def install_as_local_app(splunk, kubernetes, stack_id, pod, app):
core_api = kuberneteslib.CoreV1Api(kubernetes)
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_config = clusters.get_cluster(splunk, stack_config["cluster"])
pod_local_path = "/tmp/%s.tar" % (app.name)
temp_dir = tempfile.mkdtemp()
try:
app.render(cluster_config, stack_config, temp_dir)
render_saas_app_data(temp_dir, app)
tar_directory_to_pod(
core_api=core_api,
pod=pod.metadata.name,
namespace=stack_config["namespace"],
local_path=temp_dir,
remote_path=pod_local_path,
)
finally:
shutil.rmtree(temp_dir, ignore_errors=True)
service = instances.create_client(
core_api, stack_id, stack_config,
pod.metadata.labels["app.kubernetes.io/name"])
try:
service.post(
"apps/local",
filename=True,
name=pod_local_path,
update=True,
explicit_appname=app.name,
)
except splunklib.binding.HTTPError:
raise
logging.info("installed app '%s' to '%s" % (app.name, pod.metadata.name))
return service.restart_required
def update_deployment(splunk, kubernetes, stack_id):
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_name = stack_config["cluster"]
kubernetes = clusters.create_client(splunk, cluster_name)
cluster_config = clusters.get_cluster(splunk, cluster_name)
if stack_config["deployment_type"] == "distributed":
indexer_cluster.update(splunk, kubernetes, stack_id, stack_config)
search_head_cluster.update(splunk, kubernetes, stack_id, stack_config)
indexer_cluster.wait_until_ready(splunk, kubernetes, stack_id, stack_config)
search_head_cluster.wait_until_ready(splunk, kubernetes, stack_id, stack_config)
def update_deployment(splunk, kubernetes, stack_id):
core_api = kuberneteslib.CoreV1Api(kubernetes)
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_name = stack_config["cluster"]
kubernetes = clusters.create_client(splunk, cluster_name)
cluster_config = clusters.get_cluster(splunk, cluster_name)
if stack_config["deployment_type"] == "distributed":
indexer_cluster.update(splunk, kubernetes, stack_id, stack_config)
search_head_cluster.update(splunk, kubernetes, stack_id, stack_config)
indexer_cluster.wait_until_ready(splunk, kubernetes, stack_id,
stack_config)
search_head_cluster.wait_until_ready(splunk, kubernetes, stack_id,
stack_config)
create_load_balancers(core_api, stack_id, stack_config)
verify_load_balancers_completed(core_api, stack_id, stack_config)
def down(splunk, stack_id, force=False):
stacks.update_config(splunk, stack_id, {
"status": stacks.DELETING,
"deleted_time": time.time(),
})
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_name = stack_config["cluster"]
cluster_config = clusters.get_cluster(splunk, cluster_name)
api_client = clusters.create_client(splunk, cluster_name)
core_api = kuberneteslib.CoreV1Api(api_client)
custom_objects_api = kuberneteslib.CustomObjectsApi(api_client)
try:
services.delete_all_load_balancers(core_api, stack_id, stack_config["namespace"])
stack_deployment.delete_objects(api_client, stack_id, stack_config, cluster_config)
except:
if not force:
raise
stacks.update_config(splunk, stack_id, {
"status": stacks.DELETED,
})
def copy_app_into_folder(splunk, kubernetes, stack_id, pod, app,
target_parent_name):
core_api = kuberneteslib.CoreV1Api(kubernetes)
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_config = clusters.get_cluster(splunk, stack_config["cluster"])
temp_dir = tempfile.mkdtemp()
try:
app.render(cluster_config, stack_config, temp_dir)
render_saas_app_data(temp_dir, app)
copy_directory_to_pod(
core_api=core_api,
pod=pod.metadata.name,
namespace=stack_config["namespace"],
local_path=temp_dir,
remote_path="/opt/splunk/etc/%s/%s/" %
(target_parent_name, app.name),
)
logging.info("copied app '%s' at '%s' to '%s'" %
(app.name, target_parent_name, pod.metadata.name))
