def smallfile_run(self, es):
"""
Run the smallfiles workload so the elasticsearch server will have some data
in it for copy
Args:
es (Elasticsearch): elastic search object
Returns:
str: the UUID of the test
"""
ripsaw = RipSaw()
# Loading the main template yaml file for the benchmark and update some
# fields with new values
sf_data = templating.load_yaml(constants.SMALLFILE_BENCHMARK_YAML)
# Setting up the parameters for this test
sf_data["spec"]["elasticsearch"]["server"] = es.get_ip()
sf_data["spec"]["elasticsearch"]["port"] = es.get_port()
sf_data["spec"]["workload"]["args"]["samples"] = 1
sf_data["spec"]["workload"]["args"]["operation"] = ["create"]
sf_data["spec"]["workload"]["args"]["file_size"] = 4
sf_data["spec"]["workload"]["args"]["files"] = 500000
sf_data["spec"]["workload"]["args"]["threads"] = 4
sf_data["spec"]["workload"]["args"][
"storageclass"] = constants.DEFAULT_STORAGECLASS_RBD
sf_data["spec"]["workload"]["args"]["storagesize"] = "100Gi"
# Deploy the ripsaw operator
log.info("Apply Operator CRD")
ripsaw.apply_crd("resources/crds/ripsaw_v1alpha1_ripsaw_crd.yaml")
# deploy the smallfile workload
log.info("Running SmallFile bench")
sf_obj = OCS(**sf_data)
sf_obj.create()
# wait for benchmark pods to get created - takes a while
for bench_pod in TimeoutSampler(
240,
10,
get_pod_name_by_pattern,
"smallfile-client",
constants.RIPSAW_NAMESPACE,
):
try:
if bench_pod[0] is not None:
small_file_client_pod = bench_pod[0]
break
except IndexError:
log.info("Bench pod not ready yet")
bench_pod = OCP(kind="pod", namespace=constants.RIPSAW_NAMESPACE)
log.info("Waiting for SmallFile benchmark to Run")
bench_pod.wait_for_resource(
condition=constants.STATUS_RUNNING,
resource_name=small_file_client_pod,
sleep=30,
timeout=600,
)
for item in bench_pod.get()["items"][1]["spec"]["volumes"]:
if "persistentVolumeClaim" in item:
break
uuid = ripsaw.get_uuid(small_file_client_pod)
timeout = 600
while timeout >= 0:
logs = bench_pod.get_logs(name=small_file_client_pod)
if "RUN STATUS DONE" in logs:
break
timeout -= 30
if timeout == 0:
raise TimeoutError(
"Timed out waiting for benchmark to complete")
time.sleep(30)
ripsaw.cleanup()
return uuid
class TestFIOBenchmark(PASTest):
"""
Run FIO perf test using ripsaw benchmark
"""
def setup(self):
"""
Setting up test parameters
"""
log.info("Starting the test setup")
self.benchmark_name = "FIO"
self.client_pod_name = "fio-client"
if config.PERF.get("deploy_internal_es"):
self.es = ElasticSearch()
else:
if config.PERF.get("internal_es_server") == "":
self.es = None
return
else:
self.es = {
"server": config.PERF.get("internal_es_server"),
"port": config.PERF.get("internal_es_port"),
"url":
f"http://{config.PERF.get('internal_es_server')}:{config.PERF.get('internal_es_port')}",
"parallel": True,
}
# verify that the connection to the elasticsearch server is OK
if not super(TestFIOBenchmark, self).es_connect():
self.es = None
return
super(TestFIOBenchmark, self).setup()
# deploy the benchmark-operator (ripsaw)
self.ripsaw = RipSaw()
self.ripsaw_deploy(self.ripsaw)
def setting_storage_usage(self):
"""
Getting the storage capacity, calculate the usage of the storage and
setting the workload CR rile parameters.
