Build retention into the asset lifecycle, not into an object-storage path or a cleanup script. A promo-video API should track each source and derived asset in durable metadata, give every retained artifact an expiry policy, and mark an asset deleted only after storage confirms it is absent. Telemetry should show both whether generation works and whether expired data is actually being removed.
How should an API store and expire generated promo videos?
Separate the bytes from the record of why they exist and when they are due for removal. Object storage holds screenshots, extracted text, render parts, and finished videos; a repository holds their lifecycle metadata and policy. A path or last-modified timestamp cannot express a campaign’s retention intent.
Give every artifact its own lifecycle record
A useful record includes an asset ID, campaign ID, kind, state, object key, byte count, creation time, expiry time, and last deletion attempt. The tutorial’s example asset kinds are source_image, ocr_text, render_part, and promo_video. Keep derived artifacts in the same lifecycle system as uploaded sources: deleting a screenshot does not remove the OCR text, thumbnail, or render manifest created from it.
One workable state progression is uploaded → text_extracted → approved → rendering → ready → deletion_due → delete_requested → deleted. Not every kind must visit every state; define valid transitions for each kind rather than treating the sequence as a universal standard. Store expiry for each retained object, including outputs created after the source upload.
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Choose OCR timing around review and conversion
OCR timing is a workflow trade-off, not a universally better architecture. Run it at upload when review must happen before rendering or when extracted text is likely to be reused. That makes review available sooner, but consumes compute and creates derived text even for uploads that never become videos.
Run OCR on demand when many uploads will never be used in a video and the first generation request can tolerate the added latency and failure point. If multiple requests can trigger extraction for the same source, coordinate them so concurrent workers do not perform duplicate OCR. Whichever path you choose, assign the OCR output its own expiry and emit consistent lifecycle events.
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| Decision factor | Upload-time OCR | On-demand OCR |
|---|---|---|
| Review before rendering | Results are available for earlier review. | Results are not available until requested. |
| Uploads that never become videos | Compute may be spent on unused uploads. | Avoids OCR work for uploads that are never used. |
| First generation request | OCR work has already run. | OCR latency and possible failure move into that request. |
| Reuse of extracted text | Useful when results are reused. | Useful when reuse is uncommon. |
Expire assets with a verified, retry-safe worker
A successful delete request is not proof that an object has been removed. The sweeper should claim due records so concurrent workers cannot process the same row, request deletion, check whether the object is absent, and only then mark the record deleted. If deletion or verification fails, record the attempt and outcome so the worker can retry safely.
- Claim due records. Select records whose expiry has arrived and whose policy permits deletion. Claim them atomically to prevent competing workers from acting on the same asset.
- Record the request. Move the record to
delete_requestedand persist the deletion attempt before calling storage. - Request object deletion. Delete the object identified by its object key. Make retries safe if an earlier attempt succeeded but its result was not recorded.
- Verify absence. Check storage for the object. Do not mark the record deleted solely because the delete call returned success.
- Record the outcome. Mark it
deletedonly after absence is confirmed. Otherwise retain an actionable state, timestamp the attempt, and record its outcome for retry and diagnosis.
The tutorial uses a batch limit of 100 as a starting constraint, not a universal optimum. Tune batch size and worker concurrency to the system’s actual workload and operational limits.
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What should lifecycle telemetry measure?
Track generation flow and retention health together. A service can produce videos successfully while silently accumulating overdue objects; conversely, successful deletion counts alone do not show whether uploads are waiting too long for OCR or rendering.
Measure workflow progress and outcomes
- Time from upload to OCR start and OCR duration.
- Time from approval to render start and from ready state to first download.
- OCR and render attempt counts.
- Expired-but-unread assets, which can help reveal assets that were retained but never used.
Measure expiry and verified deletion
- Active bytes by asset kind and overdue bytes.
- Oldest expiry lag and the count of records due for processing.
- Creation and deletion throughput, including bytes for deletions verified by absence.
- Verified deletion outcomes, so failures and successful removals are distinguishable.
Alert on sustained growth in the due backlog, increasingly old overdue assets, or a halt in verified deletions while new assets continue to arrive. Use low-cardinality metric dimensions such as kind and outcome. Put asset IDs, campaign IDs, object keys, attempt numbers, and error reasons in structured logs rather than metric labels, where they would create high cardinality.
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How should holds, extensions, and deletion races work?
Represent legal or moderation holds and campaign extensions as explicit policy states with observable deadlines. The system should check a hold atomically with the transition into due or deletion work; a separate check followed by a later claim can allow a hold and a deletion worker to race.
Define extension behavior at the claim boundary
Specify whether an extension is rejected once deletion has been claimed, or whether the system must create a new render from retained sources. The policy must match what the system can actually recover: extending a finished video is not useful if its required source assets have already been removed.
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Make any recovery window explicit
If policy permits a recovery window, model it with its own state, deadline, and access controls. Do not imply that recovery is available when the applicable policy requires immediate erasure. There is no universal retention period established here; set deadlines according to the product’s approved policy and applicable obligations.
What this design does not decide
This lifecycle pattern is implementation guidance from a developer tutorial, not a storage-provider guarantee, an API standard, or legal advice. It does not establish jurisdiction-specific retention rules, provider deletion latency, or which storage vendor is best. Those choices need evidence specific to the provider, deployment, and applicable policy.
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