backfill_job_runner.py

#
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# 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
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#   http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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from __future__ import annotations

import time
from typing import TYPE_CHECKING, Any, Iterable, Iterator, Mapping, Sequence

import attr
import pendulum
from sqlalchemy import select, tuple_, update
from sqlalchemy.exc import OperationalError
from sqlalchemy.orm.session import make_transient
from tabulate import tabulate

from airflow import models
from airflow.exceptions import (
    AirflowException,
    BackfillUnfinished,
    DagConcurrencyLimitReached,
    NoAvailablePoolSlot,
    PoolNotFound,
    TaskConcurrencyLimitReached,
)
from airflow.executors.executor_loader import ExecutorLoader
from airflow.jobs.base_job_runner import BaseJobRunner
from airflow.jobs.job import Job, perform_heartbeat
from airflow.models import DAG, DagPickle
from airflow.models.dagrun import DagRun
from airflow.models.taskinstance import TaskInstance
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.dependencies_deps import BACKFILL_QUEUED_DEPS
from airflow.timetables.base import DagRunInfo
from airflow.utils import helpers, timezone
from airflow.utils.configuration import conf as airflow_conf, tmp_configuration_copy
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.types import DagRunType

if TYPE_CHECKING:
    import datetime

    from sqlalchemy.orm.session import Session

    from airflow.executors.base_executor import BaseExecutor
    from airflow.models.abstractoperator import AbstractOperator
    from airflow.models.taskinstance import TaskInstanceKey


class BackfillJobRunner(BaseJobRunner, LoggingMixin):
    """
    A backfill job runner consists of a dag or subdag for a specific time range.

    It triggers a set of task instance runs, in the right order and lasts for
    as long as it takes for the set of task instance to be completed.
    """

    job_type = "BackfillJob"

    STATES_COUNT_AS_RUNNING = (TaskInstanceState.RUNNING, TaskInstanceState.QUEUED)

    @attr.define
    class _DagRunTaskStatus:
        """
        Internal status of the backfill job.

        This class is intended to be instantiated only within a BackfillJobRunner
        instance and will track the execution of tasks, e.g. running, skipped,
        succeeded, failed, etc. Information about the dag runs related to the
        backfill job are also being tracked in this structure, e.g. finished runs, etc.
        Any other status related information related to the execution of dag runs / tasks
        can be included in this structure since it makes it easier to pass it around.

        :param to_run: Tasks to run in the backfill
        :param running: Maps running task instance key to task instance object
        :param skipped: Tasks that have been skipped
        :param succeeded: Tasks that have succeeded so far
        :param failed: Tasks that have failed
        :param not_ready: Tasks not ready for execution
        :param deadlocked: Deadlocked tasks
        :param active_runs: Active dag runs at a certain point in time
        :param executed_dag_run_dates: Datetime objects for the executed dag runs
        :param finished_runs: Number of finished runs so far
        :param total_runs: Number of total dag runs able to run
        """

        to_run: dict[TaskInstanceKey, TaskInstance] = attr.ib(factory=dict)
        running: dict[TaskInstanceKey, TaskInstance] = attr.ib(factory=dict)
        skipped: set[TaskInstanceKey] = attr.ib(factory=set)
        succeeded: set[TaskInstanceKey] = attr.ib(factory=set)
        failed: set[TaskInstanceKey] = attr.ib(factory=set)
        not_ready: set[TaskInstanceKey] = attr.ib(factory=set)
        deadlocked: set[TaskInstance] = attr.ib(factory=set)
        active_runs: list[DagRun] = attr.ib(factory=list)
        executed_dag_run_dates: set[pendulum.DateTime] = attr.ib(factory=set)
        finished_runs: int = 0
        total_runs: int = 0

    def __init__(
        self,
        job: Job,
        dag: DAG,
        start_date=None,
        end_date=None,
        mark_success=False,
        donot_pickle=False,
        ignore_first_depends_on_past=False,
        ignore_task_deps=False,
        pool=None,
        delay_on_limit_secs=1.0,
        verbose=False,
        conf=None,
        rerun_failed_tasks=False,
        run_backwards=False,
        run_at_least_once=False,
        continue_on_failures=False,
        disable_retry=False,
    ) -> None:
        """
        Create a BackfillJobRunner.

