tests.system.openlineage.example_openlineage_all_facets_dag¶

DAG with a custom operator that emits all importable openlineage.client.facet_v2 facets that OL do not overwrite.

It checks:

all run facets
all job facets
all input dataset facets
all output dataset facets
one custom facet per entity
all optional parameters are populated for comprehensive serialization coverage

Attributes¶

`DAG_ID`
`all_facets_task`
`test_run`

Classes¶

AllFacetsOperator

Abstract base class for all operators.

Module Contents¶

class tests.system.openlineage.example_openlineage_all_facets_dag.AllFacetsOperator(*, task_id, owner=DEFAULT_OWNER, email=None, email_on_retry=DEFAULT_EMAIL_ON_RETRY, email_on_failure=DEFAULT_EMAIL_ON_FAILURE, retries=DEFAULT_RETRIES, retry_delay=DEFAULT_RETRY_DELAY, retry_exponential_backoff=0, max_retry_delay=None, retry_policy=None, start_date=None, end_date=None, depends_on_past=False, ignore_first_depends_on_past=DEFAULT_IGNORE_FIRST_DEPENDS_ON_PAST, wait_for_past_depends_before_skipping=DEFAULT_WAIT_FOR_PAST_DEPENDS_BEFORE_SKIPPING, wait_for_downstream=False, dag=None, params=None, default_args=None, priority_weight=DEFAULT_PRIORITY_WEIGHT, weight_rule=DEFAULT_WEIGHT_RULE, queue=DEFAULT_QUEUE, pool=None, pool_slots=DEFAULT_POOL_SLOTS, sla=None, execution_timeout=DEFAULT_TASK_EXECUTION_TIMEOUT, on_execute_callback=None, on_failure_callback=None, on_success_callback=None, on_retry_callback=None, on_skipped_callback=None, pre_execute=None, post_execute=None, trigger_rule=DEFAULT_TRIGGER_RULE, resources=None, run_as_user=None, map_index_template=None, max_active_tis_per_dag=None, max_active_tis_per_dagrun=None, executor=None, executor_config=None, do_xcom_push=True, multiple_outputs=False, inlets=None, outlets=None, task_group=None, doc=None, doc_md=None, doc_json=None, doc_yaml=None, doc_rst=None, task_display_name=None, logger_name=None, allow_nested_operators=True, render_template_as_native_obj=None, **kwargs)[source]¶

Bases: airflow.providers.common.compat.sdk.BaseOperator

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.

Parameters:

task_id (str) – a unique, meaningful id for the task
owner (str) – the owner of the task. Using a meaningful description (e.g. user/person/team/role name) to clarify ownership is recommended.
email (str | collections.abc.Sequence[str] | None) – 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. (deprecated)
email_on_retry (bool) – Indicates whether email alerts should be sent when a task is retried (deprecated)
email_on_failure (bool) – Indicates whether email alerts should be sent when a task failed (deprecated)
retries (int | None) – the number of retries that should be performed before failing the task
retry_delay (datetime.timedelta | float) – delay between retries, can be set as timedelta or float seconds, which will be converted into timedelta, the default is timedelta(seconds=300).
retry_exponential_backoff (float) – multiplier for exponential backoff between retries. Set to 0 to disable (constant delay). Set to 2.0 for standard exponential backoff (delay doubles with each retry). For example, with retry_delay=4min and retry_exponential_backoff=5, retries occur after 4min, 20min, 100min, etc.
max_retry_delay (datetime.timedelta | float | None) – maximum delay interval between retries, can be set as timedelta or float seconds, which will be converted into timedelta.
start_date (datetime.datetime | None) – The start_date for the task, determines the logical_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 logical_date and adds the schedule_interval to determine the next logical_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 logical_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.
end_date (datetime.datetime | None) – if specified, the scheduler won’t go beyond this date
depends_on_past (bool) – 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.
wait_for_past_depends_before_skipping (bool) – 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
wait_for_downstream (bool) – 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.
dag (airflow.sdk.definitions.dag.DAG | None) – a reference to the dag the task is attached to (if any)
priority_weight (int) – 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. As not all database engines support 64-bit integers, values are capped with 32-bit. Valid range is from -2,147,483,648 to 2,147,483,647.
weight_rule (airflow.sdk.types.WeightRuleParam) – 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. Irrespective of the weight rule, resulting priority values are capped with 32-bit. This is an experimental feature. 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.
queue (str) – which queue to target when running this job. Not all executors implement queue management, the CeleryExecutor does support targeting specific queues.
pool (str | None) – the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks
pool_slots (int) – the number of pool slots this task should use (>= 1) Values less than 1 are not allowed.
sla (datetime.timedelta | None) – DEPRECATED - The SLA feature is removed in Airflow 3.0, to be replaced with a new implementation in Airflow >=3.1.
execution_timeout (datetime.timedelta | None) – max time allowed for the execution of this task instance, if it goes beyond it will raise and fail.
on_failure_callback (None | airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback | list[airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback]) – 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.
on_execute_callback (None | airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback | collections.abc.Collection[airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback]) – much like the on_failure_callback except that it is executed right before the task is executed.
on_retry_callback (None | airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback | list[airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback]) – much like the on_failure_callback except that it is executed when retries occur.
on_success_callback (None | airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback | collections.abc.Collection[airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback]) – much like the on_failure_callback except that it is executed when the task succeeds.
on_skipped_callback (None | airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback | collections.abc.Collection[airflow.sdk.definitions._internal.abstractoperator.TaskStateChangeCallback]) – 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.
pre_execute (TaskPreExecuteHook | None) – a function to be called immediately before task execution, receiving a context dictionary; raising an exception will prevent the task from being executed.
post_execute (TaskPostExecuteHook | None) – 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.
trigger_rule (str) – 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
resources (dict[str, Any] | None) – A map of resource parameter names (the argument names of the Resources constructor) to their values.
run_as_user (str | None) – unix username to impersonate while running the task
max_active_tis_per_dag (int | None) – When set, a task will be able to limit the concurrent runs across logical_dates.
max_active_tis_per_dagrun (int | None) – When set, a task will be able to limit the concurrent task instances per Dag run.
executor (str | None) – Which executor to target when running this task. NOT YET SUPPORTED
executor_config (dict | None) –
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"}})
```
do_xcom_push (bool) – if True, an XCom is pushed containing the Operator’s result
multiple_outputs (bool) – 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.
task_group (airflow.sdk.definitions.taskgroup.TaskGroup | None) – The TaskGroup to which the task should belong. This is typically provided when not using a TaskGroup as a context manager.
doc (str | None) – Add documentation or notes to your Task objects that is visible in Task Instance details View in the Webserver
doc_md (str | None) – Add documentation (in Markdown format) or notes to your Task objects that is visible in Task Instance details View in the Webserver
doc_rst (str | None) – Add documentation (in RST format) or notes to your Task objects that is visible in Task Instance details View in the Webserver
doc_json (str | None) – Add documentation (in JSON format) or notes to your Task objects that is visible in Task Instance details View in the Webserver
doc_yaml (str | None) – Add documentation (in YAML format) or notes to your Task objects that is visible in Task Instance details View in the Webserver
task_display_name (str | None) – The display name of the task which appears on the UI.
logger_name (str | None) – 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. HttpOperator will have airflow.task.operators.airflow.providers.http.operators.http.HttpOperator as logger).
allow_nested_operators (bool) –
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)
```
render_template_as_native_obj (bool | None) – If True, uses a Jinja NativeEnvironment to render templates as native Python types. If False, a Jinja Environment is used to render templates as string values. If None (default), inherits from the DAG setting.
start_from_trigger – If True, the operator starts execution directly in the triggerer, skipping the initial worker execution phase. In this mode, templated fields are rendered inside the triggerer instead of the worker. This avoids an extra round trip to a worker, but may increase load on the triggerer, since the DAG must be serialized in order to render templated fields. Use with care for DAGs with many tasks or heavy templating.
start_trigger_args – Used together with start_from_trigger to explicitly specify which operator fields should be passed to the trigger. This helps limit the amount of data serialized and sent to the triggerer.

execute(context)[source]¶

Derive when creating an operator.

The main method to execute the task. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

get_openlineage_facets_on_start()[source]¶

tests.system.openlineage.example_openlineage_all_facets_dag.DAG_ID = 'openlineage_all_facets_dag'[source]¶

tests.system.openlineage.example_openlineage_all_facets_dag.all_facets_task[source]¶

tests.system.openlineage.example_openlineage_all_facets_dag.test_run[source]¶