Practice/Meta/Design a File Downloader Library

Design a File Downloader Library

System DesignOptional

Problem Statement

Design a distributed video processing pipeline that ingests raw video files, applies transformations (transcoding, thumbnail generation, watermarking), and produces multiple output formats optimized for different devices and network conditions. The system should handle videos ranging from short social media clips (30 seconds) to full-length movies (2+ hours), processing thousands of uploads per hour during peak times.

Your pipeline must ensure reliable processing even when individual workers fail, provide visibility into processing status, and optimize for cost-efficiency by intelligently managing compute resources. The system should support adding new transformation steps without disrupting existing workflows and handle priority processing for premium users.

Key Requirements

Functional

Video ingestion and validation -- accept uploads via API, validate format/codec/size constraints, reject invalid files early
Multi-format transcoding -- convert videos to multiple resolutions (4K, 1080p, 720p, 480p) and codecs (H.264, H.265, VP9) based on configurable profiles
Metadata extraction and thumbnail generation -- extract video metadata (duration, bitrate, codec info) and generate thumbnails at multiple timestamps
Progress tracking and notifications -- provide real-time status updates on processing stages and notify users/systems when jobs complete or fail
Retry and error handling -- automatically retry transient failures with exponential backoff, handle partial failures gracefully, and provide detailed error diagnostics

Non-Functional

Scalability -- process 5,000 video uploads per hour during peak, with individual videos ranging from 50MB to 10GB; scale worker capacity dynamically based on queue depth
Reliability -- ensure 99.9% successful processing rate with no data loss; tolerate worker failures mid-processing without restarting entire jobs
Latency -- complete processing within 10 minutes for standard videos under 1GB; support priority queue for premium content requiring sub-5-minute turnaround
Cost efficiency -- minimize compute costs by batching work, using spot instances where appropriate, and avoiding redundant processing steps

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Task Orchestration and Fault Tolerance

How you coordinate multi-stage processing across distributed workers while ensuring exactly-once semantics and graceful failure recovery. Interviewers want to see if you understand idempotency, checkpointing, and dead letter queues.

Hints to consider:

Use a workflow orchestration pattern with persistent state for each processing stage
Design each transformation step to be idempotent by writing outputs to temporary locations with atomic rename
Implement checkpointing after expensive operations like transcoding to avoid reprocessing on retries
Consider circuit breakers to isolate failures in specific workers or transformation types

2. Work Distribution and Resource Management

How you partition work across heterogeneous workers, balance load, and scale capacity to meet SLAs while controlling costs. Strong candidates discuss queue-based decoupling, work stealing, and auto-scaling strategies.

Hints to consider:

Separate queues by priority (premium vs standard) and job characteristics (short vs long videos)
Use message visibility timeouts and heartbeats to detect stuck workers
Implement worker specialization for CPU-intensive (transcoding) vs I/O-bound (upload/download) tasks
Discuss spot instance strategies for batch workloads with graceful job migration on preemption

3. Data Flow and Storage Strategy

How you move large video files through the pipeline efficiently while managing storage costs and ensuring data durability. This reveals your understanding of blob storage, network bandwidth optimization, and lifecycle policies.

Hints to consider:

Stage raw and processed videos in object storage with different retention policies
Use pre-signed URLs or direct S3 transfer acceleration to avoid proxying large files through application servers
Consider multi-part upload/download for large files with parallel streams
Implement automatic cleanup of intermediate artifacts after successful processing
Use content-addressed storage or checksums to deduplicate identical uploads

4. Observability and Progress Tracking

How you provide visibility into pipeline health, individual job status, and debugging information when processing fails. Interviewers look for structured logging, metrics, and event-driven status updates.

Hints to consider:

Emit structured events at each stage transition (queued → processing → transcoding → complete)
Track fine-grained metrics like per-stage latency, worker utilization, and error rates by failure type
Store detailed processing metadata (input specs, transformation parameters, output locations) for debugging
Use distributed tracing to correlate logs across multiple workers handling the same job
Implement webhooks or WebSocket connections for real-time client progress updates

Practice/Meta/Design a File Downloader Library

Design a File Downloader Library

System DesignOptional

Problem Statement

Key Requirements

Functional

Video ingestion and validation -- accept uploads via API, validate format/codec/size constraints, reject invalid files early
Multi-format transcoding -- convert videos to multiple resolutions (4K, 1080p, 720p, 480p) and codecs (H.264, H.265, VP9) based on configurable profiles
Metadata extraction and thumbnail generation -- extract video metadata (duration, bitrate, codec info) and generate thumbnails at multiple timestamps
Progress tracking and notifications -- provide real-time status updates on processing stages and notify users/systems when jobs complete or fail
Retry and error handling -- automatically retry transient failures with exponential backoff, handle partial failures gracefully, and provide detailed error diagnostics

Non-Functional

Scalability -- process 5,000 video uploads per hour during peak, with individual videos ranging from 50MB to 10GB; scale worker capacity dynamically based on queue depth
Reliability -- ensure 99.9% successful processing rate with no data loss; tolerate worker failures mid-processing without restarting entire jobs
Latency -- complete processing within 10 minutes for standard videos under 1GB; support priority queue for premium content requiring sub-5-minute turnaround
Cost efficiency -- minimize compute costs by batching work, using spot instances where appropriate, and avoiding redundant processing steps

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Task Orchestration and Fault Tolerance

Hints to consider:

Use a workflow orchestration pattern with persistent state for each processing stage
Design each transformation step to be idempotent by writing outputs to temporary locations with atomic rename
Implement checkpointing after expensive operations like transcoding to avoid reprocessing on retries
Consider circuit breakers to isolate failures in specific workers or transformation types

2. Work Distribution and Resource Management

Hints to consider:

Separate queues by priority (premium vs standard) and job characteristics (short vs long videos)
Use message visibility timeouts and heartbeats to detect stuck workers
Implement worker specialization for CPU-intensive (transcoding) vs I/O-bound (upload/download) tasks
Discuss spot instance strategies for batch workloads with graceful job migration on preemption

3. Data Flow and Storage Strategy

Hints to consider:

Stage raw and processed videos in object storage with different retention policies
Use pre-signed URLs or direct S3 transfer acceleration to avoid proxying large files through application servers
Consider multi-part upload/download for large files with parallel streams
Implement automatic cleanup of intermediate artifacts after successful processing
Use content-addressed storage or checksums to deduplicate identical uploads

4. Observability and Progress Tracking

Hints to consider:

Emit structured events at each stage transition (queued → processing → transcoding → complete)
Track fine-grained metrics like per-stage latency, worker utilization, and error rates by failure type
Store detailed processing metadata (input specs, transformation parameters, output locations) for debugging
Use distributed tracing to correlate logs across multiple workers handling the same job
Implement webhooks or WebSocket connections for real-time client progress updates