简介
如今，软件通常会作为一种服务来交付，它们被称为网络应用程序，或软件即服务（SaaS）。12-Factor 为构建如下的 SaaS 应用提供了方法论：
- 使用标准化流程自动配置，从而使新的开发者花费最少的学习成本加入这个项目。
- 和操作系统之间尽可能的划清界限，在各个系统中提供最大的可移植性。
- 适合部署在现代的云计算平台，从而在服务器和系统管理方面节省资源。
- 将开发环境和生产环境的差异降至最低，并使用持续交付实施敏捷开发。
- 可以在工具、架构和开发流程不发生明显变化的前提下实现扩展。
这套理论适用于任意语言和后端服务（数据库、消息队列、缓存等）开发的应用程序。
背景
这套方法论由Heroku的联合创始人Adam Wiggins发掘，应用到了数以百计的应用程序的开发和部署，并通过 Heroku 平台间接见证了数十万应用程序的开发，运作以及扩展的过程。
The Twelve-Factor App系列文章，综合了Hero库关于 SaaS 应用几乎所有的经验和智慧，是开发此类应用的理想实践标准，并特别关注于应用程序如何保持良性成长，开发者之间如何进行有效的代码协作，以及如何 避免软件污染 。
英文原文
IX. X. Dev/prod parity
Keep development, staging, and production as similar as possible
Historically, there have been substantial gaps between development (a developer making live edits to a local deploy of the app) and production (a running deploy of the app accessed by end users). These gaps manifest in three areas:
- The time gap: A developer may work on code that takes days, weeks, or even months to go into production.
- The personnel gap: Developers write code, ops engineers deploy it.
- The tools gap: Developers may be using a stack like Nginx, SQLite, and OS X, while the production deploy uses Apache, MySQL, and Linux.
The twelve-factor app is designed for continuous deployment by keeping the gap between development and production small. Looking at the three gaps described above:
- Make the time gap small: a developer may write code and have it deployed hours or even just minutes later.
- Make the personnel gap small: developers who wrote code are closely involved in deploying it and watching its behavior in production.
- Make the tools gap small: keep development and production as similar as possible.
Summarizing the above into a table:
| Traditional app | Twelve-factor app | |
|---|---|---|
| Time between deploys | Weeks | Hours |
| Code authors vs code deployers | Different people | Same people |
| Dev vs production environments | Divergent | As similar as possible |
Backing services, such as the app’s database, queueing system, or cache, is one area where dev/prod parity is important. Many languages offer libraries which simplify access to the backing service, including adapters to different types of services. Some examples are in the table below.
| Type | Language | Library | Adapters |
|---|---|---|---|
| Database | Ruby/Rails | ActiveRecord | MySQL, PostgreSQL, SQLite |
| Queue | Python/Django | Celery | RabbitMQ, Beanstalkd, Redis |
| Cache | Ruby/Rails | ActiveSupport::Cache | Memory, filesystem, Memcached |
Developers sometimes find great appeal in using a lightweight backing service in their local environments, while a more serious and robust backing service will be used in production. For example, using SQLite locally and PostgreSQL in production; or local process memory for caching in development and Memcached in production.
The twelve-factor developer resists the urge to use different backing services between development and production, even when adapters theoretically abstract away any differences in backing services. Differences between backing services mean that tiny incompatibilities crop up, causing code that worked and passed tests in development or staging to fail in production. These types of errors create friction that disincentivizes continuous deployment. The cost of this friction and the subsequent dampening of continuous deployment is extremely high when considered in aggregate over the lifetime of an application.
Lightweight local services are less compelling than they once were. Modern backing services such as Memcached, PostgreSQL, and RabbitMQ are not difficult to install and run thanks to modern packaging systems, such as Homebrew and apt-get. Alternatively, declarative provisioning tools such as Chef and Puppet combined with light-weight virtual environments such as Docker and Vagrant allow developers to run local environments which closely approximate production environments. The cost of installing and using these systems is low compared to the benefit of dev/prod parity and continuous deployment.
Adapters to different backing services are still useful, because they make porting to new backing services relatively painless. But all deploys of the app (developer environments, staging, production) should be using the same type and version of each of the backing services.
中文
X. 开发环境与线上环境等价
尽可能的保持开发，预发布，线上环境相同
从以往经验来看，开发环境（即开发人员的本地 部署）和线上环境（外部用户访问的真实部署）之间存在着很多差异。这些差异表现在以下三个方面：
- 时间差异： 开发人员正在编写的代码可能需要几天，几周，甚至几个月才会上线。
- 人员差异： 开发人员编写代码，运维人员部署代码。
- 工具差异： 开发人员或许使用 Nginx，SQLite，OS X，而线上环境使用 Apache，MySQL 以及 Linux。
12-Factor 应用想要做到 持续部署 就必须缩小本地与线上差异。 再回头看上面所描述的三个差异:
- 缩小时间差异：开发人员可以几小时，甚至几分钟就部署代码。
- 缩小人员差异：开发人员不只要编写代码，更应该密切参与部署过程以及代码在线上的表现。
- 缩小工具差异：尽量保证开发环境以及线上环境的一致性。
将上述总结变为一个表格如下：
| 传统应用 | 12-Factor 应用 | |
|---|---|---|
| 每次部署间隔 | 数周 | 几小时 |
| 开发人员 vs 运维人员 | 不同的人 | 相同的人 |
| 开发环境 vs 线上环境 | 不同 | 尽量接近 |
后端服务 是保持开发与线上等价的重要部分，例如数据库，队列系统，以及缓存。许多语言都提供了简化获取后端服务的类库，例如不同类型服务的 适配器 。下列表格提供了一些例子。
| 类型 | 语言 | 类库 | 适配器 |
|---|---|---|---|
| 数据库 | Ruby/Rails | ActiveRecord | MySQL, PostgreSQL, SQLite |
| 队列 | Python/Django | Celery | RabbitMQ, Beanstalkd, Redis |
| 缓存 | Ruby/Rails | ActiveSupport::Cache | Memory, filesystem, Memcached |
开发人员有时会觉得在本地环境中使用轻量的后端服务具有很强的吸引力，而那些更重量级的健壮的后端服务应该使用在生产环境。例如，本地使用 SQLite 线上使用 PostgreSQL；又如本地缓存在进程内存中而线上存入 Memcached。
12-Factor 应用的开发人员应该反对在不同环境间使用不同的后端服务 ，即使适配器已经可以几乎消除使用上的差异。这是因为，不同的后端服务意味着会突然出现的不兼容，从而导致测试、预发布都正常的代码在线上出现问题。这些错误会给持续部署带来阻力。从应用程序的生命周期来看，消除这种阻力需要花费很大的代价。
与此同时，轻量的本地服务也不像以前那样引人注目。借助于Homebrew，apt-get等现代的打包系统，诸如Memcached、PostgreSQL、RabbitMQ 等后端服务的安装与运行也并不复杂。此外，使用类似 Chef 和 Puppet 的声明式配置工具，结合像 Vagrant 这样轻量的虚拟环境就可以使得开发人员的本地环境与线上环境无限接近。与同步环境和持续部署所带来的益处相比，安装这些系统显然是值得的。
不同后端服务的适配器仍然是有用的，因为它们可以使移植后端服务变得简单。但应用的所有部署，这其中包括开发、预发布以及线上环境，都应该使用同一个后端服务的相同版本。