ashb commented on code in PR #43076: URL: https://github.com/apache/airflow/pull/43076#discussion_r1814869498
########## task_sdk/src/airflow/sdk/definitions/baseoperator.py: ########## @@ -0,0 +1,1141 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# 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 abc +import collections.abc +import contextlib +import copy +import inspect +import warnings +from collections.abc import Iterable, Sequence +from dataclasses import dataclass +from datetime import datetime, timedelta +from functools import total_ordering, wraps +from types import FunctionType +from typing import TYPE_CHECKING, Any, ClassVar, TypeVar, cast + +import attrs + +from airflow.exceptions import FailStopDagInvalidTriggerRule +from airflow.sdk.definitions.abstractoperator import ( + DEFAULT_IGNORE_FIRST_DEPENDS_ON_PAST, + DEFAULT_OWNER, + DEFAULT_POOL_SLOTS, + DEFAULT_PRIORITY_WEIGHT, + DEFAULT_QUEUE, + DEFAULT_RETRIES, + DEFAULT_RETRY_DELAY, + DEFAULT_TASK_EXECUTION_TIMEOUT, + DEFAULT_TRIGGER_RULE, + DEFAULT_WAIT_FOR_PAST_DEPENDS_BEFORE_SKIPPING, + DEFAULT_WEIGHT_RULE, + AbstractOperator, +) +from airflow.sdk.definitions.decorators import fixup_decorator_warning_stack +from airflow.sdk.definitions.node import validate_key +from airflow.sdk.types import NOTSET, validate_instance_args +from airflow.task.priority_strategy import PriorityWeightStrategy, validate_and_load_priority_weight_strategy +from airflow.utils.trigger_rule import TriggerRule +from airflow.utils.types import AttributeRemoved + +T = TypeVar("T", bound=FunctionType) + +if TYPE_CHECKING: + # from ..execution_time.context import Context + class Context: ... + + class ParamsDict: ... + + from airflow.sdk.definitions.dag import DAG + from airflow.utils.operator_resources import Resources + +from airflow.sdk.definitions.taskgroup import TaskGroup + +# TODO: Task-SDK +AirflowException = RuntimeError +ParamsDict = dict + + +def _get_parent_defaults(dag: DAG | None, task_group: TaskGroup | None) -> tuple[dict, ParamsDict]: + if not dag: + return {}, ParamsDict() + dag_args = copy.copy(dag.default_args) + dag_params = copy.deepcopy(dag.params) + if task_group: + if task_group.default_args and not isinstance(task_group.default_args, collections.abc.Mapping): + raise TypeError("default_args must be a mapping") + dag_args.update(task_group.default_args) + return dag_args, dag_params + + +def get_merged_defaults( + dag: DAG | None, + task_group: TaskGroup | None, + task_params: collections.abc.MutableMapping | None, + task_default_args: dict | None, +) -> tuple[dict, ParamsDict]: + args, params = _get_parent_defaults(dag, task_group) + if task_params: + if not isinstance(task_params, collections.abc.Mapping): + raise TypeError(f"params must be a mapping, got {type(task_params)}") + params.update(task_params) + if task_default_args: + if not isinstance(task_default_args, collections.abc.Mapping): + raise TypeError(f"default_args must be a mapping, got {type(task_params)}") + args.update(task_default_args) + with contextlib.suppress(KeyError): + params.update(task_default_args["params"] or {}) + return args, params + + +class BaseOperatorMeta(abc.ABCMeta): + """Metaclass of BaseOperator.""" + + @classmethod + def _apply_defaults(cls, func: T) -> T: + """ + Look for an argument named "default_args", and fill the unspecified arguments from it. + + Since python2.* isn't clear about which arguments are missing when + calling a function, and that this can be quite confusing with multi-level + inheritance and argument defaults, this decorator also alerts with + specific information about the missing arguments. + """ + # Cache inspect.signature for the wrapper closure to avoid calling it + # at every decorated invocation. This is separate sig_cache created + # per decoration, i.e. each function decorated using apply_defaults will + # have a different sig_cache. + sig_cache = inspect.signature(func) + non_variadic_params = { + name: param + for (name, param) in sig_cache.parameters.items() + if param.name != "self" and param.kind not in (param.VAR_POSITIONAL, param.VAR_KEYWORD) + } + non_optional_args = { + name + for name, param in non_variadic_params.items() + if param.default == param.empty and name != "task_id" + } + + fixup_decorator_warning_stack(func) + + @wraps(func) + def apply_defaults(self: BaseOperator, *args: Any, **kwargs: Any) -> Any: + from airflow.sdk.definitions.contextmanager import DagContext, TaskGroupContext + + if args: + raise TypeError("Use keyword arguments when initializing operators") + + instantiated_from_mapped = kwargs.pop( + "_airflow_from_mapped", + getattr(self, "_BaseOperator__from_mapped", False), + ) + + dag: DAG | None = kwargs.get("dag") or DagContext.get_current() + task_group: TaskGroup | None = kwargs.get("task_group") + if dag and not task_group: + task_group = TaskGroupContext.get_current(dag) + + default_args, merged_params = get_merged_defaults( + dag=dag, + task_group=task_group, + task_params=kwargs.pop("params", None), + task_default_args=kwargs.pop("default_args", None), + ) + + for arg in sig_cache.parameters: + if arg not in kwargs and arg in default_args: + kwargs[arg] = default_args[arg] + + missing_args = non_optional_args.difference(kwargs) + if len(missing_args) == 1: + raise TypeError(f"missing keyword argument {missing_args.pop()!r}") + elif missing_args: + display = ", ".join(repr(a) for a in sorted(missing_args)) + raise TypeError(f"missing keyword arguments {display}") + + if merged_params: + kwargs["params"] = merged_params + + hook = getattr(self, "_hook_apply_defaults", None) + if hook: + args, kwargs = hook(**kwargs, default_args=default_args) + default_args = kwargs.pop("default_args", {}) + + if not hasattr(self, "_BaseOperator__init_kwargs"): + object.__setattr__(self, "_BaseOperator__init_kwargs", {}) + object.__setattr__(self, "_BaseOperator__from_mapped", instantiated_from_mapped) + + result = func(self, **kwargs, default_args=default_args) + + # Store the args passed to init -- we need them to support task.map serialization! + self._BaseOperator__init_kwargs.update(kwargs) # type: ignore + + # Set upstream task defined by XComArgs passed to template fields of the operator. + # BUT: only do this _ONCE_, not once for each class in the hierarchy + if not instantiated_from_mapped and func == self.__init__.__wrapped__: # type: ignore[misc] + self._set_xcomargs_dependencies() + # Mark instance as instantiated so that futre attr setting updates xcomarg-based deps. + object.__setattr__(self, "_BaseOperator__instantiated", True) + + return result + + apply_defaults.__non_optional_args = non_optional_args # type: ignore + apply_defaults.__param_names = set(non_variadic_params) # type: ignore + + return cast(T, apply_defaults) + + def __new__(cls, name, bases, namespace, **kwargs): + # TODO: Task-SDK + # execute_method = namespace.get("execute") + # if callable(execute_method) and not getattr(execute_method, "__isabstractmethod__", False): + # namespace["execute"] = ExecutorSafeguard().decorator(execute_method) + new_cls = super().__new__(cls, name, bases, namespace, **kwargs) + with contextlib.suppress(KeyError): + # Update the partial descriptor with the class method, so it calls the actual function + # (but let subclasses override it if they need to) + # TODO: Task-SDK + # partial_desc = vars(new_cls)["partial"] + # if isinstance(partial_desc, _PartialDescriptor): + # partial_desc.class_method = classmethod(partial) + ... + + # We patch `__init__` only if the class defines it. + if inspect.getmro(new_cls)[1].__init__ is not new_cls.__init__: + new_cls.__init__ = cls._apply_defaults(new_cls.__init__) + + return new_cls + + +# TODO: The following mapping is used to validate that the arguments passed to the BaseOperator are of the +# correct type. This is a temporary solution until we find a more sophisticated method for argument +# validation. One potential method is to use `get_type_hints` from the typing module. However, this is not +# fully compatible with future annotations for Python versions below 3.10. Once we require a minimum Python +# version that supports `get_type_hints` effectively or find a better approach, we can replace this +# manual type-checking method. +BASEOPERATOR_ARGS_EXPECTED_TYPES = { + "task_id": str, + "email": (str, Sequence), + "email_on_retry": bool, + "email_on_failure": bool, + "retries": int, + "retry_exponential_backoff": bool, + "depends_on_past": bool, + "ignore_first_depends_on_past": bool, + "wait_for_past_depends_before_skipping": bool, + "wait_for_downstream": bool, + "priority_weight": int, + "queue": str, + "pool": str, + "pool_slots": int, + "trigger_rule": str, + "run_as_user": str, + "task_concurrency": int, + "map_index_template": str, + "max_active_tis_per_dag": int, + "max_active_tis_per_dagrun": int, + "executor": str, + "do_xcom_push": bool, + "multiple_outputs": bool, + "doc": str, + "doc_md": str, + "doc_json": str, + "doc_yaml": str, + "doc_rst": str, + "task_display_name": str, + "logger_name": str, + "allow_nested_operators": bool, + "start_date": datetime, + "end_date": datetime, +} + + +# Note: BaseOperator is defined as a dataclass, and not an attrs class as we do too much metaprogramming in +# here (metaclass, custom `__setattr__` behaviour) and this fights with attrs too much to make it worth it. +# +# To future reader: if you want to try and make this a "normal" attrs class, go ahead and attempt it. If you +# get no where leave your record here for the next poor soul and what problems you ran in to. +# +# @ashb, 2024/10/14 +# - "Can't combine custom __setattr__ with on_setattr hooks" +# - Setting class-wide `define(on_setarrs=...)