HttpRunner3的$符号是如何解析的

HttpRunner3的$符号是如何解析的

变量解析

​​$​​​符号在很多测试框架中都会拿来用做变量解析，大名鼎鼎的JMeter就有这个用法。HttpRunner3也支持​​$​​符号，比如：

Step( RunRequest("登录") .post("/login") .with_headers(**{"Content-Type": "application/json"}) .with_json({"username": "dongfanger", "password": "123456"}) .extract() .with_jmespath("body.token", "token") .validate() .assert_equal("status_code", 200)),Step( RunRequest("搜索商品") .get("searchSku?skuName=电子书") .with_headers(**{"token": "$token"}) .extract() .with_jmespath("body.skuId", "skuId") .with_jmespath("body.price", "skuPrice") .validate() .assert_equal("status_code", 200)),

从​​登录​​​提取的变量​​token​​​，在​​搜索商品​​​中使用​​$token​​来引用。

parser.py

HttpRunner对​​$​​符号的解析是在parser.py文件中实现的：

这些方法可以分为四类：

url​​build_url()​​变量​​extract_variables()​​ ​​get_mapping_variable()​​ ​​parse_variables_mapping()​​ ​​regex_findall_variables()​​ ​​parse_data()​​ ​​parse_string()​​函数​​get_mapping_function()​​ ​​parse_function_params()​​ ​​regex_findall_functions()​​ ​​parse_string_value()​​ ​​parse_data()​​ ​​parse_string()​​参数化（​​"${parameterize(account.csv)}"​​）​​parse_parameters()​​

本文专门针对变量这一块源码来进行剖析。

全局变量池

HttpRunner3在运行时的所有变量都是存储在​​__session_variables​​这个字典中的：

在脚本开始时会把config的预设变量加载进来，然后会在执行Step测试步骤时，把各个步骤的变量也放到全局变量池里面。

解析过程

在runner.py的第350行代码能看到解析变量的调用：

然后parse_variables_mapping()函数需要仔细看：

def parse_variables_mapping( variables_mapping: VariablesMapping, functions_mapping: FunctionsMapping = None) -> VariablesMapping: parsed_variables: VariablesMapping = {} while len(parsed_variables) != len(variables_mapping): for var_name in variables_mapping: if var_name in parsed_variables: continue var_value = variables_mapping[var_name] variables = extract_variables(var_value) # check if reference variable itself if var_name in variables: # e.g. # variables_mapping = {"token": "abc$token"} # variables_mapping = {"key": ["$key", 2]} raise exceptions.VariableNotFound(var_name) # check if reference variable not in variables_mapping not_defined_variables = [ v_name for v_name in variables if v_name not in variables_mapping ] if not_defined_variables: # e.g. {"varA": "123$varB", "varB": "456$varC"} # e.g. {"varC": "${sum_two($a, $b)}"} raise exceptions.VariableNotFound(not_defined_variables) try: parsed_value = parse_data( var_value, parsed_variables, functions_mapping ) except exceptions.VariableNotFound: continue parsed_variables[var_name] = parsed_value return parsed_variables

​​tests/parser_test.py​​有一段测试代码：

建议打个断点，调试一把，就知道代码是怎么个原理了。

大概思路是：最外层的while循环会比较已解析和未解析的存储字典长度，只有当所有的变量都解析到值以后，才会正常退出。拿示例代码来说，第一次循环只会解析出​​varC​​​、​​a​​​、​​b​​​，第二次循环才会解析出​​varB​​​，第三次循环解析出​​varA​​。

循环内部，先是调用extract_variables()方法解析出变量集合：

def extract_variables(content: Any) -> Set: """ extract all variables in content recursively. """ if isinstance(content, (list, set, tuple)): variables = set() for item in content: variables = variables | extract_variables(item) return variables elif isinstance(content, dict): variables = set() for key, value in content.items(): variables = variables | extract_variables(value) return variables elif isinstance(content, str): return set(regex_findall_variables(content)) return set()

regex_findall_variables()函数就是用来解析字符串的，采用的是正则匹配：

def regex_findall_variables(raw_string: Text) -> List[Text]: """ extract all variable names from content, which is in format $variable Args: raw_string (str): string content Returns: list: variables list extracted from string content Examples: >>> regex_findall_variables("$variable") ["variable"] >>> regex_findall_variables("/blog/$postid") ["postid"] >>> regex_findall_variables("/$var1/$var2") ["var1", "var2"] >>> regex_findall_variables("abc") [] """ try: match_start_position = raw_string.index("$", 0) except ValueError: return [] vars_list = [] while match_start_position < len(raw_string): # Notice: notation priority # $$ > $var # search $$ dollar_match = dolloar_regex_compile.match(raw_string, match_start_position) if dollar_match: match_start_position = dollar_match.end() continue # search variable like ${var} or $var var_match = variable_regex_compile.match(raw_string, match_start_position) if var_match: var_name = var_match.group(1) or var_match.group(2) vars_list.append(var_name) match_start_position = var_match.end() continue curr_position = match_start_position try: # find next $ location match_start_position = raw_string.index("$", curr_position + 1) except ValueError: # break while loop break return vars_list

