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authorGwenael Lambrouin <gwenael.lambrouin@orange.com>2022-06-03 17:54:16 +0200
committerGwenael Lambrouin <gwenael.lambrouin@orange.com>2022-10-27 15:52:22 +0200
commit87d88eab6fe50101abc607857f13af9273aef80a (patch)
tree3cbe2963b8998b949b95ce8bea7a76378732dccf
parente2faa91512043386aa3a11ca44792bd3c009db76 (diff)
behave_tests: refactor TestAPI DB lookup
- use testapi.TestapiClient everywhere - relax search constraints: match only project name (nfvbench), test case name (characterization or non-regression), scenario tag (throughput or latency) and user_label (test chain identifier: identifies, among other things, the platform, the compute class under test, ...) - add unit tests for some of the related behave steps Change-Id: I26763f845c2286601cb958b326525b29320a1627 Signed-off-by: Gwenael Lambrouin <gwenael.lambrouin@orange.com>
Diffstat
-rw-r--r--behave_tests/features/steps/steps.py106
-rw-r--r--behave_tests/features/steps/testapi.py8
-rw-r--r--test/ut_behave_tests/test_data/project=nfvbench&case=non-regression&criteria=PASS&page=1.json8124
-rw-r--r--test/ut_behave_tests/test_steps.py151
-rw-r--r--test/ut_behave_tests/test_testapi.py21
5 files changed, 8353 insertions, 57 deletions
diff --git a/behave_tests/features/steps/steps.py b/behave_tests/features/steps/steps.py
index 512a4e5..c347871 100644
--- a/behave_tests/features/steps/steps.py
+++ b/behave_tests/features/steps/steps.py
@@ -29,7 +29,7 @@ from typing import Optional
from nfvbench.summarizer import Formatter
from nfvbench.traffic_gen.traffic_utils import parse_rate_str
-from testapi import TestapiClient, nfvbench_input_to_str
+from behave_tests.features.steps.testapi import TestapiClient, nfvbench_input_to_str
STATUS_ERROR = "ERROR"
@@ -176,8 +176,7 @@ def add_packet_rate(context, percentage: str):
"case_name": "characterization"}
nfvbench_test_conditions = deepcopy(context.json)
nfvbench_test_conditions['rate'] = 'ndr'
- testapi_client = TestapiClient(testapi_url=context.data['TEST_DB_URL'],
- logger=context.logger)
+ testapi_client = TestapiClient(testapi_url=context.data['TEST_DB_URL'])
last_result = testapi_client.find_last_result(testapi_params,
scenario_tag="throughput",
nfvbench_test_input=nfvbench_test_conditions)
@@ -185,6 +184,7 @@ def add_packet_rate(context, percentage: str):
error_msg = "No characterization result found for scenario_tag=throughput"
error_msg += " and nfvbench test conditions "
error_msg += nfvbench_input_to_str(nfvbench_test_conditions)
+ context.logger.error(error_msg)
raise AssertionError(error_msg)
# From the results report, extract the max throughput in packets per second
@@ -551,34 +551,6 @@ def latency_comparison(context, old_latency=None, threshold=None, reference_valu
max_reference_value=Formatter.standard(reference_values[1])))
-def get_result_from_input_values(input, result):
- """Check test conditions in scenario results input.
-
- Check whether the input parameters of a behave scenario results record from
- testapi match the input parameters of the latest test. In other words,
- check that the test results from testapi come from a test done under the
- same conditions (frame size, flow count, rate, ...)
-
- Args:
- input: input dict of a results dict of a behave scenario from testapi
-
- result: dict of nfvbench params used during the last test
-
- Returns:
- True if test conditions match, else False.
-
- """
- # Select required keys (other keys can be not set or unconsistent between scenarios)
- required_keys = ['duration_sec', 'frame_sizes', 'flow_count', 'rate']
- if 'user_label' in result:
- required_keys.append('user_label')
- if 'flavor_type' in result:
- required_keys.append('flavor_type')
- subset_input = dict((k, input[k]) for k in required_keys if k in input)
- subset_result = dict((k, result[k]) for k in required_keys if k in result)
- return subset_input == subset_result
-
-
def extract_value(obj, key):
"""Pull all values of specified key from nested JSON."""
arr = []
@@ -600,28 +572,58 @@ def extract_value(obj, key):
return results[0]
-def get_last_result(context, reference=None, page=None):
+def get_last_result(context, reference: bool = False):
+ """Look for a previous result in TestAPI database.
