我们使用不将 node_modules 包含在版本控制中的标准做法.但是,当通过 CI/CD 管道时,我们必须在几个地方重新安装 NPM 依赖项,这会使一切变得非常缓慢.
We use the standard practices of not including node_modules in version control. However, when moving through the CI/CD pipeline, we have to reinstall NPM dependencies in several places and it makes everything very slow.
有没有办法以某种方式使用 Docker 缓存 NPM 依赖项?我在 Google 上搜索了docker cache npm dependencies",2014 年的第一次点击产生了一个死链接.
Is there a way to somehow cache NPM dependencies with Docker? I searched Google "docker cache npm dependencies" and the first hit from 2014 yielded a dead link.
没有其他有价值的东西出现.
Nothing else of much value came up.
一种解决方案是将 node_modules 包含在版本控制中,但我认为这将是一个巨大的错误.我认为以某种方式缓存依赖项将是最好的选择.
One solution is to include node_modules in version control, but I think that would be a huge mistake. I think caching the dependencies somehow would be the best option.
这是 Dockerfile 的原样:
Here is the Dockerfile as is:
FROM node:6 COPY . . # copy all files, but node_modules does not exist ( => gitignored) RUN npm install --no-optional --only=production > /dev/null 2>&1 RUN npm install -g bower > /dev/null 2>&1 RUN bower install --config.interactive=false --allow-root > /dev/null 2>&1 ENTRYPOINT ["/bin/bash", "/root/cdt/run.sh"]这是一种可能的解决方案,但我不太清楚它是如何工作的:
Here is one possible solution, but I can't quite figure out how it works:
=> bitjudo/blog/2014/03/13/building-efficient-dockerfiles-node-dot-js/
推荐答案这个方法很神奇:
blog.playmoweb/使用 docker-cache-bfed14c051bf 加快构建速度
Docker 为您提供了一种特殊的缓存方式,显然最好使用与生俱来的缓存能力.
Docker has a special way of caching things for you, and apparently it's best to use the inborn caching ability.
不能说我完全理解它的工作原理,但它确实有效.
Cannot say I completely understand how it works, but it does work.
如果你遵循这个模式,它会为你工作:
If you follow this pattern, it will work for you:
FROM mhart/alpine-node:5.6.0 WORKDIR /src # Expose the port 3000 EXPOSE 3000 # Set the default command to run when a container starts CMD ["node", "server.js"] # Install app dependencies COPY package.json /src RUN npm install # Copy your code in the docker image COPY . /src更多推荐
加速 NPM 在 Docker 容器中的安装
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