OpenAI Backend Engineer System Design Questions

To ensure reliable messaging between OpenAI's services and asynchronous job processing, design a class MessageQueue that manages message sending and receiving among services.
Class Signature:
class MessageQueue:
- send_message(service: str, message: str) -> None: Queues a message for the given service.
- receive_message(service: str) -> Optional[str]: Retrieves the next message for the specified service, if available.
Example 1:
Input: mq = MessageQueue() followed by mq.send_message("service1", "Hello World!") and mq.receive_message("service1")
Output: "Hello World!"
Explanation: The message successfully sent can be retrieved immediately.
Example 2:
Input: mq.send_message("service2", "Goodbye!") and then mq.receive_message("service1")
Output: None
Explanation: Since no messages were sent to service1, it returns None.
Constraints:
- Each service will send up to 10^4 messages per process.
- The total number of messages can be up to 10^5.

To streamline task executions across various AI models at OpenAI, we need a JobScheduler that manages scheduling and execution of jobs. Your implementation should handle job queuing and executing based on a predefined delay.
Class Signature:
class JobScheduler:
- add_job(name: str, delay: int) -> None: Adds a job with a name and a delay before execution.
- execute_jobs(current_time: int) -> List[str]: Returns a list of job names that are executed at a given current time.
Example 1:
Input: scheduler = JobScheduler() followed by scheduler.add_job("Task1", 5) and scheduler.execute_jobs(5)
Output: ['Task1']
Explanation: The task 'Task1' is executed exactly at time 5.
Example 2:
Input: scheduler.add_job("Task2", 2) and then scheduler.execute_jobs(3)
Output: ['Task2']
Explanation: The task 'Task2' is executed at time 2, even though we checked at time 3.
Constraints:
- The total number of jobs will not exceed 10^6.
- The maximum delay for any job will not exceed 10^9.