+
+ Search TestAPI results from newest to oldest and return the first result
+ record matching the context constraints. Log an overview of the results
+ found (max rate pps, avg delay usec, test conditions, date of measurement).
+
+ The result record test case must match the current test case
+ ('characterization' or 'non-regression') unless `reference` is set to True.
+
+ The result record scenario tag must match the current scenario tag
+ ('throughput' or 'latency').
+
+ Args:
+ context: behave context including project name, test case name, traffic
+ configuration (frame size, flow count, test duration), type of the
+ compute node under test (via loop VM flavor_type) and platform (via
+ user_label).
+
+ reference: when True, look for results with the 'characterization' test
+ case name instead of the current test case name.
+
+ Returns:
+ a JSON dictionary with the results, ie a dict with the keys "input",
+ "output" and "synthesis" when the scenario tag is 'throughput' or
+ 'latency'
+ """
if reference:
case_name = 'characterization'
else:
case_name = context.CASE_NAME
- url = context.data['TEST_DB_URL'] + '?project={project_name}&case={case_name}'.format(
- project_name=context.data['PROJECT_NAME'], case_name=case_name)
- if context.data['INSTALLER_TYPE']:
- url += '&installer={installer_name}'.format(installer_name=context.data['INSTALLER_TYPE'])
- if context.data['NODE_NAME']:
- url += '&pod={pod_name}'.format(pod_name=context.data['NODE_NAME'])
- url += '&criteria=PASS'
- if page:
- url += '&page={page}'.format(page=page)
- last_results = requests.get(url)
- assert last_results.status_code == 200
- last_results = json.loads(last_results.text)
- for result in last_results["results"]:
- for tagged_result in result["details"]["results"][context.tag]:
- if get_result_from_input_values(tagged_result["input"], context.json):
- return tagged_result
- if last_results["pagination"]["current_page"] < last_results["pagination"]["total_pages"]:
- page = last_results["pagination"]["current_page"] + 1
- return get_last_result(context, reference, page)
- return None
+ testapi_params = {"project_name": context.data['PROJECT_NAME'],
+ "case_name": case_name}
+ testapi_client = TestapiClient(testapi_url=context.data['TEST_DB_URL'])
+ last_result = testapi_client.find_last_result(testapi_params,
+ scenario_tag=context.tag,
+ nfvbench_test_input=context.json)
+ if last_result is None:
+ error_msg = "get_last_result: No result found in TestAPI database:"
+ error_msg += f" case_name={case_name} scenario_tag={context.tag} "
+ error_msg += nfvbench_input_to_str(context.json)
+ context.logger.error(error_msg)
+ raise AssertionError(error_msg)
+
+ # Log an overview of the last result (latency and max throughput)
+ measurement_date = last_result["output"]["result"]["date"]
+ total_tx_rate = extract_value(last_result["output"], "total_tx_rate")
+ avg_delay_usec = extract_value(extract_value(last_result["output"], "overall"),
+ "avg_delay_usec")
+ context.logger.info(f"get_last_result: case_name={case_name} scenario_tag={context.tag}"
+ f' measurement_date="{measurement_date}"'
+ f" total_tx_rate(pps)={total_tx_rate:,}"
+ f" avg_latency_usec={round(avg_delay_usec)}")
+
+ return last_result
diff --git a/behave_tests/features/steps/testapi.py b/behave_tests/features/steps/testapi.py
index 15ef8b1..f211ee5 100644
--- a/behave_tests/features/steps/testapi.py
+++ b/behave_tests/features/steps/testapi.py
@@ -48,7 +48,7 @@ class TestapiClient:
to filter the testapi results. The following keys are currently
supported:
- mandatory keys: 'duration_sec', 'frame_sizes', 'flow_count', 'rate'
- - optional keys: 'user_label', 'flavor_type'
+ - optional keys: 'user_label'
Returns:
None if no result matching the filters can be found, else a dictionary
@@ -159,7 +159,7 @@ def equal_test_conditions(testapi_input, nfvbench_input):
The following dict keys are currently supported:
- mandatory keys: 'duration_sec', 'frame_sizes', 'flow_count', 'rate'
- - optional keys: 'user_label', 'flavor_type'
+ - optional keys: 'user_label'
Optional keys are taken into account only when they can be found in
`nfvbench_input`, else they are ignored.
