The questions
- Design a metrics monitoring system like DatadogHigh-volume time-series ingestion: bounded cardinality, correct aggregation across retries and rollups, and alerts that distinguish unhealthy services from missing data.Hard
- Design a top K trending systemContinuously changing rankings: sliding-window expiration, exact distributed top K, viral articles, and share-ID deduplication across delivery and replay.Hard
- Design Google AnalyticsHigh-volume event collection with retry-safe counting, late-arriving session data, distinct-user queries, and tenant skew.Hard
- Design Dropbox or Google DriveLarge file transfers with small metadata transactions: resumable uploads, offline conflicts, reliable change feeds, and shared subtrees whose permissions change as files move.Hard
- Design a price alert systemMatching price streams against millions of thresholds: ordered evaluation, hot-instrument fanout, and reliable one-shot notifications.Hard
- Design Facebook likes at scaleHot-post counting: correct like/unlike state, replay-safe aggregation, burst capacity, and live updates to millions of viewers.Hard
- Design a weather appLocation-based weather reads over changing external data: geographic reuse, late observations, forecast versions, and honest freshness during outages.Medium
- Design Facebook MarketplaceLocation-based discovery with photos, stale search results versus current listing state, and durable buyer-seller messaging.Hard
- Design Google PhotosLarge private media objects with small searchable metadata: reliable uploads, asynchronous image processing, library indexing, and revocable album sharing.Hard
- Design Facebook live commentsHot-stream fanout: durable comment writes, bounded live delivery, stable ordering, and reconnects across millions of viewers.Hard
- Design a system to run commands across a fleetLarge command fan-out with durable offline delivery, bounded execution, and honest handling of uncertain outcomes.Hard
- Design an ads serving systemFast ad selection over changing campaign data, with strict budget enforcement despite concurrent serving and delayed impression events.Hard
A good order to practice them
Four questions that together cover most of what the Meta list tests.
- 1Design Facebook likes at scaleCorrect like state under retries and unlike races, and bursts that must still add up exactly.
- 2Design Facebook live commentsDelivering every comment to millions of viewers, including slow ones and people who reconnect.
- 3Design an ads serving systemHard budget caps while many servers deliver at once, inside a tight latency deadline.
- 4Design Facebook MarketplaceNearby search with stable pagination, and keeping listings, photos and search in step.
What these questions test
The concepts that come up across the Meta questions, by how many questions need them.
Where interviewers push
One hard part from each of the other questions: where a strong answer separates from an average one.
- Design a price alert system: Durable triggering versus external delivery
- Changing an alert to triggered and contacting a push provider cannot be one database transaction. Crashes can lose a notification or duplicate it, and a provider timeout does not reveal whether it accepted the request.
- Design a system to run commands across a fleet: Deadlines, cancellation, and uncertain machine state
- The control plane cannot reliably distinguish a dead machine from a partitioned machine whose script is still running. Incorrect state labels can cause duplicate actions or give operators false confidence.
- Design a top K trending system: A viral article that overloads one key
- Hash partitioning balances many articles but cannot split one article's raw event rate. Any split must still produce one complete article score before global ranking, without losing deduplication or bucket identity.
- Design Google Analytics: Distinct users and ordered funnel queries
- Daily user counts cannot simply be added, and a funnel is an ordered sequence within the same session—not independent totals for its steps. Long-range reports must avoid repeated scans of billions of events.
- Design Dropbox or Google Drive: Fast change discovery without losing changes
- The service needs near-immediate updates without constant polling. Notifications can be lost, owner-log events can reach projections more than once, and a shared view can have thousands of devices catching up.
- Design a weather app: Publishing forecast releases safely
- Forecast issue time and forecast valid time are different dimensions. An hourly release contains many future time slots, and partial downloads or overlapping processing can expose mixed releases or overwrite a newer prediction with an older one.
Questions people ask
What system design questions does Meta ask?
Candidates have reported questions like a metrics monitoring system like Datadog, a top K trending system, Google Analytics, Dropbox or Google Drive, and a price alert system. Questions change and vary by team, so treat this as a sample of what comes up, not a list to memorize.
What level are these Meta questions for?
Of the 12 questions here, 11 are hard. Counting each level a question suits: 2 at mid-level, 12 at senior, 12 at staff+. The same question is asked at several levels; what changes is how deep you are expected to go and how much you drive.
What should I study for a Meta system design interview?
The concepts that come up most across these questions are hot keys and hot partitions, eventual consistency, database sharding, and back-of-the-envelope estimation. Learn how each one works and its trade-offs, then practice a few of the questions end to end, out loud, in 45 minutes.