How do I approach System Design interview questions?

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

What difficulty level is this interview question?

This is a medium difficulty System Design question, commonly asked during Onsite rounds at Google.

What role is this question designed for?

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

Design a Twitter hashtag metrics aggregator

Quick Overview

This question evaluates competency in designing scalable, low-latency real-time stream processing and aggregation systems, covering concepts such as windowing and stateful processing, data modeling, fault tolerance, hot-key handling, and API/query performance.

System Design: Hashtag Metrics Aggregator

Design a service that ingests a high-volume stream of social posts (like tweets) and produces metrics/aggregations for hashtags (similar to an ads click aggregator).

Core use cases

Ingest events: each post has post_id , user_id , timestamp , text , and derived hashtags[] .
Query:
- Get the count of a given hashtag over a time range (e.g., last 5 minutes, last 24 hours).
- Get top N hashtags over a sliding window (e.g., top 100 in the last 1 minute).
- (Optional) Breakdown by dimension such as country/region , language , or platform .

Requirements to clarify

Latency SLOs for queries (e.g., p95 < 200 ms).
Freshness (e.g., results within 5–10 seconds of real-time).
Scale assumptions (events/sec, number of unique hashtags, query QPS).
Accuracy expectations:
- Exactly-once vs at-least-once ingestion.
- Handling duplicates, late events, out-of-order timestamps.

Deliverables

Describe:

High-level architecture.
Data model and storage choices (you may start with a simple SQL placeholder, then refine).
Stream processing and windowing strategy.
APIs.
How you handle backfill/reprocessing, failures, and hot hashtags.
Caching and concurrency considerations.

Quick Overview

System Design: Hashtag Metrics Aggregator

Design a service that ingests a high-volume stream of social posts (like tweets) and produces metrics/aggregations for hashtags (similar to an ads click aggregator).

Core use cases

Ingest events: each post has post_id , user_id , timestamp , text , and derived hashtags[] .

Query:

Get the count of a given hashtag over a time range (e.g., last 5 minutes, last 24 hours).
Get top N hashtags over a sliding window (e.g., top 100 in the last 1 minute).
(Optional) Breakdown by dimension such as country/region , language , or platform .

Requirements to clarify

Latency SLOs for queries (e.g., p95 < 200 ms).

Freshness (e.g., results within 5–10 seconds of real-time).

Scale assumptions (events/sec, number of unique hashtags, query QPS).

Accuracy expectations:

Exactly-once vs at-least-once ingestion.
Handling duplicates, late events, out-of-order timestamps.

Deliverables

Describe:

High-level architecture.

Data model and storage choices (you may start with a simple SQL placeholder, then refine).

Stream processing and windowing strategy.

APIs.

How you handle backfill/reprocessing, failures, and hot hashtags.

Caching and concurrency considerations.

Design a Twitter hashtag metrics aggregator

Quick Overview

System Design: Hashtag Metrics Aggregator

Core use cases

Requirements to clarify

Deliverables

Solution

Comments (0)

Design a Twitter hashtag metrics aggregator

Quick Overview

System Design: Hashtag Metrics Aggregator

Core use cases

Requirements to clarify

Deliverables

Solution

Comments (0)