class RedpandaTest(Test):
"""
Base class for tests that use the Redpanda service.
"""
# List of topics to be created automatically when the cluster starts. Each
# topic is defined by an instance of a TopicSpec.
topics = ()
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
topics=None,
log_level='info'):
super(RedpandaTest, self).__init__(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers=num_brokers,
extra_rp_conf=extra_rp_conf,
topics=self.topics,
log_level=log_level)
@property
def topic(self):
"""
Return the name of the auto-created initial topic. Accessing this
property requires exactly one initial topic be configured.
"""
assert len(self.topics) == 1
return self.topics[0].name
def setUp(self):
self.redpanda.start()
def test_adding_nodes_to_cluster(self):
self.redpanda = RedpandaService(
self.test_context, 3, extra_rp_conf={"group_topic_partitions": 1})
# start single node cluster
self.redpanda.start(nodes=[self.redpanda.nodes[0]])
# create some topics
topics = []
# include __consumer_offsets topic replica
total_replicas = 1
for partition_count in range(1, 5):
name = f"topic{len(topics)}"
spec = TopicSpec(name=name,
partition_count=partition_count,
replication_factor=1)
total_replicas += partition_count
topics.append(spec)
for spec in topics:
DefaultClient(self.redpanda).create_topic(spec)
self.topic = spec.name
self.start_producer(1)
self.start_consumer(1)
self.await_startup()
# add second node
self.redpanda.start_node(self.redpanda.nodes[1])
kafkacat = KafkaCat(self.redpanda)
def _replicas_per_node():
node_replicas = {}
md = kafkacat.metadata()
self.redpanda.logger.info(f"metadata: {md}")
for topic in md['topics']:
for p in topic['partitions']:
for r in p['replicas']:
id = r['id']
if id not in node_replicas:
node_replicas[id] = 0
node_replicas[id] += 1
return node_replicas
def partitions_rebalanced():
per_node = _replicas_per_node()
self.redpanda.logger.info(f"replicas per node: {per_node}")
if len(per_node) < len(self.redpanda.started_nodes()):
return False
replicas = sum(per_node.values())
if replicas != total_replicas:
return False
return all(p[1] > 1 for p in per_node.items())
wait_until(partitions_rebalanced, timeout_sec=30, backoff_sec=1)
# add third node
self.redpanda.start_node(self.redpanda.nodes[2])
wait_until(partitions_rebalanced, timeout_sec=30, backoff_sec=1)
self.run_validation(enable_idempotence=False, consumer_timeout_sec=45)
def test_availability_when_one_node_failed(self):
self.redpanda = RedpandaService(
self.test_context,
3,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 1,
"default_topic_replications": 3,
})
self.redpanda.start()
spec = TopicSpec(name="test-topic",
partition_count=6,
replication_factor=3)
self.redpanda.create_topic(spec)
self.topic = spec.name
self.start_producer(1, throughput=10000)
self.start_consumer(1)
self.await_startup()
# start failure injector with default parameters
self.start_finjector()
self.validate_records()
def _start_redpanda(self, num_brokers):
self._redpanda = RedpandaService(self._context,
num_brokers,
extra_rp_conf=self._extra_rp_conf)
self._redpanda.start()
with open(LibrdkafkaTest.CONF_FILE, "w") as f:
brokers = self._redpanda.brokers()
f.write("metadata.broker.list={}".format(brokers))
def start_redpanda(self, num_nodes=1, extra_rp_conf=None):
if extra_rp_conf is not None:
# merge both configurations, the extra_rp_conf passed in
# paramter takes the precedence
self._extra_rp_conf = {**self._extra_rp_conf, **extra_rp_conf}
assert self.redpanda is None
self.redpanda = RedpandaService(self.test_context,
num_nodes,
extra_rp_conf=self._extra_rp_conf)
self.redpanda.start()
self._client = DefaultClient(self.redpanda)
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
topics=None,
log_level='info'):
super(RedpandaTest, self).__init__(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers=num_brokers,
extra_rp_conf=extra_rp_conf,
topics=self.topics,
log_level=log_level)
class RedpandaTest(Test):
"""
Base class for tests that use the Redpanda service.