"""
# for development mode - use parameters for short test run
if self.dev_mode:
log.info("Setting up parameters for development mode")
self.crd_data["spec"]["workload"]["args"]["filesize"] = "1GiB"
self.crd_data["spec"]["workload"]["args"]["storagesize"] = "5Gi"
self.crd_data["spec"]["workload"]["args"]["servers"] = 2
self.crd_data["spec"]["workload"]["args"]["samples"] = 2
self.crd_data["spec"]["workload"]["args"]["read_runtime"] = 30
self.crd_data["spec"]["workload"]["args"]["write_runtime"] = 30
self.crd_data["spec"]["workload"]["args"]["bs"] = ["64KiB"]
self.total_data_set = 20
self.filesize = 3
return
ceph_cluster = CephCluster()
ceph_capacity = ceph_cluster.get_ceph_capacity()
log.info(f"Total storage capacity is {ceph_capacity} GiB")
self.total_data_set = int(ceph_capacity * 0.4)
self.filesize = int(
self.crd_data["spec"]["workload"]["args"]["filesize"].replace(
"GiB", ""))
# To make sure the number of App pods will not be more then 50, in case
# of large data set, changing the size of the file each pod will work on
if self.total_data_set > 500:
self.filesize = int(ceph_capacity * 0.008)
self.crd_data["spec"]["workload"]["args"][
"filesize"] = f"{self.filesize}GiB"
# make sure that the storage size is larger then the file size
self.crd_data["spec"]["workload"]["args"][
"storagesize"] = f"{int(self.filesize * 1.2)}Gi"
self.crd_data["spec"]["workload"]["args"]["servers"] = int(
self.total_data_set / self.filesize)
log.info(f"Total Data set to work on is : {self.total_data_set} GiB")
def setting_io_pattern(self, io_pattern):
"""
Setting the test jobs according to the io pattern - random / sequential
Args:
io_pattern (str): the I/O pattern to run (random / sequential)
"""
if io_pattern == "sequential":
self.crd_data["spec"]["workload"]["args"]["jobs"] = [
"write", "read"
]
self.crd_data["spec"]["workload"]["args"]["iodepth"] = 1
if io_pattern == "random":
self.crd_data["spec"]["workload"]["args"]["jobs"] = [
"randwrite",
"randread",
]
def init_full_results(self, full_results):
"""
Initialize the full results object which will send to the ES server
Args:
full_results (obj): an empty FIOResultsAnalyse object
Returns:
FIOResultsAnalyse (obj): the input object fill with data
"""
for key in self.environment:
full_results.add_key(key, self.environment[key])
# Setting the global parameters of the test
full_results.add_key("dataset", f"{self.total_data_set}GiB")
full_results.add_key(
"file_size", self.crd_data["spec"]["workload"]["args"]["filesize"])
full_results.add_key(
"servers", self.crd_data["spec"]["workload"]["args"]["servers"])
full_results.add_key(
"samples", self.crd_data["spec"]["workload"]["args"]["samples"])
full_results.add_key("operations",
self.crd_data["spec"]["workload"]["args"]["jobs"])
full_results.add_key("block_sizes",
self.crd_data["spec"]["workload"]["args"]["bs"])
full_results.add_key(
"io_depth", self.crd_data["spec"]["workload"]["args"]["iodepth"])
full_results.add_key(
"jobs", self.crd_data["spec"]["workload"]["args"]["numjobs"])
full_results.add_key(
"runtime",
{
"read":
self.crd_data["spec"]["workload"]["args"]["read_runtime"],
"write":
self.crd_data["spec"]["workload"]["args"]["write_runtime"],
},
)
full_results.add_key(
"storageclass",
self.crd_data["spec"]["workload"]["args"]["storageclass"])
full_results.add_key(
"vol_size",
self.crd_data["spec"]["workload"]["args"]["storagesize"])
return full_results
def cleanup(self):
"""
Do cleanup in the benchmark-operator namespace.
delete the benchmark, an make sure no PVC's an no PV's are left.