@@ -172,8 +172,6 @@ def equal_test_conditions(testapi_input, nfvbench_input):
required_keys = ['duration_sec', 'frame_sizes', 'flow_count', 'rate']
if 'user_label' in nfvbench_input:
required_keys.append('user_label')
- if 'flavor_type' in nfvbench_input:
- required_keys.append('flavor_type')
try:
testapi_subset = {k: testapi_input[k] for k in required_keys}
@@ -191,7 +189,7 @@ def nfvbench_input_to_str(nfvbench_input: dict) -> str:
nfvbench_input: dict of nfvbench test parameters
"""
string = ""
- for key in ['user_label', 'flavor_type', 'frame_sizes', 'flow_count', 'rate', 'duration_sec']:
+ for key in ['user_label', 'frame_sizes', 'flow_count', 'rate', 'duration_sec']:
if key in nfvbench_input:
string += f"{key}={nfvbench_input[key]} "
return string
diff --git a/test/ut_behave_tests/test_data/project=nfvbench&case=non-regression&criteria=PASS&page=1.json b/test/ut_behave_tests/test_data/project=nfvbench&case=non-regression&criteria=PASS&page=1.json
new file mode 100644
index 0000000..6af5404
--- /dev/null
+++ b/test/ut_behave_tests/test_data/project=nfvbench&case=non-regression&criteria=PASS&page=1.json
@@ -0,0 +1,8124 @@
+{
+ "pagination": {
+ "current_page": 1,
+ "total_pages": 1
+ },
+ "results": [
+ {
+ "project_name": "nfvbench",
+ "scenario": "basic",
+ "stop_date": "2022-03-30 06:48:26",
+ "case_name": "non-regression",
+ "build_tag": "RPDOUC5V2ARA",
+ "version": "unknown",
+ "pod_name": "AMICAL",
+ "criteria": "PASS",
+ "installer": "unknown",
+ "_id": "6243fd3bc0d88e001ca15689",
+ "start_date": "2022-03-30 06:15:40",
+ "details": {
+ "tests": [
+ {
+ "status": "passed",
+ "elements": [
+ {
+ "status": "passed",
+ "name": "Run a NDR test for a defined frame size -- @1.1 Frame sizes",
+ "keyword": "Scenario Outline",
+ "tags": [
+ "throughput"
+ ],
+ "steps": [
+ {
+ "name": "10 sec run duration",
+ "keyword": "Given",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:6",
+ "match": {
+ "arguments": [
+ {
+ "name": "duration",
+ "value": "10"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:124"
+ },
+ "result": {
+ "duration": 0.00010585784912109375,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "64 frame size",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:7",
+ "match": {
+ "arguments": [
+ {
+ "name": "frame_size",
+ "value": "64"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:109"
+ },
+ "result": {
+ "duration": 9.799003601074219e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "100k flow count",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:8",
+ "match": {
+ "arguments": [
+ {
+ "name": "flow_count",
+ "value": "100k"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:114"
+ },
+ "result": {
+ "duration": 0.00010967254638671875,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "ndr rate",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:9",
+ "match": {
+ "arguments": [
+ {
+ "name": "rate",
+ "value": "ndr"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:119"
+ },
+ "result": {
+ "duration": 9.608268737792969e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "NFVbench API is ready",
+ "keyword": "When",
+ "step_type": "when",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:10",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:210"
+ },
+ "result": {
+ "duration": 10.035421133041382,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "3 runs are started and waiting for maximum result",
+ "keyword": "Then",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:11",
+ "match": {
+ "arguments": [
+ {
+ "name": "repeat",
+ "value": 3,
+ "original": "3"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:264"
