prefect.server.api.flow_runs

Routes for interacting with flow run objects.

average_flow_run_lateness(flows=None, flow_runs=None, task_runs=None, deployments=None, work_pools=None, work_pool_queues=None, db=Depends(provide_database_interface)) async

Query for average flow-run lateness in seconds.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/lateness")
async def average_flow_run_lateness(
    flows: Optional[schemas.filters.FlowFilter] = None,
    flow_runs: Optional[schemas.filters.FlowRunFilter] = None,
    task_runs: Optional[schemas.filters.TaskRunFilter] = None,
    deployments: Optional[schemas.filters.DeploymentFilter] = None,
    work_pools: Optional[schemas.filters.WorkPoolFilter] = None,
    work_pool_queues: Optional[schemas.filters.WorkQueueFilter] = None,
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> Optional[float]:
    """
    Query for average flow-run lateness in seconds.
    """
    async with db.session_context() as session:
        if db.dialect.name == "sqlite":
            # Since we want an _average_ of the lateness we're unable to use
            # the existing FlowRun.expected_start_time_delta property as it
            # returns a timedelta and SQLite is unable to properly deal with it
            # and always returns 1970.0 as the average. This copies the same
            # logic but ensures that it returns the number of seconds instead
            # so it's compatible with SQLite.
            base_query = sa.case(
                (
                    db.FlowRun.start_time > db.FlowRun.expected_start_time,
                    sa.func.strftime("%s", db.FlowRun.start_time)
                    - sa.func.strftime("%s", db.FlowRun.expected_start_time),
                ),
                (
                    db.FlowRun.start_time.is_(None)
                    & db.FlowRun.state_type.notin_(schemas.states.TERMINAL_STATES)
                    & (db.FlowRun.expected_start_time < sa.func.datetime("now")),
                    sa.func.strftime("%s", sa.func.datetime("now"))
                    - sa.func.strftime("%s", db.FlowRun.expected_start_time),
                ),
                else_=0,
            )
        else:
            base_query = db.FlowRun.estimated_start_time_delta

        query = await models.flow_runs._apply_flow_run_filters(
            sa.select(sa.func.avg(base_query)),
            flow_filter=flows,
            flow_run_filter=flow_runs,
            task_run_filter=task_runs,
            deployment_filter=deployments,
            work_pool_filter=work_pools,
            work_queue_filter=work_pool_queues,
        )
        result = await session.execute(query)

        avg_lateness = result.scalar()

        if avg_lateness is None:
            return None
        elif isinstance(avg_lateness, datetime.timedelta):
            return avg_lateness.total_seconds()
        else:
            return avg_lateness

count_flow_runs(flows=None, flow_runs=None, task_runs=None, deployments=None, work_pools=None, work_pool_queues=None, db=Depends(provide_database_interface)) async

Query for flow runs.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/count")
async def count_flow_runs(
    flows: schemas.filters.FlowFilter = None,
    flow_runs: schemas.filters.FlowRunFilter = None,
    task_runs: schemas.filters.TaskRunFilter = None,
    deployments: schemas.filters.DeploymentFilter = None,
    work_pools: schemas.filters.WorkPoolFilter = None,
    work_pool_queues: schemas.filters.WorkQueueFilter = None,
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> int:
    """
    Query for flow runs.
    """
    async with db.session_context() as session:
        return await models.flow_runs.count_flow_runs(
            session=session,
            flow_filter=flows,
            flow_run_filter=flow_runs,
            task_run_filter=task_runs,
            deployment_filter=deployments,
            work_pool_filter=work_pools,
            work_queue_filter=work_pool_queues,
        )

create_flow_run(flow_run, db=Depends(provide_database_interface), response=None, created_by=Depends(dependencies.get_created_by), orchestration_parameters=Depends(orchestration_dependencies.provide_flow_orchestration_parameters), api_version=Depends(dependencies.provide_request_api_version)) async

Create a flow run. If a flow run with the same flow_id and idempotency key already exists, the existing flow run will be returned.

