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airflow.models.taskinstance

Module Contents

Classes

TaskInstance	Task instances store the state of a task instance. This table is the
SimpleTaskInstance	Simplified Task Instance.
TaskInstanceNote	For storage of arbitrary notes concerning the task instance.

Functions

set_current_context(context)	Sets the current execution context to the provided context object.
clear_task_instances(tis, session[, ...])	Clears a set of task instances, but makes sure the running ones

Attributes

TR
log
hybrid_property
PAST_DEPENDS_MET
TaskInstanceStateType

airflow.models.taskinstance.TR

airflow.models.taskinstance.log

airflow.models.taskinstance.hybrid_property

airflow.models.taskinstance.PAST_DEPENDS_MET = 'past_depends_met'

airflow.models.taskinstance.set_current_context(context)

Sets the current execution context to the provided context object. This method should be called once per Task execution, before calling operator.execute.

airflow.models.taskinstance.clear_task_instances(tis, session, activate_dag_runs=None, dag=None, dag_run_state=DagRunState.QUEUED)

Clears a set of task instances, but makes sure the running ones get killed. Also sets Dagrun’s state to QUEUED and start_date to the time of execution. But only for finished DRs (SUCCESS and FAILED). Doesn’t clear DR’s state and start_date`for running DRs (QUEUED and RUNNING) because clearing the state for already running DR is redundant and clearing `start_date affects DR’s duration.

Parameters

• tis (list[TaskInstance]) – a list of task instances

• session (sqlalchemy.orm.session.Session) – current session

• dag_run_state (airflow.utils.state.DagRunState | airflow.typing_compat.Literal[False]) – state to set finished DagRuns to. If set to False, DagRuns state will not be changed.

• dag (airflow.models.dag.DAG | None) – DAG object

• activate_dag_runs (None) – Deprecated parameter, do not pass

class airflow.models.taskinstance.TaskInstance(task, execution_date=None, run_id=None, state=None, map_index=-1)

Bases: airflow.models.base.Base, airflow.utils.log.logging_mixin.LoggingMixin

Task instances store the state of a task instance. This table is the authority and single source of truth around what tasks have run and the state they are in.

The SqlAlchemy model doesn’t have a SqlAlchemy foreign key to the task or dag model deliberately to have more control over transactions.

Database transactions on this table should insure double triggers and any confusion around what task instances are or aren’t ready to run even while multiple schedulers may be firing task instances.

A value of -1 in map_index represents any of: a TI without mapped tasks; a TI with mapped tasks that has yet to be expanded (state=pending); a TI with mapped tasks that expanded to an empty list (state=skipped).

property try_number

Return the try number that this task number will be when it is actually run.

If the TaskInstance is currently running, this will match the column in the database, in all other cases this will be incremented.

property prev_attempted_tries: int

Based on this instance’s try_number, this will calculate the number of previously attempted tries, defaulting to 0.

property next_try_number: int

property log_url: str

Log URL for TaskInstance

property mark_success_url: str

URL to mark TI success

property key: airflow.models.taskinstancekey.TaskInstanceKey

Returns a tuple that identifies the task instance uniquely

property is_premature: bool

Returns whether a task is in UP_FOR_RETRY state and its retry interval has elapsed.

property previous_ti: TaskInstance | None

This attribute is deprecated. Please use airflow.models.taskinstance.TaskInstance.get_previous_ti method.

property previous_ti_success: TaskInstance | None

This attribute is deprecated. Please use airflow.models.taskinstance.TaskInstance.get_previous_ti method.

property previous_start_date_success: pendulum.DateTime | None

This attribute is deprecated. Please use airflow.models.taskinstance.TaskInstance.get_previous_start_date method.

