flow; only its location changes. You can now unit test `do_logic` independently of the flow, which is the point of the refactor. Separating logic from orchestration makes the code easier to maintain and test, especially when multiple flows or steps share the same logic.

> [tab] Testing a flow end to end
>
> To test that a flow produces the artifact values you expect, use Metaflow's Runner API from a pytest script. For example, given this flow:
>
> ```py title="simple_flow.py" theme={null}
> from metaflow import FlowSpec, step
>
> class FlowToTest(FlowSpec):
>
>     @step
>     def start(self):
>         self.x = 0
>         self.next(self.end)
>
>     @step
>     def end(self):
>         self.x += 1
>
> if __name__ == '__main__':
>     FlowToTest()
> ```
>
> To test that `x` equals `1` after the flow runs:
>
> **1. Optionally, switch Metaflow profiles**
>
> By default, Metaflow creates a profile at `~/.metaflow_config/config.json`. To separate test-run data from your actual runs, create a test profile at `~/.metaflowconfig/config_test.json`:
>
> ```json theme={null}
> {
>     "METAFLOW_DEFAULT_DATASTORE": "local"
> }
> ```
>
> **2. Write the pytest script**
>
> Define a test that runs the flow and asserts on the artifact:
>
> ```py title="test_simple_flow.py" theme={null}
> from metaflow import Runner
>
> def test_flow():
>     runner = Runner(flow_file="./simple_flow.py", profile="test")
>     result = runner.run()
>     run_obj = result.run
>     assert run_obj.data.x == 1
> ```
>
> **3. Run pytest**
>
> ```sh theme={null}
> pytest
> ```
>
> The test passes when the artifact value matches:
>
> ```text theme={null}
> ============================= test session starts ==============================
> platform darwin -- Python 3.12.4, pytest-8.2.2, pluggy-1.5.0
> plugins: anyio-4.4.0
> collected 1 item
>
> test_simple_flow.py .                                                    [100%]
>
> ============================== 1 passed in 2.01s ===============================
> ```