+ },
+ "result": {
+ "duration": 449.58826899528503,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "push result to database",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:12",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:426"
+ },
+ "result": {
+ "duration": 0.00013637542724609375,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "extract offered rate result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:13",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:327"
+ },
+ "result": {
+ "duration": 0.0003008842468261719,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "verify throughput result is in same range as the previous result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:14",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:333"
+ },
+ "result": {
+ "duration": 0.02466893196105957,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "verify throughput result is in same range as the characterization result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:15",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:409"
+ },
+ "result": {
+ "duration": 0.6800198554992676,
+ "status": "passed"
+ }
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:19",
+ "type": "scenario"
+ },
+ {
+ "status": "passed",
+ "name": "Run a NDR test for a defined frame size -- @1.2 Frame sizes",
+ "keyword": "Scenario Outline",
+ "tags": [
+ "throughput"
+ ],
+ "steps": [
+ {
+ "name": "10 sec run duration",
+ "keyword": "Given",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:6",
+ "match": {
+ "arguments": [
+ {
+ "name": "duration",
+ "value": "10"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:124"
+ },
+ "result": {
+ "duration": 0.00011587142944335938,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "768 frame size",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:7",
+ "match": {
+ "arguments": [
+ {
+ "name": "frame_size",
+ "value": "768"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:109"
+ },
+ "result": {
+ "duration": 0.00011491775512695312,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "100k flow count",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:8",
+ "match": {
+ "arguments": [
+ {
+ "name": "flow_count",
+ "value": "100k"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:114"
+ },
+ "result": {
+ "duration": 9.5367431640625e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "ndr rate",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:9",
+ "match": {
+ "arguments": [
+ {
+ "name": "rate",
+ "value": "ndr"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:119"
+ },
+ "result": {
+ "duration": 9.322166442871094e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "NFVbench API is ready",
+ "keyword": "When",
+ "step_type": "when",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:10",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:210"
+ },
+ "result": {
+ "duration": 0.008649349212646484,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "3 runs are started and waiting for maximum result",
+ "keyword": "Then",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:11",
+ "match": {
+ "arguments": [
+ {
+ "name": "repeat",
+ "value": 3,
+ "original": "3"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:264"
+ },
+ "result": {
+ "duration": 439.46048855781555,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "push result to database",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:12",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:426"
+ },
+ "result": {