finally:
shutil.rmtree(temp_dir, ignore_errors=True)
def up(splunk, stack_id):
stack_config = stacks.get_stack_config(splunk, stack_id)
cluster_name = stack_config["cluster"]
kubernetes = clusters.create_client(splunk, cluster_name)
cluster_config = clusters.get_cluster(splunk, cluster_name)
status = stack_config["status"]
if status == stacks.CREATING:
stack_deployment.create_deployment(splunk, kubernetes, stack_id, stack_config, cluster_config)
app_deployment.update_apps(splunk, kubernetes, stack_id)
logging.info("created")
stacks.update_config(splunk, stack_id, {
"status": stacks.CREATED,
})
elif status == stacks.UPDATING:
stack_deployment.update_deployment(splunk, kubernetes, stack_id)
app_deployment.update_apps(splunk, kubernetes, stack_id)
logging.info("updated")
stacks.update_config(splunk, stack_id, {
"status": stacks.CREATED,
})
elif status == stacks.CREATED:
logging.info("Everything is up-to-date")
else:
logging.warning("unexpected status: %s", status)
def handle_POST(self):
stacks = get_stacks(self.splunk)
defaults = self.splunk.confs["defaults"]["general"]
# create stack record
stack_record = {
"status": CREATING,
}
fields_names = set([
"deployment_type",
"license_master_mode",
"enterprise_license",
"indexer_count",
"search_head_count",
"cpu_per_instance",
"memory_per_instance",
"title",
"data_fabric_search",
"spark_worker_count",
"cluster",
"namespace",
"etc_storage_in_gb",
"other_var_storage_in_gb",
"indexer_var_storage_in_gb",
])
# apply request parameters
request_params = parse_qs(self.request['payload'])
stack_record.update({
k: request_params[k][0]
for k in fields_names if k in request_params
})
# apply missing fields from defaults
stack_record.update({
k: defaults[k]
for k in fields_names if k in defaults and k not in stack_record
})
# apply missing fields from cluster config
cluster_name = stack_record["cluster"]
cluster_config = clusters.get_cluster(
self.service, cluster_name)
stack_record.update(
{
k: cluster_config[k]
for k in fields_names if k in cluster_config and k not in stack_record
}
)
# add missing fields
if "data_fabric_search" not in stack_record:
stack_record["data_fabric_search"] = "false"
if "spark_worker_count" not in stack_record:
stack_record["spark_worker_count"] = "0"
if "cpu_per_instance" not in stack_record:
stack_record["cpu_per_instance"] = "1"
if "memory_per_instance" not in stack_record:
stack_record["memory_per_instance"] = "4Gi"
# save stack
stack_id = stacks.insert(json.dumps(stack_record))["_key"]
# start operator
stack_operation.start(self.service, stack_id)
# return ID
self.send_result({
"stack_id": stack_id,
})
def run_cases(splunk, test_id, test):
cases_collection = get_performance_test_cases_collection(splunk)
cases = cases_collection.query(
query=json.dumps({
"test_id": test_id,
}),
sort="index:1",
)
for case in cases:
case_id = case["_key"]
status = case["status"]
if status == CASE_FINISHED:
continue
if status == CASE_WAITING:
result = splunk.post(
"saas/stacks", **{
"deployment_type": case["deployment_type"],
"indexer_count": case["indexer_count"],
"search_head_count": case["search_head_count"],
"cpu_per_instance": case["cpu_per_instance"],
"etc_storage_in_gb": case["etc_storage_in_gb"],
"other_var_storage_in_gb": case["other_var_storage_in_gb"],
"indexer_var_storage_in_gb":
case["indexer_var_storage_in_gb"],
"memory_per_instance": case["memory_per_instance"],
"title":
"Performance Test %s and Case %s" % (test_id, case_id),
"cluster": test["cluster"],
})
response = json.loads(result.body.read())["entry"][0]["content"]
stack_id = response["stack_id"]
logging.info("created stack %s for test case %s" %
(stack_id, case_id))
case.update({
"status": CASE_STARTING,
"stack_id": stack_id,
})