"""
log.info("Deleting FIO benchmark")
self.benchmark_obj.delete()
time.sleep(180)
# Getting all PVCs created in the test (if left).
NL = "\\n" # NewLine character
command = ["oc", "get", "pvc", "-n"]
command.append(constants.RIPSAW_NAMESPACE)
command.append("-o")
command.append("template")
command.append("--template")
command.append("'{{range .items}}{{.metadata.name}}{{\"" + NL +
"\"}}{{end}}'")
pvcs_list = run_command(command, out_format="list")
log.info(f"list of all PVCs :{pvcs_list}")
for pvc in pvcs_list:
pvc = pvc.replace("'", "")
run_command(f"oc -n {constants.RIPSAW_NAMESPACE} delete pvc {pvc}")
# Getting all PVs created in the test (if left).
command[2] = "pv"
command[8] = (
"'{{range .items}}{{.metadata.name}} {{.spec.claimRef.namespace}}{{\""
+ NL + "\"}}{{end}}'")
command.remove("-n")
command.remove(constants.RIPSAW_NAMESPACE)
pvs_list = run_command(command, out_format="list")
log.info(f"list of all PVs :{pvs_list}")
for line in pvs_list:
try:
pv, ns = line.split(" ")
pv = pv.replace("'", "")
if ns == constants.RIPSAW_NAMESPACE:
log.info(f"Going to delete {pv}")
run_command(f"oc delete pv {pv}")
except Exception:
pass
def run(self):
"""
Run the test, and wait until it finished
"""
self.deploy_and_wait_for_wl_to_start(timeout=900)
# Getting the UUID from inside the benchmark pod
self.uuid = self.ripsaw.get_uuid(self.client_pod)
# Setting back the original elastic-search information
if hasattr(self, "backup_es"):
self.crd_data["spec"]["elasticsearch"] = self.backup_es
if self.dev_mode:
sleeptime = 30
else:
sleeptime = 300
self.wait_for_wl_to_finish(sleep=sleeptime)
try:
if "Fio failed to execute" not in self.test_logs:
log.info("FIO has completed successfully")
except IOError:
log.warning("FIO failed to complete")
def teardown(self):
"""
The teardown of the test environment in the end.
"""
log.info("cleanup the environment")
if hasattr(self, "ripsaw"):
self.ripsaw.cleanup()
if isinstance(self.es, ElasticSearch):
self.es.cleanup()
sleep_time = 5
log.info(
f"Going to sleep for {sleep_time} Minute, for background cleanup to complete"
)
time.sleep(sleep_time * 60)
@pytest.mark.parametrize(
argnames=["interface", "io_pattern"],
argvalues=[
pytest.param(
*[constants.CEPHBLOCKPOOL, "sequential"],
marks=pytest.mark.polarion_id("OCS-844"),
),
pytest.param(
*[constants.CEPHFILESYSTEM, "sequential"],
marks=pytest.mark.polarion_id("OCS-845"),
),
pytest.param(
*[constants.CEPHBLOCKPOOL, "random"],
marks=pytest.mark.polarion_id("OCS-846"),
),
pytest.param(
*[constants.CEPHFILESYSTEM, "random"],
marks=pytest.mark.polarion_id("OCS-847"),
),
],
)
def test_fio_workload_simple(self, interface, io_pattern):
"""
This is a basic fio perf test - non-compressed volumes
Args:
interface (str): the interface that need to be tested - CephFS / RBD
io_pattern (str): the I/O pattern to do - random / sequential
"""
# verify that there is an elasticsearch server for the benchmark
if not self.es:
log.error("This test must have an Elasticsearch server")
return False
# Getting the full path for the test logs
self.full_log_path = get_full_test_logs_path(cname=self)
self.full_log_path += f"-{interface}-{io_pattern}"
log.info(f"Logs file path name is : {self.full_log_path}")
log.info("Create resource file for fio workload")
self.crd_data = templating.load_yaml(constants.FIO_CR_YAML)
# Saving the Original elastic-search IP and PORT - if defined in yaml
self.es_info_backup(self.es)
self.set_storageclass(interface=interface)
# Setting the data set to 40% of the total storage capacity
self.setting_storage_usage()
self.get_env_info()
self.setting_io_pattern(io_pattern)
self.run()
