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239 | class AirflowPipelineWriter(BasePipelineWriter):
"""
Class for pipeline file writer. Corresponds to "AIRFLOW" framework.
"""
@property
def docker_template_name(self) -> str:
return "airflow_dockerfile.jinja"
def _write_dag(self) -> None:
# Check if the given DAG flavor is a supported/valid one
try:
dag_flavor = AirflowDagFlavor[
self.dag_config.get("dag_flavor", "PythonOperatorPerArtifact")
]
except KeyError:
raise ValueError(
f'"{dag_flavor}" is an invalid airflow dag flavor.'
)
try:
task_serialization = TaskSerializer[
self.dag_config.get("task_serialization", "LocalPickle")
]
except KeyError:
raise ValueError(
f'"{task_serialization}" is an invalid type of task serialization scheme.'
)
# Construct DAG text for the given flavor
full_code = self._write_operators(dag_flavor, task_serialization)
# Write out file
file = self.output_dir / f"{self.pipeline_name}_dag.py"
file.write_text(prettify(full_code))
logger.info(f"Generated DAG file: {file}")
def _write_operators(
self,
dag_flavor: AirflowDagFlavor,
task_serialization: TaskSerializer,
) -> str:
"""
This method implements Airflow DAG code generation corresponding
to the following flavors
- ``PythonOperatorPerSession`` flavor, where each session gets its
own Python operator.
- ``PythonOperatorPerArtifact`` flavor, where each artifact gets its own
Python operator.
Example of ``PythonOperatorPerSession`` if the two artifacts in our pipeline
(e.g., model and prediction) were created in the same session.
``` python
import pickle
import g2_z_module
...
def task_run_session_including_g2():
artifacts = g2_z_module.run_session_including_g2()
pickle.dump(artifacts["g2"], open("/tmp/g2_z/artifact_g2.pickle", "wb"))
pickle.dump(artifacts["z"], open("/tmp/g2_z/artifact_z.pickle", "wb"))
with DAG(...) as dag:
run_session_including_g2 = PythonOperator(
task_id="run_session_including_g2_task",
python_callable=task_run_session_including_g2,
)
```
Example of ``PythonOperatorPerArtifact``, if the two artifacts in our pipeline
(e.g., model and prediction) were created in the same session:
``` python
import pickle
import iris_module
...
def task_iris_model():
mod = iris_module.get_iris_model()
pickle.dump(mod, open("/tmp/iris/variable_mod.pickle", "wb"))
def task_iris_pred():
mod = pickle.load(open("/tmp/iris/variable_mod.pickle", "rb"))
pred = iris_module.get_iris_pred(mod)
pickle.dump(
pred, open("/tmp/iris/variable_pred.pickle", "wb")
)
with DAG(...) as dag:
iris_model = PythonOperator(
task_id="iris_model_task",
python_callable=task_iris_model,
)
iris_pred = PythonOperator(
task_id="iris_pred_task",
python_callable=task_iris_pred,
)
iris_model >> iris_pred
```
This way, the generated Airflow DAG file opens room for engineers
to control pipeline runs at a finer level and allows for further customization.
"""
DAG_TEMPLATE = load_plugin_template("airflow_dag_PythonOperator.jinja")
if dag_flavor == AirflowDagFlavor.PythonOperatorPerSession:
task_breakdown = DagTaskBreakdown.TaskPerSession
elif dag_flavor == AirflowDagFlavor.PythonOperatorPerArtifact:
task_breakdown = DagTaskBreakdown.TaskPerArtifact
# Get task definitions based on dag_flavor
task_defs, task_graph = get_task_graph(
self.artifact_collection,
pipeline_name=self.pipeline_name,
task_breakdown=task_breakdown,
)
# Add setup and teardown if local pickle serializer is selected
if task_serialization == TaskSerializer.LocalPickle:
task_defs["setup"] = get_localpickle_setup_task_definition(
self.pipeline_name
)
task_defs["teardown"] = get_localpickle_teardown_task_definition(
self.pipeline_name
)
# insert in order to task_names so that setup runs first and teardown runs last
task_graph.insert_setup_task("setup")
task_graph.insert_teardown_task("teardown")
rendered_task_defs = self.get_rendered_task_definitions(
task_defs, task_serialization
)
task_dependencies = sorted(
[f"{task0} >> {task1}" for task0, task1 in task_graph.graph.edges]
)
# Get DAG parameters for an Airflow pipeline
input_parameters_dict: Dict[str, Any] = {}
for parameter_name, input_spec in super().get_pipeline_args().items():
input_parameters_dict[parameter_name] = input_spec.value
full_code = DAG_TEMPLATE.render(
DAG_NAME=self.pipeline_name,
MODULE_NAME=self.pipeline_name + "_module",
OWNER=self.dag_config.get("owner", "airflow"),
RETRIES=self.dag_config.get("retries", 2),
START_DATE=self.dag_config.get("start_date", "days_ago(1)"),
SCHEDULE_INTERVAL=self.dag_config.get(
"schedule_interval", "*/15 * * * *"
),
MAX_ACTIVE_RUNS=self.dag_config.get("max_active_runs", 1),
CATCHUP=self.dag_config.get("catchup", "False"),
dag_params=input_parameters_dict,
task_definitions=rendered_task_defs,
tasks=task_defs,
task_dependencies=task_dependencies,
)
return full_code
def get_rendered_task_definitions(
self,
task_defs: Dict[str, TaskDefinition],
task_serialization: TaskSerializer,
) -> List[str]:
"""
Returns rendered tasks for the pipeline tasks.
"""
TASK_FUNCTION_TEMPLATE = load_plugin_template(
"task/task_function.jinja"
)
rendered_task_defs: List[str] = []
for task_name, task_def in task_defs.items():
loading_blocks, dumping_blocks = render_task_io_serialize_blocks(
task_def, task_serialization
)
task_def_rendered = TASK_FUNCTION_TEMPLATE.render(
function_name=task_name,
user_input_variables=", ".join(task_def.user_input_variables),
typing_blocks=task_def.typing_blocks,
loading_blocks=loading_blocks,
pre_call_block=task_def.pre_call_block,
call_block=task_def.call_block,
post_call_block=task_def.post_call_block,
dumping_blocks=dumping_blocks,
return_block="",
include_imports_locally=False,
)
rendered_task_defs.append(task_def_rendered)
# sort here to maintain deterministic behavior of writing
return sorted(rendered_task_defs)
|