44 |
45 | | Type |
46 | Language |
47 | Library |
48 | Adapters |
49 |
50 |
51 | | Database |
52 | Ruby/Rails |
53 | ActiveRecord |
54 | MySQL, PostgreSQL, SQLite |
55 |
56 |
57 | | Queue |
58 | Python/Django |
59 | Celery |
60 | RabbitMQ, Beanstalkd, Redis |
61 |
62 |
63 | | Cache |
64 | Ruby/Rails |
65 | ActiveSupport::Cache |
66 | Memory, filesystem, Memcached |
67 |
68 |
69 |
70 | 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.
71 |
72 | **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.
73 |
74 | 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](http://mxcl.github.com/homebrew/) and [apt-get](https://help.ubuntu.com/community/AptGet/Howto). Alternatively, declarative provisioning tools such as [Chef](http://www.opscode.com/chef/) and [Puppet](http://docs.puppetlabs.com/) combined with light-weight virtual environments such as [Vagrant](http://vagrantup.com/) 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.
75 |
76 | 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.
77 |
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1 | ## IX. Disposability
2 | ### Maximize robustness with fast startup and graceful shutdown
3 |
4 | **The twelve-factor app's [processes](./processes) are *disposable*, meaning they can be started or stopped at a moment's notice.** This facilitates fast elastic scaling, rapid deployment of [code](./codebase) or [config](./config) changes, and robustness of production deploys.
5 |
6 | Processes should strive to **minimize startup time**. Ideally, a process takes a few seconds from the time the launch command is executed until the process is up and ready to receive requests or jobs. Short startup time provides more agility for the [release](./build-release-run) process and scaling up; and it aids robustness, because the process manager can more easily move processes to new physical machines when warranted.
7 |
8 | Processes **shut down gracefully when they receive a [SIGTERM](http://en.wikipedia.org/wiki/SIGTERM)** signal from the process manager. For a web process, graceful shutdown is achieved by ceasing to listen on the service port (thereby refusing any new requests), allowing any current requests to finish, and then exiting. Implicit in this model is that HTTP requests are short (no more than a few seconds), or in the case of long polling, the client should seamlessly attempt to reconnect when the connection is lost.
9 |
10 | For a worker process, graceful shutdown is achieved by returning the current job to the work queue. For example, on [RabbitMQ](http://www.rabbitmq.com/) the worker can send a [`NACK`](http://www.rabbitmq.com/amqp-0-9-1-quickref.html#basic.nack); on [Beanstalkd](http://kr.github.com/beanstalkd/), the job is returned to the queue automatically whenever a worker disconnects. Lock-based systems such as [Delayed Job](https://github.com/collectiveidea/delayed_job#readme) need to be sure to release their lock on the job record. Implicit in this model is that all jobs are [reentrant](http://en.wikipedia.org/wiki/Reentrant_%28subroutine%29), which typically is achieved by wrapping the results in a transaction, or making the operation [idempotent](http://en.wikipedia.org/wiki/Idempotence).
11 |
12 | Processes should also be **robust against sudden death**, in the case of a failure in the underlying hardware. While this is a much less common occurrence than a graceful shutdown with `SIGTERM`, it can still happen. A recommended approach is use of a robust queueing backend, such as Beanstalkd, that returns jobs to the queue when clients disconnect or time out. Either way, a twelve-factor app is architected to handle unexpected, non-graceful terminations. [Crash-only design](http://lwn.net/Articles/191059/) takes this concept to its [logical conclusion](http://docs.couchdb.org/en/latest/intro/overview.html).
13 |
14 |
15 |
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/en/intro.md:
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1 | Introduction
2 | ============
3 |
4 | In the modern era, software is commonly delivered as a service: called *web apps*, or *software-as-a-service*. The twelve-factor app is a methodology for building software-as-a-service apps that:
5 |
6 | * Use **declarative** formats for setup automation, to minimize time and cost for new developers joining the project;
7 | * Have a **clean contract** with the underlying operating system, offering **maximum portability** between execution environments;
8 | * Are suitable for **deployment** on modern **cloud platforms**, obviating the need for servers and systems administration;
9 | * **Minimize divergence** between development and production, enabling **continuous deployment** for maximum agility;
10 | * And can **scale up** without significant changes to tooling, architecture, or development practices.
11 |
12 | The twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc).
