Design a scalable photo deduplication service
Design a Scalable Photo Deduplication Service
Context
Design a production system capable of detecting and removing duplicate or near-duplicate photos. This system must operate at scale, handling tens of millions of files stored across diverse locations such as object stores, Network Attached Storage (NAS), and user devices, and processed by multiple machines. The system needs to support both batch and incremental processing runs, manage multi-tenant data, and adhere to strict safety and observability requirements.
Requirements
Your design should address the following key decisions and trade-offs:
- Deduplication Index:
- Select a suitable distributed Key-Value store or database (e.g., Redis or alternatives) for the deduplication index.
- Define the key schema, estimate memory sizing, and propose an eviction strategy.
- Batch Processing Pipeline:
- Utilize a framework like MapReduce or Spark (or similar distributed processing technology) to scan and hash photos at scale.
- Detail the partitioning strategy and consider data locality across various storage locations and regions.
- Correctness Under Concurrency:
- Ensure idempotency and exactly-once processing.
- Address and handle race conditions that may arise from overlapping worker processes.
- Safety and Rollout:
- Implement verification steps before any photo deletion occurs.
- Incorporate canarying, a soft-delete/restore workflow, and maintain immutable audit logs for all operations.
- Reliability:
- Design for fault tolerance, including retry mechanisms and backpressure handling.
- Establish comprehensive monitoring for key metrics such as latency, throughput, and error rates.
- Incrementality:
- Support incremental runs for newly added files.
- Plan for periodic re-hashing of existing files, especially when deduplication algorithms are updated.
- Security and Isolation:
- Define a robust permissions model.
- Ensure multi-tenant isolation to prevent data leakage or interference between tenants.
- Cost and Throughput:
- Provide rough estimates for operational costs and system throughput.
- Outline capacity sizing assumptions based on expected load.
AI interview prep
Core dilemma
Balancing the accuracy and safety of destructive data operations (deletion) with the need for extreme scale, cost efficiency, and correctness across heterogeneous, multi-tenant data sources.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a scalable photo deduplication service
Design a Scalable Photo Deduplication Service
Context
Design a production system capable of detecting and removing duplicate or near-duplicate photos. This system must operate at scale, handling tens of millions of files stored across diverse locations such as object stores, Network Attached Storage (NAS), and user devices, and processed by multiple machines. The system needs to support both batch and incremental processing runs, manage multi-tenant data, and adhere to strict safety and observability requirements.
Requirements
Your design should address the following key decisions and trade-offs:
- Deduplication Index:
- Select a suitable distributed Key-Value store or database (e.g., Redis or alternatives) for the deduplication index.
- Define the key schema, estimate memory sizing, and propose an eviction strategy.
- Batch Processing Pipeline:
- Utilize a framework like MapReduce or Spark (or similar distributed processing technology) to scan and hash photos at scale.
- Detail the partitioning strategy and consider data locality across various storage locations and regions.
- Correctness Under Concurrency:
- Ensure idempotency and exactly-once processing.
- Address and handle race conditions that may arise from overlapping worker processes.
- Safety and Rollout:
- Implement verification steps before any photo deletion occurs.
- Incorporate canarying, a soft-delete/restore workflow, and maintain immutable audit logs for all operations.
- Reliability:
- Design for fault tolerance, including retry mechanisms and backpressure handling.
- Establish comprehensive monitoring for key metrics such as latency, throughput, and error rates.
- Incrementality:
- Support incremental runs for newly added files.
- Plan for periodic re-hashing of existing files, especially when deduplication algorithms are updated.
- Security and Isolation:
- Define a robust permissions model.
- Ensure multi-tenant isolation to prevent data leakage or interference between tenants.
- Cost and Throughput:
- Provide rough estimates for operational costs and system throughput.
- Outline capacity sizing assumptions based on expected load.