If no state is provided, the flow run will be created in a PENDING state.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/")
async def create_flow_run(
    flow_run: schemas.actions.FlowRunCreate,
    db: PrefectDBInterface = Depends(provide_database_interface),
    response: Response = None,
    created_by: Optional[schemas.core.CreatedBy] = Depends(dependencies.get_created_by),
    orchestration_parameters: Dict[str, Any] = Depends(
        orchestration_dependencies.provide_flow_orchestration_parameters
    ),
    api_version=Depends(dependencies.provide_request_api_version),
) -> schemas.responses.FlowRunResponse:
    """
    Create a flow run. If a flow run with the same flow_id and
    idempotency key already exists, the existing flow run will be returned.

    If no state is provided, the flow run will be created in a PENDING state.
    """
    # hydrate the input model into a full flow run / state model
    flow_run = schemas.core.FlowRun(**flow_run.model_dump(), created_by=created_by)

    # pass the request version to the orchestration engine to support compatibility code
    orchestration_parameters.update({"api-version": api_version})

    if not flow_run.state:
        flow_run.state = schemas.states.Pending()

    now = pendulum.now("UTC")

    async with db.session_context(begin_transaction=True) as session:
        model = await models.flow_runs.create_flow_run(
            session=session,
            flow_run=flow_run,
            orchestration_parameters=orchestration_parameters,
        )
        if model.created >= now:
            response.status_code = status.HTTP_201_CREATED

        return schemas.responses.FlowRunResponse.model_validate(
            model, from_attributes=True
        )

create_flow_run_input(flow_run_id=Path(..., description='The flow run id', alias='id'), key=Body(..., description='The input key'), value=Body(..., description='The value of the input'), sender=Body(None, description='The sender of the input'), db=Depends(provide_database_interface)) async

Create a key/value input for a flow run.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/{id}/input", status_code=status.HTTP_201_CREATED)
async def create_flow_run_input(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    key: str = Body(..., description="The input key"),
    value: bytes = Body(..., description="The value of the input"),
    sender: Optional[str] = Body(None, description="The sender of the input"),
    db: PrefectDBInterface = Depends(provide_database_interface),
):
    """
    Create a key/value input for a flow run.
    """
    async with db.session_context() as session:
        try:
            await models.flow_run_input.create_flow_run_input(
                session=session,
                flow_run_input=schemas.core.FlowRunInput(
                    flow_run_id=flow_run_id,
                    key=key,
                    sender=sender,
                    value=value.decode(),
                ),
            )
            await session.commit()

        except IntegrityError as exc:
            if "unique constraint" in str(exc).lower():
                raise HTTPException(
                    status_code=status.HTTP_409_CONFLICT,
                    detail="A flow run input with this key already exists.",
                )
            else:
                raise HTTPException(
                    status_code=status.HTTP_404_NOT_FOUND, detail="Flow run not found"
                )

delete_flow_run(flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface)) async

Delete a flow run by id.

Source code in src/prefect/server/api/flow_runs.py

@router.delete("/{id}", status_code=status.HTTP_204_NO_CONTENT)
async def delete_flow_run(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
):
    """
    Delete a flow run by id.
    """
    async with db.session_context(begin_transaction=True) as session:
        result = await models.flow_runs.delete_flow_run(
            session=session, flow_run_id=flow_run_id
        )
    if not result:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND, detail="Flow run not found"
        )

delete_flow_run_input(flow_run_id=Path(..., description='The flow run id', alias='id'), key=Path(..., description='The input key', alias='key'), db=Depends(provide_database_interface)) async

Delete a flow run input

Source code in src/prefect/server/api/flow_runs.py

@router.delete("/{id}/input/{key}", status_code=status.HTTP_204_NO_CONTENT)
async def delete_flow_run_input(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    key: str = Path(..., description="The input key", alias="key"),
    db: PrefectDBInterface = Depends(provide_database_interface),
):
    """
    Delete a flow run input
    """

    async with db.session_context() as session:
        deleted = await models.flow_run_input.delete_flow_run_input(
            session=session, flow_run_id=flow_run_id, key=key
        )
        await session.commit()

        if not deleted:
            raise HTTPException(
                status_code=status.HTTP_404_NOT_FOUND, detail="Flow run input not found"
            )

download_logs(flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface)) async

Download all flow run logs as a CSV file, collecting all logs until there are no more logs to retrieve.