+ "duration": 0.00012159347534179688,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "extract offered rate result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:13",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:327"
+ },
+ "result": {
+ "duration": 0.0003330707550048828,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "verify throughput result is in same range as the previous result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:14",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:333"
+ },
+ "result": {
+ "duration": 0.023331880569458008,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "verify throughput result is in same range as the characterization result",
+ "keyword": "And",
+ "step_type": "then",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:15",
+ "match": {
+ "arguments": [],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:409"
+ },
+ "result": {
+ "duration": 0.690040111541748,
+ "status": "passed"
+ }
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:20",
+ "type": "scenario"
+ },
+ {
+ "status": "passed",
+ "name": "Run a NDR test for a defined frame size -- @1.3 Frame sizes",
+ "keyword": "Scenario Outline",
+ "tags": [
+ "throughput"
+ ],
+ "steps": [
+ {
+ "name": "10 sec run duration",
+ "keyword": "Given",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:6",
+ "match": {
+ "arguments": [
+ {
+ "name": "duration",
+ "value": "10"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:124"
+ },
+ "result": {
+ "duration": 0.00010156631469726562,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "1518 frame size",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:7",
+ "match": {
+ "arguments": [
+ {
+ "name": "frame_size",
+ "value": "1518"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:109"
+ },
+ "result": {
+ "duration": 9.584426879882812e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "100k flow count",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:8",
+ "match": {
+ "arguments": [
+ {
+ "name": "flow_count",
+ "value": "100k"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:114"
+ },
+ "result": {
+ "duration": 9.489059448242188e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "ndr rate",
+ "keyword": "And",
+ "step_type": "given",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:9",
+ "match": {
+ "arguments": [
+ {
+ "name": "rate",
+ "value": "ndr"
+ }
+ ],
+ "location": "../../opt/nfvbench/behave_tests/features/steps/steps.py:119"
+ },
+ "result": {
+ "duration": 9.632110595703125e-05,
+ "status": "passed"
+ }
+ },
+ {
+ "name": "NFVbench API is ready",
+ "keyword": "When",
+ "step_type": "when",
+ "location": "../../opt/nfvbench/behave_tests/features/non-regression.feature:10",
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+ "check_traffic_time_sec": 200,
+ "num_mbufs": 128000,
+ "rate": "ndr",
+ "service_chain_count": 1,
+ "service_chain_shared_net": true,
+ "measurement": {
+ "NDR": 0.001,
+ "PDR": 0.1,
+ "load_epsilon": 0.1
+ },
+ "l3_router": false,
+ "debug_mask": 0,
+ "factory_class": "BasicFactory",
+ "tg-tool": "TRex",
+ "frame_sizes": [
+ "9000"
+ ],
+ "service_mode": false,
+ "edge_networks": {
+ "right": {
+ "subnet": "nfvbench-subnet3",
+ "name": "nfvbench-net3",
+ "segmentation_id": null,
+ "network_type": null,
+ "physical_network": null,
+ "cidr": "192.168.4.0/24",
+ "router_name": "router_right",
+ "gateway": null
+ },
+ "left": {
+ "subnet": "nfvbench-subnet2",
+ "name": "nfvbench-net2",
+ "segmentation_id": null,
+ "network_type": null,
+ "physical_network": null,
+ "cidr": "192.168.3.0/24",
+ "router_name": "router_left",
+ "gateway": null
+ }
+ },
+ "ndr_run": true,
+ "use_management_port": false,
