在测试过程中，经常会去JSON中的某个值，jmespath可以是除了jsonpath的另外一种选择.
下面通过几个例子来说明jmespath在python的使用
jmespath python安装
非常简单直接pip,
pipinstalljmespth
查询一个key值
source={"a":"foo","b":"bar","c":"baz"}
result=jmespath.search("a",source)
print(result)
subexpression
类似于jsonpath，通过.来表示路径的层级
source_1={"a":{"b":{"c":{"d":"value"}}}}
sub_result=jmespath.search("a.b.c",source_1)
print(sub_result)
这个例子的结果为：{'d': 'value'}
index expressions
index expression主要使用在数组上
source_2=["a","b","c","d","e","f"]
index_result=jmespath.search("[1]",source_2)
print(index_result)
这个例子的结果为:b
多个表达式综合使用
以上几种表达式可以合起来一期使用：
composite_exp="a.b.c[0].d[1][0]"
source_3={"a":{
"b":{
"c":[
{"d":[0,[1,2]]},
{"d":[3,4]}
]
}
}}
composite_result=jmespath.search(composite_exp,source_3)
print(composite_result)
这个例子的结果为1
Slicing 切片
slicing 和python本身的slicing比较像，
source_4=[0,1,2,3,4,5,6,7,8,9]
slicing_exp="[0:5]"
slicing_result=jmespath.search(slicing_exp,source_4)
print(slicing_result)
这个例子的结果为： [0, 1, 2, 3, 4]
slicing实际上和python自己的机制基本一样，同样这个也是主要给数组使用.
有一点需要记住，基本的slicing的格式其实是： [start:stop:step]
Projections
projection不知道怎么翻译，就先叫做投影吧，具体通过例子来说比较好理解.
projections主要包含一下几种情况:
List Projections
Slice Projections
Object Projections
Flatten Projections
Filter Projections
Projections- 例子
list_exp="people[*].first"
source_5={
"people":[
{"first":"James","last":"d"},
{"first":"Jacob","last":"e"},
{"first":"Jayden","last":"f"},
{"missing":"different"}
],
"foo":{"bar":"baz"}
}
proj_result1=jmespath.search(list_exp,source_5)
print(proj_result1)#['James','Jacob','Jayden']
obj_exp="reservations[*].instances[*].state"
source_6={
"reservations":[
{
"instances":[
{"state":"running"},
{"state":"stopped"}
]
},
{
"instances":[
{"state":"terminated"},
{"state":"runnning"}
]
}
]
}
proj_result2=jmespath.search(obj_exp,source_6)
print(proj_result2)#[['running','stopped'],['terminated','runnning']]
#Flattenprojections
source_7=[
[0,1],
2,
[3],
4,
[5,[6,7]]
]
flat_exp="[]"
flat_result=jmespath.search(flat_exp,source_7)
print(flat_result)#[0,1,2,3,4,5,[6,7]]
#filter
filter_exp="machines[?state=='running'].name"
filter_source={
"machines":[
{"name":"a","state":"running"},
{"name":"b","state":"stopped"},
{"name":"b","state":"running"}
]
}
filter_result=jmespath.search(filter_exp,filter_source)
print(filter_result)
#pipeexpression
pipe_exp="people[*].first | [0]"
pipe_source={
"people":[
{"first":"James","last":"d"},
{"first":"Jacob","last":"e"},
{"first":"Jayden","last":"f"},
{"missing":"different"}
],
"foo":{"bar":"baz"}
}
pipe_result=jmespath.search(pipe_exp,pipe_source)
print(pipe_result)#James
#multiselect
multi_exp="people[].[first,last]"
multiselect_result=jmespath.search(multi_exp,pipe_source)
print(multiselect_result)#[['James','d'],['Jacob','e'],['Jayden','f'],[None,None]]
基本上把网站上例子试了一下，总体感觉功能是相当强大(怀疑比jsonpath还要厉害一点).
从简单到复杂，都还是比较好用.
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