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https://github.com/keven1024/015.git
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feat(front): enhance file hash calculation by introducing engine selection for large files using native or wasm methods
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@@ -137,11 +137,14 @@ watchEffect(async () => {
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}
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})
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const LARGE_FILE_THRESHOLD = 500 * 1024 * 1024 // 500 MB
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const handleHash = async (fileId: string) => {
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const uploadfile = uploadfiles.value.find((item) => item.fileId === fileId)
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if (!uploadfile?.file) return
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uploadfile.procressType = 'hash'
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const res = await asyncWorker(calcFileHashWorker, { data: { file: uploadfile.file } })
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const engine = uploadfile.file.size >= LARGE_FILE_THRESHOLD ? 'wasm' : 'native'
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const res = await asyncWorker(calcFileHashWorker, { data: { file: uploadfile.file, engine } })
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const { hash } = res?.data || {}
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uploadfile.hash = hash
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}
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@@ -1,47 +1,34 @@
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import { noop } from 'lodash-es'
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import { md5 } from 'js-md5'
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import { createSHA1 } from 'hash-wasm'
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interface CalcFileHashProps {
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file: File
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onProgress?: (current: number) => void
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chunkSize?: number
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engine?: 'native' | 'wasm'
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}
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const calcFileHash = async (props: CalcFileHashProps) => {
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const { file, onProgress = noop, chunkSize = 100 } = props || {}
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const blob = await file.arrayBuffer()
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const hash = md5(blob)
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return hash
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// const finalChunkSize = chunkSize * 1024 * 1024;
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// const chunks = Math.ceil(file.size / finalChunkSize);
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// const spark = new SparkMD5.ArrayBuffer(); // 使用 SparkMD5 增量计算哈希
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// const fileReader = new FileReader();
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const { file, onProgress = noop, chunkSize = 100, engine = 'native' } = props || {}
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// const readChunk = (start: number): Promise<ArrayBuffer> => {
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// return new Promise((resolve, reject) => {
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// const chunk = file.slice(start, Math.min(start + finalChunkSize, file.size));
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// fileReader.onload = (e) => resolve(e.target?.result as ArrayBuffer);
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// fileReader.onerror = reject;
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// fileReader.readAsArrayBuffer(chunk);
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// });
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// };
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if (engine === 'native') {
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const buffer = await file.arrayBuffer()
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const hashBuffer = await crypto.subtle.digest('SHA-1', buffer)
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return Array.from(new Uint8Array(hashBuffer))
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.map((b) => b.toString(16).padStart(2, '0'))
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.join('')
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}
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// try {
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// const progressCallback = (current: number) => {
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// const percentage = Math.round((current / chunks) * 100);
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// onProgress(percentage);
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// };
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// for (let i = 0; i < chunks; i++) {
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// const chunk = await readChunk(i * chunkSize);
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// spark.append(chunk);
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// progressCallback(i + 1);
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// }
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// return spark.end();
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// } catch (error) {
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// throw error;
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// }
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const chunkBytes = chunkSize * 1024 * 1024
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const hasher = await createSHA1()
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let offset = 0
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while (offset < file.size) {
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const buffer = await file.slice(offset, offset + chunkBytes).arrayBuffer()
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hasher.update(new Uint8Array(buffer))
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offset += chunkBytes
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onProgress(Math.min(offset, file.size) / file.size)
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}
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return hasher.digest('hex')
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}
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export default calcFileHash
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@@ -1,8 +1,8 @@
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import calcFileHash from './calcFileHash'
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// 监听主线程消息
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self.onmessage = async (e: MessageEvent<{ file: File }>) => {
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const { file } = e.data || {}
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const hash = await calcFileHash({ file })
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self.onmessage = async (e: MessageEvent<{ file: File; engine?: 'native' | 'wasm' }>) => {
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const { file, engine } = e.data || {}
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const hash = await calcFileHash({ file, engine })
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self.postMessage({ hash })
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}
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