+ "hypervisor_hostname": "localdomain",
+ "cache_size": 16,
+ "pdr_run": false,
+ "vlan_tagging": false,
+ "fluentd": [
+ {
+ "ip": "172.20.73.203",
+ "result_tag": "nfvbench.results.amical",
+ "logging_tag": "nfvbench.logs.amical",
+ "port": 25225
+ }
+ ],
+ "use_floating_ip": false,
+ "flow_count": 100000,
+ "loop_vm_arp": false,
+ "user_id": null,
+ "openrc_file": null,
+ "disable_hdrh": false,
+ "json": "/var/lib/xtesting/results/non-regression/nfvbench-throughput-fs_9000-fc_100k-rate_ndr-2.json",
+ "vm_forwarder": "vpp",
+ "factory_module": "nfvbench.factory",
+ "no_latency_streams": false,
+ "single_run": false,
+ "no_arp": false,
+ "cores_used": 5,
+ "traffic_profile": [
+ {
+ "l2frame_size": [
+ "9000"
+ ],
+ "name": "custom_traffic_profile"
+ }
+ ],
+ "vlans": [
+ 2512,
+ 2517
+ ],
+ "tg-name": "trex-local",
+ "management_network": {
+ "subnet": "nfvbench-management-subnet",
+ "name": "nfvbench-management-net",
+ "segmentation_id": null,
+ "network_type": "vlan",
+ "physical_network": null,
+ "cidr": "192.168.0.0/24",
+ "gateway": "192.168.0.254"
+ },
+ "no_traffic": false,
+ "mpls": false,
+ "duration_sec": 10.0,
+ "clouds_detail": "openstack",
+ "mbuf_64": null,
+ "generic_poll_sec": 2,
+ "idle_interfaces_per_vm": 0,
+ "no_flow_stats": true,
+ "lat_percentiles": [
+ 25,
+ 75,
+ 99
+ ],
+ "no_cleanup": true,
+ "no_e2e_check": false,
+ "intf_speed_used": 25000000000.0,
+ "generator_profile": "trex-local",
+ "mbuf_factor": 0.2,
+ "user_label": "amical_tc12_basic",
+ "intf_speed": null,
+ "generic_retry_count": 100,
+ "flavor_type": "nfvbench.loop.basic",
+ "use_sriov_middle_net": false,
+ "json_file": "/var/lib/xtesting/results/non-regression/nfvbench-throughput-fs_9000-fc_100k-rate_ndr-2.json",
+ "std_json_path": null,
+ "unidir_reverse_traffic_pps": 1,
+ "i40e_mixed": "ignore",
+ "gratuitous_arp_pps": 1,
+ "idle_networks": {
+ "subnet": "nfvbench-idle-subnet",
+ "name": "nfvbench-idle-net",
+ "segmentation_id": null,
+ "physical_network": null,
+ "cidr": "192.169.1.0/24",
+ "network_type": "vlan"
+ },
+ "interval_sec": 10.0,
+ "no_latency_stats": false,
+ "cores": null,
+ "log_file": "/var/lib/xtesting/results/non-regression/nfvbench.log",
+ "external_networks": {
+ "right": null,
+ "left": null
+ }
+ },
+ "benchmarks": {
+ "network": {
+ "service_chain": {
+ "PVP": {
+ "result": {
+ "compute_nodes": {},
+ "profile": "custom_traffic_profile",
+ "service_chain_count": 1,
+ "result": {
+ "9000": {
+ "ndr": {
+ "load_percent_per_direction": 39.84375,
+ "timestamp_sec": 1648622722.125216,
+ "stats": {
+ "total_tx_rate": 274380,
+ "overall": {
+ "rx_pkts": 2743802,
+ "drop_percentage": 0.0,
+ "drop_pct": 0,
+ "max_delay_usec": 1244,
+ "lat_percentile": {
+ "99": "n/a",
+ "25": "n/a",
+ "75": "n/a"
+ },
+ "tx_pkts": 2743802,
+ "avg_delay_usec": 641.5002263282846,
+ "min_delay_usec": 72,
+ "hdrh": "HISTFAAAABt4nJNpmSzMgADMUJoRTM6Y1mD/ASIAAEr9BCg="
+ },
+ "1": {
+ "rx_pkts": 1371832,
+ "min_delay_usec": 74,
+ "drop_pct": 0,
+ "max_delay_usec": 1244,
+ "tx_pkts": 1371970,
+ "avg_delay_usec": 637,
+ "drop_percentage": 0.0
+ },
+ "0": {
+ "rx_pkts": 1371970,
+ "min_delay_usec": 72,
+ "drop_pct": 0,
+ "max_delay_usec": 1235,
+ "tx_pkts": 1371832,
+ "avg_delay_usec": 646,
+ "drop_percentage": 0.0
+ },
+ "theoretical_tx_rate_bps": 50000000000.0,
+ "offered_tx_rate_bps": 19799260800.0,
+ "theoretical_tx_rate_pps": 692904.6563192905
+ },
+ "initial_rate_type": "rate_percent",
+ "rate_percent": 79.6875,
+ "duration_sec": 10.0,
+ "l2frame_size": "9000",
+ "rate_pps": 276078,
+ "rate_bps": 19921875000.0,
+ "time_taken_sec": 137.65054941177368
+ },
+ "iteration_stats": {
+ "ndr_pdr": [
+ {