13 |
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1 | ## XI. Logs
2 | ### Treat logs as event streams
3 |
4 | *Logs* provide visibility into the behavior of a running app. In server-based environments they are commonly written to a file on disk (a "logfile"); but this is only an output format.
5 |
6 | Logs are the [stream](https://adam.herokuapp.com/past/2011/4/1/logs_are_streams_not_files/) of aggregated, time-ordered events collected from the output streams of all running processes and backing services. Logs in their raw form are typically a text format with one event per line (though backtraces from exceptions may span multiple lines). Logs have no fixed beginning or end, but flow continuously as long as the app is operating.
7 |
8 | **A twelve-factor app never concerns itself with routing or storage of its output stream.** It should not attempt to write to or manage logfiles. Instead, each running process writes its event stream, unbuffered, to `stdout`. During local development, the developer will view this stream in the foreground of their terminal to observe the app's behavior.
9 |
10 | In staging or production deploys, each process' stream will be captured by the execution environment, collated together with all other streams from the app, and routed to one or more final destinations for viewing and long-term archival. These archival destinations are not visible to or configurable by the app, and instead are completely managed by the execution environment. Open-source log routers (such as [Logplex](https://github.com/heroku/logplex) and [Fluent](https://github.com/fluent/fluentd)) are available for this purpose.
11 |
12 | The event stream for an app can be routed to a file, or watched via realtime tail in a terminal. Most significantly, the stream can be sent to a log indexing and analysis system such as [Splunk](http://www.splunk.com/), or a general-purpose data warehousing system such as [Hadoop/Hive](http://hive.apache.org/). These systems allow for great power and flexibility for introspecting an app's behavior over time, including:
13 |
14 | * Finding specific events in the past.
15 | * Large-scale graphing of trends (such as requests per minute).
16 | * Active alerting according to user-defined heuristics (such as an alert when the quantity of errors per minute exceeds a certain threshold).
17 |
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1 | ## VII. Port binding
2 | ### Export services via port binding
3 |
4 | Web apps are sometimes executed inside a webserver container. For example, PHP apps might run as a module inside [Apache HTTPD](http://httpd.apache.org/), or Java apps might run inside [Tomcat](http://tomcat.apache.org/).
5 |
6 | **The twelve-factor app is completely self-contained** and does not rely on runtime injection of a webserver into the execution environment to create a web-facing service. The web app **exports HTTP as a service by binding to a port**, and listening to requests coming in on that port.
7 |
8 | In a local development environment, the developer visits a service URL like `http://localhost:5000/` to access the service exported by their app. In deployment, a routing layer handles routing requests from a public-facing hostname to the port-bound web processes.
9 |
10 | This is typically implemented by using [dependency declaration](./dependencies) to add a webserver library to the app, such as [Tornado](http://www.tornadoweb.org/) for Python, [Thin](http://code.macournoyer.com/thin/) for Ruby, or [Jetty](http://jetty.codehaus.org/jetty/) for Java and other JVM-based languages. This happens entirely in *user space*, that is, within the app's code. The contract with the execution environment is binding to a port to serve requests.
11 |
12 | HTTP is not the only service that can be exported by port binding. Nearly any kind of server software can be run via a process binding to a port and awaiting incoming requests. Examples include [ejabberd](http://www.ejabberd.im/) (speaking [XMPP](http://xmpp.org/)), and [Redis](http://redis.io/) (speaking the [Redis protocol](http://redis.io/topics/protocol)).
13 |
14 | Note also that the port-binding approach means that one app can become the [backing service](./backing-services) for another app, by providing the URL to the backing app as a resource handle in the [config](./config) for the consuming app.
15 |
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1 | ## VI. Processes
2 | ### Execute the app as one or more stateless processes
3 |
4 | The app is executed in the execution environment as one or more *processes*.
5 |
6 | In the simplest case, the code is a stand-alone script, the execution environment is a developer's local laptop with an installed language runtime, and the process is launched via the command line (for example, `python my_script.py`). On the other end of the spectrum, a production deploy of a sophisticated app may use many [process types, instantiated into zero or more running processes](./concurrency).
7 |
8 | **Twelve-factor processes are stateless and [share-nothing](http://en.wikipedia.org/wiki/Shared_nothing_architecture).** Any data that needs to persist must be stored in a stateful [backing service](./backing-services), typically a database.
