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### Good for Restful API：构建更规范的RESTful API #### 概述本文将详细介绍如何使用Webmachine框架来创建符合HTTP语义的REST服务。Webmachine是一个基于Erlang语言构建的RESTful框架，其核心目标是简化HTTP业务逻辑处理，使开发者能够专注于构建业务功能，而非底层HTTP细节。 #### Webmachine框架 ##### 起源与目标 Webmachine最初是从Riak项目中重构出来的，并在Justin Sheehy的演讲《Webmachine:一个实用的HTTP执行模型》中进行了介绍。该框架基于Mochiweb（一款用Erlang编写的Web服务器）构建，旨在帮助开发者轻松构建遵循HTTP语义的服务，同时避免了在业务逻辑中直接处理HTTP相关的复杂性。 ##### 原则 - **默认行为**：Webmachine实现了一些默认行为，应用需要实现一组特定函数，这些函数将在框架内部被调用。 - **好莱坞原则**：框架调用应用提供的函数，而不是反之。 - **函数签名**：所有函数具有相同的签名`f(Req, Data, Context) -> {Result, Req, Data, Context}`。 - `Result`：通常包含布尔值，用于向决策核心发送信号。 - `ReqData`：存储请求和响应数据，可在每个函数中读取或修改，并传递到下一个函数。 - `Context`：由应用自行维护的状态信息，框架不处理这一参数。 - **无副作用编程**：由于Erlang的基础实现，Webmachine强制实施无副作用的编程模式。状态的修改只能通过传入参数的方式进行。 ##### 结构 Webmachine应用程序主要由一个或多个资源组成，这些资源是实现了特定回调和内部函数的Erlang模块。资源位于`/src`目录下，而每个应用程序都有一个`dispatch.conf`配置文件，用于定义URL访问请求到相应资源的路由规则。 #### 构建REST服务本节将通过一个具体的例子——论文库的REST API——来演示如何使用Webmachine构建REST服务。该API提供对论文的存储、更新、检索和删除操作，每篇论文包含标题和作者信息。 ##### 实现的操作 1. **存储论文**：允许用户上传新论文。 2. **更新论文**：允许用户编辑已存在的论文。 3. **检索论文**：允许用户查询特定论文。 4. **删除论文**：允许用户删除论文。 ##### 开发流程采用测试驱动开发（TDD）的方法来构建API，使用Python的`Requests`库编写API测试。这种方式有助于确保API的正确性和稳定性，同时也方便后续的功能扩展和维护。 #### 结论 Webmachine为开发者提供了一种简单高效的方式来构建符合HTTP语义的RESTful服务。通过对框架原理的理解及其提供的工具和技术的运用，开发者可以专注于业务逻辑的设计与实现，而无需过多关注底层的HTTP细节，从而提高开发效率和代码质量。对于那些希望快速构建高性能REST服务的人来说，Webmachine无疑是一个值得考虑的选择。

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Web Development with Webmachine for

Erlang

1 Webmachine – The Framework

1.1 Origin and Aims

Webmachine is a REST framework built on top of

Mochiweb

[1]

, a web server written in Erlang. It aims

at easily building a web service with correct HTTP se-

mantics while not having to deal with HTTP in the busi-

ness logic. Webmachine was refactored out of Riak as

explained in the talk “Webmachine: a Practical Exe-

cutable Model of HTTP”

[2]

by one of its creators, Justin

Sheehy. Since it was the initial implementation, this ar-

ticle mostly discusses the Erlang one and is focused on

writing a REST service with Webmachine for Erlang.

1.2 Principles

The framework implements some default behaviour and

the application has to implement a given set of functions

which will be called by Webmachine (Hollywood Princi-

ple). All functions have the same signature which is:

f(ReqData Context) -> {Result, ReqData, Context}.

Result contains mostly true/false which act as signals for

the decision core in the framework code.

ReqData stores the request and response. Every func-

tion can read and modify those and pass them on to

the next function.

Context is ignored by the framework. In this parameter

the application can store internal state for processing

of one request.

Webmachine enforces a side eﬀect free programming

model because of the underlying implementation in Er-

lang. The only possibility to modify state inside another

function is to pass an argument to it. With this in mind, a

Webmachine application can be seen as a set of functions

which call each other with their computed values.

1.3 Structure of a Webmachine Applica-

tion

An application consists of one ore more resources.

These are Erlang modules which usually implement

framework callbacks and internal functions. The re-

sources are located in the /src folder. For every appli-

cation there is also a dispatch.conf ﬁle which conﬁgures

the routes of an URL accessing request to a resource.

