prefect.engine
¶
Client-side execution and orchestration of flows and tasks.
Engine process overview¶
Flows¶
-
The flow is called by the user or an existing flow run is executed in a new process.
See
Flow.__call__
andprefect.engine.__main__
(python -m prefect.engine
) -
A synchronous function acts as an entrypoint to the engine. The engine executes on a dedicated "global loop" thread. For asynchronous flow calls, we return a coroutine from the entrypoint so the user can enter the engine without blocking their event loop.
See
enter_flow_run_engine_from_flow_call
,enter_flow_run_engine_from_subprocess
-
The thread that calls the entrypoint waits until orchestration of the flow run completes. This thread is referred to as the "user" thread and is usually the "main" thread. The thread is not blocked while waiting — it allows the engine to send work back to it. This allows us to send calls back to the user thread from the global loop thread.
See
wait_for_call_in_loop_thread
andcall_soon_in_waiting_thread
-
The asynchronous engine branches depending on if the flow run exists already and if there is a parent flow run in the current context.
See
create_then_begin_flow_run
,create_and_begin_subflow_run
, andretrieve_flow_then_begin_flow_run
-
The asynchronous engine prepares for execution of the flow run. This includes starting the task runner, preparing context, etc.
See
begin_flow_run
-
The flow run is orchestrated through states, calling the user's function as necessary. Generally the user's function is sent for execution on the user thread. If the flow function cannot be safely executed on the user thread, e.g. it is a synchronous child in an asynchronous parent it will be scheduled on a worker thread instead.
See
orchestrate_flow_run
,call_soon_in_waiting_thread
,call_soon_in_new_thread
Tasks¶
-
The task is called or submitted by the user. We require that this is always within a flow.
See
Task.__call__
andTask.submit
-
A synchronous function acts as an entrypoint to the engine. Unlike flow calls, this will not block until completion if
submit
was used.See
enter_task_run_engine
-
A future is created for the task call. Creation of the task run and submission to the task runner is scheduled as a background task so submission of many tasks can occur concurrently.
See
create_task_run_future
andcreate_task_run_then_submit
-
The engine branches depending on if a future, state, or result is requested. If a future is requested, it is returned immediately to the user thread. Otherwise, the engine will wait for the task run to complete and return the final state or result.
See
get_task_call_return_value
-
An engine function is submitted to the task runner. The task runner will schedule this function for execution on a worker. When executed, it will prepare for orchestration and wait for completion of the run.
See
create_task_run_then_submit
andbegin_task_run
-
The task run is orchestrated through states, calling the user's function as necessary. The user's function is always executed in a worker thread for isolation.
See
orchestrate_task_run
,call_soon_in_new_thread
_Ideally, for local and sequential task runners we would send the task run to the user thread as we do for flows. See #9855.
begin_flow_run
async
¶
Begins execution of a flow run; blocks until completion of the flow run
- Starts a task runner
- Determines the result storage block to use
- Orchestrates the flow run (runs the user-function and generates tasks)
- Waits for tasks to complete / shutsdown the task runner
- Sets a terminal state for the flow run
Note that the flow_run
contains a parameters
attribute which is the serialized
parameters sent to the backend while the parameters
argument here should be the
deserialized and validated dictionary of python objects.
Returns:
Type | Description |
---|---|
State
|
The final state of the run |
Source code in prefect/engine.py
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begin_task_map
async
¶
Async entrypoint for task mapping
Source code in prefect/engine.py
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begin_task_run
async
¶
Entrypoint for task run execution.
This function is intended for submission to the task runner.
This method may be called from a worker so we ensure the settings context has been entered. For example, with a runner that is executing tasks in the same event loop, we will likely not enter the context again because the current context already matches:
main thread:
--> Flow called with settings A
--> begin_task_run
executes same event loop
--> Profile A matches and is not entered again
However, with execution on a remote environment, we are going to need to ensure the settings for the task run are respected by entering the context:
main thread:
--> Flow called with settings A
--> begin_task_run
is scheduled on a remote worker, settings A is serialized
remote worker:
--> Remote worker imports Prefect (may not occur)
--> Global settings is loaded with default settings
--> begin_task_run
executes on a different event loop than the flow
--> Current settings is not set or does not match, settings A is entered
Source code in prefect/engine.py
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create_and_begin_subflow_run
async
¶
Async entrypoint for flows calls within a flow run
Subflows differ from parent flows in that they - Resolve futures in passed parameters into values - Create a dummy task for representation in the parent flow - Retrieve default result storage from the parent flow rather than the server
Returns:
Type | Description |
---|---|
Any
|
The final state of the run |
Source code in prefect/engine.py
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create_autonomous_task_run
async
¶
Create a task run in the API for an autonomous task submission and store the provided parameters using the existing result storage mechanism.
Source code in prefect/engine.py
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create_then_begin_flow_run
async
¶
Async entrypoint for flow calls
Creates the flow run in the backend, then enters the main flow run engine.
Source code in prefect/engine.py
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enter_flow_run_engine_from_flow_call
¶
Sync entrypoint for flow calls.
This function does the heavy lifting of ensuring we can get into an async context for flow run execution with minimal overhead.
Source code in prefect/engine.py
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enter_flow_run_engine_from_subprocess
¶
Sync entrypoint for flow runs that have been submitted for execution by an agent
Differs from enter_flow_run_engine_from_flow_call
in that we have a flow run id
but not a flow object. The flow must be retrieved before execution can begin.
Additionally, this assumes that the caller is always in a context without an event
loop as this should be called from a fresh process.