9 |
10 | The memory space or filesystem of the process can be used as a brief, single-transaction cache. For example, downloading a large file, operating on it, and storing the results of the operation in the database. The twelve-factor app never assumes that anything cached in memory or on disk will be available on a future request or job -- with many processes of each type running, chances are high that a future request will be served by a different process. Even when running only one process, a restart (triggered by code deploy, config change, or the execution environment relocating the process to a different physical location) will usually wipe out all local (e.g., memory and filesystem) state.
11 |
12 | Asset packagers (such as [Jammit](http://documentcloud.github.com/jammit/) or [django-compressor](http://django-compressor.readthedocs.org/)) use the filesystem as a cache for compiled assets. A twelve-factor app prefers to do this compiling during the [build stage](./build-release-run), such as the [Rails asset pipeline](http://guides.rubyonrails.org/asset_pipeline.html), rather than at runtime.
13 |
14 | Some web systems rely on ["sticky sessions"](http://en.wikipedia.org/wiki/Load_balancing_%28computing%29#Persistence) -- that is, caching user session data in memory of the app's process and expecting future requests from the same visitor to be routed to the same process. Sticky sessions are a violation of twelve-factor and should never be used or relied upon. Session state data is a good candidate for a datastore that offers time-expiration, such as [Memcached](http://memcached.org/) or [Redis](http://redis.io/).
15 |
16 |
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/en/toc.md:
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1 | The Twelve Factors
2 | ==================
3 |
4 | ## [I. Codebase](./codebase)
5 | ### One codebase tracked in revision control, many deploys
6 |
7 | ## [II. Dependencies](./dependencies)
8 | ### Explicitly declare and isolate dependencies
9 |
10 | ## [III. Config](./config)
11 | ### Store config in the environment
12 |
13 | ## [IV. Backing services](./backing-services)
14 | ### Treat backing services as attached resources
15 |
16 | ## [V. Build, release, run](./build-release-run)
17 | ### Strictly separate build and run stages
18 |
19 | ## [VI. Processes](./processes)
20 | ### Execute the app as one or more stateless processes
21 |
22 | ## [VII. Port binding](./port-binding)
23 | ### Export services via port binding
24 |
25 | ## [VIII. Concurrency](./concurrency)
26 | ### Scale out via the process model
27 |
28 | ## [IX. Disposability](./disposability)
29 | ### Maximize robustness with fast startup and graceful shutdown
30 |
31 | ## [X. Dev/prod parity](./dev-prod-parity)
32 | ### Keep development, staging, and production as similar as possible
33 |
34 | ## [XI. Logs](./logs)
35 | ### Treat logs as event streams
36 |
37 | ## [XII. Admin processes](./admin-processes)
38 | ### Run admin/management tasks as one-off processes
39 |
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1 | Who should read this document?
2 | ==============================
3 |
4 | Any developer building applications which run as a service. Ops engineers who deploy or manage such applications.
5 |
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1 | 简介
2 | ============
3 | 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：
4 |
5 | * 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。
6 | * 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**。
7 | * 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。
8 | * 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。
9 | * 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**。
10 |
11 | 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。
12 |
13 |
14 | 背景
15 | ==========
16 |
17 | 本文的贡献者者参与过数以百计的应用程序的开发和部署，并通过 [Heroku](http://www.heroku.com/) 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。
18 |
19 | 本文综合了我们关于 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](http://blog.heroku.com/archives/2011/6/28/the_new_heroku_4_erosion_resistance_explicit_contracts/) 。