# Initialize the results doc file.
full_results = self.init_full_results(
FIOResultsAnalyse(self.uuid, self.crd_data, self.full_log_path,
self.main_es))
# Setting the global parameters of the test
full_results.add_key("io_pattern", io_pattern)
# Clean up fio benchmark
self.cleanup()
log.debug(f"Full results is : {full_results.results}")
if isinstance(self.es, ElasticSearch):
# Using internal deployed elasticsearch
# if self.es:
log.info("Getting data from internal ES")
if self.main_es:
self.copy_es_data(self.es)
else:
log.info("Dumping data from the Internal ES to tar ball file")
self.es.dumping_all_data(self.full_log_path)
full_results.analyze_results(self) # Analyze the results
full_results.add_key("test_time", {
"start": self.start_time,
"end": self.end_time
})
# Writing the analyzed test results to the Elastic-Search server
if full_results.es_write():
log.info(
f"The Result can be found at : {full_results.results_link()}")
@skipif_ocs_version("<4.6")
@pytest.mark.parametrize(
argnames=["io_pattern", "bs", "cmp_ratio"],
argvalues=[
pytest.param(*["random", "1024KiB", 60]),
pytest.param(*["random", "64KiB", 60]),
pytest.param(*["random", "16KiB", 60]),
pytest.param(*["sequential", "1024KiB", 60]),
pytest.param(*["sequential", "64KiB", 60]),
pytest.param(*["sequential", "16KiB", 60]),
],
)
def test_fio_compressed_workload(self, storageclass_factory, io_pattern,
bs, cmp_ratio):
"""
This is a basic fio perf test which run on compression enabled volume
Args:
io_pattern (str): the I/O pattern to do - random / sequential
bs (str): block size to use in the test
cmp_ratio (int): the expected compression ratio
"""
# Getting the full path for the test logs
self.full_log_path = get_full_test_logs_path(cname=self)
self.full_log_path += f"-{io_pattern}-{bs}-{cmp_ratio}"
log.info(f"Logs file path name is : {self.full_log_path}")
log.info("Create resource file for fio workload")
self.crd_data = templating.load_yaml(
"ocs_ci/templates/workloads/fio/benchmark_fio_cmp.yaml")
# Saving the Original elastic-search IP and PORT - if defined in yaml
self.es_info_backup(self.es)
log.info("Creating compressed pool & SC")
sc_obj = storageclass_factory(
interface=constants.CEPHBLOCKPOOL,
new_rbd_pool=True,
replica=3,
compression="aggressive",
)
sc = sc_obj.name
pool_name = run_cmd(
f"oc get sc {sc} -o jsonpath={{'.parameters.pool'}}")
# Create fio benchmark
self.crd_data["spec"]["workload"]["args"]["bs"] = [bs]
self.crd_data["spec"]["workload"]["args"]["cmp_ratio"] = cmp_ratio
# Setting the data set to 40% of the total storage capacity
self.setting_storage_usage()
self.crd_data["spec"]["workload"]["args"][
"prefill_bs"] = self.crd_data["spec"]["workload"]["args"]["bs"][0]
self.get_env_info()
self.crd_data["spec"]["workload"]["args"]["storageclass"] = sc
self.setting_io_pattern(io_pattern)
self.run()