cases_collection.update(case_id, json.dumps(case))
raise errors.RetryOperation(
"waiting for stack %s in test case %s starting up ..." %
(stack_id, case_id))
elif status == CASE_STARTING:
stack_id = case["stack_id"]
stack = splunk.get("saas/stack/%s" % stack_id)
stack_status = json.loads(
stack.body.read())["entry"][0]["content"]["status"]
if stack_status == stacks.CREATING:
raise errors.RetryOperation()
if stack_status != stacks.CREATED:
raise Exception("unexpected stack status: %s" % stack_status)
logging.info("successfully created stack %s for case %s" %
(stack_id, case_id))
stack_config = stacks.get_stack_config(splunk, stack_id)
kube_client = clusters.create_client(splunk,
stack_config["cluster"])
cluster_config = clusters.get_cluster(splunk, test["cluster"])
node_selector_labels = cluster_config["node_selector"].split(",")
node_selector_for_generators = {}
for label in node_selector_labels:
if label:
kv = label.split("=")
if len(kv) != 2:
raise errors.ApplicationError(
"invalid node selector format (%s)" %
cluster_config.node_selector)
node_selector_for_generators[kv[0]] = kv[1]
apps_api = kubernetes.AppsV1Api(kube_client)
core_api = kubernetes.CoreV1Api(kube_client)
if stack_config["deployment_type"] == "standalone":
indexer_hosts = services.get_load_balancer_hosts(
core_api, stack_id, services.standalone_role,
stack_config["namespace"])
elif stack_config["deployment_type"] == "distributed":
indexer_hosts = services.get_load_balancer_hosts(
core_api, stack_id, services.indexer_role,
stack_config["namespace"])
else:
raise Exception("unexpected deployment type: %s" %
stack_config["deployment_type"])
data_volume_in_gb_per_day = int(case["data_volume_in_gb_per_day"])
logging.debug("data_volume_in_gb_per_day=%s" %
(data_volume_in_gb_per_day))
data_volume_in_gb_per_second = data_volume_in_gb_per_day / 24 / 60 / 60
logging.debug("data_volume_in_gb_per_second=%s" %
(data_volume_in_gb_per_second))
data_volume_in_kb_per_second = data_volume_in_gb_per_second * 1024 * 1024
logging.debug("data_volume_in_kb_per_second=%s" %
(data_volume_in_kb_per_second))
max_kb_per_second_per_data_generator = 100
logging.debug("max_kb_per_second_per_data_generator=%s" %
(max_kb_per_second_per_data_generator))
number_of_data_generators = max(
int(data_volume_in_kb_per_second /
max_kb_per_second_per_data_generator) + 1, 1)
logging.debug("number_of_data_generators=%s" %
(number_of_data_generators))
data_volume_in_kb_per_second_per_data_generator = data_volume_in_kb_per_second / \
number_of_data_generators
logging.debug(
"data_volume_in_kb_per_second_per_data_generator=%s" %
(data_volume_in_kb_per_second_per_data_generator))
deployment_name = "datagen-%s" % (stack_id)
try:
apps_api.read_namespaced_deployment(
deployment_name, namespace=stack_config["namespace"])
data_gen_deployment_already_exists = True
except kubernetes.rest.ApiException as e:
if e.status != 404:
raise
data_gen_deployment_already_exists = False
if not data_gen_deployment_already_exists:
apps_api.create_namespaced_deployment(
namespace=stack_config["namespace"],
body=kubernetes.V1Deployment(
metadata=kubernetes.V1ObjectMeta(
name=deployment_name,
namespace=stack_config["namespace"],
labels={
"app": "datagen",
"test": test_id,
"case": case_id,
},
),
spec=kubernetes.V1DeploymentSpec(
replicas=number_of_data_generators,
selector=kubernetes.V1LabelSelector(
match_labels={
"name": "datagen-%s" % (stack_id),
}),
template=kubernetes.V1PodTemplateSpec(
metadata=kubernetes.V1ObjectMeta(labels={
"name":
"datagen-%s" % (stack_id),
"app":
"datagen",
"test":
test_id,
"case":
case_id,
"stack":
stack_id,
}, ),
spec=kubernetes.V1PodSpec(
containers=[
kubernetes.V1Container(
name="datagen",
image=
"blackhypothesis/splunkeventgenerator:latest",
resources=kubernetes.