Source code in src/prefect/server/api/flow_runs.py

@router.get("/{id}/logs/download")
async def download_logs(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> StreamingResponse:
    """
    Download all flow run logs as a CSV file, collecting all logs until there are no more logs to retrieve.
    """
    async with db.session_context() as session:
        flow_run = await models.flow_runs.read_flow_run(
            session=session, flow_run_id=flow_run_id
        )

        if not flow_run:
            raise HTTPException(status.HTTP_404_NOT_FOUND, detail="Flow run not found")

        async def generate():
            data = io.StringIO()
            csv_writer = csv.writer(data)
            csv_writer.writerow(
                ["timestamp", "level", "flow_run_id", "task_run_id", "message"]
            )

            offset = 0
            limit = FLOW_RUN_LOGS_DOWNLOAD_PAGE_LIMIT

            while True:
                results = await models.logs.read_logs(
                    session=session,
                    log_filter=schemas.filters.LogFilter(
                        flow_run_id={"any_": [flow_run_id]}
                    ),
                    offset=offset,
                    limit=limit,
                    sort=schemas.sorting.LogSort.TIMESTAMP_ASC,
                )

                if not results:
                    break

                offset += limit

                for log in results:
                    csv_writer.writerow(
                        [
                            log.timestamp,
                            log.level,
                            log.flow_run_id,
                            log.task_run_id,
                            log.message,
                        ]
                    )
                    data.seek(0)
                    yield data.read()
                    data.seek(0)
                    data.truncate(0)

        return StreamingResponse(
            generate(),
            media_type="text/csv",
            headers={
                "Content-Disposition": f"attachment; filename={flow_run.name}-logs.csv"
            },
        )

filter_flow_run_input(flow_run_id=Path(..., description='The flow run id', alias='id'), prefix=Body(..., description='The input key prefix', embed=True), limit=Body(1, description='The maximum number of results to return', embed=True), exclude_keys=Body([], description='Exclude inputs with these keys', embed=True), db=Depends(provide_database_interface)) async

Filter flow run inputs by key prefix

Source code in src/prefect/server/api/flow_runs.py

@router.post("/{id}/input/filter")
async def filter_flow_run_input(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    prefix: str = Body(..., description="The input key prefix", embed=True),
    limit: int = Body(
        1, description="The maximum number of results to return", embed=True
    ),
    exclude_keys: List[str] = Body(
        [], description="Exclude inputs with these keys", embed=True
    ),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> List[schemas.core.FlowRunInput]:
    """
    Filter flow run inputs by key prefix
    """
    async with db.session_context() as session:
        return await models.flow_run_input.filter_flow_run_input(
            session=session,
            flow_run_id=flow_run_id,
            prefix=prefix,
            limit=limit,
            exclude_keys=exclude_keys,
        )

flow_run_history(history_start=Body(..., description="The history's start time."), history_end=Body(..., description="The history's end time."), history_interval=Body(..., description='The size of each history interval, in seconds. Must be at least 1 second.', json_schema_extra={'format': 'time-delta'}, alias='history_interval_seconds'), flows=None, flow_runs=None, task_runs=None, deployments=None, work_pools=None, work_queues=None, db=Depends(provide_database_interface)) async

Query for flow run history data across a given range and interval.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/history")
async def flow_run_history(
    history_start: DateTime = Body(..., description="The history's start time."),
    history_end: DateTime = Body(..., description="The history's end time."),
    # Workaround for the fact that FastAPI does not let us configure ser_json_timedelta
    # to represent timedeltas as floats in JSON.
    history_interval: float = Body(
        ...,
        description=(
            "The size of each history interval, in seconds. Must be at least 1 second."
        ),
        json_schema_extra={"format": "time-delta"},
        alias="history_interval_seconds",
    ),
    flows: schemas.filters.FlowFilter = None,
    flow_runs: schemas.filters.FlowRunFilter = None,
    task_runs: schemas.filters.TaskRunFilter = None,
    deployments: schemas.filters.DeploymentFilter = None,
    work_pools: schemas.filters.WorkPoolFilter = None,
    work_queues: schemas.filters.WorkQueueFilter = None,
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> List[schemas.responses.HistoryResponse]:
    """
    Query for flow run history data across a given range and interval.
    """
    if isinstance(history_interval, float):
        history_interval = datetime.timedelta(seconds=history_interval)

