Answer.

When designing scalable multithreaded and distributed applications, patterns are applied that ensure safety during concurrent access and effective interaction between components.

The "Actor" pattern is a model where each unit (actor) encapsulates state and interacts with others only through asynchronous messages. This eliminates race conditions as the actor processes incoming messages sequentially.

The "Message Queue" pattern is an architectural solution where separate components send data to a message queue, which is then processed by consumers. This provides task buffering, decouples the speeds of sender and consumer, and offers reliability against overloads.

Example of "Actors" in Scala (Akka):

class SimpleActor extends Actor {
  def receive = {
    case "ping" => sender() ! "pong"
  }
}

Example of a message queue in Python (RabbitMQ/Pika):

import pika
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
channel.queue_declare(queue='tasks')
channel.basic_publish(exchange='', routing_key='tasks', body='Hello')

Key features:

• Actors: state isolation and asynchronous interaction, simplicity of scaling.
• Message Queue: task buffering, delivery reliability.
• Both patterns can be combined to build fault-tolerant applications.

Trick Questions.

Can shared state objects be passed between actors?

No, actors should not share state. They communicate only via messages; otherwise, the advantage of isolation is lost, leading to data race risks.

Does a message queue guarantee delivery order for all subscribers?

No, order is only guaranteed within a single queue for one consumer. When scaled (multiple consumers), order is not guaranteed between receivers.

Are actors and message queues competitors to each other?

No, they are often combined: actors for internal logic (e.g., within a node), and message queues for task exchange between services or machines.