Requests项目如何使用pytest进行单元测试

前言

Pytest是python的单元测试框架，简单易用，在很多知名项目中应用。requests是python知名的http爬虫库，同样简单易用，是python开源项目的TOP10。关于这2个项目，之前都有过介绍，本文主要介绍requests项目如何使用pytest进行单元测试，希望达到下面3个目标：

熟练pytest的使用
学习如何对项目进行单元测试
深入requests的一些实现细节

本文分如下几个部分:

requests项目单元测试状况
简单工具类如何测试
request-api如何测试
底层API测试

requests项目单元测试状况

requests的单元测试代码全部在 tests 目录，使用 pytest.ini 进行配置。测试除pytest外，还需要安装:

上述依赖 master 版本在requirement-dev文件中定义；2.24.0版本会在pipenv中定义。

测试用例使用make命令，子命令在Makefile中定义, 使用make ci运行所有单元测试结果如下:

$ make ci
pytest tests --junitxml=report.xml
======================================================================================================= test session starts =======================================================================================================
platform linux -- Python 3.6.8, pytest-3.10.1, py-1.10.0, pluggy-0.13.1
rootdir: /home/work6/project/requests, inifile: pytest.ini
plugins: mock-2.0.0, httpbin-1.0.0, cov-2.9.0
collected 552 items                                                                                                                                                                                                               tests/test_help.py ...                                                                                                                                                                                                      [  0%]
tests/test_hooks.py ...                                                                                                                                                                                                     [  1%]
tests/test_lowlevel.py ...............                                                                                                                                                                                      [  3%]
tests/test_packages.py ...                                                                                                                                                                                                  [  4%]
tests/test_requests.py .................................................................................................................................................................................................... [ 39%]
127.0.0.1 - - [10/Aug/2021 08:41:53] "GET /stream/4 HTTP/1.1" 200 756
.127.0.0.1 - - [10/Aug/2021 08:41:53] "GET /stream/4 HTTP/1.1" 500 59
----------------------------------------
Exception happened during processing of request from ('127.0.0.1', 46048)
Traceback (most recent call last):File "/usr/lib64/python3.6/wsgiref/handlers.py", line 138, in runself.finish_response()
x.........................................................................................                                                                                                                                 [ 56%]
tests/test_structures.py ....................                                                                                                                                                                               [ 59%]
tests/test_testserver.py ......s....                                                                                                                                                                                        [ 61%]
tests/test_utils.py ..s................................................................................................................................................................................................ssss [ 98%]
ssssss.....                                                                                                                                                                                                                 [100%]----------------------------------------------------------------------------------- generated xml file: /home/work6/project/requests/report.xml -----------------------------------------------------------------------------------
======================================================================================= 539 passed, 12 skipped, 1 xfailed in 64.16 seconds ========================================================================================

可以看到requests在1分钟内，总共通过了539个测试用例，效果还是不错。使用 make coverage 查看单元测试覆盖率:

$ make coverage
----------- coverage: platform linux, python 3.6.8-final-0 -----------
Name                          Stmts   Miss  Cover
-------------------------------------------------
requests/__init__.py             71     71     0%
requests/__version__.py          10     10     0%
requests/_internal_utils.py      16      5    69%
requests/adapters.py            222     67    70%
requests/api.py                  20     13    35%
requests/auth.py                174     54    69%
requests/certs.py                 4      4     0%
requests/compat.py               47     47     0%
requests/cookies.py             238    115    52%
requests/exceptions.py           35     29    17%
requests/help.py                 63     19    70%
requests/hooks.py                15      4    73%
requests/models.py              455    119    74%
requests/packages.py             16     16     0%
requests/sessions.py            283     67    76%
requests/status_codes.py         15     15     0%
requests/structures.py           40     19    52%
requests/utils.py               465    170    63%
-------------------------------------------------
TOTAL                          2189    844    61%
Coverage XML written to file coverage.xml

结果显示requests项目总体覆盖率61%，每个模块的覆盖率也清晰可见。

单元测试覆盖率使用代码行数进行判断，Stmts显示模块的有效行数，Miss显示未执行到的行。如果生成html的报告，还可以定位到具体未覆盖到的行；pycharm的coverage也有类似功能。

tests下的文件及测试类如下表:

简单工具类如何测试

test_help 实现分析

先从最简单的test_help上手，测试类和被测试对象命名是对应的。先看看被测试的模块help.py。这个模块主要是2个函数 info 和 _implementation:

import idnadef _implementation():...def info():...system_ssl = ssl.OPENSSL_VERSION_NUMBERsystem_ssl_info = {'version': '%x' % system_ssl if system_ssl is not None else ''}idna_info = {'version': getattr(idna, '__version__', ''),}...return {'platform': platform_info,'implementation': implementation_info,'system_ssl': system_ssl_info,'using_pyopenssl': pyopenssl is not None,'pyOpenSSL': pyopenssl_info,'urllib3': urllib3_info,'chardet': chardet_info,'cryptography': cryptography_info,'idna': idna_info,'requests': {'version': requests_version,},}

info提供系统环境的信息，_implementation是其内部实现，以下划线*_*开头。再看测试类test_help:

from requests.help import infodef test_system_ssl():"""Verify we're actually setting system_ssl when it should be available."""assert info()['system_ssl']['version'] != ''class VersionedPackage(object):def __init__(self, version):self.__version__ = versiondef test_idna_without_version_attribute(mocker):"""Older versions of IDNA don't provide a __version__ attribute, verifythat if we have such a package, we don't blow up."""mocker.patch('requests.help.idna', new=None)assert info()['idna'] == {'version': ''}def test_idna_with_version_attribute(mocker):"""Verify we're actually setting idna version when it should be available."""mocker.patch('requests.help.idna', new=VersionedPackage('2.6'))assert info()['idna'] == {'version': '2.6'}

首先从头部的导入信息可以看到，仅仅对info函数进行测试，这个容易理解。info测试通过，自然覆盖到_implementation这个内部函数。这里可以得到单元测试的第1个技巧:

仅对public的接口进行测试

test_idna_without_version_attribute和test_idna_with_version_attribute均有一个mocker参数，这是pytest-mock提供的功能，会自动注入一个mock实现。使用这个mock对idna模块进行模拟

# 模拟空实现
mocker.patch('requests.help.idna', new=None)
# 模拟版本2.6
mocker.patch('requests.help.idna', new=VersionedPackage('2.6'))

可能大家会比较奇怪，这里patch模拟的是 requests.help.idna , 而我们在help中导入的是 inda 模块。这是因为在requests.packages中对inda进行了模块名重定向:

for package in ('urllib3', 'idna', 'chardet'):locals()[package] = __import__(package)# This traversal is apparently necessary such that the identities are# preserved (requests.packages.urllib3.* is urllib3.*)for mod in list(sys.modules):if mod == package or mod.startswith(package + '.'):sys.modules['requests.packages.' + mod] = sys.modules[mod]

使用mocker后，idna的__version__信息就可以进行控制，这样info中的idna结果也就可以预期。那么可以得到第2个技巧:

2.使用mock辅助单元测试

test_hooks 实现分析

我们继续查看hooks如何进行测试:

from requests import hooksdef hook(value):return value[1:]@pytest.mark.parametrize('hooks_list, result', ((hook, 'ata'),([hook, lambda x: None, hook], 'ta'),)
)
def test_hooks(hooks_list, result):assert hooks.dispatch_hook('response', {'response': hooks_list}, 'Data') == resultdef test_default_hooks():assert hooks.default_hooks() == {'response': []}

hooks模块的2个接口default_hooks和dispatch_hook都进行了测试。其中default_hooks是纯函数，无参数有返回值，这种函数最容易测试，仅仅检查返回值是否符合预期即可。dispatch_hook会复杂一些，还涉及对回调函数(hook函数)的调用:

def dispatch_hook(key, hooks, hook_data, **kwargs):"""Dispatches a hook dictionary on a given piece of data."""hooks = hooks or {}hooks = hooks.get(key)if hooks:# 判断钩子函数if hasattr(hooks, '__call__'):hooks = [hooks]for hook in hooks:_hook_data = hook(hook_data, **kwargs)if _hook_data is not None:hook_data = _hook_datareturn hook_data

