Newer
Older
# Copyright 2022-2024 ETSI SDG TeraFlowSDN (TFS) (https://tfs.etsi.org/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pandas as pd
from unittest.mock import MagicMock, patch
from common.tools.kafka.Variables import KafkaTopic
from analytics.backend.service.Streamer import DaskStreamer
from .messages_analyzer import get_batch, get_input_kpi_list, get_output_kpi_list, get_thresholds, \
get_windows_size, get_batch_size, get_agg_df
from analytics.backend.service.AnalyzerHandlers import aggregation_handler, threshold_handler
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(funcName)s - %(levelname)s - %(message)s')
# --- "test_validate_kafka_topics" should be run before the functionality tests ---
def test_validate_kafka_topics():
logger.debug(" >>> test_validate_kafka_topics: START <<< ")
response = KafkaTopic.create_all_topics()
assert isinstance(response, bool)
###########################
# Tests Implementation of Telemetry Backend
###########################
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
@pytest.fixture(autouse=True)
def log_all_methods(request):
'''
This fixture logs messages before and after each test function runs, indicating the start and end of the test.
The autouse=True parameter ensures that this logging happens automatically for all tests in the module.
'''
logger.info(f" >>> Starting test: {request.node.name} >>> ")
yield
logger.info(f" <<< Finished test: {request.node.name} <<< ")
@pytest.fixture
def dask_streamer():
with patch('analytics.backend.service.AnalyzerHelper.AnalyzerHelper.initialize_dask_client') as mock_dask_client, \
patch('analytics.backend.service.AnalyzerHelper.AnalyzerHelper.initialize_kafka_consumer') as mock_kafka_consumer, \
patch('analytics.backend.service.AnalyzerHelper.AnalyzerHelper.initialize_kafka_producer') as mock_kafka_producer:
mock_dask_client.return_value = (MagicMock(), MagicMock())
mock_kafka_consumer.return_value = MagicMock()
mock_kafka_producer.return_value = MagicMock()
return DaskStreamer(
key="test_key",
input_kpis=get_input_kpi_list(),
output_kpis=get_output_kpi_list(),
thresholds=get_thresholds(),
batch_size=get_batch_size(),
window_size=get_windows_size(),
n_workers=3,
threads_per_worker=1
)
def test_initialization(dask_streamer):
"""Test if the DaskStreamer initializes correctly."""
assert dask_streamer.key == "test_key"
assert dask_streamer.batch_size == get_batch_size()
assert dask_streamer.window_size is None
assert dask_streamer.n_workers == 3
assert dask_streamer.consumer is not None
assert dask_streamer.producer is not None
assert dask_streamer.client is not None
assert dask_streamer.cluster is not None
def test_run_stops_on_no_consumer(dask_streamer):
"""Test if the run method exits when the consumer is not initialized."""
dask_streamer.consumer = None
with patch('time.sleep', return_value=None):
dask_streamer.run()
assert not dask_streamer.running
def test_task_handler_selector_valid_handler(dask_streamer):
"""Test task handler selection with a valid handler."""
with patch('analytics.backend.service.AnalyzerHandlers.AnalyzerHandlers.is_valid_handler', return_value=True), \
patch.object(dask_streamer.client, 'submit', return_value=MagicMock()) as mock_submit, \
patch.object(dask_streamer.client, 'status', 'running'):
dask_streamer.task_handler_selector()
mock_submit.assert_called_once()
def test_task_handler_selector_invalid_handler(dask_streamer):
"""Test task handler selection with an invalid handler."""
with patch('analytics.backend.service.AnalyzerHandlers.AnalyzerHandlers.is_valid_handler', return_value=False):
dask_streamer.task_handler_selector()
assert dask_streamer.batch == []
def test_produce_result(dask_streamer):
"""Test if produce_result sends records to Kafka."""
result = [{"kpi_id": "kpi1", "value": 100}]
with patch('analytics.backend.service.AnalyzerHelper.AnalyzerHelper.delivery_report', return_value=None) as mock_delivery_report, \
patch.object(dask_streamer.producer, 'produce') as mock_produce:
dask_streamer.produce_result(result, "test_topic")
mock_produce.assert_called_once_with(
"test_topic",
key="kpi1",
value='{"kpi_id": "kpi1", "value": 100}',
callback=mock_delivery_report
)
def test_cleanup(dask_streamer):
"""Test the cleanup method."""
with patch.object(dask_streamer.consumer, 'close') as mock_consumer_close, \
patch.object(dask_streamer.producer, 'flush') as mock_producer_flush, \
patch.object(dask_streamer.client, 'close') as mock_client_close, \
patch.object(dask_streamer.cluster, 'close', MagicMock()) as mock_cluster_close:
# Mock the conditions required for the close calls
dask_streamer.client.status = 'running'
dask_streamer.cluster.close = MagicMock()
dask_streamer.cleanup()
mock_consumer_close.assert_called_once()
mock_producer_flush.assert_called_once()
mock_client_close.assert_called_once()
dask_streamer.cluster.close.assert_called_once()
def test_run_with_valid_consumer(dask_streamer):
"""Test the run method with a valid Kafka consumer."""
with patch.object(dask_streamer.consumer, 'poll') as mock_poll, \
patch.object(dask_streamer, 'task_handler_selector') as mock_task_handler_selector:
# Simulate valid messages without errors
mock_message_1 = MagicMock()
mock_message_1.value.return_value = b'{"kpi_id": "kpi1", "value": 100}'
mock_message_1.error.return_value = None # No error
mock_message_2 = MagicMock()
mock_message_2.value.return_value = b'{"kpi_id": "kpi2", "value": 200}'
mock_message_2.error.return_value = None # No error
# Mock `poll` to return valid messages
mock_poll.side_effect = [mock_message_1, mock_message_2]
# Run the `run` method in a limited loop
with patch('time.sleep', return_value=None): # Mock `sleep` to avoid delays
dask_streamer.running = True # Ensure the streamer runs
dask_streamer.batch_size = 2 # Set a small batch size for the test
# Limit the loop by breaking it after one full processing cycle
def stop_running_after_task_handler(*args, **kwargs):
logger.info("Stopping the streamer after processing the first batch.")
dask_streamer.running = False
mock_task_handler_selector.side_effect = stop_running_after_task_handler
# Execute the method
dask_streamer.run()
# Assertions
assert len(dask_streamer.batch) == 0 # Batch should be cleared after processing
mock_task_handler_selector.assert_called_once() # Task handler should be called once
mock_poll.assert_any_call(timeout=2.0) # Poll should have been called
# add a test to check the working of aggregation_handler function and threshold_handler from AnalyzerHandlers.py
def test_aggregation_handler():
# Create a sample batch
batch = get_batch()
input_kpi_list = get_input_kpi_list()
output_kpi_list = get_output_kpi_list()
thresholds = get_thresholds()
# Test aggregation_handler
aggregated_df = aggregation_handler(
"test_batch", "test_key", batch, input_kpi_list, output_kpi_list, thresholds
)
assert isinstance(aggregated_df, list)
assert all(isinstance(item, dict) for item in aggregated_df)
# Test threshold_handler
def test_threshold_handler():
# Create a sample aggregated DataFrame
agg_df = get_agg_df()
thresholds = get_thresholds()
# Test threshold_handler
result = threshold_handler("test_key", agg_df, thresholds["task_parameter"][0])
assert isinstance(result, pd.DataFrame)
assert result.shape == (1, 7)