+ "rx_pps": 273698.46051110676,
+ "rx_pkts": 2718128,
+ "time_ms": 1648622596961,
+ "drop_pct": 4163174,
+ "total_tx_pps": 688130,
+ "tx_pps": 692904,
+ "tx_pkts": 6881302,
+ "drop_percentage": 60.49980076444836
+ },
+ {
+ "rx_pps": 276582.23920405895,
+ "rx_pkts": 2748537,
+ "time_ms": 1648622609474,
+ "drop_pct": 694331,
+ "total_tx_pps": 344286,
+ "tx_pps": 346452,
+ "tx_pkts": 3442868,
+ "drop_percentage": 20.167226858537706
+ },
+ {
+ "rx_pps": 173226.0,
+ "rx_pkts": 1721434,
+ "time_ms": 1648622622000,
+ "drop_pct": 0,
+ "total_tx_pps": 172143,
+ "tx_pps": 173226,
+ "tx_pkts": 1721434,
+ "drop_percentage": 0.0
+ },
+ {
+ "rx_pps": 259838.0,
+ "rx_pkts": 2582143,
+ "time_ms": 1648622634517,
+ "drop_pct": 0,
+ "total_tx_pps": 258214,
+ "tx_pps": 259838,
+ "tx_pkts": 2582143,
+ "drop_percentage": 0.0
+ },
+ {
+ "rx_pps": 276973.17842007434,
+ "rx_pkts": 2752701,
+ "time_ms": 1648622647105,
+ "drop_pct": 260099,
+ "total_tx_pps": 301280,
+ "tx_pps": 303144,
+ "tx_pkts": 3012800,
+ "drop_percentage": 8.633131970260223
+ },
+ {
+ "rx_pps": 276794.44570930133,
+ "rx_pkts": 2750648,
+ "time_ms": 1648622659614,
+ "drop_pct": 46682,
+ "total_tx_pps": 279733,
+ "tx_pps": 281492,
+ "tx_pkts": 2797330,
+ "drop_percentage": 1.6688056110648368
+ },
+ {
+ "rx_pps": 270664.0,
+ "rx_pkts": 2694191,
+ "time_ms": 1648622672130,
+ "drop_pct": 0,
+ "total_tx_pps": 269419,
+ "tx_pps": 270664,
+ "tx_pkts": 2694191,
+ "drop_percentage": 0.0
+ },
+ {
+ "rx_pps": 276078.0,
+ "rx_pkts": 2743802,
+ "time_ms": 1648622684633,
+ "drop_pct": 0,
+ "total_tx_pps": 274380,
+ "tx_pps": 276078,
+ "tx_pkts": 2743802,
+ "drop_percentage": 0.0
+ },
+ {
+ "rx_pps": 276955.355288579,
+ "rx_pkts": 2752523,
+ "time_ms": 1648622697132,
+ "drop_pct": 18174,
+ "total_tx_pps": 277069,
+ "tx_pps": 278784,
+ "tx_pkts": 2770697,
+ "drop_percentage": 0.6559360334240807
+ },
+ {
+ "rx_pps": 276409.30910346564,
+ "rx_pkts": 2746820,
+ "time_ms": 1648622709624,
+ "drop_pct": 10163,
+ "total_tx_pps": 275698,
+ "tx_pps": 277432,
+ "tx_pkts": 2756983,
+ "drop_percentage": 0.3686275903768721
+ },
+ {
+ "rx_pps": 276744.94431529724,
+ "rx_pkts": 2750432,
+ "time_ms": 1648622722124,
+ "drop_pct": 90,
+ "total_tx_pps": 275052,
+ "tx_pps": 276754,
+ "tx_pkts": 2750522,
+ "ndr_pps": 138039,
+ "drop_percentage": 0.003272106167483845
+ }
+ ]
+ }
+ }
+ },
+ "flow_count": 100000,
+ "bidirectional": true
+ }
+ }
+ },
+ "versions": {
+ "Traffic_Generator": {
+ "build_date": "Apr 14 2021",
+ "version": "v2.89",
+ "built_by": "hhaim",
+ "mode": "STL",
+ "build_time": "11:22:15"
+ }
+ }
+ }
+ }
+ },
+ "request_id": "f621b77faf384c08b3fcb9b6a3ec9523"
+ },
+ "synthesis": {
+ "avg_delay_usec": 641.5002263282846,
+ "total_tx_rate": 274380
+ }
+ }
+ ]
+ },
+ "fail_tests": 0,
+ "total_tests": 1,
+ "pass_tests": 1
+ }
+ }
+ ]
+}
diff --git a/test/ut_behave_tests/test_steps.py b/test/ut_behave_tests/test_steps.py
new file mode 100644
index 0000000..ae6df45
--- /dev/null
+++ b/test/ut_behave_tests/test_steps.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python
+# Copyright 2022 Orange
+#
+# 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.
+#
+
+"""
+Unit tests for some of the functions found in behave_tests/features/steps/steps.py
+"""
+
+import logging
+import unittest
+from unittest.mock import call, Mock, patch
+
+from behave_tests.features.steps.steps import get_last_result
+from .test_utils import setup_logging, stub_requests_get
+
+
+def setUpModule():
+ setup_logging()
+
+
+class TestGetLastResult(unittest.TestCase):
+ def setUp(self):
+ # Mock requests.get() so that TestAPI results come from JSON files
+ # found in test_data/ directory.
