prefect.orion.models.deployments

Functions for interacting with deployment ORM objects. Intended for internal use by the Orion API.

check_work_queues_for_deployment async

Get work queues that can pick up the specified deployment.

Work queues will pick up a deployment when all of the following are met.

• The deployment has ALL tags that the work queue has (i.e. the work queue's tags must be a subset of the deployment's tags).
• The work queue's specified deployment IDs match the deployment's ID, or the work queue does NOT have specified deployment IDs.
• The work queue's specified flow runners match the deployment's flow runner or the work queue does NOT have a specified flow runner.

Notes on the query:

• Our database currently allows either "null" and empty lists as null values in filters, so we need to catch both cases with "or".
• json_contains(A, B) should be interepreted as "True if A contains B".

Returns:

Type	Description
List[schemas.core.WorkQueue]	List[db.WorkQueue]: WorkQueues

Source code in prefect/orion/models/deployments.py

@inject_db
async def check_work_queues_for_deployment(
    db: OrionDBInterface, session: sa.orm.Session, deployment_id: UUID
) -> List[schemas.core.WorkQueue]:
    """
    Get work queues that can pick up the specified deployment.

    Work queues will pick up a deployment when all of the following are met.

    - The deployment has ALL tags that the work queue has (i.e. the work
    queue's tags must be a subset of the deployment's tags).
    - The work queue's specified deployment IDs match the deployment's ID,
    or the work queue does NOT have specified deployment IDs.
    - The work queue's specified flow runners match the deployment's flow
    runner or the work queue does NOT have a specified flow runner.

    Notes on the query:

    - Our database currently allows either "null" and empty lists as
    null values in filters, so we need to catch both cases with "or".
    - `json_contains(A, B)` should be interepreted as "True if A
    contains B".

    Returns:
        List[db.WorkQueue]: WorkQueues
    """
    deployment = await session.get(db.Deployment, deployment_id)
    if not deployment:
        raise ObjectNotFoundError(f"Deployment with id {deployment_id} not found")

    query = (
        select(db.WorkQueue)
        # work queue tags are a subset of deployment tags
        .filter(
            or_(
                json_contains(deployment.tags, db.WorkQueue.filter["tags"]),
                json_contains([], db.WorkQueue.filter["tags"]),
                json_contains(None, db.WorkQueue.filter["tags"]),
            )
        )
        # deployment_ids is null or contains the deployment's ID
        .filter(
            or_(
                json_contains(
                    db.WorkQueue.filter["deployment_ids"],
                    str(deployment.id),
                ),
                json_contains(None, db.WorkQueue.filter["deployment_ids"]),
                json_contains([], db.WorkQueue.filter["deployment_ids"]),
            )
        )
    )

    result = await session.execute(query)
    return result.scalars().unique().all()

count_deployments async

Count deployments.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	A database session	required
flow_filter	schemas.filters.FlowFilter	only count deployments whose flows match these criteria	None
flow_run_filter	schemas.filters.FlowRunFilter	only count deployments whose flow runs match these criteria	None
task_run_filter	schemas.filters.TaskRunFilter	only count deployments whose task runs match these criteria	None
deployment_filter	schemas.filters.DeploymentFilter	only count deployment that match these filters	None
work_pool_filter	schemas.filters.WorkPoolFilter	only count deployments that match these work pool filters	None
work_queue_filter	schemas.filters.WorkQueueFilter	only count deployments that match these work pool queue filters	None

Returns:

Name	Type	Description
int	int	the number of deployments matching filters

Source code in prefect/orion/models/deployments.py

@inject_db
async def count_deployments(
    session: sa.orm.Session,
    db: OrionDBInterface,
    flow_filter: schemas.filters.FlowFilter = None,
    flow_run_filter: schemas.filters.FlowRunFilter = None,
    task_run_filter: schemas.filters.TaskRunFilter = None,
    deployment_filter: schemas.filters.DeploymentFilter = None,
    work_pool_filter: schemas.filters.WorkPoolFilter = None,
    work_queue_filter: schemas.filters.WorkQueueFilter = None,
) -> int:
    """
    Count deployments.

