Start by clarifying requirements and scale (e.g., upload sizes, concurrency, reliability SLAs), then outline a high-level architecture for YouTube with a deep dive on the upload pipeline. Focus on chunked, resumable uploads with integrity checks, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize idempotency and exactly-once semantics for chunk uploads to handle retries gracefully, and mention how you'd monitor and alert on upload success rates and latency percentiles in production.
Ask about expected upload sizes, peak concurrent uploads, geographic distribution, and reliability targets to scope the design appropriately.
Sketch the main components: client, API gateway, upload service, metadata service, object storage, transcoding pipeline, and CDN for playback.
Design a chunked, resumable upload protocol with checksums, retries, and idempotent chunk IDs; discuss session management and progress tracking.
Explain how to verify chunk integrity (e.g., MD5/SHA), handle partial failures, and ensure the final video is assembled correctly and atomically.
Discuss trade-offs (e.g., chunk size vs. overhead, synchronous vs. asynchronous processing) and how to scale the upload service horizontally.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the high-level pipeline stages from upload to final storage, then dive into the transcoding and thumbnail generation components, emphasizing scalability, fault tolerance, and trade-offs. Use a concrete example like a video upload to illustrate the flow and justify design decisions.
Pro tip: Highlight how you would handle failures and retries at each stage, and mention cost optimization strategies like spot instances for transcoding, as this shows production maturity.
Describe how the uploaded file is received (e.g., via API gateway or direct upload to object storage), validated for format and size, and metadata is extracted.
Explain how a message queue (e.g., SQS, Kafka) decouples ingestion from processing, and how jobs are prioritized and distributed to workers.
Detail the transcoding process: splitting the video into segments, encoding to various resolutions/bitrates (e.g., HLS/DASH), and using parallel workers for efficiency.
Describe how thumbnails are generated (e.g., extracting frames at intervals, selecting the best frame) and stored alongside the transcoded outputs.
Explain how outputs are stored in object storage, distributed via CDN, and how temporary files are cleaned up; also mention updating the database with final URLs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through separating hot metadata like title, view count, status from cold stuff.
Start by clarifying the scale and access patterns (e.g., billions of videos, high read/write throughput, low-latency lookups). Then propose a sharded, distributed database solution with a well-defined schema, caching, and replication strategy, while addressing trade-offs between consistency and availability.
Pro tip: Emphasize that metadata is typically read-heavy and can tolerate eventual consistency, so you can prioritize availability and partition tolerance. Also mention that you would decouple metadata storage from video content storage to allow independent scaling.
Ask about the expected number of videos, read/write ratio, latency requirements, and consistency needs. This ensures your design aligns with actual constraints.
Select a distributed NoSQL database (e.g., Cassandra, DynamoDB) or a sharded relational database, justifying based on scalability, schema flexibility, and query patterns.
Define a schema that supports efficient lookups (e.g., by video ID, user ID) and choose a sharding key (e.g., video ID) to distribute load evenly and avoid hotspots.
Implement caching (e.g., Redis) for hot metadata, use replication for fault tolerance, and consider denormalization for read-heavy workloads.
Explain how you handle consistency (e.g., eventual vs. strong), partition tolerance, and failure recovery (e.g., replication, backups).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I covered edge caching, cache keys by resolution and region, and cache invalidation on re-uploads.
Start by explaining the core purpose of a CDN for video: reducing latency and offloading origin by caching content at edge locations. Then walk through the end-to-end flow from client request to playback, highlighting key components like DNS steering, edge caching, and origin shield. Finally, discuss tradeoffs such as cost, cache hit ratio, consistency, and complexity, tying them to Coupang's scale and user expectations.
Pro tip: Quantify tradeoffs with metrics like cache hit ratio, egress cost per GB, and startup latency to show you think in terms of business impact, not just technology. Also mention how Coupang's global user base and peak traffic events (e.g., Black Friday) influence CDN design choices.
Describe how a CDN caches video segments at edge servers close to users, reducing latency and origin load. Mention protocols like HLS/DASH and the role of DNS or anycast routing.
Walk through a typical request: client resolves CDN edge via DNS, edge checks cache; on miss, it fetches from origin or a mid-tier cache (origin shield). Explain how segments are cached with TTLs and how invalidation works.
Cover choices like push vs. pull CDN, cache hierarchy, segment duration, and multi-CDN strategy. Explain how these affect performance, cost, and reliability.
Compare tradeoffs: cost vs. performance (e.g., more edge locations increase cost but reduce latency), cache hit ratio vs. freshness, and complexity vs. resilience. Relate to Coupang's scale and user expectations.
Summarize how you would balance tradeoffs for Coupang, considering factors like peak traffic, global reach, and cost efficiency. Mention monitoring and iterative optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered exponential backoff, dead letter queues for jobs that keep failing, and alerting on repeated failures.
Start by clarifying the pipeline stages and failure modes, then propose a layered retry strategy with idempotency and backoff, and finally discuss monitoring, dead-letter queues, and trade-offs between consistency and availability. Emphasize how you would handle partial failures and ensure data integrity.
Pro tip: Demonstrate maturity by acknowledging that retries can amplify failures and that idempotency is non-negotiable; mention circuit breakers and the importance of observability to detect when retries are ineffective.
Ask about the pipeline stages (upload, processing), expected failure types (network, service, data), and SLAs. Identify critical paths and data consistency needs.
Propose retries with exponential backoff and jitter, limited attempts, and idempotent operations. Consider synchronous vs asynchronous retries and where to place them (client, service, queue).
After retries are exhausted, route to a dead-letter queue for manual inspection or automated recovery. Implement alerting and dashboards to track failure rates.
Use unique request IDs, deduplication, and transactional writes to avoid duplicate processing. Discuss trade-offs between at-least-once and exactly-once delivery.
Balance retry aggressiveness with system load, consider circuit breakers to prevent cascading failures, and emphasize observability for debugging and capacity planning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came at the end and I think I handled it well relative to the rest of the interview.
Start by acknowledging that multi-gigabyte files break assumptions about memory, I/O, and latency, then systematically walk through the design implications at each layer (storage, processing, transfer). Emphasize streaming, chunking, and parallelism as core strategies, and quantify trade-offs with concrete numbers (e.g., 10 GB file, 1 GB/s disk, 100 MB/s network).
Pro tip: Proactively mention that you would measure and monitor I/O throughput and memory pressure in production, and that you would design for graceful degradation (e.g., backpressure) rather than assuming infinite resources.
Analyze how multi-gigabyte files stress memory (can't load fully), disk I/O (sequential vs random), network bandwidth (transfer time), and CPU (parsing/processing).
Replace whole-file operations with streaming APIs and chunk-based processing to keep memory bounded and enable incremental progress.
Split the file into chunks that can be processed in parallel across threads, processes, or machines, and consider distributed storage/compute if needed.
Handle partial failures, retries, and idempotency; ensure that chunk processing is fault-tolerant and that the final result is consistent.
Quantify performance (throughput, latency, memory) and discuss trade-offs (e.g., chunk size vs overhead, compression vs CPU).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.