20 |
21 | 我们的初衷是分享在现代软件开发过程中发现的一些系统性问题，并加深对这些问题的认识。我们提供了讨论这些问题时所需的共享词汇，同时使用相关术语给出一套针对这些问题的广义解决方案。本文格式的灵感来自于 Martin Fowler 的书籍： *[Patterns of Enterprise Application Architecture](http://books.google.com/books/about/Patterns_of_enterprise_application_archi.html?id=FyWZt5DdvFkC)* ， *[Refactoring](http://books.google.com/books/about/Refactoring.html?id=1MsETFPD3I0C)* 。
22 |
23 | 读者应该是哪些人？
24 | ==============================
25 |
26 | 任何 SaaS 应用的开发人员。部署和管理此类应用的运维工程师。
27 |
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1 | # Summary
2 |
3 | * [简介](README.md)
4 | * [I. 基准代码](codebase.md)
5 | * [II. 依赖](dependencies.md)
6 | * [III. 配置](config.md)
7 | * [IV. 后端服务](backing-services.md)
8 | * [V. 构建，发布，运行](build-release-run.md)
9 | * [VI. 进程](processes.md)
10 | * [VII. 端口绑定](port-binding.md)
11 | * [VIII. 并发](concurrency.md)
12 | * [IX. 易处理](disposability.md)
13 | * [X. 开发环境与线上环境等价](dev-prod-parity.md)
14 | * [XI. 日志](logs.md)
15 | * [XII. 管理进程](admin-processes.md)
16 |
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/zh_cn/admin-processes.md:
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1 | ## XII. 管理进程
2 | ### 后台管理任务当作一次性进程运行
3 |
4 | [进程构成](./concurrency)（process formation）是指用来处理应用的常规业务（比如处理 web 请求）的一组进程。与此不同，开发人员经常希望执行一些管理或维护应用的一次性任务，例如：
5 |
6 | * 运行数据移植（Django 中的 `manage.py migrate`, Rails 中的 `rake db:migrate`）。
7 | * 运行一个控制台（也被称为 [REPL](http://en.wikipedia.org/wiki/Read-eval-print_loop) shell），来执行一些代码或是针对线上数据库做一些检查。大多数语言都通过解释器提供了一个 REPL 工具（`python` 或 `perl`） ，或是其他命令（Ruby 使用 `irb`, Rails 使用 `rails console`）。
8 | * 运行一些提交到代码仓库的一次性脚本。
9 |
10 | 一次性管理进程应该和正常的 [常驻进程](./processes) 使用同样的环境。这些管理进程和任何其他的进程一样使用相同的 [代码](./codebase) 和 [配置](./config) ，基于某个 [发布版本](./build-release-run) 运行。后台管理代码应该随其他应用程序代码一起发布，从而避免同步问题。
11 |
12 | 所有进程类型应该使用同样的 [依赖隔离](./dependencies) 技术。例如，如果Ruby的web进程使用了命令 `bundle exec thin start` ，那么数据库移植应使用 `bundle exec rake db:migrate` 。同样的，如果一个 Python 程序使用了 Virtualenv，则需要在运行 Tornado Web 服务器和任何 `manage.py` 管理进程时引入 `bin/python` 。
13 |
14 | 12-factor 尤其青睐那些提供了 REPL shell 的语言，因为那会让运行一次性脚本变得简单。在本地部署中，开发人员直接在命令行使用 shell 命令调用一次性管理进程。在线上部署中，开发人员依旧可以使用ssh或是运行环境提供的其他机制来运行这样的进程。
15 |
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1 | 背景
2 | ==========
3 |
4 | 本文的贡献者者参与过数以百计的应用程序的开发和部署，并通过 [Heroku](http://www.heroku.com/) 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。
5 |
6 | 本文综合了我们关于 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](http://blog.heroku.com/archives/2011/6/28/the_new_heroku_4_erosion_resistance_explicit_contracts/) 。
7 |
8 | 我们的初衷是分享在现代软件开发过程中发现的一些系统性问题，并加深对这些问题的认识。我们提供了讨论这些问题时所需的共享词汇，同时使用相关术语给出一套针对这些问题的广义解决方案。本文格式的灵感来自于 Martin Fowler 的书籍： *[Patterns of Enterprise Application Architecture](http://books.google.com/books/about/Patterns_of_enterprise_application_archi.html?id=FyWZt5DdvFkC)* ， *[Refactoring](http://books.google.com/books/about/Refactoring.html?id=1MsETFPD3I0C)* 。
9 |
10 |
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1 | ## IV. 后端服务
2 | ### 把后端服务(*backing services*)当作附加资源
3 |
4 | *后端服务*是指程序运行所需要的通过网络调用的各种服务，如数据库（[MySQL](http://dev.mysql.com/)，[CouchDB](http://couchdb.apache.org/)），消息/队列系统（[RabbitMQ](http://www.rabbitmq.com/)，[Beanstalkd](http://kr.github.com/beanstalkd/)），SMTP 邮件发送服务（[ Postfix](http://www.postfix.org/)），以及缓存系统（[Memcached](http://memcached.org/)）。
5 |
6 | 类似数据库的后端服务，通常由部署应用程序的系统管理员一起管理。除了本地服务之外，应用程序有可能使用了第三方发布和管理的服务。示例包括 SMTP（例如 [Postmark](http://postmarkapp.com/)），数据收集服务（例如 [New Relic](http://newrelic.com/) 或 [Loggly](http://www.loggly.com/)），数据存储服务（如 [Amazon S3](http://http://aws.amazon.com/s3/)），以及使用 API 访问的服务（例如 [Twitter](http://dev.twitter.com/), [Google Maps](http://code.google.com/apis/maps/index.html), [Last.fm](http://www.last.fm/api)）。
7 |
8 | **12-Factor 应用不会区别对待本地或第三方服务。** 对应用程序而言，两种都是附加资源，通过一个 url 或是其他存储在 [配置](./config) 中的服务定位/服务证书来获取数据。12-Factor 应用的任意 [部署](./codebase) ，都应该可以在不进行任何代码改动的情况下，将本地 MySQL 数据库换成第三方服务（例如 [Amazon RDS](http://aws.amazon.com/rds/)）。类似的，本地 SMTP 服务应该也可以和第三方 SMTP 服务（例如 Postmark ）互换。上述 2 个例子中，仅需修改配置中的资源地址。