        :param dag: DAG object.
        :param start_date: start date for the backfill date range.
        :param end_date: end date for the backfill date range.
        :param mark_success: flag whether to mark the task auto success.
        :param donot_pickle: whether pickle
        :param ignore_first_depends_on_past: whether to ignore depend on past
        :param ignore_task_deps: whether to ignore the task dependency
        :param pool: pool to backfill
        :param delay_on_limit_secs:
        :param verbose:
        :param conf: a dictionary which user could pass k-v pairs for backfill
        :param rerun_failed_tasks: flag to whether to
                                   auto rerun the failed task in backfill
        :param run_backwards: Whether to process the dates from most to least recent
        :param run_at_least_once: If true, always run the DAG at least once even
            if no logical run exists within the time range.
        :param args:
        :param kwargs:
        """
        super().__init__(job)
        self.dag = dag
        self.dag_id = dag.dag_id
        self.bf_start_date = start_date
        self.bf_end_date = end_date
        self.mark_success = mark_success
        self.donot_pickle = donot_pickle
        self.ignore_first_depends_on_past = ignore_first_depends_on_past
        self.ignore_task_deps = ignore_task_deps
        self.pool = pool
        self.delay_on_limit_secs = delay_on_limit_secs
        self.verbose = verbose
        self.conf = conf
        self.rerun_failed_tasks = rerun_failed_tasks
        self.run_backwards = run_backwards
        self.run_at_least_once = run_at_least_once
        self.continue_on_failures = continue_on_failures
        self.disable_retry = disable_retry

    def _update_counters(self, ti_status: _DagRunTaskStatus, session: Session) -> None:
        """
        Update the counters per state of the tasks that were running.

        Can re-add to tasks to run when required.

        :param ti_status: the internal status of the backfill job tasks
        """
        tis_to_be_scheduled = []
        refreshed_tis = []
        TI = TaskInstance

        ti_primary_key_to_ti_key = {ti_key.primary: ti_key for ti_key in ti_status.running.keys()}

        filter_for_tis = TI.filter_for_tis(list(ti_status.running.values()))
        if filter_for_tis is not None:
            refreshed_tis = session.scalars(select(TI).where(filter_for_tis)).all()

        for ti in refreshed_tis:
            # Use primary key to match in memory information
            ti_key = ti_primary_key_to_ti_key[ti.key.primary]
            if ti.state == TaskInstanceState.SUCCESS:
                ti_status.succeeded.add(ti_key)
                self.log.debug("Task instance %s succeeded. Don't rerun.", ti)
                ti_status.running.pop(ti_key)
                continue
            if ti.state == TaskInstanceState.SKIPPED:
                ti_status.skipped.add(ti_key)
                self.log.debug("Task instance %s skipped. Don't rerun.", ti)
                ti_status.running.pop(ti_key)
                continue
            if ti.state == TaskInstanceState.FAILED:
                self.log.error("Task instance %s failed", ti)
                ti_status.failed.add(ti_key)
                ti_status.running.pop(ti_key)
                continue
            # special case: if the task needs to run again put it back
            if ti.state == TaskInstanceState.UP_FOR_RETRY:
                self.log.warning("Task instance %s is up for retry", ti)
                ti_status.running.pop(ti_key)
                ti_status.to_run[ti.key] = ti
            # special case: if the task needs to be rescheduled put it back
            elif ti.state == TaskInstanceState.UP_FOR_RESCHEDULE:
                self.log.warning("Task instance %s is up for reschedule", ti)
                ti_status.running.pop(ti_key)
                ti_status.to_run[ti.key] = ti
            # special case: The state of the task can be set to NONE by the task itself
            # when it reaches concurrency limits. It could also happen when the state
            # is changed externally, e.g. by clearing tasks from the ui. We need to cover
            # for that as otherwise those tasks would fall outside the scope of
            # the backfill suddenly.
            elif ti.state is None:
                self.log.warning(
                    "FIXME: task instance %s state was set to none externally or "
                    "reaching concurrency limits. Re-adding task to queue.",
                    ti,
                )
                tis_to_be_scheduled.append(ti)
                ti_status.running.pop(ti_key)
                ti_status.to_run[ti.key] = ti
            # special case: Deferrable task can go from DEFERRED to SCHEDULED;
            # when that happens, we need to put it back as in UP_FOR_RESCHEDULE
            elif ti.state == TaskInstanceState.SCHEDULED:
                self.log.debug("Task instance %s is resumed from deferred state", ti)
                ti_status.running.pop(ti_key)
                ti_status.to_run[ti.key] = ti

        # Batch schedule of task instances
        if tis_to_be_scheduled:
            filter_for_tis = TI.filter_for_tis(tis_to_be_scheduled)
            session.execute(
                update(TI)
                .where(filter_for_tis)
                .values(state=TaskInstanceState.SCHEDULED)
                .execution_options(synchronize_session=False)
            )
            session.flush()

    def _manage_executor_state(
        self,
        running: Mapping[TaskInstanceKey, TaskInstance],
        session: Session,
    ) -> Iterator[tuple[AbstractOperator, str, Sequence[TaskInstance], int]]:
        """
        Compare task instances' states with that of the executor.

        Expands downstream mapped tasks when necessary.