"""
# List of topics to be created automatically when the cluster starts. Each
# topic is defined by an instance of a TopicSpec.
topics = []
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
enable_pp=False,
enable_sr=False,
num_cores=3):
super(RedpandaTest, self).__init__(test_context)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers,
extra_rp_conf=extra_rp_conf,
enable_pp=enable_pp,
enable_sr=enable_sr,
num_cores=num_cores)
self._client = DefaultClient(self.redpanda)
@property
def topic(self):
"""
Return the name of the auto-created initial topic. Accessing this
property requires exactly one initial topic be configured.
"""
assert len(self.topics) == 1
return self.topics[0].name
def setUp(self):
self.redpanda.start()
self._create_initial_topics()
def client(self):
return self._client
def _create_initial_topics(self):
config = self.redpanda.security_config()
user = config.get("sasl_plain_username")
passwd = config.get("sasl_plain_password")
client = KafkaCliTools(self.redpanda, user=user, passwd=passwd)
for spec in self.topics:
self.logger.debug(f"Creating initial topic {spec}")
client.create_topic(spec)
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
enable_pp=False,
enable_sr=False,
num_cores=3):
super(RedpandaTest, self).__init__(test_context)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers,
extra_rp_conf=extra_rp_conf,
enable_pp=enable_pp,
enable_sr=enable_sr,
num_cores=num_cores)
self._client = DefaultClient(self.redpanda)
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
enable_pp=False,
enable_sr=False,
num_cores=3):
super(RedpandaTest, self).__init__(test_context)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers,
KafkaCliTools,
extra_rp_conf=extra_rp_conf,
enable_pp=enable_pp,
enable_sr=enable_sr,
topics=self.topics,
num_cores=num_cores)
class RedpandaTest(Test):
"""
Base class for tests that use the Redpanda service.
"""
def __init__(self,
test_context,
num_brokers=3,
extra_rp_conf=dict(),
topics=None,
log_level='info'):
super(RedpandaTest, self).__init__(test_context)
self.redpanda = RedpandaService(test_context,
num_brokers=num_brokers,
extra_rp_conf=extra_rp_conf,
topics=topics,
log_level=log_level)
def setUp(self):
self.redpanda.start()
def start_brokers(self, source_type=kafka_source):
if source_type == TestMirrorMakerService.redpanda_source:
self.source_broker = RedpandaService(self.test_context,
num_brokers=3)
else:
self.source_broker = KafkaServiceAdapter(
self.test_context,
KafkaService(self.test_context,
num_nodes=3,
zk=self.zk,
version=V_3_0_0))
self.redpanda = RedpandaService(self.test_context, num_brokers=3)
self.source_broker.start()
self.redpanda.start()
self.source_client = DefaultClient(self.source_broker)
self.topic.partition_count = 1000 if self.redpanda.dedicated_nodes else 1
self.source_client.create_topic(self.topic)
class EndToEndShadowIndexingBase(EndToEndTest):
segment_size = 1048576 # 1 Mb
s3_topic_name = "panda-topic"
num_brokers = 3
topics = (TopicSpec(
name=s3_topic_name,
partition_count=1,
replication_factor=3,
), )
def __init__(self, test_context, extra_rp_conf=None):
super(EndToEndShadowIndexingBase,
self).__init__(test_context=test_context)
self.test_context = test_context
