forked from wizhaoredhat/ocp-traffic-flow-tests
-
Notifications
You must be signed in to change notification settings - Fork 0
/
common.py
213 lines (169 loc) · 6.19 KB
/
common.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
import jinja2
from dataclasses import dataclass, fields, field, is_dataclass
from enum import Enum
from typing import List, Optional, Any, Dict, List, Union, Type, TypeVar, Generic, cast
FT_BASE_IMG = "quay.io/wizhao/ft-base-image:0.9"
TFT_TOOLS_IMG = "quay.io/wizhao/tft-tools:latest"
TFT_TESTS = "tft-tests"
MEASURE_POWER_PLUGIN = "measure_power"
MEASURE_CPU_PLUGIN = "measure_cpu"
VALIDATE_OFFLOAD_PLUGIN = "validate_offload"
@dataclass
class Result:
out: str
err: str
returncode: int
E = TypeVar("E", bound=Enum)
def enum_convert(enum_type: Type[E], value: Union[E, str, int]) -> E:
if isinstance(value, enum_type):
return value
elif isinstance(value, str):
try:
return enum_type[value]
except KeyError:
raise ValueError(f"Cannot convert {value} to {enum_type}")
elif isinstance(value, int):
try:
return enum_type(value)
except ValueError:
raise ValueError(f"Cannot convert {value} to {enum_type}")
else:
raise ValueError(f"Invalid type for conversion to {enum_type}")
class TestType(Enum):
IPERF_TCP = 1
IPERF_UDP = 2
HTTP = 3
class PodType(Enum):
NORMAL = 1
SRIOV = 2
HOSTBACKED = 3
class TestCaseType(Enum):
POD_TO_POD_SAME_NODE = 1
POD_TO_POD_DIFF_NODE = 2
POD_TO_HOST_SAME_NODE = 3
POD_TO_HOST_DIFF_NODE = 4
POD_TO_CLUSTER_IP_TO_POD_SAME_NODE = 5
POD_TO_CLUSTER_IP_TO_POD_DIFF_NODE = 6
POD_TO_CLUSTER_IP_TO_HOST_SAME_NODE = 7
POD_TO_CLUSTER_IP_TO_HOST_DIFF_NODE = 8
POD_TO_NODE_PORT_TO_POD_SAME_NODE = 9
POD_TO_NODE_PORT_TO_POD_DIFF_NODE = 10
POD_TO_NODE_PORT_TO_HOST_SAME_NODE = 11
POD_TO_NODE_PORT_TO_HOST_DIFF_NODE = 12
HOST_TO_HOST_SAME_NODE = 13
HOST_TO_HOST_DIFF_NODE = 14
HOST_TO_POD_SAME_NODE = 15
HOST_TO_POD_DIFF_NODE = 16
HOST_TO_CLUSTER_IP_TO_POD_SAME_NODE = 17
HOST_TO_CLUSTER_IP_TO_POD_DIFF_NODE = 18
HOST_TO_CLUSTER_IP_TO_HOST_SAME_NODE = 19
HOST_TO_CLUSTER_IP_TO_HOST_DIFF_NODE = 20
HOST_TO_NODE_PORT_TO_POD_SAME_NODE = 21
HOST_TO_NODE_PORT_TO_POD_DIFF_NODE = 22
HOST_TO_NODE_PORT_TO_HOST_SAME_NODE = 23
HOST_TO_NODE_PORT_TO_HOST_DIFF_NODE = 24
POD_TO_EXTERNAL = 25
HOST_TO_EXTERNAL = 26
class ConnectionMode(Enum):
POD_IP = 1
CLUSTER_IP = 2
NODE_PORT_IP = 3
EXTERNAL_IP = 4
class NodeLocation(Enum):
SAME_NODE = 1
DIFF_NODE = 2
@dataclass
class PodInfo:
name: str
pod_type: PodType
is_tenant: bool
index: int
@dataclass
class TestMetadata:
reverse: bool
test_case_id: TestCaseType
test_type: TestType
server: PodInfo
client: PodInfo
def __post_init__(self) -> None:
self.test_case_id = enum_convert(TestCaseType, self.test_case_id)
self.test_type = enum_convert(TestType, self.test_type)
if isinstance(self.server, dict):
self.server = dataclass_from_dict(PodInfo, self.server)
if isinstance(self.client, dict):
self.client = dataclass_from_dict(PodInfo, self.client)
@dataclass
class BaseOutput:
command: str
result: dict
@dataclass
class IperfOutput(BaseOutput):
tft_metadata: TestMetadata
def __post_init__(self) -> None:
if isinstance(self.tft_metadata, dict):
self.tft_metadata = dataclass_from_dict(TestMetadata, self.tft_metadata)
elif not isinstance(self.tft_metadata, TestMetadata):
raise ValueError("tft_metadata must be a TestMetadata instance or a dict")
@dataclass
class PluginOutput(BaseOutput):
plugin_metadata: dict
name: str
@dataclass
class TftAggregateOutput:
"""Aggregated output of a single tft run. A single run of a trafficFlowTests._run_tests() will
pass a reference to an instance of TftAggregateOutput to each task to which the task will append
it's respective output. A list of this class will be the expected format of input provided to
evaluator.py.
Attributes:
flow_test: an object of type IperfOutput containing the results of a flow test run
plugins: a list of objects derivated from type PluginOutput for each optional plugin to append
resulting output to."""
flow_test: Optional[IperfOutput] = None
plugins: List[PluginOutput] = field(default_factory=list)
def __post_init__(self) -> None:
if isinstance(self.flow_test, dict):
self.flow_test = dataclass_from_dict(IperfOutput, self.flow_test)
elif self.flow_test is not None and not isinstance(self.flow_test, IperfOutput):
raise ValueError("flow_test must be an IperfOutput instance or a dict")
self.plugins = [
(
dataclass_from_dict(PluginOutput, plugin)
if isinstance(plugin, dict)
else plugin
)
for plugin in self.plugins
]
def j2_render(in_file_name: str, out_file_name: str, kwargs: Dict[str, Any]) -> None:
with open(in_file_name) as inFile:
contents = inFile.read()
template = jinja2.Template(contents)
rendered = template.render(**kwargs)
with open(out_file_name, "w") as outFile:
outFile.write(rendered)
def serialize_enum(
data: Union[Enum, Dict[Any, Any], List[Any], Any]
) -> Union[str, Dict[Any, Any], List[Any], Any]:
if isinstance(data, Enum):
return data.name
elif isinstance(data, dict):
return {k: serialize_enum(v) for k, v in data.items()}
elif isinstance(data, list):
return [serialize_enum(item) for item in data]
else:
return data
T = TypeVar("T")
# Takes a dataclass and the dict you want to convert from
# If your dataclass has a dataclass member, it handles that recursively
def dataclass_from_dict(cls: Type[T], data: Dict[str, Any]) -> T:
assert is_dataclass(
cls
), "dataclass_from_dict() should only be used with dataclasses."
field_values = {}
for field in fields(cls):
field_name = field.name
field_type = field.type
if is_dataclass(field_type) and field_name in data:
field_values[field_name] = dataclass_from_dict(field_type, data[field_name])
elif field_name in data:
field_values[field_name] = data[field_name]
return cast(T, cls(**field_values))