    if history_interval < datetime.timedelta(seconds=1):
        raise HTTPException(
            status.HTTP_422_UNPROCESSABLE_ENTITY,
            detail="History interval must not be less than 1 second.",
        )

    async with db.session_context() as session:
        return await run_history(
            session=session,
            run_type="flow_run",
            history_start=history_start,
            history_end=history_end,
            history_interval=history_interval,
            flows=flows,
            flow_runs=flow_runs,
            task_runs=task_runs,
            deployments=deployments,
            work_pools=work_pools,
            work_queues=work_queues,
        )

paginate_flow_runs(sort=Body(schemas.sorting.FlowRunSort.ID_DESC), limit=dependencies.LimitBody(), page=Body(1, ge=1), flows=None, flow_runs=None, task_runs=None, deployments=None, work_pools=None, work_pool_queues=None, db=Depends(provide_database_interface)) async

Pagination query for flow runs.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/paginate", response_class=ORJSONResponse)
async def paginate_flow_runs(
    sort: schemas.sorting.FlowRunSort = Body(schemas.sorting.FlowRunSort.ID_DESC),
    limit: int = dependencies.LimitBody(),
    page: int = Body(1, ge=1),
    flows: Optional[schemas.filters.FlowFilter] = None,
    flow_runs: Optional[schemas.filters.FlowRunFilter] = None,
    task_runs: Optional[schemas.filters.TaskRunFilter] = None,
    deployments: Optional[schemas.filters.DeploymentFilter] = None,
    work_pools: Optional[schemas.filters.WorkPoolFilter] = None,
    work_pool_queues: Optional[schemas.filters.WorkQueueFilter] = None,
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> FlowRunPaginationResponse:
    """
    Pagination query for flow runs.
    """
    offset = (page - 1) * limit

    async with db.session_context() as session:
        runs = await models.flow_runs.read_flow_runs(
            session=session,
            flow_filter=flows,
            flow_run_filter=flow_runs,
            task_run_filter=task_runs,
            deployment_filter=deployments,
            work_pool_filter=work_pools,
            work_queue_filter=work_pool_queues,
            offset=offset,
            limit=limit,
            sort=sort,
        )

        count = await models.flow_runs.count_flow_runs(
            session=session,
            flow_filter=flows,
            flow_run_filter=flow_runs,
            task_run_filter=task_runs,
            deployment_filter=deployments,
            work_pool_filter=work_pools,
            work_queue_filter=work_pool_queues,
        )

        # Instead of relying on fastapi.encoders.jsonable_encoder to convert the
        # response to JSON, we do so more efficiently ourselves.
        # In particular, the FastAPI encoder is very slow for large, nested objects.
        # See: https://github.com/tiangolo/fastapi/issues/1224
        results = [
            schemas.responses.FlowRunResponse.model_validate(
                run, from_attributes=True
            ).model_dump(mode="json")
            for run in runs
        ]

        response = FlowRunPaginationResponse(
            results=results,
            count=count,
            limit=limit,
            pages=(count + limit - 1) // limit,
            page=page,
        ).model_dump(mode="json")

        return ORJSONResponse(content=response)

read_flow_run(flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface)) async

Get a flow run by id.

Source code in src/prefect/server/api/flow_runs.py

@router.get("/{id}")
async def read_flow_run(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> schemas.responses.FlowRunResponse:
    """
    Get a flow run by id.
    """
    async with db.session_context() as session:
        flow_run = await models.flow_runs.read_flow_run(
            session=session, flow_run_id=flow_run_id
        )
        if not flow_run:
            raise HTTPException(status.HTTP_404_NOT_FOUND, detail="Flow run not found")
        return schemas.responses.FlowRunResponse.model_validate(
            flow_run, from_attributes=True
        )

read_flow_run_graph_v1(flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface)) async

Get a task run dependency map for a given flow run.

