This one looked like a display problem until I actually started drawing it out.
Start by clarifying requirements and scale, then design a two-part system: one for computing the total unique player count (likely using a scalable counting service) and one for retrieving the viewer's friends who played (using a graph store or precomputed friend lists). Discuss trade-offs between real-time and batch processing, and how to handle data consistency and latency.
Pro tip: Emphasize the importance of precomputation and caching for the friends list, as real-time friend intersection is expensive; also mention using approximate counting for the total player count to handle scale efficiently.
Ask about expected QPS, latency requirements, data freshness, and scale (number of users, friends per user, games). This sets the stage for design decisions.
Propose a system with separate services: one for total unique player count (e.g., using a counting service like Redis HyperLogLog or a custom counter) and one for friends who played (using a graph database or precomputed friend-game mappings).
Design schemas: for total count, use a scalable counter with sharding; for friends, store user-game relationships and friend lists, possibly in a graph DB or denormalized tables for fast lookups.
Describe how data is ingested (e.g., play events) and processed: real-time for total count, batch for friend list updates. Discuss trade-offs between push vs. pull for friend list updates.
Discuss consistency vs. latency, cost of exact vs. approximate counting, caching strategies, and how to handle edge cases like new friends or deleted accounts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The at-least-once part is what makes this interesting.
Start by clarifying the requirements: what defines a unique player (e.g., user ID) and the time window for deduplication. Then propose a two-layer solution: first, deduplicate events at the ingestion layer using idempotent processing with unique event IDs and a distributed store like Redis or a database with TTL; second, aggregate unique players per session or day using a set-based data structure (e.g., HyperLogLog for approximate counts or exact sets for smaller scale). Discuss trade-offs between accuracy, memory, and latency, and how to handle late or out-of-order events.
Pro tip: Mention that at-least-once delivery means you must design for idempotency, not just deduplication—use event IDs and a dedup cache with TTL, but also consider that the same user may have multiple events with different IDs, so dedup at the user level requires a separate aggregation step. Also, highlight the importance of defining the deduplication window (e.g., per session, per day) as it affects storage and accuracy.
Ask questions to understand what constitutes a unique player (e.g., user ID), the time window for deduplication (per session, daily, etc.), and the expected scale (events per second, number of unique users). Also confirm the delivery semantics (at-least-once) and acceptable latency/accuracy trade-offs.
Assign a unique event ID to each play event at the source. At ingestion, use a distributed cache or database to track processed event IDs and ignore duplicates. Ensure the dedup store has a TTL aligned with the maximum expected delay for retries.
After deduplicating events, aggregate unique players using a set-based approach. For exact counts, use a distributed set (e.g., Redis Sets) partitioned by time window; for approximate counts at scale, use HyperLogLog. Consider using a streaming framework (e.g., Flink, Spark Streaming) with windowing and state management.
Use event timestamps and watermarks to handle late data. Allow for a grace period and update aggregates accordingly. Ensure that deduplication logic works across windows, e.g., if an event arrives late, it should still be deduplicated and counted if within the window.
Compare exact vs. approximate counting (memory vs. accuracy), synchronous vs. asynchronous deduplication (latency vs. correctness), and storage options (Redis vs. Cassandra vs. BigQuery). Mention partitioning strategies to scale and how to monitor for duplicates or missed events.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: is the player count needed in real-time or can it be eventually consistent? Then propose a scalable architecture that avoids a single hot counter, such as sharded counters with periodic aggregation, and discuss trade-offs between accuracy, latency, and complexity.
Pro tip: Mention that you would first try to avoid the problem by using an approximate count or a distributed counter like Redis with sharding, and only fall back to exact counting if absolutely necessary. This shows you prioritize scalability and pragmatism.
Ask whether the count must be exact and real-time, or if eventual consistency is acceptable. Determine the read/write patterns and the scale (thousands of events per second).
Explain that a single counter creates a hotspot, leading to contention, increased latency, and potential failure under high write load.
Describe sharding the counter across multiple nodes (e.g., by player ID or random shard), where each shard maintains a partial count. Writes are distributed, reducing contention.
Explain that reads require summing all shards, which can be done periodically or on-demand. Discuss using a background job to aggregate into a single value for efficient reads.
Compare with other approaches like approximate counting (HyperLogLog), CRDTs, or message queues with batch processing. Highlight trade-offs in accuracy, latency, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements, then propose an inverted index or precomputed intersection approach. Discuss trade-offs between precomputation and on-the-fly computation, and suggest optimizations like caching, sharding, or approximate algorithms.
Pro tip: Mention that you would precompute intersections for power users and use a hybrid approach with Bloom filters or MinHash for approximate results, showing awareness of real-world constraints.