9 |
10 | 每个不同的后端服务是一份 *资源* 。例如，一个 MySQL 数据库是一个资源，两个 MySQL 数据库（用来数据分区）就被当作是 2 个不同的资源。12-Factor 应用将这些数据库都视作 *附加资源* ，这些资源和它们附属的部署保持松耦合。
11 |
12 | 
13 |
14 | Resources can be attached and detached to deploys at will. For example, if the app's database is misbehaving due to a hardware issue, the app's administrator might spin up a new database server restored from a recent backup. The current production database could be detached, and the new database attached -- all without any code changes.
15 |
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1 | ## V. Build, release, run
2 | ### Strictly separate build and run stages
3 |
4 | A [codebase](./codebase) is transformed into a (non-development) deploy through three stages:
5 |
6 | * The *build stage* is a transform which converts a code repo into an executable bundle known as a *build*. Using a version of the code at a commit specified by the deployment process, the build stage fetches vendors [dependencies](./dependencies) and compiles binaries and assets.
7 | * The *release stage* takes the build produced by the build stage and combines it with the deploy's current [config](./config). The resulting *release* contains both the build and the config and is ready for immediate execution in the execution environment.
8 | * The *run stage* (also known as "runtime") runs the app in the execution environment, by launching some set of the app's [processes](./processes) against a selected release.
9 |
10 | 
11 |
12 | **The twelve-factor app uses strict separation between the build, release, and run stages.** For example, it is impossible to make changes to the code at runtime, since there is no way to propagate those changes back to the build stage.
13 |
14 | Deployment tools typically offer release management tools, most notably the ability to roll back to a previous release. For example, the [Capistrano](https://github.com/capistrano/capistrano/wiki) deployment tool stores releases in a subdirectory named `releases`, where the current release is a symlink to the current release directory. Its `rollback` command makes it easy to quickly roll back to a previous release.
15 |
16 | Every release should always have a unique release ID, such as a timestamp of the release (such as `2011-04-06-20:32:17`) or an incrementing number (such as `v100`). Releases are an append-only ledger and a release cannot be mutated once it is created. Any change must create a new release.
17 |
18 | Builds are initiated by the app's developers whenever new code is deployed. Runtime execution, by contrast, can happen automatically in cases such as a server reboot, or a crashed process being restarted by the process manager. Therefore, the run stage should be kept to as few moving parts as possible, since problems that prevent an app from running can cause it to break in the middle of the night when no developers are on hand. The build stage can be more complex, since errors are always in the foreground for a developer who is driving the deploy.
19 |
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/en/codebase.md:
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1 | ## I. Codebase
2 | ### One codebase tracked in revision control, many deploys
3 |
4 | A twelve-factor app is always tracked in a version control system, such as [Git](http://git-scm.com/), [Mercurial](http://mercurial.selenic.com/), or [Subversion](http://subversion.apache.org/). A copy of the revision tracking database is known as a *code repository*, often shortened to *code repo* or just *repo*.
5 |
6 | A *codebase* is any single repo (in a centralized revision control system like Subversion), or any set of repos who share a root commit (in a decentralized revision control system like Git).
7 |
8 | 
9 |
10 | There is always a one-to-one correlation between the codebase and the app:
11 |
12 | * If there are multiple codebases, it's not an app -- it's a distributed system. Each component in a distributed system is an app, and each can individually comply with twelve-factor.