# Initialize the results doc file.
full_results = self.init_full_results(
FIOResultsAnalyse(self.uuid, self.crd_data, self.full_log_path,
self.main_es))
# Setting the global parameters of the test
full_results.add_key("io_pattern", io_pattern)
if isinstance(self.es, ElasticSearch):
# Using internal deployed elasticsearch
# if self.es:
log.info("Getting data from internal ES")
if self.main_es:
self.copy_es_data(self.es)
else:
log.info("Dumping data from the Internal ES to tar ball file")
self.es.dumping_all_data(self.full_log_path)
log.info("verifying compression ratio")
ratio = calculate_compression_ratio(pool_name)
full_results.add_key("cmp_ratio", {
"expected": cmp_ratio,
"actual": ratio
})
log.debug(f"Full results is : {full_results.results}")
full_results.analyze_results(self) # Analyze the results
if (cmp_ratio + 5) < ratio or ratio < (cmp_ratio - 5):
log.warning(f"The compression ratio is {ratio}% "
f"while the expected ratio is {cmp_ratio}%")
else:
log.info(f"The compression ratio is {ratio}%")
full_results.add_key("test_time", {
"start": self.start_time,
"end": self.end_time
})
# Writing the analyzed test results to the Elastic-Search server
if full_results.es_write():
log.info(
f"The Result can be found at : {full_results.results_link()}")
# Clean up fio benchmark
self.cleanup()
sc_obj.delete()
sc_obj.ocp.wait_for_delete(resource_name=sc, timeout=300, sleep=5)
class TestSmallFileWorkload(PASTest):
"""
Deploy Ripsaw operator and run SmallFile workload
SmallFile workload using https://github.com/distributed-system-analysis/smallfile
smallfile is a python-based distributed POSIX workload generator which can be
used to quickly measure performance for a variety of metadata-intensive
workloads
"""
def setup(self):
"""
Setting up test parameters
"""
log.info("Starting the test setup")
self.benchmark_name = "SmallFiles"
self.client_pod_name = "smallfile-client"
if config.PERF.get("deploy_internal_es"):
self.es = ElasticSearch()
else:
if config.PERF.get("internal_es_server") == "":
self.es = None
return
else:
self.es = {
"server":
config.PERF.get("internal_es_server"),
"port":
config.PERF.get("internal_es_port"),
"url":
f"http://{config.PERF.get('internal_es_server')}:{config.PERF.get('internal_es_port')}",
}
# verify that the connection to the elasticsearch server is OK
if not super(TestSmallFileWorkload, self).es_connect():
self.es = None
return
super(TestSmallFileWorkload, self).setup()
# deploy the benchmark-operator (ripsaw)
self.ripsaw = RipSaw()
self.ripsaw_deploy(self.ripsaw)
def setting_storage_usage(self, file_size, files, threads, samples):
"""
Getting the storage capacity, calculate the usage of the storage and
setting the workload CR rile parameters.
Args:
file_size (int) : the size of the file to be used
files (int) : number of files to use
threads (int) : number of threads to be use in the test
samples (int) : how meany samples to run for each test
"""
self.crd_data["spec"]["workload"]["args"]["file_size"] = file_size
self.crd_data["spec"]["workload"]["args"]["files"] = files
self.crd_data["spec"]["workload"]["args"]["threads"] = threads
self.crd_data["spec"]["workload"]["args"]["samples"] = samples
# Calculating the size of the volume that need to be test, it should
# be at least twice in the size then the size of the files, and at
# least 100Gi.
# Since the file_size is in Kb and the vol_size need to be in Gb, more
# calculation is needed.
vol_size = int(files * threads * file_size * 3)
vol_size = int(vol_size / constants.GB2KB)
if vol_size < 100:
vol_size = 100
self.crd_data["spec"]["workload"]["args"][
"storagesize"] = f"{vol_size}Gi"
def init_full_results(self, full_results):
"""
Initialize the full results object which will send to the ES server
Args:
full_results (obj): an empty SmallFileResultsAnalyse object
Returns:
SmallFileResultsAnalyse (obj): the input object fill with data
"""
for key in self.environment:
full_results.add_key(key, self.environment[key])
# Calculating the total size of the working data set - in GB
full_results.add_key(
"dataset",
self.crd_data["spec"]["workload"]["args"]["file_size"] *
self.crd_data["spec"]["workload"]["args"]["files"] *
self.crd_data["spec"]["workload"]["args"]["threads"] *
full_results.results["clients"] / constants.GB2KB,
)
full_results.add_key(
"global_options",
{
"files":
self.crd_data["spec"]["workload"]["args"]["files"],
"file_size":
self.crd_data["spec"]["workload"]["args"]["file_size"],
"storageclass":
self.crd_data["spec"]["workload"]["args"]["storageclass"],
"vol_size":
self.crd_data["spec"]["workload"]["args"]["storagesize"],
},
)
return full_results
def run(self):
log.info("Running SmallFile bench")
self.deploy_and_wait_for_wl_to_start(timeout=240, sleep=10)
# Getting the UUID from inside the benchmark pod
self.uuid = self.ripsaw.get_uuid(self.client_pod)
self.wait_for_wl_to_finish(sleep=30)
try:
if "RUN STATUS DONE" in self.test_logs:
log.info("SmallFiles has completed successfully")
return True
except IOError:
log.warning("SmallFiles failed to complete")
return False
def teardown(self):
"""
The teardown of the test environment in the end.