        :param running: dict of key, task to verify
        :return: An iterable of expanded TaskInstance per MappedTask
        """
        executor = self.job.executor
        # list of tuples (dag_id, task_id, execution_date, map_index) of running tasks in executor
        buffered_events = list(executor.get_event_buffer().items())
        running_tis_ids = [
            (key.dag_id, key.task_id, key.run_id, key.map_index)
            for key, _ in buffered_events
            if key in running
        ]
        # list of TaskInstance of running tasks in executor (refreshed from db in batch)
        refreshed_running_tis = session.scalars(
            select(TaskInstance).where(
                tuple_(
                    TaskInstance.dag_id,
                    TaskInstance.task_id,
                    TaskInstance.run_id,
                    TaskInstance.map_index,
                ).in_(running_tis_ids)
            )
        ).all()
        # dict of refreshed TaskInstance by key to easily find them
        running_dict = {(ti.dag_id, ti.task_id, ti.run_id, ti.map_index): ti for ti in refreshed_running_tis}
        need_refresh = False

        for key, value in buffered_events:
            state, info = value
            ti_key = (key.dag_id, key.task_id, key.run_id, key.map_index)
            if ti_key not in running_dict:
                self.log.warning("%s state %s not in running=%s", key, state, running.values())
                continue

            ti = running_dict[ti_key]
            if need_refresh:
                ti.refresh_from_db(session=session)

            self.log.debug("Executor state: %s task %s", state, ti)

            if (
                state in (TaskInstanceState.FAILED, TaskInstanceState.SUCCESS)
                and ti.state in self.STATES_COUNT_AS_RUNNING
            ):
                msg = (
                    f"Executor reports task instance {ti} finished ({state}) although the task says its "
                    f"{ti.state}. Was the task killed externally? Info: {info}"
                )
                self.log.error(msg)
                ti.handle_failure(error=msg)
                continue

            def _iter_task_needing_expansion() -> Iterator[AbstractOperator]:
                from airflow.models.mappedoperator import AbstractOperator

                for node in self.dag.get_task(ti.task_id, include_subdags=True).iter_mapped_dependants():
                    if isinstance(node, AbstractOperator):
                        yield node
                    else:  # A (mapped) task group. All its children need expansion.
                        yield from node.iter_tasks()

            if ti.state not in self.STATES_COUNT_AS_RUNNING:
                # Don't use ti.task; if this task is mapped, that attribute
                # would hold the unmapped task. We need to original task here.
                for node in _iter_task_needing_expansion():
                    new_tis, num_mapped_tis = node.expand_mapped_task(ti.run_id, session=session)
                    yield node, ti.run_id, new_tis, num_mapped_tis

    @provide_session
    def _get_dag_run(
        self,
        dagrun_info: DagRunInfo,
        dag: DAG,
        session: Session = NEW_SESSION,
    ) -> DagRun | None:
        """
        Return an existing dag run for the given run date or create one.

        If the max_active_runs limit is reached, this function will return None.

        :param dagrun_info: Schedule information for the dag run
        :param dag: DAG
        :param session: the database session object
        :return: a DagRun in state RUNNING or None
        """
        run_date = dagrun_info.logical_date

        # consider max_active_runs but ignore when running subdags
        respect_dag_max_active_limit = bool(dag.timetable.can_be_scheduled and not dag.is_subdag)

        current_active_dag_count = dag.get_num_active_runs(external_trigger=False)

        # check if we are scheduling on top of an already existing DAG run
        # we could find a "scheduled" run instead of a "backfill"
        runs = DagRun.find(dag_id=dag.dag_id, execution_date=run_date, session=session)
        run: DagRun | None
        if runs:
            run = runs[0]
            if run.state == DagRunState.RUNNING:
                respect_dag_max_active_limit = False
            # Fixes --conf overwrite for backfills with already existing DagRuns
            run.conf = self.conf or {}
            # start_date is cleared for existing DagRuns
            run.start_date = timezone.utcnow()
        else:
            run = None

        # enforce max_active_runs limit for dag, special cases already
        # handled by respect_dag_max_active_limit
        if respect_dag_max_active_limit and current_active_dag_count >= dag.max_active_runs:
            return None

        run = run or dag.create_dagrun(
            execution_date=run_date,
            data_interval=dagrun_info.data_interval,
            start_date=timezone.utcnow(),
            state=DagRunState.RUNNING,
            external_trigger=False,
            session=session,
            conf=self.conf,
            run_type=DagRunType.BACKFILL_JOB,
            creating_job_id=self.job.id,
        )

        # set required transient field
        run.dag = dag

        # explicitly mark as backfill and running
        run.state = DagRunState.RUNNING
        run.run_type = DagRunType.BACKFILL_JOB
        run.verify_integrity(session=session)

        run.notify_dagrun_state_changed(msg="started")
        return run

    @provide_session
    def _task_instances_for_dag_run(
        self,
        dag: DAG,
        dag_run: DagRun,
        session: Session = NEW_SESSION,
    ) -> dict[TaskInstanceKey, TaskInstance]:
        """
        Return a map of task instance keys to task instance objects for the given dag run.