2 Building a REST Service

The running example will be a paper repository. It pro-

vides an API to store, change, retrieve and delete papers

with their title and author.

The operations which are implemented in the example are

the following:

The example will be developed in a test driven manner.

The API tests are written with the excellent Python li-

brary Requests

[3]

by Kenneth Reitz. Erlang unit tests are

written with EUnit

[4]

All source code can be found in a Github repository

[5]

2.1 Start a Webmachine Application

2.1.1 What we expect from the fresh Application

A barebones Webmachine application listens to port 8888

and returns a short html response stating “Hello world”.

# -*- coding: utf-8 -*- #!/usr/bin/python import requests

import unittest class TestPaperAPI(unittest.TestCase):

def setUp(self): self.base_url = "http://localhost:8888"

def test_get_on_root_returns_html_hello_world(self):

resp = requests.get(self.base_url)

self.assertEqual(resp.content, "<html><body>Hello,

new world</body></html>") if

__name__ == "__main__": suite =

unittest.TestLoader(verbosity=2).loadTestsFromTestCase(TestPaperAPI)

unittest.TextTestRunner.run(suite)

Running the test with

python -m unittest api_tests.py -v

should produce a failing assertion. If this test succeeds, it

2 2 BUILDING A REST SERVICE

means that there is already an application running on that

port.

2.1.2 Install and run a basic Webmachine Applica-

tion

For this tutorial a working installation of Erlang is

needed. There are precompiled binaries available

[6] [7]

and it is also accesible via package managers.

After this, clone the Webmachine code repository

[8]

from

github.

git clone git://github.com/basho/webmachine.git

Then let it generate the skeleton code for our application

which we will call prp – a short hand for paper repository

and the namespace used for this tutorial code.

webmachine/scripts/new_webmachine.sh prp

After generating the code we build and run it:

make ./start.sh

Note: Everytime the code is built, the changed modules

are reloaded into the Erlang VM with hot code reloading.

Because a full run of make checks all dependencies with

rebar and is therefore comparatively slow. To speed up

develpment it is recommended to run

rebar compile

if no new dependencies were added and just the written

Webmachine application needs to be compile.

Now the test should pass.

2.1.3 Adding the testrunner to the Makeﬁle

For convenience it is recommended to add the

api_tests.py to a new test directory at the applica-

tion root. mkdir test The directory structure should look

like:

tree -L 1 . ├── Makeﬁle ├── deps ├── ebin

├── include ├── priv ├── rebar ├── re-

bar.conﬁg ├── src ├── start.sh └── test

In addition the testrunner is added to the Makeﬁle as a

new target:

api-tests: $(shell python -m unittest discover test

'*_tests.py' -v)

Now type

make api-tests

to execute the test suite.

2.2 Conﬁgure Routes

For every request a Webmachine application decides

which function in which module it has to call. This

conﬁguration is done in a ﬁle in priv/dispatch.conf.

The syntax for this conﬁguration can be found at the

documentation

[9]

In priv/dispatch.conf:

{["paper”, id], prp_paper, []}. % The canonical resource

for everything else {[], prp_resource, []}.

The ﬁrst line tells Webmachine to look into the resource

ﬁle prp_paper.erl to generate a response for a request to

/paper/id. In addition, for a URL path /paper/foobar the

part after the second slash, in this case “foobar” is acces-

sible via the atom 'id'. With this help we can later get the

id of a paper via:

wrq:path_info(id).

2.3 Adding a storage layer

The service which is built in this tutorial requires a stor-

age. A simple abstraction layer exists around it whose

creation is not discussed in this tutorial. It uses an in-

memory Mnesia database, a distributed database written

in Erlang which is part of the Erlang distribution as well

so there is no need to install drivers and databases.

Please get the source code from the Github repository

[5]

. The relevant ﬁles are in src/prp_schema.erl and

priv/prp_datatypes.hrl. The corresponding unit tests are

located in test/prp_schema_tests.erl.

2.4 GET /paper/id

2.4.1 Writing a ﬁrst test

At ﬁrst a test method is added to the TestPaperAPI class:

def test_get_on_paper_returns_id_in_html(self):

for id in 1,2,3: resp = requests.get(self.paper_url

+ str(id)) self.assertEqual(resp.status_code, 200)

self.assertEqual(resp.content, "<html><body>" + str(id)

+ "</body></html>")

Running the test via

make api_tests

should fail.

2.4.2 Implementing a GET response in HTML

As conﬁgured in dispatch.conf the request gets routed

to src/prp_paper.erl. On application startup the init/1

method is called. It ﬁlls the in-memory Mnesia database

wich is used for testing with inital data to test with

(Fixtures).

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2018-12-28

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