Source code in src/prefect/server/api/flow_runs.py

@router.get("/{id}/graph")
async def read_flow_run_graph_v1(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> List[DependencyResult]:
    """
    Get a task run dependency map for a given flow run.
    """
    async with db.session_context() as session:
        return await models.flow_runs.read_task_run_dependencies(
            session=session, flow_run_id=flow_run_id
        )

read_flow_run_graph_v2(flow_run_id=Path(..., description='The flow run id', alias='id'), since=Query(datetime.datetime.min, description='Only include runs that start or end after this time.'), db=Depends(provide_database_interface)) async

Get a graph of the tasks and subflow runs for the given flow run

Source code in src/prefect/server/api/flow_runs.py

@router.get("/{id:uuid}/graph-v2")
async def read_flow_run_graph_v2(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    since: datetime.datetime = Query(
        datetime.datetime.min,
        description="Only include runs that start or end after this time.",
    ),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> Graph:
    """
    Get a graph of the tasks and subflow runs for the given flow run
    """
    async with db.session_context() as session:
        try:
            return await read_flow_run_graph(
                session=session,
                flow_run_id=flow_run_id,
                since=since,
            )
        except FlowRunGraphTooLarge as e:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=str(e),
            )

read_flow_run_input(flow_run_id=Path(..., description='The flow run id', alias='id'), key=Path(..., description='The input key', alias='key'), db=Depends(provide_database_interface)) async

Create a value from a flow run input

Source code in src/prefect/server/api/flow_runs.py

@router.get("/{id}/input/{key}")
async def read_flow_run_input(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    key: str = Path(..., description="The input key", alias="key"),
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> PlainTextResponse:
    """
    Create a value from a flow run input
    """

    async with db.session_context() as session:
        flow_run_input = await models.flow_run_input.read_flow_run_input(
            session=session, flow_run_id=flow_run_id, key=key
        )

    if flow_run_input:
        return PlainTextResponse(flow_run_input.value)
    else:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND, detail="Flow run input not found"
        )

read_flow_runs(sort=Body(schemas.sorting.FlowRunSort.ID_DESC), limit=dependencies.LimitBody(), offset=Body(0, ge=0), flows=None, flow_runs=None, task_runs=None, deployments=None, work_pools=None, work_pool_queues=None, db=Depends(provide_database_interface)) async

Query for flow runs.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/filter", response_class=ORJSONResponse)
async def read_flow_runs(
    sort: schemas.sorting.FlowRunSort = Body(schemas.sorting.FlowRunSort.ID_DESC),
    limit: int = dependencies.LimitBody(),
    offset: int = Body(0, ge=0),
    flows: Optional[schemas.filters.FlowFilter] = None,
    flow_runs: Optional[schemas.filters.FlowRunFilter] = None,
    task_runs: Optional[schemas.filters.TaskRunFilter] = None,
    deployments: Optional[schemas.filters.DeploymentFilter] = None,
    work_pools: Optional[schemas.filters.WorkPoolFilter] = None,
    work_pool_queues: Optional[schemas.filters.WorkQueueFilter] = None,
    db: PrefectDBInterface = Depends(provide_database_interface),
) -> List[schemas.responses.FlowRunResponse]:
    """
    Query for flow runs.
    """
    async with db.session_context() as session:
        db_flow_runs = await models.flow_runs.read_flow_runs(
            session=session,
            flow_filter=flows,
            flow_run_filter=flow_runs,
            task_run_filter=task_runs,
            deployment_filter=deployments,
            work_pool_filter=work_pools,
            work_queue_filter=work_pool_queues,
            offset=offset,
            limit=limit,
            sort=sort,
        )

        # Instead of relying on fastapi.encoders.jsonable_encoder to convert the
        # response to JSON, we do so more efficiently ourselves.
        # In particular, the FastAPI encoder is very slow for large, nested objects.
        # See: https://github.com/tiangolo/fastapi/issues/1224
        encoded = [
            schemas.responses.FlowRunResponse.model_validate(
                fr, from_attributes=True
            ).model_dump(mode="json")
            for fr in db_flow_runs
        ]
        return ORJSONResponse(content=encoded)

resume_flow_run(flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface), run_input=Body(default=None, embed=True), response=None, flow_policy=Depends(orchestration_dependencies.provide_flow_policy), task_policy=Depends(orchestration_dependencies.provide_task_policy), orchestration_parameters=Depends(orchestration_dependencies.provide_flow_orchestration_parameters), api_version=Depends(dependencies.provide_request_api_version)) async

