100 lines
4.7 KiB
Markdown
100 lines
4.7 KiB
Markdown
# Video Clipping Service Planning Prompt
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Use this prompt to ask an AI to create a production-grade implementation plan for a Java Spring Boot video clipping microservice.
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```text
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You are a senior Java/Spring Boot backend architect specializing in large-scale media-processing systems.
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Create a production-grade implementation plan for a Java Spring Boot microservice that accepts very large video files and generates 8-second video clips.
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Before answering, research current official or primary documentation for:
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- Spring Boot and Java supported production versions.
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- FFmpeg segmenting behavior and keyframe accuracy.
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- Object-storage multipart/resumable upload best practices.
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- Queue-based async processing.
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- Kubernetes deployment, probes, resource limits, and observability.
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- Spring Boot testing and Testcontainers.
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Use citations or source links for important version and architecture claims.
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Design principles:
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- Do not load entire video files into application memory.
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- Prefer direct-to-object-storage upload using signed URLs and multipart/resumable upload.
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- Separate API service from video-processing workers.
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- Use durable job state, retryable queues, idempotency, and cleanup.
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- Explain the performance tradeoff between fast keyframe-aligned clipping and exact 8-second frame-accurate clipping.
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- Use FFmpeg safely through isolated worker processes, not inline request processing.
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Include these sections:
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1. Goal and Scope
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Define the service, assumptions, non-goals, supported formats, max file size, expected concurrency, and cloud/runtime assumptions.
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2. Architecture
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Design the full flow:
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Client -> API -> signed upload URL -> object storage -> job record -> queue -> worker -> FFmpeg -> generated clips -> object storage -> job status.
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Explain API service, worker service, database, queue, object storage, temporary disk, and cleanup responsibilities.
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3. Technology Stack
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Recommend Java version, Spring Boot version, database, queue, object storage, FFmpeg packaging, container base image, observability tools, and testing tools. Explain each choice.
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4. API Contract
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Design versioned REST endpoints using noun-based paths:
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- Create video asset/upload session.
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- Complete upload/register uploaded object.
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- Create or start clip-generation job.
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- Get job status.
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- List generated clips.
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- Get signed download URLs.
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- Cancel/delete job.
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For each endpoint include method, path, auth, idempotency, request schema, response schema, and examples.
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5. Validation and Security
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Include authn/authz, file size limits, media type checks, video probing, malware scanning, rate limiting, signed URL expiry, object key safety, tenant isolation, sensitive logging rules, and error handling.
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6. Video Processing Design
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Explain FFmpeg commands and strategy for:
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- Fast segmentation using stream copy where acceptable.
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- Accurate 8-second segmentation using transcoding/forced keyframes where required.
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- Handling the final shorter segment.
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- Audio/video sync.
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- Variable frame rate videos.
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- Corrupt inputs.
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- Progress tracking.
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- Retry behavior.
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- Partial-output cleanup.
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7. Data Model
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Define tables/entities for video assets, upload sessions, processing jobs, generated clips, and job events. Include indexes, lifecycle states, and timestamps.
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8. Performance and Scalability
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Cover streaming, object-storage bandwidth, worker concurrency, CPU/memory/disk sizing, queue backpressure, autoscaling, timeout handling, large-file resilience, benchmarking, and cost tradeoffs.
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9. Error Model
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Use `application/problem+json` for non-2xx errors. Map 400, 401, 403, 404, 409, 413, 415, 422, 429, 500, and 503 with examples.
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10. Testing Strategy
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Include unit, integration, API contract, FFmpeg, queue, object-storage, large-file, failure/retry, security, load, and end-to-end tests. Use JUnit 5, Spring Boot test support, Testcontainers, realistic video fixtures, and mocked/sandboxed cloud services where appropriate.
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11. Deployment and Operations
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Include Docker, Kubernetes API and worker deployments, readiness/liveness/startup probes, resource requests/limits, ephemeral storage, horizontal autoscaling, metrics, logs, traces, alerts, dashboards, dead-letter queues, cleanup jobs, and disaster recovery.
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12. Project Structure
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Propose a clean/hexagonal Spring Boot package structure separating API, application services, domain, persistence, queue, storage, FFmpeg adapter, config, and tests.
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13. Implementation Milestones
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Break implementation into MVP, production hardening, performance tuning, and operational readiness.
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14. Open Questions
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List decisions needed before implementation, especially:
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- Exact vs keyframe-aligned 8-second clips.
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- Maximum file size.
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- Supported codecs/containers.
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- Cloud provider.
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- Auth provider.
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- Expected upload and processing volume.
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- Retention policy.
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```
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