pytest.mark.parametrize提供了2组参数进行测试。第一组参数hook和ata很简单，hook是一个函数，会对参数裁剪，去掉首位，ata是期望的返回值。test_hooks的response的参数是Data，所以结果应该是ata。第二组参数中的第一个参数会复杂一些，变成了一个数组，首位还是hook函数，中间使用一个匿名函数，匿名函数没有返回值，这样覆盖到 if _hook_data is not None: 的旁路分支。执行过程如下:

hook函数裁剪Data首位，剩余ata
匿名函数不对结果修改，剩余ata
hook函数继续裁剪ata首位，剩余ta

经过测试可以发现dispatch_hook的设计十分巧妙，使用pipeline模式，将所有的钩子串起来，这是和事件机制不一样的地方。细心的话，我们可以发现 if hooks: 并未进行旁路测试，这个不够严谨，有违我们的第3个技巧:

3.测试尽可能覆盖目标函数的所有分支

test_structures 实现分析

LookupDict的测试用例如下:

class TestLookupDict:@pytest.fixture(autouse=True)def setup(self):"""LookupDict instance with "bad_gateway" attribute."""self.lookup_dict = LookupDict('test')self.lookup_dict.bad_gateway = 502def test_repr(self):assert repr(self.lookup_dict) == "<lookup 'test'>"get_item_parameters = pytest.mark.parametrize('key, value', (('bad_gateway', 502),('not_a_key', None)))@get_item_parametersdef test_getitem(self, key, value):assert self.lookup_dict[key] == value@get_item_parametersdef test_get(self, key, value):assert self.lookup_dict.get(key) == value

可以发现使用setup方法配合@pytest.fixture，给所有测试用例初始化了一个lookup_dict对象；同时pytest.mark.parametrize可以在不同的测试用例之间复用的，我们可以得到第4个技巧:

4.使用pytest.fixture复用被测试对象，使用pytest.mark.parametriz复用测试参数

通过TestLookupDict的test_getitem和test_get可以更直观的了解LookupDict的get和__getitem__方法的作用:

class LookupDict(dict):...def __getitem__(self, key):# We allow fall-through here, so values default to Nonereturn self.__dict__.get(key, None)def get(self, key, default=None):return self.__dict__.get(key, default)

get自定义字典，使其可以使用 get 方法获取值
__getitem__自定义字典，使其可以使用 [] 符合获取值

CaseInsensitiveDict的测试用例在test_structures和test_requests中都有测试，前者主要是基础测试，后者偏向业务使用层面，我们可以看到这两种差异：

class TestCaseInsensitiveDict:# 类测试def test_repr(self):assert repr(self.case_insensitive_dict) == "{'Accept': 'application/json'}"def test_copy(self):copy = self.case_insensitive_dict.copy()assert copy is not self.case_insensitive_dictassert copy == self.case_insensitive_dictclass TestCaseInsensitiveDict:# 使用方法测试def test_delitem(self):cid = CaseInsensitiveDict()cid['Spam'] = 'someval'del cid['sPam']assert 'spam' not in cidassert len(cid) == 0def test_contains(self):cid = CaseInsensitiveDict()cid['Spam'] = 'someval'assert 'Spam' in cidassert 'spam' in cidassert 'SPAM' in cidassert 'sPam' in cidassert 'notspam' not in cid

借鉴上面的测试方法，不难得出第5个技巧:

5.可以从不同的层面对同一个对象进行单元测试

后面的test_lowlevel和test_requests也应用了这种技巧

utils.py

utils中构建了一个可以写入env的生成器(由yield关键字提供)，可以当上下文装饰器使用:

import contextlib
import os@contextlib.contextmanager
def override_environ(**kwargs):save_env = dict(os.environ)for key, value in kwargs.items():if value is None:del os.environ[key]else:os.environ[key] = valuetry:yieldfinally:os.environ.clear()os.environ.update(save_env)

下面是使用方法示例:

# test_requests.pykwargs = {var: proxy
}
# 模拟控制proxy环境变量
with override_environ(**kwargs):proxies = session.rebuild_proxies(prep, {})def rebuild_proxies(self, prepared_request, proxies):  bypass_proxy = should_bypass_proxies(url, no_proxy=no_proxy)def should_bypass_proxies(url, no_proxy):...get_proxy = lambda k: os.environ.get(k) or os.environ.get(k.upper())...

