Virtual Actors


Workflows also provides a virtual actors abstraction, which can be thought of as syntactic sugar on top of a dynamic workflow. Virtual actors are like Ray actors, but backed by durable storage instead of a running process. You can also launch sub-workflows from the methods of each virtual actor (e.g., train models in parallel). Here is a basic example:

from ray import workflow
import ray

class Counter:
    def __init__(self, init_val):
        self._val = init_val

    def incr(self, val=1):
        self._val += val

    def value(self):
        return self._val


# Initialize a Counter actor with id="my_counter".
counter = Counter.get_or_create("my_counter", 0)

# Similar to workflow steps, actor methods support:
# - `run()`, which will return the value
# - `run_async()`, which will return a ObjectRef
assert == 10

# Non-blocking execution.
assert 30 == ray.get(counter.value.run_async())

In the code above, we define a Counter virtual actor. When the Counter is created, its class definition and initial state is logged into storage as a dynamic workflow with workflow_id="my_counter". When actor methods are called, new steps are dynamically appended to the workflow and executed, returning the new actor state and result.

__dict__ in virtual actors must be able to json serializable, otherwise __getstate__ and __setstate__ must be defined, which will be called on each step to restore and save the actor.

We can retrieve the actor via its workflow_id in another process, to get the value:

counter = workflow.get_actor(workflow_id="counter")
assert 30 ==

Readonly methods are not only lower overhead since they skip action logging, but can be executed concurrently with respect to mutating methods on the actor.

Launching sub-workflows from actor methods

Inside virtual actor methods, sub-workflow involving other methods of the virtual actor can be launched. These sub-workflows can also include workflow steps defined outside the actor class, for example:

def double(s):
    return 2 * s

class Actor:
    def __init__(self):
        self.val = 1

    def double(self, update):
        step = double.step(self.val)
        if not update:
            # inside the method, a workflow can be launched
            return step
            # workflow can also be passed to anthoer method
            return self.update.step(step)

    def update(self, v):
        self.val = v
        return self.val

handler = Actor.get_or_create("actor")
assert == 2
assert == 2
assert == 2
assert == 4

Actor method ordering

Workflow virtual actors provide similar ordering guarantees as Ray actors: the methods will be executed in the same order as they are submitted, provided they are submitted from the same thread. This applies both to .run() (trivially true) and .run_async()`, and is also guaranteed to hold under cluster failures. Hence, you can use actor methods as a short-lived queue of work to process for the actor.

When an actor method launches a sub-workflow, that entire sub-workflow will be run as part of the actor method step. This means all steps of the sub-workflow will be guaranteed to complete before any other queued actor method calls are run. However, note that the sub-workflow is not transactional, that is, read-only methods can read intermediate actor state written by steps of the sub-workflow.

Long-lived sub-workflows

We do not recommend running long-lived workflows as sub-workflows of a virtual actor. This is because sub-workflows block future actor methods calls from executing while they are running. Instead, you can launch a separate workflow and track its execution using workflow API methods. By generating the workflow id deterministically (ensuring idempotency), no duplicate workflows will be launched even if there is a failure.

Long-lived sub-workflow (bad).
class ShoppingCart:
    # BAD: blocks until shipping completes, which could be
    # slow. Until that workflow finishes, no mutating methods
    # can be called on this actor.
    def do_checkout():
        # Run shipping workflow as sub-workflow of this method.
        return ship_items.step(self.items)
Launching separate workflows (good).
class ShoppingCart:
    # GOOD: the checkout method is non-blocking, and the shipment
    # status can be monitored via ``self.shipment_workflow_id``.
    def do_checkout():
        # Deterministically generate a workflow id for idempotency.
        self.shipment_workflow_id = "ship_{}".format(self.order_id)
        # Run shipping workflow as a separate async workflow.

Receiving external events

Note: This feature is not yet implemented.