` isn't called for non-attrs subclasses +@total_ordering +@dataclass(repr=False, kw_only=True) +class BaseOperator(AbstractOperator, metaclass=BaseOperatorMeta): + r""" + Abstract base class for all operators. + + Since operators create objects that become nodes in the DAG, BaseOperator + contains many recursive methods for DAG crawling behavior. To derive from + this class, you are expected to override the constructor and the 'execute' + method. + + Operators derived from this class should perform or trigger certain tasks + synchronously (wait for completion). Example of operators could be an + operator that runs a Pig job (PigOperator), a sensor operator that + waits for a partition to land in Hive (HiveSensorOperator), or one that + moves data from Hive to MySQL (Hive2MySqlOperator). Instances of these + operators (tasks) target specific operations, running specific scripts, + functions or data transfers. + + This class is abstract and shouldn't be instantiated. Instantiating a + class derived from this one results in the creation of a task object, + which ultimately becomes a node in DAG objects. Task dependencies should + be set by using the set_upstream and/or set_downstream methods. + + :param task_id: a unique, meaningful id for the task + :param owner: the owner of the task. Using a meaningful description + (e.g. user/person/team/role name) to clarify ownership is recommended. + :param email: the 'to' email address(es) used in email alerts. This can be a + single email or multiple ones. Multiple addresses can be specified as a + comma or semicolon separated string or by passing a list of strings. + :param email_on_retry: Indicates whether email alerts should be sent when a + task is retried + :param email_on_failure: Indicates whether email alerts should be sent when + a task failed + :param retries: the number of retries that should be performed before + failing the task + :param retry_delay: delay between retries, can be set as ``timedelta`` or + ``float`` seconds, which will be converted into ``timedelta``, + the default is ``timedelta(seconds=300)``. + :param retry_exponential_backoff: allow progressively longer waits between + retries by using exponential backoff algorithm on retry delay (delay + will be converted into seconds) + :param max_retry_delay: maximum delay interval between retries, can be set as + ``timedelta`` or ``float`` seconds, which will be converted into ``timedelta``. + :param start_date: The ``start_date`` for the task, determines + the ``execution_date`` for the first task instance. The best practice + is to have the start_date rounded + to your DAG's ``schedule_interval``. Daily jobs have their start_date + some day at 00:00:00, hourly jobs have their start_date at 00:00 + of a specific hour. Note that Airflow simply looks at the latest + ``execution_date`` and adds the ``schedule_interval`` to determine + the next ``execution_date``. It is also very important + to note that different tasks' dependencies + need to line up in time. If task A depends on task B and their + start_date are offset in a way that their execution_date don't line + up, A's dependencies will never be met. If you are looking to delay + a task, for example running a daily task at 2AM, look into the + ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using + dynamic ``start_date`` and recommend using fixed ones. Read the + FAQ entry about start_date for more information. + :param end_date: if specified, the scheduler won't go beyond this date + :param depends_on_past: when set to true, task instances will run + sequentially and only if the previous instance has succeeded or has been skipped. + The task instance for the start_date is allowed to run. + :param wait_for_past_depends_before_skipping: when set to true, if the task instance + should be marked as skipped, and depends_on_past is true, the ti will stay on None state + waiting the task of the previous run + :param wait_for_downstream: when set to true, an instance of task + X will wait for tasks immediately downstream of the previous instance + of task X to finish successfully or be skipped before it runs. This is useful if the + different instances of a task X alter the same asset, and this asset + is used by tasks downstream of task X. Note that depends_on_past + is forced to True wherever wait_for_downstream is used. Also note that + only tasks *immediately* downstream of the previous task instance are waited + for; the statuses of any tasks further downstream are ignored. + :param dag: a reference to the dag the task is attached to (if any) + :param priority_weight: priority weight of this task against other task. + This allows the executor to trigger higher priority tasks before + others when things get backed up. Set priority_weight as a higher + number for more important tasks. + :param weight_rule: weighting method used for the effective total + priority weight of the task. Options are: + ``{ downstream | upstream | absolute }`` default is ``downstream`` + When set