.NET微服务：Docker容器化的应用架构指南

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"NET-Microservices-Architecture-for-Containerized-NET-Applications.pdf" 是一本由微软开发部门发布的关于使用 .NET Core 和 Docker 实现微服务架构的教程文档。本书适用于那些想要在容器环境中构建可扩展、高可用的 .NET 应用程序的开发者。本书内容涵盖： 1. **微服务基础**：首先介绍了微服务架构的基本概念，包括为什么从单体应用转向微服务，以及微服务架构的优点，如独立部署、技术多样性、容错性和可伸缩性。 2. **.NET Core 与微服务**：深入讨论了 .NET Core 如何适应微服务架构，强调了其跨平台能力、高性能和轻量级特性。书中可能涵盖了如何使用 .NET Core 创建服务、API 设计原则以及依赖注入等话题。 3. **Docker 容器化**：详细解释了如何利用 Docker 将 .NET Core 应用程序容器化，以实现更高效的部署和隔离。这部分内容可能包括 Dockerfile 的编写、Docker 镜像创建、容器网络配置和容器编排。 4. **服务发现与注册**：讨论了在分布式系统中如何实现服务间的通信，可能包括 Eureka、Consul 或者 Kubernetes 的服务发现机制。 5. **持续集成与持续部署(CI/CD)**：介绍了如何设置 CI/CD 管道来自动化构建、测试和部署微服务，可能涉及 Jenkins、Azure DevOps 或 GitHub Actions。 6. **监控与日志记录**：讲述了如何对微服务进行监控和日志收集，以确保系统的稳定性和性能，可能会提及 Prometheus、Grafana、ELK Stack（Elasticsearch, Logstash, Kibana）等工具。 7. **容错与弹性设计**：讨论了如何设计容错和弹性策略，比如断路器模式、重试策略和死信队列，以增强系统的健壮性。 8. **安全性考虑**：涵盖微服务间的认证、授权和数据安全，可能包括 OAuth2、OpenID Connect、HTTPS 以及其他安全最佳实践。 9. **微服务架构的挑战与最佳实践**：分析了实施微服务架构可能遇到的问题，如数据一致性、服务边界定义、分布式事务等，并提供了相应的解决策略。 10. **案例研究**：通过具体的示例项目展示了如何将这些理论应用到实践中，帮助读者更好地理解和应用所学知识。这本书是开发者了解和掌握 .NET Core 和 Docker 在微服务架构中的应用的宝贵资源，适合希望构建现代云原生应用程序的开发团队。

4 CHAPTER 2 | Introduction to Containers and Docker

similar to instantiating a process like a service or web app. For reliability, however, when you run

multiple instances of the same image across multiple host servers, you typically want each container

(image instance) to run in a different host server or VM in different fault domains.

In short, containers offer the benefits of isolation, portability, agility, scalability, and control across the

whole application lifecycle workflow. The most important benefit is the environment’s isolation

provided between Dev and Ops.

What is Docker?

Docker is an open-source project for automating the deployment of applications as portable, self-

sufficient containers that can run on the cloud or on-premises. Docker is also a company that

promotes and evolves this technology, working in collaboration with cloud, Linux, and Windows

vendors, including Microsoft.

Figure 2-2. Docker deploys containers at all layers of the hybrid cloud.

Docker containers can run anywhere, on-premises in the customer datacenter, in an external service

provider or in the cloud, on Azure. Docker image containers can run natively on Linux and Windows.

However, Windows images can run only on Windows hosts and Linux images can run on Linux hosts

and Windows hosts (using a Hyper-V Linux VM, so far), where host means a server or a VM.

Developers can use development environments on Windows, Linux, or macOS. On the development

computer, the developer runs a Docker host where Docker images are deployed, including the app

and its dependencies. Developers who work on Linux or on macOS use a Docker host that is Linux

based, and they can create images only for Linux containers. (Developers working on macOS can edit

code or run the Docker CLI from macOS, but as of the time of this writing, containers don’t run

directly on macOS.) Developers who work on Windows can create images for either Linux or Windows

Containers.