6.涉及环境变量的地方，可以使用上下文装饰器进行模拟多种环境变量

utils测试用例

utils的测试用例较多，我们选择部分进行分析。先看to_key_val_list函数:

# 对象转列表
def to_key_val_list(value):if value is None:return Noneif isinstance(value, (str, bytes, bool, int)):raise ValueError('cannot encode objects that are not 2-tuples')if isinstance(value, Mapping):value = value.items()return list(value)

对应的测试用例TestToKeyValList:

class TestToKeyValList:@pytest.mark.parametrize('value, expected', (([('key', 'val')], [('key', 'val')]),((('key', 'val'), ), [('key', 'val')]),({'key': 'val'}, [('key', 'val')]),(None, None)))def test_valid(self, value, expected):assert to_key_val_list(value) == expecteddef test_invalid(self):with pytest.raises(ValueError):to_key_val_list('string')

重点是test_invalid中使用pytest.raise对异常的处理:

7.使用pytest.raises对异常进行捕获处理

TestSuperLen介绍了几种进行IO模拟测试的方法:

class TestSuperLen:@pytest.mark.parametrize('stream, value', ((StringIO.StringIO, 'Test'),(BytesIO, b'Test'),pytest.param(cStringIO, 'Test',marks=pytest.mark.skipif('cStringIO is None')),))def test_io_streams(self, stream, value):"""Ensures that we properly deal with different kinds of IO streams."""assert super_len(stream()) == 0assert super_len(stream(value)) == 4def test_super_len_correctly_calculates_len_of_partially_read_file(self):"""Ensure that we handle partially consumed file like objects."""s = StringIO.StringIO()s.write('foobarbogus')assert super_len(s) == 0@pytest.mark.parametrize('mode, warnings_num', (('r', 1),('rb', 0),))def test_file(self, tmpdir, mode, warnings_num, recwarn):file_obj = tmpdir.join('test.txt')file_obj.write('Test')with file_obj.open(mode) as fd:assert super_len(fd) == 4assert len(recwarn) == warnings_numdef test_super_len_with_tell(self):foo = StringIO.StringIO('12345')assert super_len(foo) == 5foo.read(2)assert super_len(foo) == 3def test_super_len_with_fileno(self):with open(__file__, 'rb') as f:length = super_len(f)file_data = f.read()assert length == len(file_data)

使用StringIO来模拟IO操作，可以配置各种IO的测试。当然也可以使用BytesIO/cStringIO, 不过单元测试用例一般不关注性能，StringIO简单够用。
pytest提供tmpdir的fixture，可以进行文件读写操作测试
可以使用__file__来进行文件的只读测试，__file__表示当前文件，不会产生副作用。

8.使用IO模拟配合进行单元测试

request-api如何测试

requests的测试需要httpbin和pytest-httpbin，前者会启动一个本地服务，后者会安装一个pytest插件，测试用例中可以得到httpbin的fixture，用来操作这个服务的URL。

坦率的讲：这个测试用例内容庞大，达到2500行。看起来是针对各种业务的零散case，我并没有完全理顺其组织逻辑。我选择一些感兴趣的业务进行介绍, 先看TimeOut的测试:

TARPIT = 'http://10.255.255.1'class TestTimeout:def test_stream_timeout(self, httpbin):try:requests.get(httpbin('delay/10'), timeout=2.0)except requests.exceptions.Timeout as e:assert 'Read timed out' in e.args[0].args[0]@pytest.mark.parametrize('timeout', ((0.1, None),Urllib3Timeout(connect=0.1, read=None)))def test_connect_timeout(self, timeout):try:requests.get(TARPIT, timeout=timeout)pytest.fail('The connect() request should time out.')except ConnectTimeout as e:assert isinstance(e, ConnectionError)assert isinstance(e, Timeout)

test_stream_timeout利用httpbin创建了一个延迟10s响应的接口，然后请求本身设置成2s，这样可以收到一个本地timeout的错误。test_connect_timeout则是访问一个不存在的服务，捕获连接超时的错误。