to ``downstream`` the effective weight of the task is the + aggregate sum of all downstream descendants. As a result, upstream + tasks will have higher weight and will be scheduled more aggressively + when using positive weight values. This is useful when you have + multiple dag run instances and desire to have all upstream tasks to + complete for all runs before each dag can continue processing + downstream tasks. When set to ``upstream`` the effective weight is the + aggregate sum of all upstream ancestors. This is the opposite where + downstream tasks have higher weight and will be scheduled more + aggressively when using positive weight values. This is useful when you + have multiple dag run instances and prefer to have each dag complete + before starting upstream tasks of other dags. When set to + ``absolute``, the effective weight is the exact ``priority_weight`` + specified without additional weighting. You may want to do this when + you know exactly what priority weight each task should have. + Additionally, when set to ``absolute``, there is bonus effect of + significantly speeding up the task creation process as for very large + DAGs. Options can be set as string or using the constants defined in + the static class ``airflow.utils.WeightRule`` + |experimental| + Since 2.9.0, Airflow allows to define custom priority weight strategy, + by creating a subclass of + ``airflow.task.priority_strategy.PriorityWeightStrategy`` and registering + in a plugin, then providing the class path or the class instance via + ``weight_rule`` parameter. The custom priority weight strategy will be + used to calculate the effective total priority weight of the task instance. + :param queue: which queue to target when running this job. Not + all executors implement queue management, the CeleryExecutor + does support targeting specific queues. + :param pool: the slot pool this task should run in, slot pools are a + way to limit concurrency for certain tasks + :param pool_slots: the number of pool slots this task should use (>= 1) + Values less than 1 are not allowed. + :param sla: time by which the job is expected to succeed. Note that + this represents the ``timedelta`` after the period is closed. For + example if you set an SLA of 1 hour, the scheduler would send an email + soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance + has not succeeded yet. + The scheduler pays special attention for jobs with an SLA and + sends alert + emails for SLA misses. SLA misses are also recorded in the database + for future reference. All tasks that share the same SLA time + get bundled in a single email, sent soon after that time. SLA + notification are sent once and only once for each task instance. + :param execution_timeout: max time allowed for the execution of + this task instance, if it goes beyond it will raise and fail. + :param on_failure_callback: a function or list of functions to be called when a task instance + of this task fails. a context dictionary is passed as a single + parameter to this function. Context contains references to related + objects to the task instance and is documented under the macros + section of the API. + :param on_execute_callback: much like the ``on_failure_callback`` except + that it is executed right before the task is executed. + :param on_retry_callback: much like the ``on_failure_callback`` except + that it is executed when retries occur. + :param on_success_callback: much like the ``on_failure_callback`` except + that it is executed when the task succeeds. + :param on_skipped_callback: much like the ``on_failure_callback`` except + that it is executed when skipped occur; this callback will be called only if AirflowSkipException get raised. + Explicitly it is NOT called if a task is not started to be executed because of a preceding branching + decision in the DAG or a trigger rule which causes execution to skip so that the task execution + is never scheduled. + :param pre_execute: a function to be called immediately before task + execution, receiving a context dictionary; raising an exception will + prevent the task from being executed. + + |experimental| + :param post_execute: a function to be called immediately after task + execution, receiving a context dictionary and task result; raising an + exception will prevent the task from succeeding. + + |experimental| + :param trigger_rule: defines the rule by which dependencies are applied + for the task to get triggered. Options are: + ``{ all_success | all_failed | all_done | all_skipped | one_success | one_done | + one_failed | none_failed | none_failed_min_one_success | none_skipped | always}`` + default is ``all_success``. Options can be set as string or + using the constants defined in the static class + ``airflow.utils.TriggerRule`` + :param resources: A map of resource parameter names (the argument names of the + Resources constructor) to