self.topic = EndToEndShadowIndexingTest.s3_topic_name
self.si_settings = SISettings(
cloud_storage_reconciliation_interval_ms=500,
cloud_storage_max_connections=5,
log_segment_size=EndToEndShadowIndexingTest.segment_size, # 1MB
)
self.s3_bucket_name = self.si_settings.cloud_storage_bucket
self.si_settings.load_context(self.logger, test_context)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(context=self.test_context,
num_brokers=self.num_brokers,
si_settings=self.si_settings,
extra_rp_conf=extra_rp_conf)
self.kafka_tools = KafkaCliTools(self.redpanda)
def setUp(self):
self.redpanda.start()
for topic in EndToEndShadowIndexingBase.topics:
self.kafka_tools.create_topic(topic)
def tearDown(self):
self.s3_client.empty_bucket(self.s3_bucket_name)
def __init__(self, test_context, extra_rp_conf=None):
super(EndToEndShadowIndexingBase,
self).__init__(test_context=test_context)
self.test_context = test_context
self.topic = EndToEndShadowIndexingTest.s3_topic_name
self.si_settings = SISettings(
cloud_storage_reconciliation_interval_ms=500,
cloud_storage_max_connections=5,
log_segment_size=EndToEndShadowIndexingTest.segment_size, # 1MB
)
self.s3_bucket_name = self.si_settings.cloud_storage_bucket
self.si_settings.load_context(self.logger, test_context)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(context=self.test_context,
num_brokers=self.num_brokers,
si_settings=self.si_settings,
extra_rp_conf=extra_rp_conf)
self.kafka_tools = KafkaCliTools(self.redpanda)
def __init__(self, test_context):
super(EndToEndShadowIndexingTest,
self).__init__(test_context=test_context)
self.s3_bucket_name = f"panda-bucket-{uuid.uuid1()}"
self.topic = EndToEndShadowIndexingTest.s3_topic_name
self._extra_rp_conf = dict(
cloud_storage_enabled=True,
cloud_storage_enable_remote_read=True,
cloud_storage_enable_remote_write=True,
cloud_storage_access_key=EndToEndShadowIndexingTest.s3_access_key,
cloud_storage_secret_key=EndToEndShadowIndexingTest.s3_secret_key,
cloud_storage_region=EndToEndShadowIndexingTest.s3_region,
cloud_storage_bucket=self.s3_bucket_name,
cloud_storage_disable_tls=True,
cloud_storage_api_endpoint=EndToEndShadowIndexingTest.s3_host_name,
cloud_storage_api_endpoint_port=9000,
cloud_storage_reconciliation_interval_ms=500,
cloud_storage_max_connections=5,
log_segment_size=EndToEndShadowIndexingTest.segment_size, # 1MB
)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(
context=test_context,
num_brokers=3,
client_type=KafkaCliTools,
extra_rp_conf=self._extra_rp_conf,
topics=EndToEndShadowIndexingTest.topics,
)
self.kafka_tools = KafkaCliTools(self.redpanda)
self.s3_client = S3Client(
region=EndToEndShadowIndexingTest.s3_region,
access_key=EndToEndShadowIndexingTest.s3_access_key,
secret_key=EndToEndShadowIndexingTest.s3_secret_key,
endpoint=f"http://{EndToEndShadowIndexingTest.s3_host_name}:9000",
logger=self.logger,
)
def __init__(self,
test_context,
num_brokers=None,
extra_rp_conf=dict(),
enable_pp=False,
enable_sr=False,
si_settings=None,
**kwargs):
"""
Any trailing keyword arguments are passed through to the
RedpandaService constructor.
"""
super(RedpandaTest, self).__init__(test_context)
self.scale = Scale(test_context)
self.si_settings = si_settings
if num_brokers is None:
