## Problem Build a simple streaming pipeline framework. A `Stream` accepts items, applies a transform function, and fans out to multiple downstream consumers. If a consumer is slow (its internal queue is full), the producer should block (backpressure) rather than drop items. ```python class Stream: def __init__(self, transform: Callable, buffer_size: int): ... def subscribe(self, consumer: 'Stream') -> None: """Add a downstream consumer.""" def publish(self, item: Any) -> None: """Apply transform and push to all consumers. Block if any consumer is full.""" def start(self) -> None: ... def stop(self) -> None: ... ``` **Example:** ``` source = Stream(transform=lambda x: x * 2, buffer_size=100) logger = Stream(transform=print, buffer_size=50) source.subscribe(logger) source.publish(5) # logger receives 10 ``` ## Follow-ups 1. What synchronization primitives do you use — threading.Queue, asyncio.Queue, or semaphores? 2. If one consumer is permanently slow, should it block all other consumers? How do you design around this? 3. How would you add a `filter` operator that drops items not matching a predicate without losing backpressure? 4. Describe how Kafka solves the same fan-out and backpressure problems at scale.

InterviewDB Question

Streams: Implement Concurrent Data Stream Processing With Backpressure and Fan-Out

phone

Question Details

Problem Build a simple streaming pipeline framework. A Stream accepts items, applies a transform function, and fans out to multiple downstream consumers. If a consumer is slow (its internal queue is full), the producer should block (backpressure) rather than drop items. Example: Follow-ups What synchronization primitives do you use — threading.Queue, asyncio.Queue, or semaphores? If one consumer is permanently slow, should it block all other consumers? How do you design around this? How would yo…

Full Details

🔒

Unlock all Sierra questions

Full insider details, leaked discussions, and candidate experiences.

Get full access — $100 a year, unlimited access

About This Question

This is a reported interview question from a sierra interview during the phone round.

It covers the following topics: Coding, Queue, Phone, Onsite .

Topics

Coding Queue Phone Onsite

Streams: Implement Concurrent Data Stream Processing With Backpressure and Fan-Out

Question Details

Full Details

About This Question

Topics

More Sierra Interview Questions