    Args:
        session: A database session
        flow_filter: only count deployments whose flows match these criteria
        flow_run_filter: only count deployments whose flow runs match these criteria
        task_run_filter: only count deployments whose task runs match these criteria
        deployment_filter: only count deployment that match these filters
        work_pool_filter: only count deployments that match these work pool filters
        work_queue_filter: only count deployments that match these work pool queue filters

    Returns:
        int: the number of deployments matching filters
    """

    query = select(sa.func.count(sa.text("*"))).select_from(db.Deployment)

    query = await _apply_deployment_filters(
        query=query,
        flow_filter=flow_filter,
        flow_run_filter=flow_run_filter,
        task_run_filter=task_run_filter,
        deployment_filter=deployment_filter,
        work_pool_filter=work_pool_filter,
        work_queue_filter=work_queue_filter,
        db=db,
    )

    result = await session.execute(query)
    return result.scalar()

create_deployment async

Upserts a deployment.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	a database session	required
deployment	schemas.core.Deployment	a deployment model	required

Returns:

Type	Description
	db.Deployment: the newly-created or updated deployment

Source code in prefect/orion/models/deployments.py

@inject_db
async def create_deployment(
    session: sa.orm.Session, deployment: schemas.core.Deployment, db: OrionDBInterface
):
    """Upserts a deployment.

    Args:
        session: a database session
        deployment: a deployment model

    Returns:
        db.Deployment: the newly-created or updated deployment

    """

    # set `updated` manually
    # known limitation of `on_conflict_do_update`, will not use `Column.onupdate`
    # https://docs.sqlalchemy.org/en/14/dialects/sqlite.html#the-set-clause
    deployment.updated = pendulum.now("UTC")

    insert_values = deployment.dict(shallow=True, exclude_unset=True)

    insert_stmt = (
        (await db.insert(db.Deployment))
        .values(**insert_values)
        .on_conflict_do_update(
            index_elements=db.deployment_unique_upsert_columns,
            set_={
                **deployment.dict(
                    shallow=True,
                    exclude_unset=True,
                    exclude={"id", "created", "created_by"},
                ),
            },
        )
    )

    await session.execute(insert_stmt)

    query = (
        sa.select(db.Deployment)
        .where(
            sa.and_(
                db.Deployment.flow_id == deployment.flow_id,
                db.Deployment.name == deployment.name,
            )
        )
        .execution_options(populate_existing=True)
    )
    result = await session.execute(query)
    model = result.scalar()

    if model.work_queue_name:
        await models.work_queues._ensure_work_queue_exists(
            session=session, name=model.work_queue_name, db=db
        )

    # because this could upsert a different schedule, delete any runs from the old
    # deployment
    await _delete_scheduled_runs(
        session=session, deployment_id=model.id, db=db, auto_scheduled_only=True
    )

    return model

delete_deployment async

Delete a deployment by id.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	A database session	required
deployment_id	UUID	a deployment id	required

Returns:

Name	Type	Description
bool	bool	whether or not the deployment was deleted

Source code in prefect/orion/models/deployments.py

@inject_db
async def delete_deployment(
    session: sa.orm.Session, deployment_id: UUID, db: OrionDBInterface
) -> bool:
    """
    Delete a deployment by id.

    Args:
        session: A database session
        deployment_id: a deployment id

    Returns:
        bool: whether or not the deployment was deleted
    """

    # delete scheduled runs, both auto- and user- created.
    await _delete_scheduled_runs(
        session=session, deployment_id=deployment_id, auto_scheduled_only=False
    )

    result = await session.execute(
        delete(db.Deployment).where(db.Deployment.id == deployment_id)
    )
    return result.rowcount > 0

read_deployment async

Reads a deployment by id.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	A database session	required
deployment_id	UUID	a deployment id	required

Returns:

Type	Description
	db.Deployment: the deployment

Source code in prefect/orion/models/deployments.py

@inject_db
async def read_deployment(
    session: sa.orm.Session, deployment_id: UUID, db: OrionDBInterface
):
    """Reads a deployment by id.

