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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 Figma.

What role is this question designed for?

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

Design a trending-articles platform | Figma Interview Question

Quick Overview

This question evaluates system design competence for building large-scale, near-real-time trending and feed systems, testing skills in streaming ingestion, event modeling, ranking algorithms, caching, consistency and fault-tolerance, capacity planning, and operational observability within the System Design domain.

Context

Design a backend that surfaces trending articles in near real time for a large-scale consumer product. The system should support global, regional, and category-specific feeds, and scale to hundreds of thousands of events per second at peak. You will specify requirements, constraints, and propose an end-to-end architecture.

Functional Requirements to Clarify

Feed surfaces and APIs
- Global, local (country/region), and category feeds
- Optional personalization vs. purely global ranking
- Pagination (cursor-based), page size limits, and stable ordering
- Language and locale support (e.g., en-US) and fallback behavior
Freshness and latency
- Feed freshness target (e.g., new trends visible within X seconds)
- Read latency SLAs (P50/P95/P99)
Abuse and spam resilience
- Bot and fraud mitigation, deduplication, rate limits, downweighting
- Editorial blocks and safety filters
Observability and controls
- Feature flags, explainability of ranking, metrics and alerting

Constraints to Define (provide your assumptions if not given)

Scale assumptions
- DAU, sessions per user per day
- Feed QPS (avg and peak), event rates (views, clicks, dwell, shares)
- Peak write rates to caches and data stores
SLOs and availability
- P99 read latency target
- Freshness and end-to-end pipeline delay (P99)
- Availability target (e.g., 99.9% or higher)
Data retention
- Hot, warm, and cold retention periods

What to Design and Deliver

Ingestion and storage architecture for user and content events (views, clicks, dwell time, shares)
- Event schema design and enrichment
- Hot/warm/cold storage tiers and partitioning
- Indexing strategy for lookups by article, locale, category
Streaming and batch compute
- Sliding windows and time-decayed counts
- Top-K per segment (global, locale, category) and merging strategies
- Handling late/out-of-order events, deduplication, and watermarks
- Backfill/reprocessing strategy
Caching, invalidation, and ranking service integration
- How to cache and update top lists; cursor design for pagination
- Personalization vs. global ranking trade-offs
Consistency model and fault tolerance
- Exactly-once or at-least-once guarantees; idempotency
- Failure modes and graceful degradation
Capacity planning and sharding
- Kafka/stream partitions, cache shards, storage sizing
- Rough capacity estimates and headroom
Technology choices and trade-offs
- Justify choices (e.g., Kafka vs. Kinesis, Flink vs. Spark, Redis vs. Aerospike, DynamoDB vs. Cassandra, ClickHouse, OpenSearch, S3/Parquet, etc.)

Provide diagrams verbally (component-by-component), API examples, formulas (e.g., exponential decay), small numeric examples, and clear justifications.

Quick Overview

Context

Functional Requirements to Clarify

Feed surfaces and APIs

Global, local (country/region), and category feeds
Optional personalization vs. purely global ranking
Pagination (cursor-based), page size limits, and stable ordering
Language and locale support (e.g., en-US) and fallback behavior

Freshness and latency

Feed freshness target (e.g., new trends visible within X seconds)
Read latency SLAs (P50/P95/P99)

Abuse and spam resilience

Bot and fraud mitigation, deduplication, rate limits, downweighting
Editorial blocks and safety filters

Observability and controls

Feature flags, explainability of ranking, metrics and alerting

Constraints to Define (provide your assumptions if not given)

Scale assumptions

DAU, sessions per user per day
Feed QPS (avg and peak), event rates (views, clicks, dwell, shares)
Peak write rates to caches and data stores

SLOs and availability

P99 read latency target
Freshness and end-to-end pipeline delay (P99)
Availability target (e.g., 99.9% or higher)

Data retention

Hot, warm, and cold retention periods

What to Design and Deliver

Ingestion and storage architecture for user and content events (views, clicks, dwell time, shares)

Event schema design and enrichment
Hot/warm/cold storage tiers and partitioning
Indexing strategy for lookups by article, locale, category

Streaming and batch compute

Sliding windows and time-decayed counts
Top-K per segment (global, locale, category) and merging strategies
Handling late/out-of-order events, deduplication, and watermarks
Backfill/reprocessing strategy

Caching, invalidation, and ranking service integration

How to cache and update top lists; cursor design for pagination
Personalization vs. global ranking trade-offs

Consistency model and fault tolerance

Exactly-once or at-least-once guarantees; idempotency
Failure modes and graceful degradation

Capacity planning and sharding

Kafka/stream partitions, cache shards, storage sizing
Rough capacity estimates and headroom

Technology choices and trade-offs

Justify choices (e.g., Kafka vs. Kinesis, Flink vs. Spark, Redis vs. Aerospike, DynamoDB vs. Cassandra, ClickHouse, OpenSearch, S3/Parquet, etc.)

Provide diagrams verbally (component-by-component), API examples, formulas (e.g., exponential decay), small numeric examples, and clear justifications.

Design a trending-articles platform

Quick Overview

Context

Functional Requirements to Clarify

Constraints to Define (provide your assumptions if not given)

What to Design and Deliver

Solution

Comments (0)

Design a trending-articles platform

Quick Overview

Context

Functional Requirements to Clarify

Constraints to Define (provide your assumptions if not given)

What to Design and Deliver

Solution

Comments (0)

Design a trending-articles platform

Quick Overview