Bdd Steps

BDD Steps — Python Step Definition Generation

Generate Python step definitions for Behave that implement Gherkin steps using the Databricks SDK. Read existing .feature files, identify undefined steps, and produce well-typed implementations.

When to use

• Implementing step definitions for new or existing feature files
• Adding Databricks SDK calls to step implementations
• Refactoring step definitions for reusability across features

Process

1. Identify undefined steps

Read the target feature files, then run a dry-run to find undefined steps:

uv run behave --dry-run features/<target>.feature 2>&1

Behave prints suggested snippets for each undefined step. Use these as the starting point.

2. Write step definitions

Place step files in features/steps/ organized by domain:

Step definition structure:

from __future__ import annotations

from behave import given, when, then
from behave.runner import Context


@given('a descriptive step pattern with "{parameter}"')
def step_impl(context: Context, parameter: str) -> None:
    """Docstring explaining what this step does."""
    # Implementation using context.workspace (set in environment.py)
    ...

Refer to references/step-library.md for a comprehensive library of reusable Databricks step definitions covering:

• Workspace connection and SQL execution
• Table/schema existence and row count assertions
• Grant and permission verification
• Pipeline triggering and status polling
• Job submission and completion waiting
• HTTP endpoint testing with SSO header simulation

3. Step writing principles

Use context for state passing. Store results in context attributes so downstream Then steps can assert on them:

@when('I execute a query on "{table_name}"')
def step_execute(context: Context, table_name: str) -> None:
    context.query_result = context.workspace.statement_execution.execute_statement(...)

@then('the result should have {count:d} rows')
def step_check_rows(context: Context, count: int) -> None:
    actual = len(context.query_result.result.data_array or [])
    assert actual == count, f"Expected {count}, got {actual}"

Type all parameters. Use Behave's parse types ({name:d} for int, {name:f} for float) or register custom types.

Assertion messages must be diagnostic. Always include expected vs. actual values:

assert actual == expected, f"Expected {expected}, got {actual}"

Substitute {test_schema} references. Feature files may use {test_schema} as a placeholder. Step definitions should resolve it from context.test_schema:

table_fqn = table_name.replace("{test_schema}", context.test_schema)

Poll with timeout for async operations. Jobs, pipelines, and app deployments need polling loops with configurable timeouts.

4. Validate steps compile

After writing, verify all steps resolve:

uv run behave --dry-run

Zero undefined steps = ready to run.

Additional resources

• references/step-library.md — Complete reusable step definition library for all Databricks domains