# Default to a 3 node cluster if sufficient nodes are available, else
# a single node cluster. This is just a default: tests are welcome
# to override constructor to pass an explicit size. This logic makes
# it convenient to mix 3 node and 1 node cases in the same class, by
# just modifying the @cluster node count per test.
if test_context.cluster.available().size() >= 3:
num_brokers = 3
else:
num_brokers = 1
if self.si_settings:
self.si_settings.load_context(self.logger, test_context)
self.redpanda = RedpandaService(test_context,
num_brokers,
extra_rp_conf=extra_rp_conf,
enable_pp=enable_pp,
enable_sr=enable_sr,
si_settings=self.si_settings,
**kwargs)
self._client = DefaultClient(self.redpanda)
def test_recovery_after_catastrophic_failure(self):
self.redpanda = RedpandaService(
self.test_context,
3,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 1,
"default_topic_replications": 3,
})
self.redpanda.start()
spec = TopicSpec(name="test-topic",
partition_count=6,
replication_factor=3)
self.redpanda.create_topic(spec)
self.topic = spec.name
self.start_producer(1, throughput=10000)
self.start_consumer(1)
self.await_startup()
# inject permanent random failure
f_spec = FailureSpec(random.choice(FailureSpec.FAILURE_TYPES),
random.choice(self.redpanda.nodes[0:1]))
self.inject_failure(f_spec)
# inject transient failure on other node
f_spec = FailureSpec(random.choice(FailureSpec.FAILURE_TYPES),
self.redpanda.nodes[2],
length=2.0 if self.scale.local else 15.0)
self.inject_failure(f_spec)
self.validate_records()
class AvailabilityTests(EndToEndFinjectorTest):
def validate_records(self):
min_records = 40000
producer_timeout_sec = 60
consumer_timeout_sec = 60
if self.scale.ci or self.scale.release:
min_records = 100000
producer_timeout_sec = 180
consumer_timeout_sec = 180
self.run_validation(min_records=min_records,
enable_idempotence=False,
producer_timeout_sec=producer_timeout_sec,
consumer_timeout_sec=consumer_timeout_sec)
@cluster(num_nodes=5)
def test_availability_when_one_node_failed(self):
self.redpanda = RedpandaService(
self.test_context,
3,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 1,
"default_topic_replications": 3,
})
self.redpanda.start()
spec = TopicSpec(name="test-topic",
partition_count=6,
replication_factor=3)
self.redpanda.create_topic(spec)
self.topic = spec.name
self.start_producer(1, throughput=10000)
self.start_consumer(1)
self.await_startup()
# start failure injector with default parameters
self.start_finjector()
self.validate_records()
@cluster(num_nodes=5)
def test_recovery_after_catastrophic_failure(self):
self.redpanda = RedpandaService(
self.test_context,
3,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 1,
"default_topic_replications": 3,
})
self.redpanda.start()
spec = TopicSpec(name="test-topic",
partition_count=6,
replication_factor=3)
self.redpanda.create_topic(spec)
self.topic = spec.name
self.start_producer(1, throughput=10000)
self.start_consumer(1)
self.await_startup()
# inject permanent random failure
f_spec = FailureSpec(random.choice(FailureSpec.FAILURE_TYPES),
random.choice(self.redpanda.nodes[0:1]))
self.inject_failure(f_spec)
# inject transient failure on other node
f_spec = FailureSpec(random.choice(FailureSpec.FAILURE_TYPES),
self.redpanda.nodes[2],
length=2.0 if self.scale.local else 15.0)
self.inject_failure(f_spec)
self.validate_records()
class NodeOperationFuzzyTest(EndToEndTest):
def generate_random_workload(self, count, skip_nodes):
op_types = [ADD, DECOMMISSION]
tp_op_types = [ADD_TOPIC, DELETE_TOPIC]
# current state
active_nodes = [1, 2, 3, 4, 5]
decommissioned_nodes = []
operations = []
topics = []
def eligible_active_nodes():
return list(
filter(lambda n: not (n == 1 or n in skip_nodes),
active_nodes))
def decommission(id):