To host containers in development environments and provide additional developer tools, Docker

ships Docker Community Edition (CE) for Windows or for macOS. These products install the necessary

VM (the Docker host) to host the containers. Docker also makes available Docker Enterprise Edition

6 CHAPTER 2 | Introduction to Containers and Docker

For VMs, there are three base layers in the host server, from the bottom-up: infrastructure, Host

Operating System and a Hypervisor and on top of all that each VM has its own OS and all necessary

libraries. For Docker, the host server only has the infrastructure and the OS and on top of that, the

container engine, that keeps container isolated but sharing the base OS services.

Because containers require far fewer resources (for example, they don’t need a full OS), they’re easy to

deploy and they start fast. This allows you to have higher density, meaning that it allows you to run

more services on the same hardware unit, thereby reducing costs.

As a side effect of running on the same kernel, you get less isolation than VMs.

The main goal of an image is that it makes the environment (dependencies) the same across different

deployments. This means that you can debug it on your machine and then deploy it to another

machine with the same environment guaranteed.

A container image is a way to package an app or service and deploy it in a reliable and reproducible

way. You could say that Docker isn’t only a technology but also a philosophy and a process.

When using Docker, you won’t hear developers say, “It works on my machine, why not in production?”

They can simply say, “It runs on Docker”, because the packaged Docker application can be executed

on any supported Docker environment, and it runs the way it was intended to on all deployment

targets (such as Dev, QA, staging, and production).

A simple analogy

Perhaps a simple analogy can help getting the grasp of the core concept of Docker.

Let’s go back in time to the 1950s for a moment. There were no word processors, and the

photocopiers were used everywhere (kind of).

Imagine you’re responsible for quickly issuing batches of letters as required, to mail them to

customers, using real paper and envelopes, to be delivered physically to each customer’s address

(there was no email back then).

At some point, you realize the letters are just a composition of a large set of paragraphs, which are

picked and arranged as needed, according to the purpose of the letter, so you devise a system to

issue letters quickly, expecting to get a hefty raise.

The system is simple:

1. You begin with a deck of transparent sheets containing one paragraph each.

2. To issue a set of letters, you pick the sheets with the paragraphs you need, then you stack and

align them so they look and read fine.

3. Finally, you place the set in the photocopier and press start to produce as many letters as

required.

So, simplifying, that’s the core idea of Docker.

In Docker, each layer is the resulting set of changes that happen to the filesystem after executing a

command, such as, installing a program.

7 CHAPTER 2 | Introduction to Containers and Docker

So, when you “look” at the filesystem after the layer has been copied, you see all the files, included the

layer when the program was installed.

You can think of an image as an auxiliary read-only hard disk ready to be installed in a “computer”

where the operating system is already installed.

Similarly, you can think of a container as the “computer” with the image hard disk installed. The

container, just like a computer, can be powered on or off.

Docker terminology

This section lists terms and definitions you should be familiar with before getting deeper into Docker.

For further definitions, see the extensive glossary provided by Docker.

Container image: A package with all the dependencies and information needed to create a container.

An image includes all the dependencies (such as frameworks) plus deployment and execution

configuration to be used by a container runtime. Usually, an image derives from multiple base images

that are layers stacked on top of each other to form the container’s filesystem. An image is immutable

once it has been created.

Dockerfile: A text file that contains instructions for how to build a Docker image. It’s like a batch

script, the first line states the base image to begin with and then follow the instructions to install

required programs, copy files and so on, until you get the working environment you need.

Build: The action of building a container image based on the information and context provided by its

Dockerfile, plus additional files in the folder where the image is built. You can build images with the

Docker docker build command.