Ask about the scale (number of friends, frequency of queries), latency requirements, and whether approximate results are acceptable.
Suggest using an inverted index where each game maps to a set of users who played it, or precomputing intersections for each user.
For users with millions of friends, use techniques like sharding the friend list, caching frequent intersections, or using approximate set intersection algorithms.
Compare precomputation (fast reads, expensive writes) vs. on-the-fly (flexible, slower) and suggest a hybrid approach.
Explain how to keep the index up-to-date with new plays, possibly using a write-ahead log or incremental updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sliding windows on unique counts are painful.
Start by contrasting the two requirements: 'total players ever' is a monotonic, append-only metric, while 'players who played this week' requires a sliding window with deduplication and expiration. Then walk through the necessary changes in data model, storage, and query patterns, emphasizing trade-offs between accuracy, latency, and cost.
Pro tip: Mention that you'd clarify the exact semantics of 'played this week'—does it mean distinct players with at least one session in the last 7 days, or something else?—and discuss how you'd handle late-arriving data and timezone boundaries, showing you think about edge cases.
Define what 'played this week' means: distinct players, sliding window of exactly 7 days, and whether it's rolling or calendar-based. Also confirm update frequency and acceptable staleness.
Move from a simple counter to a model that tracks player activity with timestamps, such as a set of player IDs per day or a time-series of events. Consider using a probabilistic data structure like HyperLogLog for approximate distinct counts if scale demands it.
Select a storage solution that supports efficient time-window queries, like a time-series database or a partitioned table by date. For real-time updates, consider stream processing with windowing (e.g., Flink, Kafka Streams) or batch aggregation with periodic recomputation.
Discuss trade-offs between exact vs. approximate counts, latency vs. cost, and complexity of maintaining sliding windows. Propose optimizations like pre-aggregating daily active users and combining them with set unions or HLL merges.
Cover late data, timezone handling, backfill, and how to scale to millions of players. Mention techniques like incremental updates, TTL for old data, and sharding by player ID or time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the idempotency design pays off.
Start by clarifying the event pipeline and the definition of 'play event' and 'count'. Then discuss whether the design is idempotent (e.g., using unique event IDs) and if not, propose a repair strategy such as deduplication or reconciliation. Emphasize trade-offs between real-time correction and batch repair.
Pro tip: Mention that you'd add a unique constraint or idempotency key at the ingestion layer to prevent double-publishing, and that you'd monitor for duplicates with anomaly detection. This shows you think about prevention, not just repair.
Ask questions to understand how play events are generated, published, and consumed. Identify where duplication could occur (e.g., producer retries, at-least-once delivery).
Determine if the design already has idempotency (e.g., unique event IDs, dedup at consumer). If not, explain how you'd add it to absorb duplicates.
Discuss how double-publishing affects counts (e.g., inflated play counts) and any downstream aggregations or dashboards. Consider real-time vs. batch processing.
Outline steps to correct the count: identify affected time window, deduplicate events, recompute aggregates, and backfill corrected data. Mention using a unique key or event ID to filter duplicates.
Suggest adding idempotency keys, exactly-once semantics where possible, and monitoring for duplicate rates. Discuss trade-offs between complexity and reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I asked about whether the numeric total includes the named friends or excludes them, what event qualifies as a play (session start vs minimum duration), and what privacy rules govern showing a friend's name.
Start by restating the problem and explicitly listing the ambiguities around 'played' and privacy. Then ask targeted clarifying questions that probe scale, data sources, and policy constraints, while stating your working assumptions so the interviewer can correct you. Finally, outline how those answers would shape the system design.
Pro tip: Treat privacy as a first-class design constraint, not an afterthought—propose concrete mechanisms like mutual-friend checks, visibility flags, and audit logging to show you understand Roblox's trust-and-safety priorities.
Briefly restate the system goal and call out the two ambiguous terms: what counts as 'played' and what privacy rules govern friend names. This shows you can identify ambiguity before diving into design.
Ask focused questions about scale (DAU, events per second), data sources (game servers, client telemetry), definition of 'played' (join, duration threshold, completion), and privacy requirements (mutual friends, user opt-out, age gating).
For each ambiguity, state a reasonable default assumption (e.g., 'played' means joined for at least 60 seconds; friend names visible only to mutual friends) and note that you'd validate these with product and legal.
Explain how each assumption impacts architecture—e.g., a stricter 'played' definition reduces write volume, while privacy rules affect whether you can precompute friend lists or must filter at read time.
Recap the key questions and assumptions, then ask the interviewer if any should be adjusted before you proceed with the high-level design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.