13 | * Multiple apps sharing the same code is a violation of twelve-factor. The solution here is to factor shared code into libraries which can be included through the [dependency manager](./dependencies).
14 |
15 | There is only one codebase per app, but there will be many deploys of the app. A *deploy* is a running instance of the app. This is typically a production site, and one or more staging sites. Additionally, every developer has a copy of the app running in their local development environment, each of which also qualifies as a deploy.
16 |
17 | The codebase is the same across all deploys, although different versions may be active in each deploy. For example, a developer has some commits not yet deployed to staging; staging has some commits not yet deployed to production. But they all share the same codebase, thus making them identifiable as different deploys of the same app.
18 |
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/en/concurrency.md:
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1 | ## VIII. Concurrency
2 | ### Scale out via the process model
3 |
4 | Any computer program, once run, is represented by one or more processes. Web apps have taken a variety of process-execution forms. For example, PHP processes run as child processes of Apache, started on demand as needed by request volume. Java processes take the opposite approach, with the JVM providing one massive uberprocess that reserves a large block of system resources (CPU and memory) on startup, with concurrency managed internally via threads. In both cases, the running process(es) are only minimally visible to the developers of the app.
5 |
6 | 
7 |
8 | **In the twelve-factor app, processes are a first class citizen.** Processes in the twelve-factor app take strong cues from [the unix process model for running service daemons](https://adam.herokuapp.com/past/2011/5/9/applying_the_unix_process_model_to_web_apps/). Using this model, the developer can architect their app to handle diverse workloads by assigning each type of work to a *process type*. For example, HTTP requests may be handled by a web process, and long-running background tasks handled by a worker process.
9 |
10 | This does not exclude individual processes from handling their own internal multiplexing, via threads inside the runtime VM, or the async/evented model found in tools such as [EventMachine](http://rubyeventmachine.com/), [Twisted](http://twistedmatrix.com/trac/), or [Node.js](http://nodejs.org/). But an individual VM can only grow so large (vertical scale), so the application must also be able to span multiple processes running on multiple physical machines.
11 |
12 | The process model truly shines when it comes time to scale out. The [share-nothing, horizontally partitionable nature of twelve-factor app processes](./processes) means that adding more concurrency is a simple and reliable operation. The array of process types and number of processes of each type is known as the *process formation*.
13 |
14 | Twelve-factor app processes [should never daemonize](http://dustin.github.com/2010/02/28/running-processes.html) or write PID files. Instead, rely on the operating system's process manager (such as [Upstart](http://upstart.ubuntu.com/), a distributed process manager on a cloud platform, or a tool like [Foreman](http://blog.daviddollar.org/2011/05/06/introducing-foreman.html) in development) to manage [output streams](./logs), respond to crashed processes, and handle user-initiated restarts and shutdowns.
15 |
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/en/config.md:
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1 | ## III. Config
2 | ### Store config in the environment
3 |
4 | An app's *config* is everything that is likely to vary between [deploys](./codebase) (staging, production, developer environments, etc). This includes:
5 |
6 | * Resource handles to the database, Memcached, and other [backing services](./backing-services)
7 | * Credentials to external services such as Amazon S3 or Twitter
8 | * Per-deploy values such as the canonical hostname for the deploy
9 |
10 | Apps sometimes store config as constants in the code. This is a violation of twelve-factor, which requires **strict separation of config from code**. Config varies substantially across deploys, code does not.
11 |
12 | A litmus test for whether an app has all config correctly factored out of the code is whether the codebase could be made open source at any moment, without compromising any credentials.
13 |
14 | Note that this definition of "config" does **not** include internal application config, such as `config/routes.rb` in Rails, or how [code modules are connected](http://docs.spring.io/spring/docs/current/spring-framework-reference/html/beans.html) in [Spring](http://spring.io/). This type of config does not vary between deploys, and so is best done in the code.
15 |
16 | Another approach to config is the use of config files which are not checked into revision control, such as `config/database.yml` in Rails. This is a huge improvement over using constants which are checked into the code repo, but still has weaknesses: it's easy to mistakenly check in a config file to the repo; there is a tendency for config files to be scattered about in different places and different formats, making it hard to see and manage all the config in one place. Further, these formats tend to be language- or framework-specific.
