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292 | class Scheduler(LoopService):
"""
A loop service that schedules flow runs from deployments.
"""
# the main scheduler takes its loop interval from
# PREFECT_API_SERVICES_SCHEDULER_LOOP_SECONDS
loop_seconds = None
def __init__(self, loop_seconds: float = None, **kwargs):
super().__init__(
loop_seconds=(
loop_seconds
or self.loop_seconds
or PREFECT_API_SERVICES_SCHEDULER_LOOP_SECONDS.value()
),
**kwargs,
)
self.deployment_batch_size: int = (
PREFECT_API_SERVICES_SCHEDULER_DEPLOYMENT_BATCH_SIZE.value()
)
self.max_runs: int = PREFECT_API_SERVICES_SCHEDULER_MAX_RUNS.value()
self.min_runs: int = PREFECT_API_SERVICES_SCHEDULER_MIN_RUNS.value()
self.max_scheduled_time: datetime.timedelta = (
PREFECT_API_SERVICES_SCHEDULER_MAX_SCHEDULED_TIME.value()
)
self.min_scheduled_time: datetime.timedelta = (
PREFECT_API_SERVICES_SCHEDULER_MIN_SCHEDULED_TIME.value()
)
self.insert_batch_size = (
PREFECT_API_SERVICES_SCHEDULER_INSERT_BATCH_SIZE.value()
)
@inject_db
async def run_once(self, db: PrefectDBInterface):
"""
Schedule flow runs by:
- Querying for deployments with active schedules
- Generating the next set of flow runs based on each deployments schedule
- Inserting all scheduled flow runs into the database
All inserted flow runs are committed to the database at the termination of the
loop.
"""
total_inserted_runs = 0
last_id = None
while True:
async with db.session_context(begin_transaction=False) as session:
query = self._get_select_deployments_to_schedule_query()
# use cursor based pagination
if last_id:
query = query.where(db.Deployment.id > last_id)
result = await session.execute(query)
deployment_ids = result.scalars().unique().all()
# collect runs across all deployments
try:
runs_to_insert = await self._collect_flow_runs(
session=session, deployment_ids=deployment_ids
)
except TryAgain:
continue
# bulk insert the runs based on batch size setting
for batch in batched_iterable(runs_to_insert, self.insert_batch_size):
async with db.session_context(begin_transaction=True) as session:
inserted_runs = await self._insert_scheduled_flow_runs(
session=session, runs=batch
)
total_inserted_runs += len(inserted_runs)
# if this is the last page of deployments, exit the loop
if len(deployment_ids) < self.deployment_batch_size:
break
else:
# record the last deployment ID
last_id = deployment_ids[-1]
self.logger.info(f"Scheduled {total_inserted_runs} runs.")
@inject_db
def _get_select_deployments_to_schedule_query(self, db: PrefectDBInterface):
"""
Returns a sqlalchemy query for selecting deployments to schedule.
The query gets the IDs of any deployments with:
- an active schedule
- EITHER:
- fewer than `min_runs` auto-scheduled runs
- OR the max scheduled time is less than `max_scheduled_time` in the future
"""
now = pendulum.now("UTC")
query = (
sa.select(db.Deployment.id)
.select_from(db.Deployment)
# TODO: on Postgres, this could be replaced with a lateral join that
# sorts by `next_scheduled_start_time desc` and limits by
# `self.min_runs` for a ~ 50% speedup. At the time of writing,
# performance of this universal query appears to be fast enough that
# this optimization is not worth maintaining db-specific queries
.join(
db.FlowRun,
# join on matching deployments, only picking up future scheduled runs
sa.and_(
db.Deployment.id == db.FlowRun.deployment_id,
db.FlowRun.state_type == StateType.SCHEDULED,
db.FlowRun.next_scheduled_start_time >= now,
db.FlowRun.auto_scheduled.is_(True),
),
isouter=True,
)