Container: An instance of a Docker image. A container represents the execution of a single

application, process, or service. It consists of the contents of a Docker image, an execution

environment, and a standard set of instructions. When scaling a service, you create multiple instances

of a container from the same image. Or a batch job can create multiple containers from the same

image, passing different parameters to each instance.

Volumes: Offer a writable filesystem that the container can use. Since images are read-only but most

programs need to write to the filesystem, volumes add a writable layer, on top of the container image,

so the programs have access to a writable filesystem. The program doesn’t know it is accessing a

layered filesystem, it is just the filesystem as usual. Volumes live in the host system and are managed

by Docker.

Tag: A mark or label you can apply to images so that different images or versions of the same image

(depending on the version number or the target environment) can be identified.

Multi-stage Build: Is a feature, since Docker 17.05 or higher, that helps to reduce the size of the final

images. In a few sentences, with multi-stage build you can use, for example, a large base image,

containing the SDK, for compiling and publishing the application and then using the publishing folder

with a small runtime-only base image, to produce a much smaller final image

Repository (repo): A collection of related Docker images, labeled with a tag that indicates the image

version. Some repos contain multiple variants of a specific image, such as an image containing SDKs

8 CHAPTER 2 | Introduction to Containers and Docker

(heavier), an image containing only runtimes (lighter), etc. Those variants can be marked with tags. A

single repo can contain platform variants, such as a Linux image and a Windows image.

Registry: A service that provides access to repositories. The default registry for most public images is

Docker Hub (owned by Docker as an organization). A registry usually contains repositories from

multiple teams. Companies often have private registries to store and manage images they’ve created.

Azure Container Registry is another example.

Multi-arch image: For multi-architecture, it’s a feature that simplifies the selection of the appropriate

image, according to the platform where Docker is running. For example, when a Dockerfile requests a

base image FROM mcr.microsoft.com/dotnet/core/sdk:3.1 from the registry, it actually gets 3.1-

sdk-nanoserver-1909, 3.1-sdk-nanoserver-1809 or 3.1-sdk-buster-slim, depending on the

operating system and version where Docker is running.

Docker Hub: A public registry to upload images and work with them. Docker Hub provides Docker

image hosting, public or private registries, build triggers and web hooks, and integration with GitHub

and Bitbucket.

Azure Container Registry: A public resource for working with Docker images and its components in

Azure. This provides a registry that is close to your deployments in Azure and that gives you control

over access, making it possible to use your Azure Active Directory groups and permissions.

Docker Trusted Registry (DTR): A Docker registry service (from Docker) that can be installed on-

premises so it lives within the organization’s datacenter and network. It is convenient for private

images that should be managed within the enterprise. Docker Trusted Registry is included as part of

the Docker Datacenter product. For more information, see Docker Trusted Registry (DTR).

Docker Community Edition (CE): Development tools for Windows and macOS for building, running,

and testing containers locally. Docker CE for Windows provides development environments for both

Linux and Windows Containers. The Linux Docker host on Windows is based on a Hyper-V virtual

machine. The host for Windows Containers is directly based on Windows. Docker CE for Mac is based

on the Apple Hypervisor framework and the xhyve hypervisor, which provides a Linux Docker host

virtual machine on Mac OS X. Docker CE for Windows and for Mac replaces Docker Toolbox, which

was based on Oracle VirtualBox.

Docker Enterprise Edition (EE): An enterprise-scale version of Docker tools for Linux and Windows

development.

Compose: A command-line tool and YAML file format with metadata for defining and running multi-

container applications. You define a single application based on multiple images with one or more

.yml files that can override values depending on the environment. After you have created the

definitions, you can deploy the whole multi-container application with a single command (docker-

compose up) that creates a container per image on the Docker host.

Cluster: A collection of Docker hosts exposed as if it were a single virtual Docker host, so that the

application can scale to multiple instances of the services spread across multiple hosts within the

cluster. Docker clusters can be created with Kubernetes, Azure Service Fabric, Docker Swarm and

Mesosphere DC/OS.

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