+ patcher = patch('behave_tests.features.steps.testapi.requests')
+ self._mock_requests = patcher.start()
+ self._mock_requests.get.side_effect = stub_requests_get
+ self.addCleanup(patcher.stop)
+
+ # Setup a mock for behave context
+ self._context = Mock()
+ self._context.data = {
+ 'PROJECT_NAME': "nfvbench",
+ 'TEST_DB_URL': "http://127.0.0.1:8000/api/v1/results"
+ }
+ self._context.logger = logging.getLogger("behave_tests")
+
+ def test_get_last_result_throughput_characterization(self):
+ self._context.json = {
+ "frame_sizes": ['64'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "ndr",
+ "user_label": "amical_tc18_loopback"
+ }
+ self._context.tag = "throughput"
+
+ last_result = get_last_result(self._context, reference=True)
+
+ self.assertIsNotNone(last_result)
+ self.assertEqual(16765582, last_result["synthesis"]["total_tx_rate"])
+ self.assertEqual(25, round(last_result["synthesis"]["avg_delay_usec"]))
+
+ self._mock_requests.get.assert_called_once_with(
+ "http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=1")
+
+ def test_get_last_result_latency_characterization(self):
+ self._context.json = {
+ "frame_sizes": ['768'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "90%",
+ "user_label": "amical_tc6_intensive"
+ }
+ self._context.tag = "latency"
+
+ last_result = get_last_result(self._context, reference=True)
+
+ self.assertIsNotNone(last_result)
+ self.assertEqual(262275, last_result["synthesis"]["total_tx_rate"])
+ self.assertEqual(353, round(last_result["synthesis"]["avg_delay_usec"]))
+
+ self._mock_requests.get.assert_has_calls([
+ call("http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=1"),
+ call("http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=2")])
+
+ def test_last_result_not_found(self):
+ self._context.json = {
+ "frame_sizes": ['64'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "ndr",
+ "user_label": "toto_titi_tata" # User label not in test data
+ }
+ self._context.tag = "throughput"
+
+ with self.assertRaises(AssertionError):
+ get_last_result(self._context, reference=True)
+
+ self._mock_requests.get.assert_has_calls([
+ call("http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=1"),
+ call("http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=2")])
+
+ def test_get_last_result_throughput_non_regression(self):
+ self._context.CASE_NAME = "non-regression"
+ self._context.json = {
+ "frame_sizes": ['1518'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "ndr",
+ "user_label": "amical_tc12_basic"
+ }
+ self._context.tag = "throughput"
+
+ last_result = get_last_result(self._context)
+
+ self.assertIsNotNone(last_result)
+ self.assertEqual(512701, last_result["synthesis"]["total_tx_rate"])
+ self.assertEqual(148, round(last_result["synthesis"]["avg_delay_usec"]))
+
+ self._mock_requests.get.assert_called_once_with(
+ "http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=non-regression&criteria=PASS&page=1")
+
+ def test_get_last_result_latency_non_regression(self):
+ self._context.CASE_NAME = "non-regression"
+ self._context.json = {
+ "frame_sizes": ['1518'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "70%",
+ "user_label": "amical_tc12_basic"
+ }
+ self._context.tag = "latency"
+
+ last_result = get_last_result(self._context)
+
+ self.assertIsNotNone(last_result)
+ self.assertEqual(352040, last_result["synthesis"]["total_tx_rate"])
+ self.assertEqual(114, round(last_result["synthesis"]["avg_delay_usec"]))
+
+ self._mock_requests.get.assert_called_once_with(
+ "http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=non-regression&criteria=PASS&page=1")
diff --git a/test/ut_behave_tests/test_testapi.py b/test/ut_behave_tests/test_testapi.py
index 2b57483..0fbcd96 100644
--- a/test/ut_behave_tests/test_testapi.py
+++ b/test/ut_behave_tests/test_testapi.py
@@ -109,3 +109,24 @@ class TestTestapiClient(unittest.TestCase):
with self.assertRaises(ValueError):
client.find_last_result(testapi_params, scenario_tag="throughput",
nfvbench_test_input=nfvbench_test_input)
+
+ def test_flavor_is_ignored(self):
+ """Check that lookup in TestAPI does not filter on the flavor_type."""
+ client = TestapiClient("http://127.0.0.1:8000/api/v1/results")
+ testapi_params = {"project_name": "nfvbench", "case_name": "characterization"}
+ nfvbench_test_input = {"frame_sizes": ['64'],
+ "flow_count": "100k",
+ "duration_sec": '10',
+ "rate": "ndr",
+ "user_label": "amical_tc18_loopback",
+ "flavor_type": "no_such_flavor"}
+ last_result = client.find_last_result(testapi_params,
+ scenario_tag="throughput",
+ nfvbench_test_input=nfvbench_test_input)
+ self.assertIsNotNone(last_result)
+ self.assertEqual(16765582, last_result["synthesis"]["total_tx_rate"])
+ self.assertEqual(25, round(last_result["synthesis"]["avg_delay_usec"]))
+
+ self.mock_requests.get.assert_called_once_with(
+ "http://127.0.0.1:8000/api/v1/results?"
+ "project=nfvbench&case=characterization&criteria=PASS&page=1")