        :param dag_run: the dag run to get the tasks from
        :param session: the database session object
        """
        tasks_to_run = {}

        if dag_run is None:
            return tasks_to_run

        # check if we have orphaned tasks
        self.reset_state_for_orphaned_tasks(filter_by_dag_run=dag_run, session=session)

        # for some reason if we don't refresh the reference to run is lost
        dag_run.refresh_from_db(session=session)
        make_transient(dag_run)

        dag_run.dag = dag
        info = dag_run.task_instance_scheduling_decisions(session=session)
        schedulable_tis = info.schedulable_tis
        try:
            for ti in dag_run.get_task_instances(session=session):
                if ti in schedulable_tis:
                    ti.set_state(TaskInstanceState.SCHEDULED)
                if ti.state != TaskInstanceState.REMOVED:
                    tasks_to_run[ti.key] = ti
            session.commit()
        except Exception:
            session.rollback()
            raise
        return tasks_to_run

    def _log_progress(self, ti_status: _DagRunTaskStatus) -> None:
        self.log.info(
            "[backfill progress] | finished run %s of %s | tasks waiting: %s | succeeded: %s | "
            "running: %s | failed: %s | skipped: %s | deadlocked: %s | not ready: %s",
            ti_status.finished_runs,
            ti_status.total_runs,
            len(ti_status.to_run),
            len(ti_status.succeeded),
            len(ti_status.running),
            len(ti_status.failed),
            len(ti_status.skipped),
            len(ti_status.deadlocked),
            len(ti_status.not_ready),
        )

        self.log.debug("Finished dag run loop iteration. Remaining tasks %s", ti_status.to_run.values())

    def _process_backfill_task_instances(
        self,
        ti_status: _DagRunTaskStatus,
        executor: BaseExecutor,
        pickle_id: int | None,
        start_date: datetime.datetime | None = None,
        *,
        session: Session,
    ) -> list:
        """
        Process a set of task instances from a set of DAG runs.

        Special handling is done to account for different task instance states
        that could be present when running them in a backfill process.

        :param ti_status: the internal status of the job
        :param executor: the executor to run the task instances
        :param pickle_id: the pickle_id if dag is pickled, None otherwise
        :param start_date: the start date of the backfill job
        :param session: the current session object
        :return: the list of execution_dates for the finished dag runs
        """
        executed_run_dates = []

        is_unit_test = airflow_conf.getboolean("core", "unit_test_mode")

        while (ti_status.to_run or ti_status.running) and not ti_status.deadlocked:
            self.log.debug("*** Clearing out not_ready list ***")
            ti_status.not_ready.clear()

            # we need to execute the tasks bottom to top
            # or leaf to root, as otherwise tasks might be
            # determined deadlocked while they are actually
            # waiting for their upstream to finish
            def _per_task_process(key, ti: TaskInstance, session):
                ti.refresh_from_db(lock_for_update=True, session=session)

                task = self.dag.get_task(ti.task_id, include_subdags=True)
                ti.task = task

                self.log.debug("Task instance to run %s state %s", ti, ti.state)

                # The task was already marked successful or skipped by a
                # different Job. Don't rerun it.
                if ti.state == TaskInstanceState.SUCCESS:
                    ti_status.succeeded.add(key)
                    self.log.debug("Task instance %s succeeded. Don't rerun.", ti)
                    ti_status.to_run.pop(key)
                    if key in ti_status.running:
                        ti_status.running.pop(key)
                    return
                elif ti.state == TaskInstanceState.SKIPPED:
                    ti_status.skipped.add(key)
                    self.log.debug("Task instance %s skipped. Don't rerun.", ti)
                    ti_status.to_run.pop(key)
                    if key in ti_status.running:
                        ti_status.running.pop(key)
                    return

                if self.rerun_failed_tasks:
                    # Rerun failed tasks or upstreamed failed tasks
                    if ti.state in (TaskInstanceState.FAILED, TaskInstanceState.UPSTREAM_FAILED):
                        self.log.error("Task instance %s with state %s", ti, ti.state)
                        if key in ti_status.running:
                            ti_status.running.pop(key)
                        # Reset the failed task in backfill to scheduled state
                        ti.set_state(TaskInstanceState.SCHEDULED, session=session)
                else:
                    # Default behaviour which works for subdag.
                    if ti.state in (TaskInstanceState.FAILED, TaskInstanceState.UPSTREAM_FAILED):
                        self.log.error("Task instance %s with state %s", ti, ti.state)
                        ti_status.failed.add(key)
                        ti_status.to_run.pop(key)
                        if key in ti_status.running:
                            ti_status.running.pop(key)
                        return

                if self.ignore_first_depends_on_past:
                    dagrun = ti.get_dagrun(session=session)
                    ignore_depends_on_past = dagrun.execution_date == (start_date or ti.start_date)
                else:
                    ignore_depends_on_past = False

                backfill_context = DepContext(
                    deps=BACKFILL_QUEUED_DEPS,
                    ignore_depends_on_past=ignore_depends_on_past,
                    ignore_task_deps=self.ignore_task_deps,
                    wait_for_past_depends_before_skipping=False,
                    flag_upstream_failed=True,
                )

