[PWA] 0. Introduce to Offline First

本文探讨了在网络连接不稳定的情况下如何优化用户体验。通过采用离线优先的策略,在页面加载时同时从缓存和服务器请求数据,确保即使在弱网环境下也能为用户提供即时的内容展示,从而改善整体体验。

Why offline first?

Imagin you are visiting a website, it is fine if wifi connection is good. It might be also "fine" if show you "Your don't have internet connection", so you give up. The worse case is you have really poor wifi connection and the page is trying to loading, but nothing comes up. So you just wait and wait... 

 

Noramlly online first soultion is trying to connect network (server) first. If cannot connect then fetch data from cache. This is not so good, because you still need to wait and wait util network fallback then you will get cache data. How about we do:

  1. When page loading, send two request.
  2. One request going to cache to fetch as much as we can to display on the screen.
  3. Another reqest going to the reall server, get data update.
  4. If we are in really poor wifi connection, at least we get something, we see something, better than nothing.
  5. If the connection is good, the cache data will be replaced with real data and interface update immediately. This will provide a better user experence.

 

根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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