17 |
18 | **The twelve-factor app stores config in *environment variables*** (often shortened to *env vars* or *env*). Env vars are easy to change between deploys without changing any code; unlike config files, there is little chance of them being checked into the code repo accidentally; and unlike custom config files, or other config mechanisms such as Java System Properties, they are a language- and OS-agnostic standard.
19 |
20 | Another aspect of config management is grouping. Sometimes apps batch config into named groups (often called "environments") named after specific deploys, such as the `development`, `test`, and `production` environments in Rails. This method does not scale cleanly: as more deploys of the app are created, new environment names are necessary, such as `staging` or `qa`. As the project grows further, developers may add their own special environments like `joes-staging`, resulting in a combinatorial explosion of config which makes managing deploys of the app very brittle.
21 |
22 | In a twelve-factor app, env vars are granular controls, each fully orthogonal to other env vars. They are never grouped together as "environments", but instead are independently managed for each deploy. This is a model that scales up smoothly as the app naturally expands into more deploys over its lifetime.
23 |
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/en/dependencies.md:
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1 | ## II. Dependencies
2 | ### Explicitly declare and isolate dependencies
3 |
4 | Most programming languages offer a packaging system for distributing support libraries, such as [CPAN](http://www.cpan.org/) for Perl or [Rubygems](http://rubygems.org/) for Ruby. Libraries installed through a packaging system can be installed system-wide (known as "site packages") or scoped into the directory containing the app (known as "vendoring" or "bundling").
5 |
6 | **A twelve-factor app never relies on implicit existence of system-wide packages.** It declares all dependencies, completely and exactly, via a *dependency declaration* manifest. Furthermore, it uses a *dependency isolation* tool during execution to ensure that no implicit dependencies "leak in" from the surrounding system. The full and explicit dependency specification is applied uniformly to both production and development.
7 |
8 | For example, [Bundler](https://bundler.io/) for Ruby offers the `Gemfile` manifest format for dependency declaration and `bundle exec` for dependency isolation. In Python there are two separate tools for these steps -- [Pip](http://www.pip-installer.org/en/latest/) is used for declaration and [Virtualenv](http://www.virtualenv.org/en/latest/) for isolation. Even C has [Autoconf](http://www.gnu.org/s/autoconf/) for dependency declaration, and static linking can provide dependency isolation. No matter what the toolchain, dependency declaration and isolation must always be used together -- only one or the other is not sufficient to satisfy twelve-factor.
9 |
10 | One benefit of explicit dependency declaration is that it simplifies setup for developers new to the app. The new developer can check out the app's codebase onto their development machine, requiring only the language runtime and dependency manager installed as prerequisites. They will be able to set up everything needed to run the app's code with a deterministic *build command*. For example, the build command for Ruby/Bundler is `bundle install`, while for Clojure/[Leiningen](https://github.com/technomancy/leiningen#readme) it is `lein deps`.
11 |
12 | Twelve-factor apps also do not rely on the implicit existence of any system tools. Examples include shelling out to ImageMagick or `curl`. While these tools may exist on many or even most systems, there is no guarantee that they will exist on all systems where the app may run in the future, or whether the version found on a future system will be compatible with the app. If the app needs to shell out to a system tool, that tool should be vendored into the app.
13 |
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/en/dev-prod-parity.md:
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1 | ## X. Dev/prod parity
2 | ### Keep development, staging, and production as similar as possible
3 |
4 | Historically, there have been substantial gaps between development (a developer making live edits to a local [deploy](./codebase) of the app) and production (a running deploy of the app accessed by end users). These gaps manifest in three areas:
5 |
6 | * **The time gap:** A developer may work on code that takes days, weeks, or even months to go into production.
7 | * **The personnel gap**: Developers write code, ops engineers deploy it.
8 | * **The tools gap**: Developers may be using a stack like Nginx, SQLite, and OS X, while the production deploy uses Apache, MySQL, and Linux.
9 |
10 | **The twelve-factor app is designed for [continuous deployment](http://www.avc.com/a_vc/2011/02/continuous-deployment.html) by keeping the gap between development and production small.** Looking at the three gaps described above:
11 |
12 | * Make the time gap small: a developer may write code and have it deployed hours or even just minutes later.
13 | * Make the personnel gap small: developers who wrote code are closely involved in deploying it and watching its behavior in production.
14 | * Make the tools gap small: keep development and production as similar as possible.
15 |
16 | Summarizing the above into a table:
17 |
18 |