ObjectRefs are not just handles to data; they are asynchronous execution contexts that allow you to express complex data dependencies and control flow in a distributed system.
Let’s see this in action. Imagine we have a simple Ray task that simulates some work and returns a result.
import ray
import time
ray.init()
@ray.remote
def slow_computation(x):
time.sleep(2)
return x * 2
# Launch the task
future = slow_computation.remote(5)
print(f"Task launched, got future: {future}")
# We can do other things while the task is running
print("Doing other work...")
time.sleep(1)
print("Finished other work.")
# When we need the result, we get it
result = ray.get(future)
print(f"Result: {result}")
ray.shutdown()
Here, future is an ObjectRef. When we call slow_computation.remote(5), Ray schedules this task on a worker and immediately returns an ObjectRef. This ObjectRef is a placeholder for the actual result, which will be computed asynchronously. We don’t have the result yet, but we have a way to get it later. The ray.get(future) call blocks until the result is available and then retrieves it.
The real power emerges when you have multiple tasks and dependencies. Ray’s ObjectRefs naturally represent these dependencies.
Consider this:
import ray
import time
ray.init()
@ray.remote
def fetch_data(source_id):
print(f"Fetching data from {source_id}...")
time.sleep(1)
return f"data_from_{source_id}"
@ray.remote
def process_data(data1, data2):
print(f"Processing '{data1}' and '{data2}'...")
time.sleep(2)
return f"processed({data1},{data2})"
@ray.remote
def aggregate_results(processed_data):
print(f"Aggregating '{processed_data}'...")
time.sleep(1)
return f"final_result({processed_data})"
# Launch data fetching tasks concurrently
ref1 = fetch_data.remote("source_A")
ref2 = fetch_data.remote("source_B")
print(f"Launched fetch tasks, got refs: {ref1}, {ref2}")
# Launch the processing task, it will wait for ref1 and ref2 to be ready
# Ray automatically handles passing ObjectRefs as arguments.
# The worker for process_data will only start when data for ref1 and ref2 is available.
processed_ref = process_data.remote(ref1, ref2)
print(f"Launched process task, got ref: {processed_ref}")
# Launch the aggregation task, it waits for processed_ref
final_ref = aggregate_results.remote(processed_ref)
print(f"Launched aggregate task, got ref: {final_ref}")
# Get the final result
final_result = ray.get(final_ref)
print(f"Final Result: {final_result}")
ray.shutdown()
In this example, ref1 and ref2 are ObjectRefs returned by fetch_data.remote(). When we call process_data.remote(ref1, ref2), we are passing these ObjectRefs directly as arguments. Ray understands this: it knows that process_data cannot actually start executing its logic until the data corresponding to ref1 and ref2 is available. Ray’s scheduler will automatically stage the necessary data to the worker that is executing process_data once it’s ready. This is the core of asynchronous dependency management. The aggregate_results task then waits for processed_ref, building a chain of dependencies.
The mental model you should build is one of a directed acyclic graph (DAG) of tasks. Each task is a node, and an ObjectRef represents an edge carrying data from one task’s output to another task’s input. Ray’s scheduler is responsible for executing this DAG efficiently, identifying tasks whose dependencies are met and scheduling them on available workers. When you call ray.get(some_object_ref), you are essentially asking Ray to traverse the DAG up to that point and return the final computed value.
A crucial aspect of ObjectRefs, especially when dealing with large datasets or complex workflows, is that they represent remote data. When you pass an ObjectRef to another task, you are not copying the data itself; you are passing a reference. Ray then handles the data transfer (or avoids it if the tasks are on the same node) behind the scenes. This is why ray.get can block: it might be waiting for data to be transferred from a remote worker.
The next frontier to explore is how to manage these asynchronous patterns beyond simple task dependencies, particularly when you need to react to multiple futures completing or implement more sophisticated control flow like retries or timeouts.