Advanced Concepts in Ray Actors: Named Actors, Actor Lifecycles, and Actor Pooling

by Thomas Memenga on 2023-05-10

Advanced Concepts in Ray Actors: Named Actors, Actor Lifecycles, and Actor Pooling

Ray Actors are a powerful feature of the Ray framework, enabling stateful and asynchronous computing in distributed systems. In this blog post, we’ll explore three advanced concepts of Ray Actors: Named Actors, Actor Lifecycles, and Actor Pooling, each accompanied by practical examples.

Named Actors

Named Actors in Ray provide a way to name and easily reference actors across different parts of your application. This is particularly useful in large-scale distributed systems where you need to access the same actor instance from different nodes.

Example: Using Named Actors

Suppose you have a global configuration actor that needs to be accessed from various parts of your application.

import ray

class ConfigActor:
    def __init__(self):
        self.config = {}

    def update_config(self, key, value):
        self.config[key] = value

    def get_config(self):
        return self.config

# Start Ray

# Create a named actor
config_actor = ConfigActor.options(name="global_config").remote()

# Access the named actor from anywhere in the cluster
config_actor_ref = ray.get_actor("global_config")
ray.get(config_actor_ref.update_config.remote("setting1", "value1"))

In this example, the ConfigActor is created with a name global_config. This actor can then be retrieved using ray.get_actor("global_config") from any node in the Ray cluster.

Note: Named actors are scoped by namespace. If no namespace is assigned, they will be placed in an anonymous namespace by default.

Actor Lifecycles

Understanding and managing the lifecycle of an actor is crucial for effective resource management and ensuring the consistency of the application state.

Example: Managing Actor Lifecycle

Consider a scenario where you have a temporary data processing actor that should be removed after its task is completed.

import ray

class DataProcessor:
    def process(self, data):
        # Process data
        return processed_data

# Start Ray

# Create an actor
processor = DataProcessor.remote()

# Process data
processed_data = ray.get(processor.process.remote(data))

# Destroy the actor when done

In this example, after the DataProcessor actor completes its task, it is explicitly destroyed using ray.kill(processor). This helps in freeing up resources and managing the actor’s lifecycle effectively. Killing a named actor allows the name to be reused.

Actor Pooling

Actor pooling is a technique to manage a group of actors, allowing for load balancing and parallel processing of tasks.

Example: Implementing Actor Pooling

Imagine you have a web scraping application where multiple actors are needed to scrape different websites concurrently.

import ray

class ScraperActor:
    def scrape(self, url):
        # Scrape data from the URL
        return scraped_data

# Start Ray

# Create a pool of actors
num_actors = 5
scrapers = [ScraperActor.remote() for _ in range(num_actors)]

# Distribute URLs across the actor pool
urls = ["", "", ...]
scraped_data = ray.get([scraper.scrape.remote(url) for scraper, url in zip(scrapers, urls)])

In this example, a pool of ScraperActor instances is created. Each actor in the pool is assigned a URL to scrape, allowing for concurrent scraping of multiple websites.


Named Actors, Actor Lifecycles, and Actor Pooling are advanced features in Ray that provide greater control and efficiency in managing stateful computations in distributed systems. By leveraging these concepts, developers can build more robust, scalable, and maintainable distributed applications.

For more detailed information and advanced use cases, the Ray documentation is an excellent resource, offering in-depth guides and examples on these concepts.