V1ResourceRequirements(
requests={
"memory": "10Mi",
"cpu": "500m",
},
limits={
"memory": "50Mi",
"cpu": "1",
},
),
env=[
kubernetes.V1EnvVar(
name="DSTHOST",
value=";".join(
map(
lambda host: host +
":9996",
indexer_hosts)),
),
kubernetes.V1EnvVar(
name="KB_S",
value="%s" %
data_volume_in_kb_per_second_per_data_generator,
),
],
),
],
node_selector=node_selector_for_generators,
),
),
),
),
)
logging.info("created %s data generators for case %s" %
(number_of_data_generators, case_id))
if stack_config["deployment_type"] == "standalone":
search_head_host = services.get_load_balancer_hosts(
core_api, stack_id, services.standalone_role,
stack_config["namespace"])[0]
elif stack_config["deployment_type"] == "distributed":
search_head_host = services.get_load_balancer_hosts(
core_api, stack_id, services.search_head_role,
stack_config["namespace"])[0]
else:
raise Exception("unexpected deployment type: %s" %
stack_config["deployment_type"])
searches_per_day = int(case["searches_per_day"])
logging.debug("searches_per_day=%s" % (searches_per_day))
searches_per_second = searches_per_day / 24 / 60 / 60
logging.debug("searches_per_second=%s" % (searches_per_second))
max_searches_per_second_per_generator = 5
logging.debug("max_searches_per_second_per_generator=%s" %
(max_searches_per_second_per_generator))
number_of_search_generators = max(
int(searches_per_second /
max_searches_per_second_per_generator) + 1, 1)
logging.debug("number_of_search_generators=%s" %
(number_of_search_generators))
searches_per_second_per_generator = searches_per_second / \
number_of_search_generators
logging.debug("searches_per_second_per_generator=%s" %
(searches_per_second_per_generator))
search_template = case["search_template"]
if searches_per_day > 0 and search_template:
deployment_name = "searchgen-%s" % (stack_id)
try:
apps_api.read_namespaced_deployment(
deployment_name, namespace=stack_config["namespace"])
search_gen_deployment_already_exists = True
except kubernetes.rest.ApiException as e:
if e.status != 404:
raise
search_gen_deployment_already_exists = False
if not search_gen_deployment_already_exists:
admin_password = instances.get_admin_password(
core_api, stack_id, stack_config,
services.search_head_role)
apps_api.create_namespaced_deployment(
namespace=stack_config["namespace"],
body=kubernetes.V1Deployment(
metadata=kubernetes.V1ObjectMeta(
name=deployment_name,
namespace=stack_config["namespace"],
labels={
"app": "searchgen",
"test": test_id,
"case": case_id,
},
),
spec=kubernetes.V1DeploymentSpec(
replicas=number_of_search_generators,
selector=kubernetes.V1LabelSelector(
match_labels={
"name": "searchgen-%s" % (stack_id),
}),
template=kubernetes.V1PodTemplateSpec(
metadata=kubernetes.V1ObjectMeta(labels={
"name":
"searchgen-%s" % (stack_id),
"app":
"searchgen",
"test":
test_id,
"case":
case_id,
"stack":
stack_id,
}, ),
spec=kubernetes.V1PodSpec(
containers=[
kubernetes.V1Container(
name="searchgen",
image=
"hovu96/splunk-searchgen:latest",
resources=kubernetes.
V1ResourceRequirements(
requests={
"memory": "10Mi",
"cpu": "500m",
},
limits={
"memory": "50Mi",
"cpu": "1",
},
),
env=[
kubernetes.V1EnvVar(
name="SEARCH_GEN_SPL",
value=search_template,
),
kubernetes.V1EnvVar(
name="SEARCH_GEN_HOST",
value=search_head_host,
),
kubernetes.V1EnvVar(
name="SEARCH_GEN_USER",
value="admin",
),
kubernetes.V1EnvVar(
name=
"SEARCH_GEN_PASSWORD",
value=admin_password,
),
kubernetes.V1EnvVar(
name="SEARCH_GEN_SPS",
value="%s" %
searches_per_second_per_generator,
),
],
),
],
node_selector=
node_selector_for_generators,
),
),
),
),
)
logging.info("created %s search generators for case %s" %
(number_of_search_generators, case_id))
else:
logging.info("no search generators started")
case.update({
"status": CASE_RUNNING,
"time_started_running": time.time(),
})
cases_collection.update(case_id, json.dumps(case))
raise errors.RetryOperation("running test case %s ..." % case_id)
elif status == CASE_RUNNING:
time_started_running = case["time_started_running"]
time_now = time.time()
seconds_running_to_far = time_now - time_started_running
target_run_duration = test["run_duration"]
logging.debug(
"time_started_running=%s time_now=%s seconds_running_to_far=%s"
% (time_started_running, time_now, seconds_running_to_far))
if seconds_running_to_far < (target_run_duration * 60):
logging.debug("still waiting")
raise errors.RetryOperation()
logging.info("time elapsed for case %s" % (case_id))
case.update({
"status": CASE_STOPPING,
"time_finished_running": time.time(),
})
cases_collection.update(case_id, json.dumps(case))
raise errors.RetryOperation("stopping test case %s" % case_id)
elif status == CASE_STOPPING:
stop_case(splunk, test_id, case_id, case)
case.update({
"status": CASE_FINISHED,
})
cases_collection.update(case_id, json.dumps(case))
logging.info("finished test case %s" % case_id)
else:
logging.error("run_cases: unexpected status for test case %s: %s" %
(case_id, status))
raise errors.RetryOperation()