__tablename__ = 'task_instance'

task_id

dag_id

run_id

map_index

start_date

end_date

duration

state

max_tries

hostname

unixname

job_id

pool

pool_slots

queue

priority_weight

operator

queued_dttm

queued_by_job_id

pid

executor_config

updated_at

external_executor_id

trigger_id

trigger_timeout

next_method

next_kwargs

__table_args__ = ()

dag_model

trigger

triggerer_job

dag_run

rendered_task_instance_fields

execution_date

task_instance_note

note

task: airflow.models.operator.Operator

init_on_load()

Initialize the attributes that aren’t stored in the DB

command_as_list(mark_success=False, ignore_all_deps=False, ignore_task_deps=False, ignore_depends_on_past=False, wait_for_past_depends_before_skipping=False, ignore_ti_state=False, local=False, pickle_id=None, raw=False, job_id=None, pool=None, cfg_path=None)

Returns a command that can be executed anywhere where airflow is installed. This command is part of the message sent to executors by the orchestrator.

static generate_command(dag_id, task_id, run_id, mark_success=False, ignore_all_deps=False, ignore_depends_on_past=False, wait_for_past_depends_before_skipping=False, ignore_task_deps=False, ignore_ti_state=False, local=False, pickle_id=None, file_path=None, raw=False, job_id=None, pool=None, cfg_path=None, map_index=-1)

Generates the shell command required to execute this task instance.

Parameters

• dag_id (str) – DAG ID

• task_id (str) – Task ID

• run_id (str) – The run_id of this task’s DagRun

• mark_success (bool) – Whether to mark the task as successful

• ignore_all_deps (bool) – Ignore all ignorable dependencies. Overrides the other ignore_* parameters.

• ignore_depends_on_past (bool) – Ignore depends_on_past parameter of DAGs (e.g. for Backfills)

• wait_for_past_depends_before_skipping (bool) – Wait for past depends before marking the ti as skipped

• ignore_task_deps (bool) – Ignore task-specific dependencies such as depends_on_past and trigger rule

• ignore_ti_state (bool) – Ignore the task instance’s previous failure/success

• local (bool) – Whether to run the task locally

• pickle_id (int | None) – If the DAG was serialized to the DB, the ID associated with the pickled DAG

• file_path (pathlib.PurePath | str | None) – path to the file containing the DAG definition

• raw (bool) – raw mode (needs more details)

• job_id (str | None) – job ID (needs more details)

• pool (str | None) – the Airflow pool that the task should run in

• cfg_path (str | None) – the Path to the configuration file

Returns

shell command that can be used to run the task instance

Return type

list[str]

current_state(session=NEW_SESSION)

Get the very latest state from the database, if a session is passed, we use and looking up the state becomes part of the session, otherwise a new session is used.

sqlalchemy.inspect is used here to get the primary keys ensuring that if they change it will not regress

Parameters

session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

error(session=NEW_SESSION)

Forces the task instance’s state to FAILED in the database.

Parameters

session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

refresh_from_db(session=NEW_SESSION, lock_for_update=False)

Refreshes the task instance from the database based on the primary key

Parameters

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

• lock_for_update (bool) – if True, indicates that the database should lock the TaskInstance (issuing a FOR UPDATE clause) until the session is committed.

refresh_from_task(task, pool_override=None)

Copy common attributes from the given task.

Parameters

• task (airflow.models.operator.Operator) – The task object to copy from

• pool_override (str | None) – Use the pool_override instead of task’s pool

clear_xcom_data(session=NEW_SESSION)

Clear all XCom data from the database for the task instance.

If the task is unmapped, all XComs matching this task ID in the same DAG run are removed. If the task is mapped, only the one with matching map index is removed.

Parameters

session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

set_state(state, session=NEW_SESSION)

Set TaskInstance state.

Parameters

• state (str | None) – State to set for the TI

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

Returns

Was the state changed

Return type

bool

are_dependents_done(session=NEW_SESSION)

Checks whether the immediate dependents of this task instance have succeeded or have been skipped. This is meant to be used by wait_for_downstream.

This is useful when you do not want to start processing the next schedule of a task until the dependents are done. For instance, if the task DROPs and recreates a table.

Parameters

session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

get_previous_dagrun(state=None, session=None)

The DagRun that ran before this task instance’s DagRun.

Parameters

• state (airflow.utils.state.DagRunState | None) – If passed, it only take into account instances of a specific state.

• session (sqlalchemy.orm.session.Session | None) – SQLAlchemy ORM Session.

get_previous_ti(state=None, session=NEW_SESSION)

The task instance for the task that ran before this task instance.

Parameters

• state (airflow.utils.state.DagRunState | None) – If passed, it only take into account instances of a specific state.

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

get_previous_execution_date(state=None, session=NEW_SESSION)

The execution date from property previous_ti_success.