Source code in prefect/engine.py
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enter_task_run_engine
¶
Sync entrypoint for task calls
Source code in prefect/engine.py
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orchestrate_flow_run
async
¶
Executes a flow run.
Note on flow timeouts
Since async flows are run directly in the main event loop, timeout behavior will
match that described by anyio. If the flow is awaiting something, it will
immediately return; otherwise, the next time it awaits it will exit. Sync flows
are being task runner in a worker thread, which cannot be interrupted. The worker
thread will exit at the next task call. The worker thread also has access to the
status of the cancellation scope at FlowRunContext.timeout_scope.cancel_called
which allows it to raise a TimeoutError
to respect the timeout.
Returns:
Type | Description |
---|---|
State
|
The final state of the run |
Source code in prefect/engine.py
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orchestrate_task_run
async
¶
Execute a task run
This function should be submitted to a task runner. We must construct the context here instead of receiving it already populated since we may be in a new environment.
Proposes a RUNNING state, then - if accepted, the task user function will be run - if rejected, the received state will be returned
When the user function is run, the result will be used to determine a final state
- if an exception is encountered, it is trapped and stored in a FAILED state
- otherwise, return_value_to_state
is used to determine the state
If the final state is COMPLETED, we generate a cache key as specified by the task
The final state is then proposed - if accepted, this is the final state and will be returned - if rejected and a new final state is provided, it will be returned - if rejected and a non-final state is provided, we will attempt to enter a RUNNING state again
Returns:
Type | Description |
---|---|
State
|
The final state of the run |
Source code in prefect/engine.py
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pause_flow_run
async
¶
Pauses the current flow run by blocking execution until resumed.
When called within a flow run, execution will block and no downstream tasks will run until the flow is resumed. Task runs that have already started will continue running. A timeout parameter can be passed that will fail the flow run if it has not been resumed within the specified time.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
flow_run_id |
UUID
|
a flow run id. If supplied, this function will attempt to pause
the specified flow run outside of the flow run process. When paused, the
flow run will continue execution until the NEXT task is orchestrated, at
which point the flow will exit. Any tasks that have already started will
run until completion. When resumed, the flow run will be rescheduled to
finish execution. In order pause a flow run in this way, the flow needs to
have an associated deployment and results need to be configured with the
|
None
|
timeout |
int
|
the number of seconds to wait for the flow to be resumed before failing. Defaults to 1 hour (3600 seconds). If the pause timeout exceeds any configured flow-level timeout, the flow might fail even after resuming. |
3600
|
poll_interval |
int
|
The number of seconds between checking whether the flow has been resumed. Defaults to 10 seconds. |
10
|
reschedule |
bool
|
Flag that will reschedule the flow run if resumed. Instead of
blocking execution, the flow will gracefully exit (with no result returned)
instead. To use this flag, a flow needs to have an associated deployment and
results need to be configured with the |
False
|
key |
str
|
An optional key to prevent calling pauses more than once. This defaults to the number of pauses observed by the flow so far, and prevents pauses that use the "reschedule" option from running the same pause twice. A custom key can be supplied for custom pausing behavior. |
None
|
wait_for_input |
Optional[Type[T]]
|
a subclass of |
None
|
@task
def task_one():
for i in range(3):
sleep(1)
@flow
def my_flow():
terminal_state = task_one.submit(return_state=True)
if terminal_state.type == StateType.COMPLETED:
print("Task one succeeded! Pausing flow run..")
pause_flow_run(timeout=2)
else:
print("Task one failed. Skipping pause flow run..")
Source code in prefect/engine.py
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report_flow_run_crashes
async
¶
Detect flow run crashes during this context and update the run to a proper final state.
This context must reraise the exception to properly exit the run.
Source code in prefect/engine.py
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report_task_run_crashes
async
¶
Detect task run crashes during this context and update the run to a proper final state.
This context must reraise the exception to properly exit the run.
Source code in prefect/engine.py
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resume_flow_run
async
¶
Resumes a paused flow.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
flow_run_id |
the flow_run_id to resume |
required | |
run_input |
Optional[Dict]
|
a dictionary of inputs to provide to the flow run. |
None
|
Source code in prefect/engine.py
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retrieve_flow_then_begin_flow_run
async
¶
Async entrypoint for flow runs that have been submitted for execution by an agent
- Retrieves the deployment information
- Loads the flow object using deployment information
- Updates the flow run version
Source code in prefect/engine.py
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suspend_flow_run
async
¶
Suspends a flow run by stopping code execution until resumed.
When suspended, the flow run will continue execution until the NEXT task is
orchestrated, at which point the flow will exit. Any tasks that have
already started will run until completion. When resumed, the flow run will
be rescheduled to finish execution. In order suspend a flow run in this
way, the flow needs to have an associated deployment and results need to be
configured with the persist_results
option.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
flow_run_id |
Optional[UUID]
|
a flow run id. If supplied, this function will attempt to suspend the specified flow run. If not supplied will attempt to suspend the current flow run. |
None
|
timeout |
Optional[int]
|
the number of seconds to wait for the flow to be resumed before failing. Defaults to 1 hour (3600 seconds). If the pause timeout exceeds any configured flow-level timeout, the flow might fail even after resuming. |
3600
|
key |
Optional[str]
|
An optional key to prevent calling suspend more than once. This defaults to a random string and prevents suspends from running the same suspend twice. A custom key can be supplied for custom suspending behavior. |
None
|
wait_for_input |
Optional[Type[T]]
|
a subclass of |
None
|
Source code in prefect/engine.py
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