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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

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This is a hard difficulty System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Meta during technical interviews.

Design a content processing service | Meta Interview Question

Quick Overview

This question evaluates understanding of designing scalable, reliable content-processing systems that handle high-throughput ingestion, ML-based classification and rule evaluation, priority/backpressure mechanisms, storage and API design, experimentation hooks, and operational concerns like observability and cost.

System Design: High-Throughput Content Processing Service

Context

Design a content processing and moderation service for a large-scale media app. Content includes images and short videos uploaded by users. The service must ingest content reliably, classify it using ML, apply policy rules, and route outcomes for auto-action or human review.

Assume 10M items/day on average with bursty traffic (e.g., 10× spikes around events). Near-real-time means prioritized items should be fully processed within minutes, and non-priority can tolerate longer latency.

Requirements

Ingestion
- Ingest 10M items/day with bursty traffic.
- Handle authentication, idempotency, and client-side retries.
Processing Pipeline
- Near-real-time ML classification and rule evaluation.
- Priority queues (e.g., P0, P1) and backpressure so bursts do not overload the system.
Storage
- Choose storage for media (binary) and metadata (records, decisions, audit logs).
APIs
- Expose APIs for upload, review (moderator tooling), and appeals.
Experimentation
- Add A/B testing hooks to try multiple models/rules and support offline evaluation.
Non-Functional
- Scalability, fault tolerance, observability, and cost efficiency.

Deliverables

Provide a high-level architecture diagram.
Justify trade-offs and key technology choices.

Quick Overview

Context

Requirements

Ingestion

Ingest 10M items/day with bursty traffic.
Handle authentication, idempotency, and client-side retries.

Processing Pipeline

Near-real-time ML classification and rule evaluation.
Priority queues (e.g., P0, P1) and backpressure so bursts do not overload the system.

Storage

Choose storage for media (binary) and metadata (records, decisions, audit logs).

APIs

Expose APIs for upload, review (moderator tooling), and appeals.

Experimentation

Add A/B testing hooks to try multiple models/rules and support offline evaluation.

Non-Functional

Scalability, fault tolerance, observability, and cost efficiency.

Design a content processing service

Quick Overview

System Design: High-Throughput Content Processing Service

Context

Requirements

Deliverables

Solution

Submit Your Answer to Earn 20XP

Design a content processing service

Quick Overview

System Design: High-Throughput Content Processing Service

Context

Requirements

Deliverables

Solution

Submit Your Answer to Earn 20XP