                # Is the task runnable? -- then run it
                # the dependency checker can change states of tis
                if ti.are_dependencies_met(
                    dep_context=backfill_context, session=session, verbose=self.verbose
                ):
                    if executor.has_task(ti):
                        self.log.debug("Task Instance %s already in executor waiting for queue to clear", ti)
                    else:
                        self.log.debug("Sending %s to executor", ti)
                        # Skip scheduled state, we are executing immediately
                        ti.state = TaskInstanceState.QUEUED
                        ti.queued_by_job_id = self.job.id
                        ti.queued_dttm = timezone.utcnow()
                        session.merge(ti)
                        try:
                            session.commit()
                        except OperationalError:
                            self.log.exception("Failed to commit task state change due to operational error")
                            session.rollback()
                            # early exit so the outer loop can retry
                            return

                        cfg_path = None

                        if executor.is_local:
                            cfg_path = tmp_configuration_copy()

                        executor.queue_task_instance(
                            ti,
                            mark_success=self.mark_success,
                            pickle_id=pickle_id,
                            ignore_task_deps=self.ignore_task_deps,
                            ignore_depends_on_past=ignore_depends_on_past,
                            wait_for_past_depends_before_skipping=False,
                            pool=self.pool,
                            cfg_path=cfg_path,
                        )
                        ti_status.running[key] = ti
                        ti_status.to_run.pop(key)
                    return

                if ti.state == TaskInstanceState.UPSTREAM_FAILED:
                    self.log.error("Task instance %s upstream failed", ti)
                    ti_status.failed.add(key)
                    ti_status.to_run.pop(key)
                    if key in ti_status.running:
                        ti_status.running.pop(key)
                    return

                # special case
                if ti.state == TaskInstanceState.UP_FOR_RETRY:
                    self.log.debug("Task instance %s retry period not expired yet", ti)
                    if key in ti_status.running:
                        ti_status.running.pop(key)
                    ti_status.to_run[key] = ti
                    return

                # special case
                if ti.state == TaskInstanceState.UP_FOR_RESCHEDULE:
                    self.log.debug("Task instance %s reschedule period not expired yet", ti)
                    if key in ti_status.running:
                        ti_status.running.pop(key)
                    ti_status.to_run[key] = ti
                    return

                # all remaining tasks
                self.log.debug("Adding %s to not_ready", ti)
                ti_status.not_ready.add(key)

            try:
                for task in self.dag.topological_sort(include_subdag_tasks=True):
                    for key, ti in list(ti_status.to_run.items()):
                        # Attempt to workaround deadlock on backfill by attempting to commit the transaction
                        # state update few times before giving up
                        max_attempts = 5
                        for i in range(max_attempts):
                            if task.task_id != ti.task_id:
                                continue

                            pool = session.scalar(
                                select(models.Pool).where(models.Pool.pool == task.pool).limit(1)
                            )
                            if not pool:
                                raise PoolNotFound(f"Unknown pool: {task.pool}")

                            open_slots = pool.open_slots(session=session)
                            if open_slots <= 0:
                                raise NoAvailablePoolSlot(
                                    f"Not scheduling since there are {open_slots} "
                                    f"open slots in pool {task.pool}"
                                )

                            num_running_task_instances_in_dag = DAG.get_num_task_instances(
                                self.dag_id,
                                states=self.STATES_COUNT_AS_RUNNING,
                                session=session,
                            )

                            if num_running_task_instances_in_dag >= self.dag.max_active_tasks:
                                raise DagConcurrencyLimitReached(
                                    "Not scheduling since DAG max_active_tasks limit is reached."
                                )

                            if task.max_active_tis_per_dag is not None:
                                num_running_task_instances_in_task = DAG.get_num_task_instances(
                                    dag_id=self.dag_id,
                                    task_ids=[task.task_id],
                                    states=self.STATES_COUNT_AS_RUNNING,
                                    session=session,
                                )

                                if num_running_task_instances_in_task >= task.max_active_tis_per_dag:
                                    raise TaskConcurrencyLimitReached(
                                        "Not scheduling since Task concurrency limit is reached."
                                    )

                            if task.max_active_tis_per_dagrun is not None:
                                num_running_task_instances_in_task_dagrun = DAG.get_num_task_instances(
                                    dag_id=self.dag_id,
                                    run_id=ti.run_id,
                                    task_ids=[task.task_id],
                                    states=self.STATES_COUNT_AS_RUNNING,
                                    session=session,
                                )

