胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6264

LucaslEliane · 2019-08-01T05:04:19Z

译文翻译完成，resolve #6046

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Ultrasteve

翻译幸苦了，提了一些建议

Ultrasteve · 2019-08-02T05:35:44Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


 ![](https://cdn-images-1.medium.com/max/2000/1*fsseXIPGEhwmg6kfgXyIjA.jpeg)

-If you’ve been keeping up with my writing, a few weeks ago, I published a [blog](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33) talking about a variety of ways to use Node.js to read really large datasets.
+如果你最近都在阅读我的文章，你应该会看到我几周前发布的一篇[博客](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33)，这篇博客讨论了使用 Node.js 来读取大型数据集的各种方法。


[如果你最近都在阅读我的文章] -> [如果你一直在关注我的文章]

Ultrasteve · 2019-08-02T05:39:19Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-As I worked towards my ultimate end goal of processing a large dataset, I came up with three solutions in Node.js.
+当我努力去实现处理大型数据集这个目标的过程中，我在 Node.js 中提出了三个解决方案。


【提出】-》【想到】

Ultrasteve · 2019-08-02T05:43:02Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-In the end, I came up with only one solution in Node.js that was able to handle the full 2.55GB file I wanted to parse through, at one time.
+最后，我在 Node.js 中提出了唯一能够处理完整的 2.55 GB 的文件的解决方案。


【提出】-》【想到】

Ultrasteve · 2019-08-02T05:48:37Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-Below are code snippets implementing `console.time()` and `performance-now` in each of my scripts. These are only snippets of one function each — for the full code, you can see my repo [here](https://github.com/paigen11/file-read-challenge).
+下面是我在每个脚本中接入 `console.time()` 以及 `performance-now` 的代码片段。这些代码只是每个函数的片段 - 相对于完整代码来说，你可以在[这里](https://github.com/paigen11/file-read-challenge)查看我的代码仓库。


【相对于完整代码来说】-》【如果你想看全套代码】
异译会好一些

Ultrasteve · 2019-08-02T05:51:07Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


 ![](https://cdn-images-1.medium.com/max/2568/1*n48UZ77lvktwjN6IDR0x1g.png)

-Since this script is using the `fs.readFile()` implementation, where the entire file is read into memory before any functions are executed on it, this is the most synchronous-looking code. It’s not actually synchronous, that’s an entirely separate Node method called `fs.readFileSync()`, it just resembles it .
+由于这个脚本使用 `fs.readFile()` 实现，整个文件都会在执行函数之前被读取到内存中，看起来这是最同步的代码。但是它实际上不是同步的，同步地读取文件在 Node 中有一个专用的方法，叫做 `fs.readFileSync()`，两者看起来很相似。


【看起来这是最同步的代码】-》【这看起来很想同步代码】
觉得直接翻译有点奇怪

Ultrasteve · 2019-08-02T05:54:05Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-In the end, streams both native to Node.js and not, are way, WAY more efficient at processing large data sets.
+最后，Node.js 原生的流和非原生的流，在处理大型数据集的时候会更加有效。


【Node.js 原生的流和非原生的流】-》【流式处理，不论是Node.js 原生还是非原生的】

Ultrasteve · 2019-08-03T04:46:43Z

校对认领

fanyijihua · 2019-08-03T04:46:46Z

@Ultrasteve 好的呢 🍺

xilihuasi · 2019-08-05T01:44:42Z

校对认领

fanyijihua · 2019-08-05T01:44:44Z

@xilihuasi 妥妥哒 🍻

xilihuasi

@LucaslEliane @Glowin 校对完成

xilihuasi · 2019-08-05T15:42:13Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-I won’t go into the specifics of the challenge and solution, because you can read my first post for all the details [here](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33), but I will give you the high level overview.
+我不会详细介绍上一篇博客中的挑战和解决方案，因为你可以去阅读我的第一篇文章，了解所有的细节[这里](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33)，但是我会给你进行一下简单的介绍。


了解所有的细节这里
=>
在这了解所有的细节

xilihuasi · 2019-08-05T16:01:04Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md


