Rippling Backend Engineer System Design Questions

Rippling needs a robust message queue to handle real-time notifications for its users, allowing messages to be sent and processed efficiently across different parts of the system.
Class Design: Define a class MessageQueue with methods to enqueue new messages, dequeue messages for processing, and check the status of messages in the queue.- enqueue(message: str): Adds a message to the queue.
- dequeue() -> str: Retrieves and removes the oldest message from the queue.
- getQueueSize() -> int: Returns the current number of messages in the queue.Example 1:
Input: enqueue('Welcome to Rippling')
Input: enqueue('New Notification')
Input: dequeue()
Output: 'Welcome to Rippling' Example 2:
Input: getQueueSize()
Output: 1
Explanation: After dequeueing a message, only one message remains in the queue.Constraints:
- 1 <= message.length <= 100
- At most 10^6 messages can be processed in a year.

Rippling manages numerous tasks for employees, requiring an efficient job scheduler that can prioritize tasks based on dependencies and timings. This scheduler must operate at scale to accommodate all employees seamlessly.
Class Design: Define a class JobScheduler that manages and schedules multiple jobs, ensuring that dependencies are resolved and jobs are executed at the right time.- addJob(jobId: int, dependencies: List[int]): Adds a new job with its dependencies.
- executeJobs(time: int): Executes all jobs that are due at the specified time.
- getJobStatus(jobId: int) -> str: Returns the current status (pending, executed, failed) of a specified job.Example 1:
Input: addJob(1, [])
Input: addJob(2, [1])
Input: executeJobs(1)
Output: Job 1 executed successfully. Example 2:
Input: addJob(3, [2])
Input: executeJobs(1)
Output: Job 2 is still pending due to dependencies. Constraints:
- 1 <= jobId <= 1000
- Each job can have at most 5 dependencies.