their values. + :param run_as_user: unix username to impersonate while running the task + :param max_active_tis_per_dag: When set, a task will be able to limit the concurrent + runs across execution_dates. + :param max_active_tis_per_dagrun: When set, a task will be able to limit the concurrent + task instances per DAG run. + :param executor: Which executor to target when running this task. NOT YET SUPPORTED + :param executor_config: Additional task-level configuration parameters that are + interpreted by a specific executor. Parameters are namespaced by the name of + executor. + + **Example**: to run this task in a specific docker container through + the KubernetesExecutor :: + + MyOperator(..., executor_config={"KubernetesExecutor": {"image": "myCustomDockerImage"}}) + + :param do_xcom_push: if True, an XCom is pushed containing the Operator's + result + :param multiple_outputs: if True and do_xcom_push is True, pushes multiple XComs, one for each + key in the returned dictionary result. If False and do_xcom_push is True, pushes a single XCom. + :param task_group: The TaskGroup to which the task should belong. This is typically provided when not + using a TaskGroup as a context manager. + :param doc: Add documentation or notes to your Task objects that is visible in + Task Instance details View in the Webserver + :param doc_md: Add documentation (in Markdown format) or notes to your Task objects + that is visible in Task Instance details View in the Webserver + :param doc_rst: Add documentation (in RST format) or notes to your Task objects + that is visible in Task Instance details View in the Webserver + :param doc_json: Add documentation (in JSON format) or notes to your Task objects + that is visible in Task Instance details View in the Webserver + :param doc_yaml: Add documentation (in YAML format) or notes to your Task objects + that is visible in Task Instance details View in the Webserver + :param task_display_name: The display name of the task which appears on the UI. + :param logger_name: Name of the logger used by the Operator to emit logs. + If set to `None` (default), the logger name will fall back to + `airflow.task.operators.{class.__module__}.{class.__name__}` (e.g. SimpleHttpOperator will have + *airflow.task.operators.airflow.providers.http.operators.http.SimpleHttpOperator* as logger). + :param allow_nested_operators: if True, when an operator is executed within another one a warning message + will be logged. If False, then an exception will be raised if the operator is badly used (e.g. nested + within another one). In future releases of Airflow this parameter will be removed and an exception + will always be thrown when operators are nested within each other (default is True). + + **Example**: example of a bad operator mixin usage:: + + @task(provide_context=True) + def say_hello_world(**context): + hello_world_task = BashOperator( + task_id="hello_world_task", + bash_command="python -c \"print('Hello, world!')\"", + dag=dag, + ) + hello_world_task.execute(context) + """ + + task_id: str + owner: str = DEFAULT_OWNER + email: str | Sequence[str] | None = None + email_on_retry: bool = True + email_on_failure: bool = True + retries: int | None = DEFAULT_RETRIES + retry_delay: timedelta | float = DEFAULT_RETRY_DELAY + retry_exponential_backoff: bool = False + max_retry_delay: timedelta | float | None = None + start_date: datetime | None = None + end_date: datetime | None = None + depends_on_past: bool = False + ignore_first_depends_on_past: bool = DEFAULT_IGNORE_FIRST_DEPENDS_ON_PAST + wait_for_past_depends_before_skipping: bool = DEFAULT_WAIT_FOR_PAST_DEPENDS_BEFORE_SKIPPING + wait_for_downstream: bool = False + dag: DAG | None = None + params: collections.abc.MutableMapping | None = None + default_args: dict | None = None + priority_weight: int = DEFAULT_PRIORITY_WEIGHT + # TODO: + weight_rule: PriorityWeightStrategy | str = DEFAULT_WEIGHT_RULE + queue: str = DEFAULT_QUEUE + pool: str = "default" + pool_slots: int = DEFAULT_POOL_SLOTS + execution_timeout: timedelta | None = DEFAULT_TASK_EXECUTION_TIMEOUT + # on_execute_callback: None | TaskStateChangeCallback | list[TaskStateChangeCallback] = None + # on_failure_callback: None | TaskStateChangeCallback | list[TaskStateChangeCallback] = None + # on_success_callback: None | TaskStateChangeCallback | list[TaskStateChangeCallback] = None + # on_retry_callback: None | TaskStateChangeCallback | list[TaskStateChangeCallback] = None + # on_skipped_callback: None | TaskStateChangeCallback | list[TaskStateChangeCallback] = None + # pre_execute: TaskPreExecuteHook | None = None + # post_execute: TaskPostExecuteHook | None = None Review Comment: Nope, openlineage uses "Listeners" which are global to the deployment, not specific to one dag or task. -- This is an automated message from the Apache Git Service. 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