Resume a paused flow run.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/{id}/resume")
async def resume_flow_run(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
    run_input: Optional[Dict] = Body(default=None, embed=True),
    response: Response = None,
    flow_policy: Type[BaseOrchestrationPolicy] = Depends(
        orchestration_dependencies.provide_flow_policy
    ),
    task_policy: BaseOrchestrationPolicy = Depends(
        orchestration_dependencies.provide_task_policy
    ),
    orchestration_parameters: Dict[str, Any] = Depends(
        orchestration_dependencies.provide_flow_orchestration_parameters
    ),
    api_version=Depends(dependencies.provide_request_api_version),
) -> OrchestrationResult:
    """
    Resume a paused flow run.
    """
    now = pendulum.now("UTC")

    async with db.session_context(begin_transaction=True) as session:
        flow_run = await models.flow_runs.read_flow_run(session, flow_run_id)
        state = flow_run.state

        if state is None or state.type != schemas.states.StateType.PAUSED:
            result = OrchestrationResult(
                state=None,
                status=schemas.responses.SetStateStatus.ABORT,
                details=schemas.responses.StateAbortDetails(
                    reason="Cannot resume a flow run that is not paused."
                ),
            )
            return result

        orchestration_parameters.update({"api-version": api_version})

        keyset = state.state_details.run_input_keyset

        if keyset:
            run_input = run_input or {}

            try:
                hydration_context = await schema_tools.HydrationContext.build(
                    session=session,
                    raise_on_error=True,
                    render_jinja=True,
                    render_workspace_variables=True,
                )
                run_input = schema_tools.hydrate(run_input, hydration_context) or {}
            except schema_tools.HydrationError as exc:
                return OrchestrationResult(
                    state=state,
                    status=schemas.responses.SetStateStatus.REJECT,
                    details=schemas.responses.StateAbortDetails(
                        reason=f"Error hydrating run input: {exc}",
                    ),
                )

            schema_json = await models.flow_run_input.read_flow_run_input(
                session=session, flow_run_id=flow_run.id, key=keyset["schema"]
            )

            if schema_json is None:
                return OrchestrationResult(
                    state=state,
                    status=schemas.responses.SetStateStatus.REJECT,
                    details=schemas.responses.StateAbortDetails(
                        reason="Run input schema not found."
                    ),
                )

            try:
                schema = orjson.loads(schema_json.value)
            except orjson.JSONDecodeError:
                return OrchestrationResult(
                    state=state,
                    status=schemas.responses.SetStateStatus.REJECT,
                    details=schemas.responses.StateAbortDetails(
                        reason="Run input schema is not valid JSON."
                    ),
                )

            try:
                schema_tools.validate(run_input, schema, raise_on_error=True)
            except schema_tools.ValidationError as exc:
                return OrchestrationResult(
                    state=state,
                    status=schemas.responses.SetStateStatus.REJECT,
                    details=schemas.responses.StateAbortDetails(
                        reason=f"Reason: {exc}"
                    ),
                )
            except schema_tools.CircularSchemaRefError:
                return OrchestrationResult(
                    state=state,
                    status=schemas.responses.SetStateStatus.REJECT,
                    details=schemas.responses.StateAbortDetails(
                        reason="Invalid schema: Unable to validate schema with circular references.",
                    ),
                )

        if state.state_details.pause_reschedule:
            orchestration_result = await models.flow_runs.set_flow_run_state(
                session=session,
                flow_run_id=flow_run_id,
                state=schemas.states.Scheduled(
                    name="Resuming", scheduled_time=pendulum.now("UTC")
                ),
                flow_policy=flow_policy,
                orchestration_parameters=orchestration_parameters,
            )
        else:
            orchestration_result = await models.flow_runs.set_flow_run_state(
                session=session,
                flow_run_id=flow_run_id,
                state=schemas.states.Running(),
                flow_policy=flow_policy,
                orchestration_parameters=orchestration_parameters,
            )

        if (
            keyset
            and run_input
            and orchestration_result.status == schemas.responses.SetStateStatus.ACCEPT
        ):
            # The state change is accepted, go ahead and store the validated
            # run input.
            await models.flow_run_input.create_flow_run_input(
                session=session,
                flow_run_input=schemas.core.FlowRunInput(
                    flow_run_id=flow_run_id,
                    key=keyset["response"],
                    value=orjson.dumps(run_input).decode("utf-8"),
                ),
            )