active_nodes.remove(id)
decommissioned_nodes.append(id)
def add(id):
active_nodes.append(id)
decommissioned_nodes.remove(id)
for _ in range(0, count):
if len(decommissioned_nodes) == 2:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
elif len(decommissioned_nodes) == 0:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
else:
op = random.choice(op_types)
if op == DECOMMISSION:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
elif op == ADD:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
# topic operation
if len(topics) == 0:
op = ADD_TOPIC
else:
op = random.choice(tp_op_types)
if op == ADD_TOPIC:
operations.append((
ADD_TOPIC,
f"test-topic-{random.randint(0,2000)}-{round(time.time()*1000000)}",
random.choice(ALLOWED_REPLICATION), 3))
else:
operations.append((DELETE_TOPIC, random.choice(topics)))
return operations
def _create_random_topics(self, count):
max_partitions = 10
topics = []
for i in range(0, count):
name = f"topic-{i}"
spec = TopicSpec(
name=name,
partition_count=random.randint(1, max_partitions),
replication_factor=random.choice(ALLOWED_REPLICATION))
topics.append(spec)
for spec in topics:
self.redpanda.create_topic(spec)
return topics
"""
Adding nodes to the cluster should result in partition reallocations to new
nodes
"""
@cluster(num_nodes=7)
@parametrize(enable_failures=True)
@parametrize(enable_failures=False)
def test_node_opeartions(self, enable_failures):
# allocate 5 nodes for the cluster
self.redpanda = RedpandaService(
self.test_context,
5,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 3,
"default_topic_replications": 3,
})
self.active_nodes = set([1, 2, 3, 4, 5])
self.redpanda.start()
# create some topics
topics = self._create_random_topics(10)
self.redpanda.logger.info(f"using topics: {topics}")
# select one of the topics to use in consumer/producer
self.topic = random.choice(topics).name
self.start_producer(1, throughput=100)
self.start_consumer(1)
self.await_startup()
NODE_OP_TIMEOUT = 360
def failure_injector_loop():
f_injector = FailureInjector(self.redpanda)
while enable_failures:
f_type = random.choice(FailureSpec.FAILURE_TYPES)
length = 0
# allow suspending any node
if f_type == FailureSpec.FAILURE_SUSPEND:
length = random.randint(1, 10)
node = random.choice(self.redpanda.nodes)
else:
#kill/termianate only active nodes (not to influence the test outcome)
idx = random.choice(list(self.active_nodes)) - 1
node = self.redpanda.nodes[idx]
f_injector.inject_failure(
FailureSpec(node=node, type=f_type, length=length))
delay = random.randint(20, 45)
self.redpanda.logger.info(
f"waiting {delay} seconds before next failure")
time.sleep(delay)
if enable_failures:
finjector_thread = threading.Thread(target=failure_injector_loop,
args=())
finjector_thread.daemon = True
finjector_thread.start()
def decommission(node_id):
self.logger.info(f"decommissioning node: {node_id}")
def decommissioned():
try:
admin = Admin(self.redpanda)
# if broker is already draining, it is suceess
brokers = admin.get_brokers()
for b in brokers:
if b['node_id'] == node_id and b[
'membership_status'] == 'draining':
return True
r = admin.decommission_broker(id=node_id)
return r.status_code == 200
except requests.exceptions.RetryError:
return False
except requests.exceptions.ConnectionError:
return False
except requests.exceptions.HTTPError:
return False
wait_until(decommissioned,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
def node_removed():
admin = Admin(self.redpanda)
try:
brokers = admin.get_brokers(node=self.redpanda.nodes[0])
for b in brokers:
if b['node_id'] == node_id:
return False
return True
except:
return False
wait_until(node_removed,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
kafkacat = KafkaCat(self.redpanda)
