← Verkada Inc. Interview Insights
I started with the ingestion to playback pipeline which felt right, but I spent too long on it and left myself almost no buffer for the deep dives.
Start by clarifying requirements and scale (e.g., 500 hours uploaded per minute, billions of daily views) to set context. Then walk through the end-to-end pipeline: upload/ingestion, storage, encoding, CDN distribution, playback, and metadata/recommendation layers. Emphasize trade-offs (e.g., latency vs. cost, consistency vs. availability) and how components interact at scale.
Pro tip: Focus on the critical path for a single video upload and playback, then discuss how the system scales horizontally and handles failures. Mention concrete numbers (e.g., video sizes, encoding bitrates) to show practical understanding.
Ask about expected upload volume, viewership, geographic distribution, and latency requirements. Define functional and non-functional requirements to scope the design.
Sketch the main components: ingestion service, storage (raw and encoded), encoding pipeline, CDN, metadata database, and recommendation service. Explain data flow from upload to playback.
Detail how uploads are handled (resumable uploads, chunking), stored in blob storage (e.g., S3), and how metadata is captured. Discuss durability, redundancy, and cost optimization.
Explain transcoding into multiple formats/resolutions (e.g., H.264, VP9, AV1) using a distributed encoding farm. Describe how encoded segments are pushed to CDN edge servers for low-latency playback.
Cover adaptive bitrate streaming (HLS/DASH), metadata storage (SQL/NoSQL), and the recommendation pipeline (batch/real-time) that personalizes content. Discuss caching and consistency trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale (e.g., billions of videos, low-latency search, personalization). Then propose a high-level architecture covering indexing (inverted index, metadata), ranking (learning-to-rank with engagement signals), autocomplete (trie or prefix-based), and personalization (user embeddings, real-time features). Finally, discuss trade-offs and how you'd measure success.
Pro tip: Emphasize the importance of separating offline and online components: offline indexing and model training vs. online serving with low latency. Also, mention how you'd handle cold-start and freshness for new videos.
Ask about scale (number of videos, queries per second), latency requirements, and key features (e.g., autocomplete, personalization). Define success metrics like CTR, watch time, and search latency.
Describe how to ingest video metadata (title, description, tags, transcripts) and build an inverted index for text search. Consider using a distributed search engine like Elasticsearch or building a custom index with sharding and replication.
Explain a two-stage retrieval: first, use the inverted index to get candidate videos (e.g., BM25), then apply a learning-to-rank model using features like relevance, engagement, and freshness. Discuss online vs. offline feature computation.
Propose a prefix-based autocomplete system using a trie or a dedicated service (e.g., Elasticsearch completion suggester). Include ranking of suggestions based on popularity and personalization.
Incorporate user history and embeddings to personalize results. Discuss trade-offs: latency vs. personalization, freshness vs. relevance, and how to A/B test changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Chunked uploads and presigned S3 URLs came out clean.
Start by clarifying requirements (file sizes, concurrency, latency, reliability) and then walk through the end-to-end upload and processing pipeline. Structure your answer around the client upload flow, the ingestion layer, the transcoding pipeline, and CDN distribution, highlighting trade-offs at each stage. Emphasize scalability, fault tolerance, and cost efficiency.
Pro tip: Mention that presigned URLs offload bandwidth from your servers and that chunked uploads with checksums enable resumability and integrity verification. Also, discuss how you'd handle failures and retries in the transcoding pipeline to show operational maturity.
Ask about expected file sizes, peak concurrency, geographic distribution, latency SLAs, and budget. This shapes decisions like chunk size, storage tier, and transcoding priority.
Use presigned URLs to let clients upload directly to object storage (e.g., S3) in chunks. Implement chunked uploads with checksums and resumability, and consider parallel chunk uploads for speed.
Trigger transcoding jobs upon upload completion via events (e.g., S3 notifications to SQS). Use a scalable, queue-based system with worker pools (e.g., AWS Lambda, ECS, or Kubernetes) to process videos into multiple formats/resolutions.
Store transcoded outputs in a CDN-backed origin (e.g., S3 + CloudFront). Use signed URLs or tokens for secure access, and configure caching policies for optimal performance and cost.
Discuss auto-scaling, dead-letter queues for failed jobs, retry logic, and monitoring (e.g., CloudWatch, Prometheus). Also cover cost optimization and potential bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the failure modes and the core design goals: resumability, idempotency, and cleanup. Then walk through the upload protocol (e.g., chunked, multipart, or tus) and how each component handles failures, retries, and orphaned data. Finally, describe the user-facing recovery experience and trade-offs between complexity and reliability.
Pro tip: Emphasize idempotency keys and server-side deduplication to prevent duplicate chunks or uploads, and mention that orphaned chunks should be cleaned up via a background job with a TTL, not synchronously, to avoid impacting user experience.
Identify where uploads can fail (network, client crash, server error) and state the goals: resumability, exactly-once processing, and minimal user disruption.
Choose a chunked or multipart upload approach with unique upload IDs and chunk sequence numbers. Ensure each chunk is independently verifiable and retryable.
Store upload state on the server (e.g., which chunks received) and allow clients to query and resume. Use exponential backoff with jitter for retries, and cap retry attempts.
Use idempotency keys per chunk or upload session to deduplicate. Server should ignore duplicate chunks and only assemble when all chunks are present and verified.
Run a background job to delete incomplete uploads after a TTL. For users, show progress, allow pause/resume, and provide clear error messages with retry options.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.