    Args:
        session: A database session
        deployment_id: a deployment id

    Returns:
        db.Deployment: the deployment
    """

    return await session.get(db.Deployment, deployment_id)

read_deployment_by_name async

Reads a deployment by name.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	A database session	required
name	str	a deployment name	required
flow_name	str	the name of the flow the deployment belongs to	required

Returns:

Type	Description
	db.Deployment: the deployment

Source code in prefect/orion/models/deployments.py

@inject_db
async def read_deployment_by_name(
    session: sa.orm.Session, name: str, flow_name: str, db: OrionDBInterface
):
    """Reads a deployment by name.

    Args:
        session: A database session
        name: a deployment name
        flow_name: the name of the flow the deployment belongs to

    Returns:
        db.Deployment: the deployment
    """

    result = await session.execute(
        select(db.Deployment)
        .join(db.Flow, db.Deployment.flow_id == db.Flow.id)
        .where(
            sa.and_(
                db.Flow.name == flow_name,
                db.Deployment.name == name,
            )
        )
        .limit(1)
    )
    return result.scalar()

read_deployments async

Read deployments.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	A database session	required
offset	int	Query offset	None
limit	int	Query limit	None
flow_filter	schemas.filters.FlowFilter	only select deployments whose flows match these criteria	None
flow_run_filter	schemas.filters.FlowRunFilter	only select deployments whose flow runs match these criteria	None
task_run_filter	schemas.filters.TaskRunFilter	only select deployments whose task runs match these criteria	None
deployment_filter	schemas.filters.DeploymentFilter	only select deployment that match these filters	None
work_pool_filter	schemas.filters.WorkPoolFilter	only select deployments whose work pools match these criteria	None
work_queue_filter	schemas.filters.WorkQueueFilter	only select deployments whose work pool queues match these criteria	None
sort	schemas.sorting.DeploymentSort	the sort criteria for selected deployments. Defaults to name ASC.	schemas.sorting.DeploymentSort.NAME_ASC

Returns:

Type	Description
	List[db.Deployment]: deployments

Source code in prefect/orion/models/deployments.py

@inject_db
async def read_deployments(
    session: sa.orm.Session,
    db: OrionDBInterface,
    offset: int = None,
    limit: int = None,
    flow_filter: schemas.filters.FlowFilter = None,
    flow_run_filter: schemas.filters.FlowRunFilter = None,
    task_run_filter: schemas.filters.TaskRunFilter = None,
    deployment_filter: schemas.filters.DeploymentFilter = None,
    work_pool_filter: schemas.filters.WorkPoolFilter = None,
    work_queue_filter: schemas.filters.WorkQueueFilter = None,
    sort: schemas.sorting.DeploymentSort = schemas.sorting.DeploymentSort.NAME_ASC,
):
    """
    Read deployments.

    Args:
        session: A database session
        offset: Query offset
        limit: Query limit
        flow_filter: only select deployments whose flows match these criteria
        flow_run_filter: only select deployments whose flow runs match these criteria
        task_run_filter: only select deployments whose task runs match these criteria
        deployment_filter: only select deployment that match these filters
        work_pool_filter: only select deployments whose work pools match these criteria
        work_queue_filter: only select deployments whose work pool queues match these criteria
        sort: the sort criteria for selected deployments. Defaults to `name` ASC.

    Returns:
        List[db.Deployment]: deployments
    """

    query = select(db.Deployment).order_by(sort.as_sql_sort(db=db))

    query = await _apply_deployment_filters(
        query=query,
        flow_filter=flow_filter,
        flow_run_filter=flow_run_filter,
        task_run_filter=task_run_filter,
        deployment_filter=deployment_filter,
        work_pool_filter=work_pool_filter,
        work_queue_filter=work_queue_filter,
        db=db,
    )

    if offset is not None:
        query = query.offset(offset)
    if limit is not None:
        query = query.limit(limit)

    result = await session.execute(query)
    return result.scalars().unique().all()

schedule_runs async

Schedule flow runs for a deployment

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	a database session	required
deployment_id	UUID	the id of the deployment to schedule	required
start_time	datetime.datetime	the time from which to start scheduling runs	None
end_time	datetime.datetime	runs will be scheduled until at most this time	None
min_time	datetime.timedelta	runs will be scheduled until at least this far in the future	None
min_runs	int	a minimum amount of runs to schedule	None
max_runs	int	a maximum amount of runs to schedule	None