.where(
sa.and_(
db.Deployment.paused.is_not(True),
(
# Only include deployments that have at least one
# active schedule.
sa.select(db.DeploymentSchedule.deployment_id)
.where(
sa.and_(
db.DeploymentSchedule.deployment_id == db.Deployment.id,
db.DeploymentSchedule.active.is_(True),
)
)
.exists()
),
)
)
.group_by(db.Deployment.id)
# having EITHER fewer than three runs OR runs not scheduled far enough out
.having(
sa.or_(
sa.func.count(db.FlowRun.next_scheduled_start_time) < self.min_runs,
sa.func.max(db.FlowRun.next_scheduled_start_time)
< now + self.min_scheduled_time,
)
)
.order_by(db.Deployment.id)
.limit(self.deployment_batch_size)
)
return query
async def _collect_flow_runs(
self,
session: sa.orm.Session,
deployment_ids: List[UUID],
) -> List[Dict]:
runs_to_insert = []
for deployment_id in deployment_ids:
now = pendulum.now("UTC")
# guard against erroneously configured schedules
try:
runs_to_insert.extend(
await self._generate_scheduled_flow_runs(
session=session,
deployment_id=deployment_id,
start_time=now,
end_time=now + self.max_scheduled_time,
min_time=self.min_scheduled_time,
min_runs=self.min_runs,
max_runs=self.max_runs,
)
)
except Exception:
self.logger.exception(
f"Error scheduling deployment {deployment_id!r}.",
)
finally:
connection = await session.connection()
if connection.invalidated:
# If the error we handled above was the kind of database error that
# causes underlying transaction to rollback and the connection to
# become invalidated, rollback this session. Errors that may cause
# this are connection drops, database restarts, and things of the
# sort.
#
# This rollback _does not rollback a transaction_, since that has
# actually already happened due to the error above. It brings the
# Python session in sync with underlying connection so that when we
# exec the outer with block, the context manager will not attempt to
# commit the session.
#
# Then, raise TryAgain to break out of these nested loops, back to
# the outer loop, where we'll begin a new transaction with
# session.begin() in the next loop iteration.
await session.rollback()
raise TryAgain()
return runs_to_insert
@inject_db
async def _generate_scheduled_flow_runs(
self,
session: sa.orm.Session,
deployment_id: UUID,
start_time: datetime.datetime,
end_time: datetime.datetime,
min_time: datetime.timedelta,
min_runs: int,
max_runs: int,
db: PrefectDBInterface,
) -> List[Dict]:
"""
Given a `deployment_id` and schedule params, generates a list of flow run
objects and associated scheduled states that represent scheduled flow runs.
Pass-through method for overrides.
Args:
session: a database session
deployment_id: the id of the deployment to schedule
start_time: the time from which to start scheduling runs
end_time: runs will be scheduled until at most this time
min_time: runs will be scheduled until at least this far in the future
min_runs: a minimum amount of runs to schedule
max_runs: a maximum amount of runs to schedule
This function will generate the minimum number of runs that satisfy the min
and max times, and the min and max counts. Specifically, the following order
will be respected:
- Runs will be generated starting on or after the `start_time`
- No more than `max_runs` runs will be generated
- No runs will be generated after `end_time` is reached
- At least `min_runs` runs will be generated
- Runs will be generated until at least `start_time + min_time` is reached
"""
return await models.deployments._generate_scheduled_flow_runs(
session=session,
deployment_id=deployment_id,
start_time=start_time,
end_time=end_time,
min_time=min_time,
min_runs=min_runs,
max_runs=max_runs,
)
@inject_db
async def _insert_scheduled_flow_runs(
self,
session: sa.orm.Session,
runs: List[Dict],
db: PrefectDBInterface,
) -> List[UUID]:
"""
Given a list of flow runs to schedule, as generated by
`_generate_scheduled_flow_runs`, inserts them into the database. Note this is a
separate method to facilitate batch operations on many scheduled runs.
Pass-through method for overrides.
"""
return await models.deployments._insert_scheduled_flow_runs(
session=session, runs=runs
)
|