-#### The Three Different Solutions Possible For Smaller Datasets
+#### 三种解决方案与小数据集情况下的不同点


三种解决方案与小数据集情况下的不同点
=>
三种小数据集场景下的解决方案

xilihuasi · 2019-08-05T16:14:17Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md

 ```

-That’s one method I used to figure out how long it took to process the dataset.
+这是我用来计算处理数据集所需要的时间的一种方法。


这是我用来计算处理数据集所需要的时间的一种方法。
=>
这是我用来计算处理数据集所需时间的一种方法。

…-for-reading-large-datasets-pt-2.md

LucaslEliane · 2019-08-08T03:43:20Z

@xilihuasi @Ultrasteve 感谢二位细心校对，已按照二位校对意见修改译文

LucaslEliane · 2019-08-14T09:36:57Z

@xilihuasi @Ultrasteve 麻烦二位检查一下修改后的译文是否还有译错之处~ 感谢

xilihuasi · 2019-08-14T13:57:22Z

...ms-for-the-win-a-performance-comparison-of-nodejs-methods-for-reading-large-datasets-pt-2.md

@@ -48,7 +48,7 @@

 **解决方案 3：[`event-stream`](https://www.npmjs.com/package/event-stream)**

-最后，我在 Node.js 中提出了唯一能够处理完整的 2.55 GB 的文件的解决方案。
+最后，我在想到了 Node.js 中唯一能够处理完整的 2.55 GB 的文件的解决方案。


我在想到了 => 我想到了在

已修改感谢

…-for-reading-large-datasets-pt-2.md

LucaslEliane · 2019-08-20T03:42:58Z

@xilihuasi @Ultrasteve 感谢校对，已按照意见进行修改~ 如没有其他问题，请麻烦 approve 一下~

lsvih · 2019-08-25T08:59:56Z

@LucaslEliane 已经 merge 啦~ 快快麻溜发布到掘金然后给我发下链接，方便及时添加积分哟。

掘金翻译计划有自己的知乎专栏，你也可以投稿哈，推荐使用一个好用的插件。
专栏地址：https://zhuanlan.zhihu.com/juejinfanyi

lsvih · 2019-10-24T07:46:39Z

地址：https://juejin.im/post/5d67c71e51882537930bc593

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胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6046

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胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6264

胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6264

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lsvih commented Oct 24, 2019


		As I worked towards my ultimate end goal of processing a large dataset, I came up with three solutions in Node.js.
		当我努力去实现处理大型数据集这个目标的过程中，我在 Node.js 中提出了三个解决方案。


		In the end, I came up with only one solution in Node.js that was able to handle the full 2.55GB file I wanted to parse through, at one time.
		最后，我在 Node.js 中提出了唯一能够处理完整的 2.55 GB 的文件的解决方案。


		Below are code snippets implementing `console.time()` and `performance-now` in each of my scripts. These are only snippets of one function each — for the full code, you can see my repo [here](https://github.com/paigen11/file-read-challenge).
		下面是我在每个脚本中接入 `console.time()` 以及 `performance-now` 的代码片段。这些代码只是每个函数的片段 - 相对于完整代码来说，你可以在[这里](https://github.com/paigen11/file-read-challenge)查看我的代码仓库。


		In the end, streams both native to Node.js and not, are way, WAY more efficient at processing large data sets.
		最后，Node.js 原生的流和非原生的流，在处理大型数据集的时候会更加有效。


		I won’t go into the specifics of the challenge and solution, because you can read my first post for all the details [here](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33), but I will give you the high level overview.
		我不会详细介绍上一篇博客中的挑战和解决方案，因为你可以去阅读我的第一篇文章，了解所有的细节[这里](https://itnext.io/using-node-js-to-read-really-really-large-files-pt-1-d2057fe76b33)，但是我会给你进行一下简单的介绍。


		#### The Three Different Solutions Possible For Smaller Datasets
		#### 三种解决方案与小数据集情况下的不同点

胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6264

胜者是 Stream：NodeJS 读取大数据集合几种方法的性能比较 #6264

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