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

Returns:

Type	Description
List[UUID]	a list of flow run ids scheduled for the deployment

Source code in prefect/orion/models/deployments.py

async def schedule_runs(
    session: sa.orm.Session,
    deployment_id: UUID,
    start_time: datetime.datetime = None,
    end_time: datetime.datetime = None,
    min_time: datetime.timedelta = None,
    min_runs: int = None,
    max_runs: int = None,
    auto_scheduled: bool = True,
) -> List[UUID]:
    """
    Schedule flow runs for a deployment

    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

    Returns:
        a list of flow run ids scheduled for the deployment
    """
    if min_runs is None:
        min_runs = PREFECT_ORION_SERVICES_SCHEDULER_MIN_RUNS.value()
    if max_runs is None:
        max_runs = PREFECT_ORION_SERVICES_SCHEDULER_MAX_RUNS.value()
    if start_time is None:
        start_time = pendulum.now("UTC")
    if end_time is None:
        end_time = start_time + (
            PREFECT_ORION_SERVICES_SCHEDULER_MAX_SCHEDULED_TIME.value()
        )
    if min_time is None:
        min_time = PREFECT_ORION_SERVICES_SCHEDULER_MIN_SCHEDULED_TIME.value()

    start_time = pendulum.instance(start_time)
    end_time = pendulum.instance(end_time)

    runs = await _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,
        auto_scheduled=auto_scheduled,
    )
    return await _insert_scheduled_flow_runs(session=session, runs=runs)

update_deployment async

Updates a deployment.

Parameters:

Name	Type	Description	Default
session	sa.orm.Session	a database session	required
deployment_id	UUID	the ID of the deployment to modify	required
deployment	schemas.actions.DeploymentUpdate	changes to a deployment model	required

Returns:

Name	Type	Description
bool	bool	whether the deployment was updated

Source code in prefect/orion/models/deployments.py

@inject_db
async def update_deployment(
    session: sa.orm.Session,
    deployment_id: UUID,
    deployment: schemas.actions.DeploymentUpdate,
    db: OrionDBInterface,
) -> bool:
    """Updates a deployment.

    Args:
        session: a database session
        deployment_id: the ID of the deployment to modify
        deployment: changes to a deployment model

    Returns:
        bool: whether the deployment was updated

    """

    # exclude_unset=True allows us to only update values provided by
    # the user, ignoring any defaults on the model
    update_data = deployment.dict(
        shallow=True,
        exclude_unset=True,
        exclude={"work_pool_name"},
    )
    if deployment.work_pool_name and deployment.work_queue_name:
        # If a specific pool name/queue name combination was provided, get the
        # ID for that work pool queue.
        update_data[
            "work_queue_id"
        ] = await WorkerLookups()._get_work_queue_id_from_name(
            session=session,
            work_pool_name=deployment.work_pool_name,
            work_queue_name=deployment.work_queue_name,
            create_queue_if_not_found=True,
        )
    elif deployment.work_pool_name:
        # If just a pool name was provided, get the ID for its default
        # work pool queue.
        update_data[
            "work_queue_id"
        ] = await WorkerLookups()._get_default_work_queue_id_from_work_pool_name(
            session=session,
            work_pool_name=deployment.work_pool_name,
        )
    elif deployment.work_queue_name:
        # If just a queue name was provided, ensure the queue exists and
        # get its ID.
        work_queue = await models.work_queues._ensure_work_queue_exists(
            session=session, name=update_data["work_queue_name"], db=db
        )
        update_data["work_queue_id"] = work_queue.id

    update_stmt = (
        sa.update(db.Deployment)
        .where(db.Deployment.id == deployment_id)
        .values(**update_data)
    )
    result = await session.execute(update_stmt)

    # delete any auto scheduled runs that would have reflected the old deployment config
    await _delete_scheduled_runs(
        session=session, deployment_id=deployment_id, db=db, auto_scheduled_only=True
    )

    # create work queue if it doesn't exist
    if update_data.get("work_queue_name"):
        await models.work_queues._ensure_work_queue_exists(
            session=session, name=update_data["work_queue_name"], db=db
        )

    return result.rowcount > 0