airflow/jobs.py
def _process_task_instances(self, dag, queue, session=None):
        """
        This method schedules the tasks for a single DAG by looking at the
        active DAG runs and adding task instances that should run to the
        queue.
        """

        # update the state of the previously active dag runs
        dag_runs = DagRun.find(dag_id=dag.dag_id, state=State.RUNNING, session=session)
        active_dag_runs = []
        for run in dag_runs:
            self.log.info("Examining DAG run %s", run)
            # don't consider runs that are executed in the future
            if run.execution_date > timezone.utcnow():
                self.log.error(
                    "Execution date is in future: %s",
                    run.execution_date
                )
                continue

            if len(active_dag_runs) >= dag.max_active_runs:
                self.log.info("Number of active dag runs reached max_active_run.")
                break

            # skip backfill dagruns for now as long as they are not really scheduled
            if run.is_backfill:
                continue

            # todo: run.dag is transient but needs to be set
            run.dag = dag
            # todo: preferably the integrity check happens at dag collection time
            run.verify_integrity(session=session)
            run.update_state(session=session)
            if run.state == State.RUNNING:
                make_transient(run)
                active_dag_runs.append(run)

        for run in active_dag_runs:
            self.log.debug("Examining active DAG run: %s", run)
            # this needs a fresh session sometimes tis get detached
            tis = run.get_task_instances(state=(State.NONE,
                                                State.UP_FOR_RETRY,
                                                State.UP_FOR_RESCHEDULE))

            # this loop is quite slow as it uses are_dependencies_met for
            # every task (in ti.is_runnable). This is also called in
            # update_state above which has already checked these tasks
            for ti in tis:
                task = dag.get_task(ti.task_id)

                # fixme: ti.task is transient but needs to be set
                ti.task = task

                if ti.are_dependencies_met(
                        dep_context=DepContext(flag_upstream_failed=True),
                        session=session):
                    self.log.debug('Queuing task: %s', ti)
                    queue.append(ti.key)
def _process_task_instances(self, dag, queue, session=None):
        """
        This method schedules the tasks for a single DAG by looking at the
        active DAG runs and adding task instances that should run to the
        queue.
        """

        # update the state of the previously active dag runs
        dag_runs = DagRun.find(dag_id=dag.dag_id, state=State.RUNNING, session=session)
        active_dag_runs = []
        for run in dag_runs:
            self.log.info("Examining DAG run %s", run)
            # don't consider runs that are executed in the future
            if run.execution_date > timezone.utcnow():
                self.log.error(
                    "Execution date is in future: %s",
                    run.execution_date
                )
                continue

            if len(active_dag_runs) >= dag.max_active_runs:
                self.log.info("Number of active dag runs reached max_active_run.")
                break

            # skip backfill dagruns for now as long as they are not really scheduled
            if run.is_backfill:
                continue

            # todo: run.dag is transient but needs to be set
            run.dag = dag
            # todo: preferably the integrity check happens at dag collection time
            run.verify_integrity(session=session)
            run.update_state(session=session)
            if run.state == State.RUNNING:
                make_transient(run)
                active_dag_runs.append(run)

        for run in active_dag_runs:
            self.log.debug("Examining active DAG run: %s", run)
            # this needs a fresh session sometimes tis get detached
            tis = run.get_task_instances(state=(State.NONE,
                                                State.UP_FOR_RETRY,
                                                State.UP_FOR_RESCHEDULE))

            # this loop is quite slow as it uses are_dependencies_met for
            # every task (in ti.is_runnable). This is also called in
            # update_state above which has already checked these tasks
            for ti in tis:
                task = dag.get_task(ti.task_id)

                # fixme: ti.task is transient but needs to be set
                ti.task = task

                if ti.are_dependencies_met(
                        dep_context=DepContext(flag_upstream_failed=True),
                        session=session):
                    self.log.debug('Queuing task: %s', ti)
                    queue.append(ti.key)
This method schedules the tasks for a single DAG by looking at the
        active DAG runs and adding task instances that should run to the
        queue.