1. Problem Analysis
1. Slow uploading speed of personal network disk: As an important platform for users to store and share videos, the slow upload speed of personal network disk will affect the user experience, especially for uploading large files.
2. Slow preloading of video playback: When the video is playing, the slow preloading speed will cause the user to wait too long and affect the user's viewing experience.
3. Slow response speed of video text retrieval: When users search video text in their personal network disk, if the response speed is slow, the user's retrieval efficiency and experience will be reduced.
2. Solution Design
1. The optimization solution for the slow uploading speed of personal network disk:
(1) Front-end optimization: Use fragmentation upload technology to divide large files into small pieces for uploading, and upload multiple small pieces at the same time through multi-threading to improve the upload speed. At the same time, use a progress bar to display the progress of the upload, so that users can clearly see the progress of the upload.
(2) Back-end optimization: use a distributed storage system to store uploaded video files on multiple servers, reduce the load pressure on a single server, and increase the upload speed. At the same time, using caching technology, popular videos are pre-cached to high-speed storage devices to speed up uploading.
2. The optimization solution for slow video playback preloading:
(1) Front-end optimization: Use video preloading technology to load video data in advance to reduce user waiting time. When the page is loaded, the key frame data of the video can be loaded through an asynchronous request to display the thumbnail or preview of the video in advance, allowing users to have a better preview experience.
(2) Back-end optimization: Use CDN (Content Distribution Network) technology to distribute video resources to node servers around the world, allowing users to obtain video data from the server closest to them, reducing network delays and improving video loading speed.
3. Optimization scheme for slow response speed of video text retrieval:
(1) Front-end optimization: use asynchronous request technology to send the user's retrieval request to the back-end, and display the loading animation on the front-end to improve the user's interactive experience. At the same time, the user's search keywords are cached locally to reduce repeated requests and improve search speed.
(2) Back-end optimization: Use a full-text search engine, such as Elasticsearch, to index the text information of the video to improve retrieval efficiency. At the same time, using caching technology, the index results of popular videos are cached in memory, reducing the number of database visits and improving response speed.
3. Realization of some codes for front-end and back-end interaction
1. Front-end code example:
```javascript
// video upload
function uploadVideo(file) { // multipart upload logic // ... }
// Video preload
function preloadVideo(videoUrl) { // Asynchronously request video key frame data // ... }
// video text retrieval
function searchVideo(keyword) { // asynchronous request backend interface // ... } ```
2. Backend code example:
```python
# Video upload
def upload_video(file):
# Distributed storage logic
# ...
# Video preload
def preload_video(video_url):
# CDN distribution logic
# ...
# Video text retrieval
def search_video(keyword):
# Full-text search engine query logic
# ...
```
Through the above optimization scheme design and front-end and back-end interactive code implementation, the speed and user experience of personal network disk video upload, playback loading and text retrieval can be effectively improved, and better services can be provided for users.