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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import base64
import os
import pickle
from tempfile import TemporaryDirectory
from typing import TYPE_CHECKING, Callable, Sequence
import dill
from airflow.decorators.base import DecoratedOperator, task_decorator_factory
from airflow.providers.docker.operators.docker import DockerOperator
from airflow.utils.python_virtualenv import write_python_script
if TYPE_CHECKING:
from airflow.decorators.base import TaskDecorator
from airflow.utils.context import Context
def _generate_decode_command(env_var, file, python_command):
# We don't need `f.close()` as the interpreter is about to exit anyway
return (
f'{python_command} -c "import base64, os;'
rf"x = base64.b64decode(os.environ[\"{env_var}\"]);"
rf'f = open(\"{file}\", \"wb\"); f.write(x);"'
)
def _b64_encode_file(filename):
with open(filename, "rb") as file_to_encode:
return base64.b64encode(file_to_encode.read())
class _DockerDecoratedOperator(DecoratedOperator, DockerOperator):
"""
Wraps a Python callable and captures args/kwargs when called for execution.
:param python_callable: A reference to an object that is callable
:param python: Python binary name to use
:param use_dill: Whether dill should be used to serialize the callable
:param expect_airflow: whether to expect airflow to be installed in the docker environment. if this
one is specified, the script to run callable will attempt to load Airflow macros.
:param op_kwargs: a dictionary of keyword arguments that will get unpacked
in your function (templated)
:param op_args: a list of positional arguments that will get unpacked when
calling your callable (templated)
:param multiple_outputs: if set, function return value will be
unrolled to multiple XCom values. Dict will unroll to xcom values with keys as keys.
Defaults to False.
"""
custom_operator_name = "@task.docker"
template_fields: Sequence[str] = (*DockerOperator.template_fields, "op_args", "op_kwargs")
def __init__(
self,
use_dill=False,
python_command="python3",
expect_airflow: bool = True,
**kwargs,
) -> None:
command = "placeholder command"
self.python_command = python_command
self.expect_airflow = expect_airflow
self.use_dill = use_dill
super().__init__(
command=command, retrieve_output=True, retrieve_output_path="/tmp/script.out", **kwargs
)
def generate_command(self):
return (
f"""bash -cx '{_generate_decode_command("__PYTHON_SCRIPT", "/tmp/script.py",
self.python_command)} &&"""
f'{_generate_decode_command("__PYTHON_INPUT", "/tmp/script.in", self.python_command)} &&'
f"{self.python_command} /tmp/script.py /tmp/script.in /tmp/script.out'"
)
def execute(self, context: Context):
with TemporaryDirectory(prefix="venv") as tmp_dir:
input_filename = os.path.join(tmp_dir, "script.in")
script_filename = os.path.join(tmp_dir, "script.py")
with open(input_filename, "wb") as file:
if self.op_args or self.op_kwargs:
self.pickling_library.dump({"args": self.op_args, "kwargs": self.op_kwargs}, file)
py_source = self.get_python_source()
write_python_script(
jinja_context={
"op_args": self.op_args,
"op_kwargs": self.op_kwargs,
"pickling_library": self.pickling_library.__name__,
"python_callable": self.python_callable.__name__,
"python_callable_source": py_source,
"expect_airflow": self.expect_airflow,
"string_args_global": False,
},
filename=script_filename,
)
# Pass the python script to be executed, and the input args, via environment variables. This is
# more than slightly hacky, but it means it can work when Airflow itself is in the same Docker
# engine where this task is going to run (unlike say trying to mount a file in)
self.environment["__PYTHON_SCRIPT"] = _b64_encode_file(script_filename)
if self.op_args or self.op_kwargs:
self.environment["__PYTHON_INPUT"] = _b64_encode_file(input_filename)
else:
self.environment["__PYTHON_INPUT"] = ""
self.command = self.generate_command()
return super().execute(context)
@property
def pickling_library(self):
if self.use_dill:
return dill
return pickle
[docs]def docker_task(
python_callable: Callable | None = None,
multiple_outputs: bool | None = None,
**kwargs,
) -> TaskDecorator:
"""
Python operator decorator; wraps a function into an Airflow operator.
Also accepts any argument that DockerOperator will via ``kwargs``. Can be reused in a single DAG.
:param python_callable: Function to decorate
:param multiple_outputs: If set, function return value will be unrolled to multiple XCom values.
Dict will unroll to XCom values with keys as XCom keys. Defaults to False.
"""
return task_decorator_factory(
python_callable=python_callable,
multiple_outputs=multiple_outputs,
decorated_operator_class=_DockerDecoratedOperator,
**kwargs,
)