        # set the 201 if a new state was created
        if orchestration_result.state and orchestration_result.state.timestamp >= now:
            response.status_code = status.HTTP_201_CREATED
        else:
            response.status_code = status.HTTP_200_OK

        return orchestration_result

set_flow_run_state(flow_run_id=Path(..., description='The flow run id', alias='id'), state=Body(..., description='The intended state.'), force=Body(False, description='If false, orchestration rules will be applied that may alter or prevent the state transition. If True, orchestration rules are not applied.'), db=Depends(provide_database_interface), response=None, flow_policy=Depends(orchestration_dependencies.provide_flow_policy), orchestration_parameters=Depends(orchestration_dependencies.provide_flow_orchestration_parameters), api_version=Depends(dependencies.provide_request_api_version)) async

Set a flow run state, invoking any orchestration rules.

Source code in src/prefect/server/api/flow_runs.py

@router.post("/{id}/set_state")
async def set_flow_run_state(
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    state: schemas.actions.StateCreate = Body(..., description="The intended state."),
    force: bool = Body(
        False,
        description=(
            "If false, orchestration rules will be applied that may alter or prevent"
            " the state transition. If True, orchestration rules are not applied."
        ),
    ),
    db: PrefectDBInterface = Depends(provide_database_interface),
    response: Response = None,
    flow_policy: Type[BaseOrchestrationPolicy] = Depends(
        orchestration_dependencies.provide_flow_policy
    ),
    orchestration_parameters: Dict[str, Any] = Depends(
        orchestration_dependencies.provide_flow_orchestration_parameters
    ),
    api_version=Depends(dependencies.provide_request_api_version),
) -> OrchestrationResult:
    """Set a flow run state, invoking any orchestration rules."""

    # pass the request version to the orchestration engine to support compatibility code
    orchestration_parameters.update({"api-version": api_version})

    now = pendulum.now("UTC")

    # create the state
    async with db.session_context(
        begin_transaction=True, with_for_update=True
    ) as session:
        orchestration_result = await models.flow_runs.set_flow_run_state(
            session=session,
            flow_run_id=flow_run_id,
            # convert to a full State object
            state=schemas.states.State.model_validate(state),
            force=force,
            flow_policy=flow_policy,
            orchestration_parameters=orchestration_parameters,
        )

    # set the 201 if a new state was created
    if orchestration_result.state and orchestration_result.state.timestamp >= now:
        response.status_code = status.HTTP_201_CREATED
    else:
        response.status_code = status.HTTP_200_OK

    return orchestration_result

update_flow_run(flow_run, flow_run_id=Path(..., description='The flow run id', alias='id'), db=Depends(provide_database_interface)) async

Updates a flow run.

Source code in src/prefect/server/api/flow_runs.py

@router.patch("/{id}", status_code=status.HTTP_204_NO_CONTENT)
async def update_flow_run(
    flow_run: schemas.actions.FlowRunUpdate,
    flow_run_id: UUID = Path(..., description="The flow run id", alias="id"),
    db: PrefectDBInterface = Depends(provide_database_interface),
):
    """
    Updates a flow run.
    """
    async with db.session_context(begin_transaction=True) as session:
        if flow_run.job_variables is not None:
            this_run = await models.flow_runs.read_flow_run(
                session, flow_run_id=flow_run_id
            )
            if this_run is None:
                raise HTTPException(
                    status.HTTP_404_NOT_FOUND, detail="Flow run not found"
                )
            if not this_run.state:
                raise HTTPException(
                    status.HTTP_400_BAD_REQUEST,
                    detail="Flow run state is required to update job variables but none exists",
                )
            if this_run.state.type != schemas.states.StateType.SCHEDULED:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=f"Job variables for a flow run in state {this_run.state.type.name} cannot be updated",
                )
            if this_run.deployment_id is None:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail="A deployment for the flow run could not be found",
                )

            deployment = await models.deployments.read_deployment(
                session=session, deployment_id=this_run.deployment_id
            )
            if deployment is None:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail="A deployment for the flow run could not be found",
                )

            await validate_job_variables_for_deployment_flow_run(
                session, deployment, flow_run
            )

        result = await models.flow_runs.update_flow_run(
            session=session, flow_run=flow_run, flow_run_id=flow_run_id
        )
    if not result:
        raise HTTPException(status.HTTP_404_NOT_FOUND, detail="Flow run not found")