def replicas_per_node():
node_replicas = {}
md = kafkacat.metadata()
self.redpanda.logger.info(f"metadata: {md}")
for topic in md['topics']:
for p in topic['partitions']:
for r in p['replicas']:
id = r['id']
if id not in node_replicas:
node_replicas[id] = 0
node_replicas[id] += 1
return node_replicas
def restart_node(node_id, cleanup=True):
self.logger.info(f"restarting node: {node_id}")
self.redpanda.stop_node(self.redpanda.nodes[node_id - 1])
if cleanup:
self.redpanda.clean_node(self.redpanda.nodes[node_id - 1],
preserve_logs=True)
self.redpanda.start_node(self.redpanda.nodes[node_id - 1])
def has_new_replicas():
per_node = replicas_per_node()
self.logger.info(f"replicas per node: {per_node}")
return node_id in per_node
wait_until(has_new_replicas,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
def is_topic_present(name):
kcl = KCL(self.redpanda)
lines = kcl.list_topics().splitlines()
self.redpanda.logger.debug(
f"checking if topic {name} is present in {lines}")
for l in lines:
if l.startswith(name):
return True
return False
def create_topic(spec):
try:
self.redpanda.create_topic(spec)
except Exception as e:
self.redpanda.logger.warn(
f"error creating topic {spec.name} - {e}")
try:
return is_topic_present(spec.name)
except Exception as e:
self.redpanda.logger.warn(f"error while listing topics - {e}")
return False
def delete_topic(name):
try:
self.redpanda.delete_topic(name)
except Exception as e:
self.redpanda.logger.warn(f"error deleting topic {name} - {e}")
try:
return not is_topic_present(name)
except Exception as e:
self.redpanda.logger.warn(f"error while listing topics - {e}")
return False
work = self.generate_random_workload(10, skip_nodes=set())
self.redpanda.logger.info(f"node operations to execute: {work}")
for op in work:
op_type = op[0]
self.logger.info(f"executing - {op}")
if op_type == ADD:
id = op[1]
self.active_nodes.add(id)
restart_node(id)
if op_type == DECOMMISSION:
id = op[1]
self.active_nodes.remove(id)
decommission(id)
elif op_type == ADD_TOPIC:
spec = TopicSpec(name=op[1],
replication_factor=op[2],
partition_count=op[3])
wait_until(lambda: create_topic(spec) == True,
timeout_sec=180,
backoff_sec=2)
elif op_type == DELETE_TOPIC:
wait_until(lambda: delete_topic(op[1]) == True,
timeout_sec=180,
backoff_sec=2)
enable_failures = False
self.run_validation(enable_idempotence=False,
producer_timeout_sec=60,
consumer_timeout_sec=180)
class NodeOperationFuzzyTest(EndToEndTest):
def generate_random_workload(self, count, skip_nodes):
op_types = [ADD, DECOMMISSION]
tp_op_types = [ADD_TOPIC, DELETE_TOPIC]
# current state
active_nodes = [1, 2, 3, 4, 5]
decommissioned_nodes = []
operations = []
topics = []
def eligible_active_nodes():
return list(
filter(lambda n: not (n == 1 or n in skip_nodes),
active_nodes))
def decommission(id):
active_nodes.remove(id)
decommissioned_nodes.append(id)
def add(id):
active_nodes.append(id)
decommissioned_nodes.remove(id)
for _ in range(0, count):
if len(decommissioned_nodes) == 2:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
elif len(decommissioned_nodes) == 0:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
else:
op = random.choice(op_types)
if op == DECOMMISSION:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
elif op == ADD:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
# topic operation
if len(topics) == 0:
op = ADD_TOPIC
else:
op = random.choice(tp_op_types)
if op == ADD_TOPIC:
operations.append((
ADD_TOPIC,
f"test-topic-{random.randint(0,2000)}-{time.time()*1000.0}",
random.choice(ALLOWED_REPLICATION), 3))
else:
operations.append((DELETE_TOPIC, random.choice(topics)))
return operations
def _create_random_topics(self, count):
max_partitions = 10
topics = []
for i in range(0, count):
name = f"topic-{i}"