调用extract_variables()方法解析出变量集合以后，就会进行异常校验：变量是否引用自己和变量未定义：

如果校验通过就会调用parse_data()解析出变量值，存入已解析的字典parsed_variables中。

parse_data()和parse_string()两个函数和主要流程无关了，它们的作用就是解析出变量值，感兴趣的读者朋友可以自行研究一下：

def parse_data( raw_data: Any, variables_mapping: VariablesMapping = None, functions_mapping: FunctionsMapping = None,) -> Any: """ parse raw data with evaluated variables mapping. Notice: variables_mapping should not contain any variable or function. """ if isinstance(raw_data, str): # content in string format may contains variables and functions variables_mapping = variables_mapping or {} functions_mapping = functions_mapping or {} # only strip whitespaces and tabs, \n\r is left because they maybe used in changeset raw_data = raw_data.strip(" \t") return parse_string(raw_data, variables_mapping, functions_mapping) elif isinstance(raw_data, (list, set, tuple)): return [ parse_data(item, variables_mapping, functions_mapping) for item in raw_data ] elif isinstance(raw_data, dict): parsed_data = {} for key, value in raw_data.items(): parsed_key = parse_data(key, variables_mapping, functions_mapping) parsed_value = parse_data(value, variables_mapping, functions_mapping) parsed_data[parsed_key] = parsed_value return parsed_data else: # other types, e.g. None, int, float, bool return raw_data

def parse_string( raw_string: Text, variables_mapping: VariablesMapping, functions_mapping: FunctionsMapping,) -> Any: """ parse string content with variables and functions mapping. Args: raw_string: raw string content to be parsed. variables_mapping: variables mapping. functions_mapping: functions mapping. Returns: str: parsed string content. Examples: >>> raw_string = "abc${add_one($num)}def" >>> variables_mapping = {"num": 3} >>> functions_mapping = {"add_one": lambda x: x + 1} >>> parse_string(raw_string, variables_mapping, functions_mapping) "abc4def" """ try: match_start_position = raw_string.index("$", 0) parsed_string = raw_string[0:match_start_position] except ValueError: parsed_string = raw_string return parsed_string while match_start_position < len(raw_string): # Notice: notation priority # $$ > ${func($a, $b)} > $var # search $$ dollar_match = dolloar_regex_compile.match(raw_string, match_start_position) if dollar_match: match_start_position = dollar_match.end() parsed_string += "$" continue # search function like ${func($a, $b)} func_match = function_regex_compile.match(raw_string, match_start_position) if func_match: func_name = func_match.group(1) func = get_mapping_function(func_name, functions_mapping) func_params_str = func_match.group(2) function_meta = parse_function_params(func_params_str) args = function_meta["args"] kwargs = function_meta["kwargs"] parsed_args = parse_data(args, variables_mapping, functions_mapping) parsed_kwargs = parse_data(kwargs, variables_mapping, functions_mapping) try: func_eval_value = func(*parsed_args, **parsed_kwargs) except Exception as ex: logger.error( f"call function error:\n" f"func_name: {func_name}\n" f"args: {parsed_args}\n" f"kwargs: {parsed_kwargs}\n" f"{type(ex).__name__}: {ex}" ) raise func_raw_str = "${" + func_name + f"({func_params_str})" + "}" if func_raw_str == raw_string: # raw_string is a function, e.g. "${add_one(3)}", return its eval value directly return func_eval_value # raw_string contains one or many functions, e.g. "abc${add_one(3)}def" parsed_string += str(func_eval_value) match_start_position = func_match.end() continue # search variable like ${var} or $var var_match = variable_regex_compile.match(raw_string, match_start_position) if var_match: var_name = var_match.group(1) or var_match.group(2) var_value = get_mapping_variable(var_name, variables_mapping) if f"${var_name}" == raw_string or "${" + var_name + "}" == raw_string: # raw_string is a variable, $var or ${var}, return its value directly return var_value # raw_string contains one or many variables, e.g. "abc${var}def" parsed_string += str(var_value) match_start_position = var_match.end() continue curr_position = match_start_position try: # find next $ location match_start_position = raw_string.index("$", curr_position + 1) remain_string = raw_string[curr_position:match_start_position] except ValueError: remain_string = raw_string[curr_position:] # break while loop match_start_position = len(raw_string) parsed_string += remain_string return parsed_string

技术都是相通的

通过阅读源码可以发现，技术都是相通的。对于HttpRunner来说，它之所以要费这么大周章设计​​$​​符号，就是因为它的定位是要让不怎么会写代码的同学，也能使用yaml轻松写自动化。但是V3版本出来以后，官方强烈建议采用直接编写Python代码，但仍然遵循yaml这种约定，是不是反而成为了一种约束呢？跳出这个框架，直接编写pytest，岂不是效率会更高一些？这些问题的答案需要我们根据自身特点和业务场景去把握了。就我个人而言，仍然且始终坚持，纯Python代码写自动化的方式。

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。