Parameters

• state (airflow.utils.state.DagRunState | None) – If passed, it only take into account instances of a specific state.

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

get_previous_start_date(state=None, session=NEW_SESSION)

The start date from property previous_ti_success.

Parameters

• state (airflow.utils.state.DagRunState | None) – If passed, it only take into account instances of a specific state.

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

are_dependencies_met(dep_context=None, session=NEW_SESSION, verbose=False)

Returns whether or not all the conditions are met for this task instance to be run given the context for the dependencies (e.g. a task instance being force run from the UI will ignore some dependencies).

Parameters

• dep_context (airflow.ti_deps.dep_context.DepContext | None) – The execution context that determines the dependencies that should be evaluated.

• session (sqlalchemy.orm.session.Session) – database session

• verbose (bool) – whether log details on failed dependencies on info or debug log level

get_failed_dep_statuses(dep_context=None, session=NEW_SESSION)

Get failed Dependencies

__repr__()

Return repr(self).

next_retry_datetime()

Get datetime of the next retry if the task instance fails. For exponential backoff, retry_delay is used as base and will be converted to seconds.

ready_for_retry()

Checks on whether the task instance is in the right state and timeframe to be retried.

get_dagrun(session=NEW_SESSION)

Returns the DagRun for this TaskInstance

Parameters

session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

Returns

DagRun

Return type

airflow.models.dagrun.DagRun

check_and_change_state_before_execution(verbose=True, ignore_all_deps=False, ignore_depends_on_past=False, wait_for_past_depends_before_skipping=False, ignore_task_deps=False, ignore_ti_state=False, mark_success=False, test_mode=False, job_id=None, pool=None, external_executor_id=None, session=NEW_SESSION)

Checks dependencies and then sets state to RUNNING if they are met. Returns True if and only if state is set to RUNNING, which implies that task should be executed, in preparation for _run_raw_task

Parameters

• verbose (bool) – whether to turn on more verbose logging

• ignore_all_deps (bool) – Ignore all of the non-critical dependencies, just runs

• ignore_depends_on_past (bool) – Ignore depends_on_past DAG attribute

• wait_for_past_depends_before_skipping (bool) – Wait for past depends before mark the ti as skipped

• ignore_task_deps (bool) – Don’t check the dependencies of this TaskInstance’s task

• ignore_ti_state (bool) – Disregards previous task instance state

• mark_success (bool) – Don’t run the task, mark its state as success

• test_mode (bool) – Doesn’t record success or failure in the DB

• job_id (str | None) – Job (BackfillJob / LocalTaskJob / SchedulerJob) ID

• pool (str | None) – specifies the pool to use to run the task instance

• external_executor_id (str | None) – The identifier of the celery executor

• session (sqlalchemy.orm.session.Session) – SQLAlchemy ORM Session

Returns

whether the state was changed to running or not

Return type

bool

clear_next_method_args()

run(verbose=True, ignore_all_deps=False, ignore_depends_on_past=False, wait_for_past_depends_before_skipping=False, ignore_task_deps=False, ignore_ti_state=False, mark_success=False, test_mode=False, job_id=None, pool=None, session=NEW_SESSION)

Run TaskInstance

dry_run()

Only Renders Templates for the TI

handle_failure(error, test_mode=None, context=None, force_fail=False, session=NEW_SESSION)

Handle Failure for the TaskInstance

is_eligible_to_retry()

Is task instance is eligible for retry

get_template_context(session=None, ignore_param_exceptions=True)

Return TI Context

get_rendered_template_fields(session=NEW_SESSION)

Update task with rendered template fields for presentation in UI. If task has already run, will fetch from DB; otherwise will render.

get_rendered_k8s_spec(session=NEW_SESSION)

Fetch rendered template fields from DB

overwrite_params_with_dag_run_conf(params, dag_run)

Overwrite Task Params with DagRun.conf

render_templates(context=None)

Render templates in the operator fields.

If the task was originally mapped, this may replace self.task with the unmapped, fully rendered BaseOperator. The original self.task before replacement is returned.

render_k8s_pod_yaml()

Render k8s pod yaml

get_email_subject_content(exception, task=None)

Get the email subject content for exceptions.

email_alert(exception, task)

Send alert email with exception information.

set_duration()

Set TI duration

xcom_push(key, value, execution_date=None, session=NEW_SESSION)

Make an XCom available for tasks to pull.

Parameters

• key (str) – Key to store the value under.

• value (Any) – Value to store. What types are possible depends on whether enable_xcom_pickling is true or not. If so, this can be any picklable object; only be JSON-serializable may be used otherwise.

• execution_date (datetime.datetime | None) – Deprecated parameter that has no effect.

xcom_pull(task_ids=None, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=False, session=NEW_SESSION, *, map_indexes=None, default=None)

Pull XComs that optionally meet certain criteria.

Parameters

• key (str) – A key for the XCom. If provided, only XComs with matching keys will be returned. The default key is 'return_value', also available as constant XCOM_RETURN_KEY. This key is automatically given to XComs returned by tasks (as opposed to being pushed manually). To remove the filter, pass None.

• task_ids (str | Iterable[str] | None) – Only XComs from tasks with matching ids will be pulled. Pass None to remove the filter.

• dag_id (str | None) – If provided, only pulls XComs from this DAG. If None (default), the DAG of the calling task is used.

• map_indexes (int | Iterable[int] | None) – If provided, only pull XComs with matching indexes. If None (default), this is inferred from the task(s) being pulled (see below for details).

• include_prior_dates (bool) – If False, only XComs from the current execution_date are returned. If True, XComs from previous dates are returned as well.

When pulling one single task (task_id is None or a str) without specifying map_indexes, the return value is inferred from whether the specified task is mapped. If not, value from the one single task instance is returned. If the task to pull is mapped, an iterator (not a list) yielding XComs from mapped task instances is returned. In either case, default (None if not specified) is returned if no matching XComs are found.

When pulling multiple tasks (i.e. either task_id or map_index is a non-str iterable), a list of matching XComs is returned. Elements in the list is ordered by item ordering in task_id and map_index.

get_num_running_task_instances(session, same_dagrun=False)

Return Number of running TIs from the DB

init_run_context(raw=False)

Sets the log context.

static filter_for_tis(tis)

Returns SQLAlchemy filter to query selected task instances

schedule_downstream_tasks(session=NEW_SESSION)

The mini-scheduler for scheduling downstream tasks of this task instance :meta: private

get_relevant_upstream_map_indexes(upstream, ti_count, *, session)

Infer the map indexes of an upstream “relevant” to this ti.

The bulk of the logic mainly exists to solve the problem described by the following example, where ‘val’ must resolve to different values, depending on where the reference is being used:

@task
def this_task(v):  # This is self.task.
    return v * 2

@task_group
def tg1(inp):
    val = upstream(inp)  # This is the upstream task.
    this_task(val)  # When inp is 1, val here should resolve to 2.
    return val

# This val is the same object returned by tg1.
val = tg1.expand(inp=[1, 2, 3])

@task_group
def tg2(inp):
    another_task(inp, val)  # val here should resolve to [2, 4, 6].

tg2.expand(inp=["a", "b"])

The surrounding mapped task groups of upstream and self.task are inspected to find a common “ancestor”. If such an ancestor is found, we need to return specific map indexes to pull a partial value from upstream XCom.

Parameters

• upstream (airflow.models.operator.Operator) – The referenced upstream task.

• ti_count (int | None) – The total count of task instance this task was expanded by the scheduler, i.e. expanded_ti_count in the template context.

Returns

Specific map index or map indexes to pull, or None if we want to “whole” return value (i.e. no mapped task groups involved).

Return type

int | range | None

airflow.models.taskinstance.TaskInstanceStateType

class airflow.models.taskinstance.SimpleTaskInstance(dag_id, task_id, run_id, start_date, end_date, try_number, map_index, state, executor_config, pool, queue, key, run_as_user=None, priority_weight=None)

Simplified Task Instance.

Used to send data between processes via Queues.

__eq__(other)

Return self==value.

as_dict()

classmethod from_ti(ti)

classmethod from_dict(obj_dict)

class airflow.models.taskinstance.TaskInstanceNote(content, user_id=None)

Bases: airflow.models.base.Base

For storage of arbitrary notes concerning the task instance.

__tablename__ = 'task_instance_note'

user_id

task_id

dag_id

run_id

map_index

content

created_at

updated_at

task_instance

__table_args__ = ()

__repr__()

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