TestRequests都是对requests的业务进程测试，可以看到至少是2种:

class TestRequests:def test_basic_building(self):req = requests.Request()req.url = 'http://kennethreitz.org/'req.data = {'life': '42'}pr = req.prepare()assert pr.url == req.urlassert pr.body == 'life=42'def test_path_is_not_double_encoded(self):request = requests.Request('GET', "http://0.0.0.0/get/test case").prepare()assert request.path_url == '/get/test%20case...def test_HTTP_200_OK_GET_ALTERNATIVE(self, httpbin):r = requests.Request('GET', httpbin('get'))s = requests.Session()s.proxies = getproxies()r = s.send(r.prepare())assert r.status_code == 200ef test_set_cookie_on_301(self, httpbin):s = requests.session()url = httpbin('cookies/set?foo=bar')s.get(url)assert s.cookies['foo'] == 'bar'

对url进行校验，只需要对request进行prepare，这种情况下，请求并未发送，少了网络传输，测试用例会更迅速
需要响应数据的情况，需要使用httbin构建真实的请求-响应数据

底层API测试

testserver构建一个简单的基于线程的tcp服务，这个tcp服务具有__enter__和__exit__方法，还可以当一个上下文环境使用。

class TestTestServer:def test_basic(self):"""messages are sent and received properly"""question = b"success?"answer = b"yeah, success"def handler(sock):text = sock.recv(1000)assert text == questionsock.sendall(answer)with Server(handler) as (host, port):sock = socket.socket()sock.connect((host, port))sock.sendall(question)text = sock.recv(1000)assert text == answersock.close()def test_text_response(self):"""the text_response_server sends the given text"""server = Server.text_response_server("HTTP/1.1 200 OK\r\n" +"Content-Length: 6\r\n" +"\r\nroflol")with server as (host, port):r = requests.get('http://{}:{}'.format(host, port))assert r.status_code == 200assert r.text == u'roflol'assert r.headers['Content-Length'] == '6'

test_basic方法对Server进行基础校验，确保收发双方可以正确的发送和接收数据。先是客户端的sock发送question，然后服务端在handler中判断收到的数据是question，确认后返回answer，最后客户端再确认可以正确收到answer响应。 test_text_response方法则不完整的测试了http协议。按照http协议的规范发送了http请求，Server.text_response_server会回显请求。下面是模拟浏览器的锚点定位不会经过网络传输的testcase:

def test_fragment_not_sent_with_request():"""Verify that the fragment portion of a URI isn't sent to the server."""def response_handler(sock):req = consume_socket_content(sock, timeout=0.5)sock.send(b'HTTP/1.1 200 OK\r\n'b'Content-Length: '+bytes(len(req))+b'\r\n'b'\r\n'+req)close_server = threading.Event()server = Server(response_handler, wait_to_close_event=close_server)with server as (host, port):url = 'http://{}:{}/path/to/thing/#view=edit&token=hunter2'.format(host, port)r = requests.get(url)raw_request = r.contentassert r.status_code == 200headers, body = raw_request.split(b'\r\n\r\n', 1)status_line, headers = headers.split(b'\r\n', 1)assert status_line == b'GET /path/to/thing/ HTTP/1.1'for frag in (b'view', b'edit', b'token', b'hunter2'):assert frag not in headersassert frag not in bodyclose_server.set()

可以看到请求的path是 /path/to/thing/#view=edit&token=hunter2，其中 # 后面的部分是本地锚点，不应该进行网络传输。上面测试用例中，对接收到的响应进行判断，鉴别响应头和响应body中不包含这些关键字。

结合requests的两个层面的测试，我们可以得出第9个技巧:

9.构造模拟服务配合测试

总结

简单小结一下，从requests的单元测试实践中，可以得到下面9个技巧: