A promo-video API needs more than an upload directory and a scheduled cleanup job. Give every source and derived asset a durable lifecycle record, assign expiry to each artifact, and mark an object deleted only after storage confirms it is absent. Then instrument the lifecycle so operators can see overdue bytes, expiry lag, and verified deletion outcomes.
The implementation guidance below follows a developer tutorial by FrozenSigh2853916, published October 2, 2026. It is a design pattern, not a storage-vendor guarantee, retention standard, or universal legal rule. Read the tutorial on DEV Community.
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How should an API store and expire generated promo videos?
Separate the bytes from the policy that governs them. Store image, text, render, and video bytes in object storage; keep lifecycle metadata and retention policy in a repository that workers can query and update. A storage path or last-modified time alone cannot say why an asset exists, when it is due for deletion, or whether deletion was verified.
Represent each artifact as its own lifecycle record
Use one record per source or derived object. A practical set of fields is:
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- Identity and association: asset ID and campaign ID.
- Purpose and lifecycle: kind and state.
- Storage and size: object key and byte count.
- Timing: creation time and expiry time.
- Deletion evidence: last deletion attempt, its outcome, and whether absence was confirmed.
Useful kinds include source_image, ocr_text, render_part, and promo_video. A representative path through processing is uploaded → text_extracted → approved → rendering → ready → deletion_due → delete_requested → deleted. Adapt states to the actual workflow, but do not collapse deletion requested and deletion verified into one state.
Derived objects need explicit retention too. OCR text can contain sensitive material extracted from an image; thumbnails, render manifests, and intermediate render parts may also persist after the source disappears. Give every derived artifact its own expiry and deletion tracking rather than assuming that removing the original removes its derivatives.
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Choose when OCR runs based on the workflow
Upload-time OCR is appropriate when approval or moderation must happen before rendering, or when extracted text is reused. It makes review available earlier, but consumes compute and creates derived text for uploads that may never become a video.
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Make expiry a verified, retryable operation
- Claim due records. Query assets whose expiry has passed and atomically claim them so two sweepers do not process the same row. Check hold and extension policy in the same transition into deletion work.
- Record the request. Move the record to
delete_requestedand persist the attempt details before or as the storage deletion call is made. - Request storage deletion. Treat an accepted delete request as an attempt, not proof that the bytes are gone.
- Verify absence. Check whether the object still exists. Record
deletedonly after absence is confirmed. - Retry safely. If the call fails or the object remains, record the outcome and attempt time, retain a retryable state, and let a later pass try again. Make repeated deletion requests safe.
The tutorial’s sample uses a batch limit of 100 as a starting constraint, not a generally optimal batch size. Select and adjust a limit based on observed backlog and the behavior of the chosen storage system.
What should telemetry measure?
Measure both workflow latency and retention health. The following signals distinguish a slow generation path from a cleanup system that is falling behind.
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| Area | Useful measures | What they reveal |
|---|---|---|
| Processing flow | Upload-to-OCR-start, OCR duration, approval-to-render-start, and ready-to-first-download | Where users wait between upload, review, rendering, and first use |
| Work and outcomes | OCR and render attempt counts; expired-unread assets | Retries, processing failures, and assets that expire without being used |
| Storage exposure | Active bytes by kind; overdue bytes | Which classes of objects occupy storage and how much expired data remains |
| Expiry backlog | Due-record count, oldest expiry lag, and backlog trend | Whether the sweeper is keeping up and how long the oldest due asset has waited |
| Deletion proof | Verified deletion bytes and deletion outcomes | Whether requested cleanup is actually resulting in confirmed absence |
Alert on sustained backlog growth, aging overdue assets, or a stoppage in verified deletions while new assets continue to arrive. Keep metric dimensions low-cardinality—for example, asset kind and outcome. Put asset ID, campaign ID, object key, attempt number, and error reason in structured logs, not metric labels.
What about legal holds and campaign extensions?
Represent holds and extensions as explicit policy states with observable deadlines; do not make them informal exceptions to the expiry worker. The worker should check them atomically as it claims an asset for deletion, so a concurrent hold cannot be overlooked between eligibility checking and deletion work.
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Define what happens if a campaign extension arrives after a deletion claim: reject it, or require a new render from retained sources. If the design includes a recovery window, model it with its own state, deadline, and access controls. Where policy requires immediate erasure, do not imply that recovery is available.
No universal retention period or legal rule follows from this architecture guidance. Set retention and hold behavior according to the applicable policy and jurisdiction; the tutorial does not establish either.
How to judge whether the lifecycle is healthy
Review trends rather than treating a successful upload or an empty queue at one moment as proof of a sound lifecycle. Compare active and overdue bytes by asset kind, expiry lag, due-record backlog, and verified deletion outcomes. Investigate whether creation is outpacing verified deletion; a growing gap can indicate accumulating retained data, but the tutorial provides no numeric threshold or universal target.
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There is no provider comparison, deletion-latency benchmark, or durability finding in the cited guidance. These lifecycle practices help define application behavior and observability; they do not establish which storage vendor to choose or what a provider guarantees.
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