                                if (
                                    num_running_task_instances_in_task_dagrun
                                    >= task.max_active_tis_per_dagrun
                                ):
                                    raise TaskConcurrencyLimitReached(
                                        "Not scheduling since Task concurrency per DAG run limit is reached."
                                    )

                            _per_task_process(key, ti, session)
                            try:
                                session.commit()
                            except OperationalError:
                                self.log.error(
                                    "Failed to commit task state due to operational error. "
                                    "The job will retry this operation so if your backfill succeeds, "
                                    "you can safely ignore this message.",
                                    exc_info=True,
                                )
                                session.rollback()
                                if i == max_attempts - 1:
                                    raise
                                # retry the loop
                            else:
                                # break the retry loop
                                break
            except (NoAvailablePoolSlot, DagConcurrencyLimitReached, TaskConcurrencyLimitReached) as e:
                self.log.debug(e)

            perform_heartbeat(
                job=self.job, heartbeat_callback=self.heartbeat_callback, only_if_necessary=is_unit_test
            )
            # execute the tasks in the queue
            executor.heartbeat()

            # If the set of tasks that aren't ready ever equals the set of
            # tasks to run and there are no running tasks then the backfill
            # is deadlocked
            if ti_status.not_ready and ti_status.not_ready == set(ti_status.to_run) and not ti_status.running:
                self.log.warning("Deadlock discovered for ti_status.to_run=%s", ti_status.to_run.values())
                ti_status.deadlocked.update(ti_status.to_run.values())
                ti_status.to_run.clear()

            # check executor state -- and expand any mapped TIs
            for node, run_id, new_mapped_tis, max_map_index in self._manage_executor_state(
                ti_status.running, session
            ):

                def to_keep(key: TaskInstanceKey) -> bool:
                    if key.dag_id != node.dag_id or key.task_id != node.task_id or key.run_id != run_id:
                        # For another Dag/Task/Run -- don't remove
                        return True
                    return 0 <= key.map_index <= max_map_index

                # remove the old unmapped TIs for node -- they have been replaced with the mapped TIs
                ti_status.to_run = {key: ti for (key, ti) in ti_status.to_run.items() if to_keep(key)}

                ti_status.to_run.update({ti.key: ti for ti in new_mapped_tis})

                for new_ti in new_mapped_tis:
                    new_ti.set_state(TaskInstanceState.SCHEDULED, session=session)

            # Set state to failed for running TIs that are set up for retry if disable-retry flag is set
            for ti in ti_status.running.values():
                if self.disable_retry and ti.state == TaskInstanceState.UP_FOR_RETRY:
                    ti.set_state(TaskInstanceState.FAILED, session=session)

            # update the task counters
            self._update_counters(ti_status=ti_status, session=session)
            session.commit()

            # update dag run state
            _dag_runs = ti_status.active_runs[:]
            for run in _dag_runs:
                run.update_state(session=session)
                if run.state in State.finished_dr_states:
                    ti_status.finished_runs += 1
                    ti_status.active_runs.remove(run)
                    executed_run_dates.append(run.execution_date)

            self._log_progress(ti_status)
            session.commit()

        # return updated status
        return executed_run_dates

    @provide_session
    def _collect_errors(self, ti_status: _DagRunTaskStatus, session: Session = NEW_SESSION) -> Iterator[str]:
        def tabulate_ti_keys_set(ti_keys: Iterable[TaskInstanceKey]) -> str:
            # Sorting by execution date first
            sorted_ti_keys: Any = sorted(
                ti_keys,
                key=lambda ti_key: (
                    ti_key.run_id,
                    ti_key.dag_id,
                    ti_key.task_id,
                    ti_key.map_index,
                    ti_key.try_number,
                ),
            )

            if all(key.map_index == -1 for key in ti_keys):
                headers = ["DAG ID", "Task ID", "Run ID", "Try number"]
                sorted_ti_keys = (k[0:4] for k in sorted_ti_keys)
            else:
                headers = ["DAG ID", "Task ID", "Run ID", "Map Index", "Try number"]

            return tabulate(sorted_ti_keys, headers=headers)