"""
log.info("cleanup the environment")
if hasattr(self, "ripsaw"):
self.ripsaw.cleanup()
if isinstance(self.es, ElasticSearch):
self.es.cleanup()
sleep_time = 5
log.info(
f"Going to sleep for {sleep_time} Minute, for background cleanup to complete"
)
time.sleep(sleep_time * 60)
@pytest.mark.parametrize(
argnames=["file_size", "files", "threads", "samples", "interface"],
argvalues=[
pytest.param(
*[4, 50000, 4, 3, constants.CEPHBLOCKPOOL],
marks=pytest.mark.polarion_id("OCS-1295"),
),
pytest.param(
*[16, 50000, 4, 3, constants.CEPHBLOCKPOOL],
marks=pytest.mark.polarion_id("OCS-2020"),
),
pytest.param(
*[16, 200000, 4, 3, constants.CEPHBLOCKPOOL],
marks=pytest.mark.polarion_id("OCS-2021"),
),
pytest.param(
*[4, 50000, 4, 3, constants.CEPHFILESYSTEM],
marks=pytest.mark.polarion_id("OCS-2022"),
),
pytest.param(
*[16, 50000, 4, 3, constants.CEPHFILESYSTEM],
marks=pytest.mark.polarion_id("OCS-2023"),
),
],
)
@pytest.mark.polarion_id("OCS-1295")
def test_smallfile_workload(self, file_size, files, threads, samples,
interface):
"""
Run SmallFile Workload
Args:
file_size (int) : the size of the file to be used
files (int) : number of files to use
threads (int) : number of threads to be use in the test
samples (int) : how meany samples to run for each test
interface (str) : the volume type (rbd / cephfs)
"""
# verify that there is an elasticsearch server for the benchmark
if not self.es:
log.error("This test must have an Elasticsearch server")
return False
# Getting the full path for the test logs
self.full_log_path = get_full_test_logs_path(cname=self)
self.full_log_path += f"-{file_size}-{files}-{threads}-{samples}-{interface}"
log.info(f"Logs file path name is : {self.full_log_path}")
# Loading the main template yaml file for the benchmark
log.info("Create resource file for smallfiles workload")
self.crd_data = templating.load_yaml(
constants.SMALLFILE_BENCHMARK_YAML)
# Saving the Original elastic-search IP and PORT - if defined in yaml
self.es_info_backup(self.es)
self.set_storageclass(interface=interface)
# Setting the data set to 40% of the total storage capacity
self.setting_storage_usage(file_size, files, threads, samples)
self.get_env_info()
if not self.run():
log.error("The benchmark failed to run !")
return
# Setting back the original elastic-search information
if self.backup_es:
self.crd_data["spec"]["elasticsearch"] = self.backup_es
# Initialize the results doc file.
full_results = self.init_full_results(
SmallFileResultsAnalyse(self.uuid, self.crd_data,
self.full_log_path, self.main_es))
log.info(f"Full results is : {full_results.results}")
if isinstance(self.es, ElasticSearch):
# Using internal deployed elasticsearch
log.info("Getting data from internal ES")
if self.main_es:
self.copy_es_data(self.es)
full_results.read()
else:
log.info("Dumping data from the Internal ES to tar ball file")
self.es.dumping_all_data(self.full_log_path)
else:
log.info(self.es)
self.es = Elasticsearch(hosts=[{
"host": self.es["server"],
"port": self.es["port"]
}])
full_results.read()
full_results.add_key("test_time", {
"start": self.start_time,
"end": self.end_time
})
if self.main_es:
full_results.es = self.main_es
if not full_results.dont_check:
full_results.add_key("hosts", full_results.get_clients_list())
full_results.init_full_results()
full_results.aggregate_host_results()
test_status = full_results.aggregate_samples_results()
full_results.all_results = None
if full_results.es_write():
log.info(
f"The Result can be found at : {full_results.results_link()}"
)
else:
test_status = True
assert test_status, "Test Failed !"