States are the "currency" of Prefect. All information about tasks and flows is transmitted via rich
State objects. While you don't need to know the details of the state system to use Prefect, you can give your workflows superpowers by taking advantage of it.
At any moment, you can learn anything you need to know about a task or flow by examining its current state or the history of its states. For example, a state could tell you:
- that a task is scheduled to make a third run attempt in an hour
- that a task succeeded and what data it produced
- that a task is paused and waiting for a user to resume it
- that a task's output is cached and will be reused on future runs
- that a task failed because it timed out
- etc. etc.
By manipulating a relatively small number of task states, Prefect workflows can harness this emergent complexity.
Only runs have states
Though we often refer to the "state" of a flow or a task, what we really mean is the state of a flow run or a task run. Flows and tasks are templates that describe what a system does; only when we run the system does it also take on a state. So while we might refer to a task as "running" or being "successful", we really mean that a specific instance of the task is in that state.
State objects have three important characteristics: a
result. Some states have additional fields, as well. For example, a
Retrying state has a field that says when the task should be retried.
State messages usually explain why the state was entered. In the case of
Failed states, they often contain the error message associated with the failure.
Success(message="The task succeeded!")
Pending(message="This task is waiting to start")
State results carry data associated with the state. For task
Success states, this is the data produced by the task. For
Failed states, it is often the Python
Exception object that led to the failure.
Because all states have a
result field, it means that tasks can work with the results of failed upstream tasks. This may seem surprising, but it's incredibly powerful. For example, a task that runs after a failed task could look at the failed result to see exactly why the failure took place. A task following a skipped task could receive a message indicating why the task was skipped.
To be clear: the default trigger will not run tasks that follow failed tasks, so users will have to opt-in to this functionality.
There are three main types of states:
Finished. Flows and tasks typically progress through them in that order, possibly more than once. Each state type has many children. For example,
Retrying are both
Failed are both
At each stage of the execution pipeline, the current state determines what actions are taken. For example, if you attempt to run a task in a
Success state it will exit the pipeline, because
Finished states are never re-run. If you attempt to run a task in a
Retrying state, it will proceed only as long as the state's scheduled retry time has already passed. In this way, states carry all of the critical information the Prefect engine uses to make decisions about workflow logic.
There's actually a fourth kind of state, called a
MetaState, but it doesn't affect the execution pipeline. Instead, meta-states are used by Prefect to enhance existing states with additional information. For example, two meta-states are
Queued. These are used to "wrap" other states in a way that makes the original state recoverable. For example, a
Scheduled state might be put into a
Submitted state to indicate that it's been submitted for execution, but the original
Scheduled state is needed by the engine to perform runtime logic. By wrapping the
Scheduled state with the
Submitted meta-state, rather than replacing it, the engine is able to recover the original information it needs.
State handlers & callbacks
It is often desirable to take action when a certain event happens, for example when a task fails. Prefect provides
state_handlers for this purpose. Flows and Tasks may have one or more state handler functions that are called whenever the task's state changes. The signature of a state handler is:
def state_handler(obj: Union[Flow, Task], old_state: State, new_state: State) -> State: return new_state
Whenever the task's state changes, the handler will be called with the task itself, the old (previous) state, and then new (current) state. The handler must return a
State object, which is used as the task's new state. This provides an opportunity to either react to certain states or even modify them. If multiple handlers are provided, then they are called in sequence with the state returned by one becoming the
new_state value of the next.
For example, to send a notification whenever a task is retried:
def notify_on_retry(task, old_state, new_state): if isinstance(new_state, state.Retrying): send_notification() # function that sends a notification return new_state task_that_notifies = Task(state_handlers=[notify_on_retry])
Flow state transitions
Flows transition through a relatively small number of states.
Scheduled -> Running -> Success / Failed
Typically, flow runs begin in a
Scheduled state that indicates when the run should start.
Scheduled is a subclass of
Pending. When the run begins, it transitions in to a
Running state. Finally, when all terminal tasks are finished, the flow moves to a
Finished state. The final state will either be
Failed, depending on the states of the reference tasks.
Task state transitions
Tasks transition through a much greater variety of states, as their execution can lead to many different outcomes. In general, they will repeatedly move from
Pending states to
Running states until they finally enter a
While tasks can move through any combination of states, the following patterns are most common.
Pending -> Running -> Success
The most common pattern for tasks is to be created in a pending state, run, and succeed.
Pending -> Running -> Failed
The second most common pattern is for tasks to encounter an error while running and end up in a
Failure (before running)
Pending -> TriggerFailed
If a task's trigger function doesn't return
True, then the task will fail before it even runs, ending up in a
Failed -> Retrying -> Running
Failed state, appropriately configured tasks can automatically move into a
Retrying state. Once the specified amount of time has passed, the task will move back into a
Skip (while running)
Running -> Skipped
Users can cause tasks to skip themselves by raising a
SKIP signal. Skipped states are generally treated as success states, with some additional caveats. For example, tasks downstream of skipped tasks will automatically skip themselves by default.
Skip (before running)
Pending -> Skipped
If an upstream task is
Skipped and a task has
skip_on_upstream_skip=True (the default setting), then it will automatically skip itself before it runs. This allows users to bypass entire chains of tasks without needing to configure each one.
Special task state transitions
Tasks also have more some unusual but important state transition patterns.
Pause transition (while running)
Running -> Paused -> Resume -> Running
Users can pause tasks by raising a
PAUSE signal. Once paused, tasks must be put in a
Resume state in order to move back into a running state. Both
Resume are subclasses of
Pause transition (before running)
Pending -> Paused
Tasks will also enter a paused state if they have a
manual_only trigger. This will happen before they run, and users will have to explicitly start those tasks to continue.
Running -> Mapped
If a task is mapped over its inputs, then it will enter a
Mapped state after it runs. This indicates that it did not do any work, but rather dynamically generated children tasks to carry out the mapped function. The children states can be accessed as
Mapped is a
Finished state that subclasses