Why the Entire Film Stores No Real Video URLs: Deep Considerations on Keeping Everything in the Jimeng Cloud

When you open any dbvideo_process storyboard record in the Yuncao Academy system and inspect its vdurl field, you will find that it is not an HTTPS file link but a string of digits and letters representing a Jimeng task ID, for example 57347289. If you try to open this ID directly in a browser, you will only get an error page. This practice may confuse people encountering the system for the first time, because under the intuition of traditional file management, every video clip should correspond to an mp4 file address that can be downloaded via a download manager or browser. But in fact, across all 1,029 shots in "The First Avenue," not a single vdurl field contains a real video file path. Every one of them is a unique task number assigned by the Jimeng platform to each video generation task. This means all finished video footage for the entire film exists only in the cloud, and the local database does not store a single byte of video. The first reason behind this design starts with local hard drive capacity limits. Each storyboard's generated video has the specification of 2560x1440 resolution, 30 frames per second, H.264 encoding, an average bitrate of approximately 7.7 Mbps, and a duration of five seconds, putting the single video file size at roughly 6.5 MB to 10 MB. If calculated at an average of 8 MB, downloading all 1,029 shots locally would require approximately 8 GB of storage space. If estimated using the 65 MB figure mentioned in the documentation—which might represent a version with longer single-shot durations or higher bitrates—then the total video file volume for the entire film would reach 65 GB or even more. For a work with a final runtime of two hours and twenty minutes, if you include ultra-high-definition or quadruple compression, the exported file for the entire film typically falls between 10 and 20 GB as well. At first glance, 8 GB does not seem like a big deal, but the problem is that these video files are not the final cut; they are large volumes of raw material that need to be edited, filtered, and discarded. During work, you often need to keep multiple versions of trial-and-error results. For example, if one fight scene is regenerated five times, that produces five different video files, bringing the cumulative capacity to over 40 GB. Add in intermediate assets such as first-frame and last-frame images, character images, and location images, and the site's total storage demand can easily exceed 200 GB, which is a considerable strain on a personal workstation. The validity period of temporary download links is equally important. The Jimeng platform generates a temporary HTTPS link for each video task that can be used for downloading, and this link typically expires within a few hours or a few days. If you fail to transfer the file to local storage before expiration, you need to re-request a new download address from the platform using the task ID. But the trouble is that this request requires calling Jimeng's query interface, and it only works if the task ID is still saved in the database. If you only saved the temporary link and did not save the task ID, once the link expires, you will never be able to retrieve that video again. The Yuncao Academy system chooses to abandon saving links altogether and saves only the task ID. Every time a preview or download is needed, it calls the current Jimeng API to obtain a new valid address. This completely sidesteps the link expiration problem. Asking the platform for a fresh link on demand is actually more reliable than storing an address that may expire at any moment. The Jimeng console provides a very convenient access method: as long as you log into your account and search by task ID in the task list, you can find the corresponding video generation record. The video itself is stored on Jimeng's cloud servers, and users can play or download it by clicking the thumbnail. The flexibility of this model is that even if the original task was generated long ago, as long as the task ID still exists, the system can precisely locate the generation record, the input first-frame and last-frame images, the text prompt used, and all metadata. If you had saved a temporary download link instead, all of this information would be lost once the link expired, because a link contains no information about the generation parameters at all. Keeping the task ID is effectively equivalent to keeping a complete credential of the video generation content—it is both a file pointer and a metadata entry point. When you want to trace the generation conditions of a particular shot, you only need to pull up the complete information corresponding to that task ID, including the references to the first and last frames, the full prompt text, generation time, model version, and parameter configuration. These are crucial for fine-tuning the film. For long-term data integrity, keeping the task ID has advantages over keeping local files. Local video files are exposed to risks such as disk bad sectors, accidental deletion, and formatting. A cloud-based task ID, by contrast, can always recover the original record as long as the account is not closed. The Jimeng platform's own storage strategy uses distributed redundant replicas, so files are not lost because one hard drive fails, and Jimeng typically also provides version history functionality, allowing users to review past generation records. This durability far exceeds local storage. Even if video files are eventually purged due to platform policy, as long as the task ID is in the database, you know the file once existed and can look up logs or backup archives by the ID. If the system had only stored vdurl download addresses, once the link expired and the task ID had been cleaned from the database, that shot would be permanently unrecoverable. In other words, the data model chooses not to store the file itself but to store a pointer to the file, sacrificing instant access in exchange for long-term reliability and recoverability. The actual workflow further validates the correctness of this design. During the editing phase, when a creator needs to view a shot's video, the system first takes the vdurl task ID from the local database, calls the Jimeng API with that ID, obtains a fresh temporary download link within a few milliseconds, and then begins playback. Because the API call is fast, the latency before each playback is almost imperceptible. If the video had already been downloaded locally, playback would indeed be somewhat faster, but it would come at storage cost, and when three computers are working in parallel, syncing those local files would become a new problem. The current task-ID-based remote access approach naturally has multi-machine sharing characteristics, because all machines point to the same cloud task ID, with no need to maintain any file replica synchronization mechanism. During final export, the system pulls the high-definition video file for each task ID one by one from the Jimeng platform for concatenation and compression. Throughout the entire process, only task IDs exist in the database, while finished film files are temporarily generated in the export directory and immediately cleaned up after export is complete and verified. This on-demand retrieval model is in the same spirit as storing only primary keys and foreign key relationships in a database rather than redundant large fields—it replaces entity duplication with standardized ID references, avoids data redundancy, and comprehensively reduces system operation complexity. Therefore, the fact that the entire film contains no real video addresses is not a technical flaw, but rather the optimal solution after weighing four factors: storage requirements, data reliability, traceability, and multi-machine collaboration. The core philosophy of this design is: store metadata rather than files, store references rather than content. When you encounter a similar problem in your own video production pipeline, instead of agonizing over how to save massive numbers of video files, seriously consider storing the task ID and its complete metadata as the sole task credential, and treat the files themselves as derived assets that can be regenerated or retrieved on demand at any time. That way, no matter how the platform evolves or how temporary links change, your project will always have a trail to follow.