        if ti_status.failed:
            yield "Some task instances failed:\n"
            yield tabulate_ti_keys_set(ti_status.failed)
        if ti_status.deadlocked:
            yield "BackfillJob is deadlocked."
            deadlocked_depends_on_past = any(
                t.are_dependencies_met(
                    dep_context=DepContext(ignore_depends_on_past=False),
                    session=session,
                    verbose=self.verbose,
                )
                != t.are_dependencies_met(
                    dep_context=DepContext(ignore_depends_on_past=True), session=session, verbose=self.verbose
                )
                for t in ti_status.deadlocked
            )
            if deadlocked_depends_on_past:
                yield (
                    "Some of the deadlocked tasks were unable to run because "
                    'of "depends_on_past" relationships. Try running the '
                    "backfill with the option "
                    '"ignore_first_depends_on_past=True" or passing "-I" at '
                    "the command line."
                )
            yield "\nThese tasks have succeeded:\n"
            yield tabulate_ti_keys_set(ti_status.succeeded)
            yield "\n\nThese tasks are running:\n"
            yield tabulate_ti_keys_set(ti_status.running)
            yield "\n\nThese tasks have failed:\n"
            yield tabulate_ti_keys_set(ti_status.failed)
            yield "\n\nThese tasks are skipped:\n"
            yield tabulate_ti_keys_set(ti_status.skipped)
            yield "\n\nThese tasks are deadlocked:\n"
            yield tabulate_ti_keys_set([ti.key for ti in ti_status.deadlocked])

    def _get_dag_with_subdags(self) -> list[DAG]:
        return [self.dag, *self.dag.subdags]

    @provide_session
    def _execute_dagruns(
        self,
        dagrun_infos: Iterable[DagRunInfo],
        ti_status: _DagRunTaskStatus,
        executor: BaseExecutor,
        pickle_id: int | None,
        start_date: datetime.datetime | None,
        session: Session = NEW_SESSION,
    ) -> None:
        """
        Compute and execute dag runs and their respective task instances for the given dates.

        Returns a list of execution dates of the dag runs that were executed.

        :param dagrun_infos: Schedule information for dag runs
        :param ti_status: internal BackfillJobRunner status structure to tis track progress
        :param executor: the executor to use, it must be previously started
        :param pickle_id: numeric id of the pickled dag, None if not pickled
        :param start_date: backfill start date
        :param session: the current session object
        """
        for dagrun_info in dagrun_infos:
            for dag in self._get_dag_with_subdags():
                dag_run = self._get_dag_run(dagrun_info, dag, session=session)
                if dag_run is not None:
                    tis_map = self._task_instances_for_dag_run(dag, dag_run, session=session)
                    ti_status.active_runs.append(dag_run)
                    ti_status.to_run.update(tis_map or {})

        processed_dag_run_dates = self._process_backfill_task_instances(
            ti_status=ti_status,
            executor=executor,
            pickle_id=pickle_id,
            start_date=start_date,
            session=session,
        )

        ti_status.executed_dag_run_dates.update(processed_dag_run_dates)

    @provide_session
    def _set_unfinished_dag_runs_to_failed(
        self,
        dag_runs: Iterable[DagRun],
        session: Session = NEW_SESSION,
    ) -> None:
        """
        Update the state of each dagrun based on the task_instance state and set unfinished runs to failed.

        :param dag_runs: DAG runs
        :param session: session
        :return: None
        """
        for dag_run in dag_runs:
            dag_run.update_state()
            if dag_run.state not in State.finished_dr_states:
                dag_run.set_state(DagRunState.FAILED)
            session.merge(dag_run)

    @provide_session
    def _execute(self, session: Session = NEW_SESSION) -> None:
        """
        Initialize all required components of a dag for a specified date range and execute the tasks.

        :meta private:
        """
        ti_status = BackfillJobRunner._DagRunTaskStatus()

        start_date = self.bf_start_date

        # Get DagRun schedule between the start/end dates, which will turn into dag runs.
        dagrun_start_date = timezone.coerce_datetime(start_date)
        if self.bf_end_date is None:
            dagrun_end_date = pendulum.now(timezone.utc)
        else:
            dagrun_end_date = pendulum.instance(self.bf_end_date)
        dagrun_infos = list(self.dag.iter_dagrun_infos_between(dagrun_start_date, dagrun_end_date))
        if self.run_backwards:
            tasks_that_depend_on_past = [t.task_id for t in self.dag.task_dict.values() if t.depends_on_past]
            if tasks_that_depend_on_past:
                raise AirflowException(
                    f"You cannot backfill backwards because one or more "
                    f'tasks depend_on_past: {",".join(tasks_that_depend_on_past)}'
                )
            dagrun_infos = dagrun_infos[::-1]

        if not dagrun_infos:
            if not self.run_at_least_once:
                self.log.info("No run dates were found for the given dates and dag interval.")
                return
            dagrun_infos = [DagRunInfo.interval(dagrun_start_date, dagrun_end_date)]

        dag_with_subdags_ids = [d.dag_id for d in self._get_dag_with_subdags()]
        running_dagruns = DagRun.find(
            dag_id=dag_with_subdags_ids,
            execution_start_date=self.bf_start_date,
            execution_end_date=self.bf_end_date,
            no_backfills=True,
            state=DagRunState.RUNNING,
        )

        if running_dagruns:
            for run in running_dagruns:
                self.log.error(
                    "Backfill cannot be created for DagRun %s in %s, as there's already %s in a RUNNING "
                    "state.",
                    run.run_id,
                    run.execution_date.strftime("%Y-%m-%dT%H:%M:%S"),
                    run.run_type,
                )
            self.log.error(
                "Changing DagRun into BACKFILL would cause scheduler to lose track of executing "
                "tasks. Not changing DagRun type into BACKFILL, and trying insert another DagRun into "
                "database would cause database constraint violation for dag_id + execution_date "
                "combination. Please adjust backfill dates or wait for this DagRun to finish.",
            )
            return
        # picklin'
        pickle_id = None

        executor_class, _ = ExecutorLoader.import_default_executor_cls()

        if not self.donot_pickle and executor_class.supports_pickling:
            pickle = DagPickle(self.dag)
            session.add(pickle)
            session.commit()
            pickle_id = pickle.id

        executor = self.job.executor
        executor.job_id = self.job.id
        executor.start()

        ti_status.total_runs = len(dagrun_infos)  # total dag runs in backfill

        try:
            remaining_dates = ti_status.total_runs
            while remaining_dates > 0:
                dagrun_infos_to_process = [
                    dagrun_info
                    for dagrun_info in dagrun_infos
                    if dagrun_info.logical_date not in ti_status.executed_dag_run_dates
                ]
                self._execute_dagruns(
                    dagrun_infos=dagrun_infos_to_process,
                    ti_status=ti_status,
                    executor=executor,
                    pickle_id=pickle_id,
                    start_date=start_date,
                    session=session,
                )

                remaining_dates = ti_status.total_runs - len(ti_status.executed_dag_run_dates)
                err = "".join(self._collect_errors(ti_status=ti_status, session=session))
                if err:
                    if not self.continue_on_failures or ti_status.deadlocked:
                        raise BackfillUnfinished(err, ti_status)

                if remaining_dates > 0:
                    self.log.info(
                        "max_active_runs limit for dag %s has been reached "
                        " - waiting for other dag runs to finish",
                        self.dag_id,
                    )
                    time.sleep(self.delay_on_limit_secs)
        except (KeyboardInterrupt, SystemExit):
            self.log.warning("Backfill terminated by user.")

            # TODO: we will need to terminate running task instances and set the
            # state to failed.
            self._set_unfinished_dag_runs_to_failed(ti_status.active_runs)
        except OperationalError:
            self.log.error(
                "Backfill job dead-locked. The job will retry the job so it is likely "
                "to heal itself. If your backfill succeeds you can ignore this exception.",
                exc_info=True,
            )
            raise
        finally:
            session.commit()
            executor.end()

        self.log.info("Backfill done for DAG %s. Exiting.", self.dag)

    @provide_session
    def reset_state_for_orphaned_tasks(
        self,
        filter_by_dag_run: DagRun | None = None,
        session: Session = NEW_SESSION,
    ) -> int | None:
        """
        Reset state of orphaned tasks.

        This function checks if there are any tasks in the dagrun (or all) that
        have a schedule or queued states but are not known by the executor. If
        it finds those it will reset the state to None so they will get picked
        up again.  The batch option is for performance reasons as the queries
        are made in sequence.

        :param filter_by_dag_run: the dag_run we want to process, None if all
        :return: the number of TIs reset
        """
        queued_tis = self.job.executor.queued_tasks
        # also consider running as the state might not have changed in the db yet
        running_tis = self.job.executor.running

        # Can't use an update here since it doesn't support joins.
        resettable_states = [TaskInstanceState.SCHEDULED, TaskInstanceState.QUEUED]
        if filter_by_dag_run is None:
            resettable_tis = (
                session.scalars(
                    select(TaskInstance)
                    .join(TaskInstance.dag_run)
                    .where(
                        DagRun.state == DagRunState.RUNNING,
                        DagRun.run_type != DagRunType.BACKFILL_JOB,
                        TaskInstance.state.in_(resettable_states),
                    )
                )
            ).all()
        else:
            resettable_tis = filter_by_dag_run.get_task_instances(state=resettable_states, session=session)

        tis_to_reset = [ti for ti in resettable_tis if ti.key not in queued_tis and ti.key not in running_tis]
        if not tis_to_reset:
            return 0

        def query(result, items):
            if not items:
                return result

            filter_for_tis = TaskInstance.filter_for_tis(items)
            reset_tis = session.scalars(
                select(TaskInstance)
                .where(filter_for_tis, TaskInstance.state.in_(resettable_states))
                .with_for_update()
            ).all()

            for ti in reset_tis:
                ti.state = None
                session.merge(ti)

            return result + reset_tis

        reset_tis = helpers.reduce_in_chunks(query, tis_to_reset, [], self.job.max_tis_per_query)

        task_instance_str = "\n".join(f"\t{x!r}" for x in reset_tis)
        session.flush()

        self.log.info("Reset the following %s TaskInstances:\n%s", len(reset_tis), task_instance_str)
        return len(reset_tis)