spec = TopicSpec(
name=name,
partition_count=random.randint(1, max_partitions),
replication_factor=random.choice(ALLOWED_REPLICATION))
topics.append(spec)
for spec in topics:
self.redpanda.create_topic(spec)
return topics
"""
Adding nodes to the cluster should result in partition reallocations to new
nodes
"""
@cluster(num_nodes=7)
def test_node_opeartions(self):
# allocate 5 nodes for the cluster
self.redpanda = RedpandaService(
self.test_context,
5,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 3,
"default_topic_replications": 3,
})
# start 3 nodes
self.redpanda.start()
# create some topics
topics = self._create_random_topics(10)
self.redpanda.logger.info(f"using topics: {topics}")
# select one of the topics to use in consumer/producer
self.topic = random.choice(topics).name
self.start_producer(1, throughput=100)
self.start_consumer(1)
self.await_startup()
def decommission(node_id):
self.logger.info(f"decommissioning node: {node_id}")
admin = Admin(self.redpanda)
admin.decommission_broker(id=node_id)
def node_removed():
admin = Admin(self.redpanda)
brokers = admin.get_brokers()
for b in brokers:
if b['node_id'] == node_id:
return False
return True
wait_until(node_removed, timeout_sec=240, backoff_sec=2)
kafkacat = KafkaCat(self.redpanda)
def replicas_per_node():
node_replicas = {}
md = kafkacat.metadata()
self.redpanda.logger.info(f"metadata: {md}")
for topic in md['topics']:
for p in topic['partitions']:
for r in p['replicas']:
id = r['id']
if id not in node_replicas:
node_replicas[id] = 0
node_replicas[id] += 1
return node_replicas
def restart_node(node_id, cleanup=True):
self.logger.info(f"restarting node: {node_id}")
self.redpanda.stop_node(self.redpanda.nodes[node_id - 1])
if cleanup:
self.redpanda.clean_node(self.redpanda.nodes[node_id - 1])
self.redpanda.start_node(self.redpanda.nodes[node_id - 1])
admin = Admin(self.redpanda)
admin.set_log_level("cluster", "trace")
def has_new_replicas():
per_node = replicas_per_node()
self.logger.info(f"replicas per node: {per_node}")
return node_id in per_node
wait_until(has_new_replicas, timeout_sec=240, backoff_sec=2)
admin = Admin(self.redpanda)
admin.set_log_level("cluster", "trace")
work = self.generate_random_workload(10, skip_nodes=set())
self.redpanda.logger.info(f"node operations to execute: {work}")
for op in work:
op_type = op[0]
self.logger.info(f"executing - {op}")
if op_type == ADD:
id = op[1]
restart_node(id)
if op_type == DECOMMISSION:
id = op[1]
decommission(id)
elif op_type == ADD_TOPIC:
spec = TopicSpec(name=op[1],
replication_factor=op[2],
partition_count=op[3])
self.redpanda.create_topic(spec)
elif op_type == DELETE_TOPIC:
self.redpanda.delete_topic(op[1])
self.run_validation(enable_idempotence=False, consumer_timeout_sec=180)
def test_node_opeartions(self):
# allocate 5 nodes for the cluster
self.redpanda = RedpandaService(
self.test_context,
5,
KafkaCliTools,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 3,
"default_topic_replications": 3,
})
# start 3 nodes
self.redpanda.start()
# create some topics
topics = self._create_random_topics(10)
self.redpanda.logger.info(f"using topics: {topics}")
# select one of the topics to use in consumer/producer
self.topic = random.choice(topics).name
self.start_producer(1, throughput=100)
self.start_consumer(1)
self.await_startup()
def decommission(node_id):
self.logger.info(f"decommissioning node: {node_id}")
admin = Admin(self.redpanda)
admin.decommission_broker(id=node_id)
def node_removed():
admin = Admin(self.redpanda)
brokers = admin.get_brokers()
for b in brokers:
if b['node_id'] == node_id:
return False
return True
wait_until(node_removed, timeout_sec=240, backoff_sec=2)
kafkacat = KafkaCat(self.redpanda)
def replicas_per_node():
node_replicas = {}
md = kafkacat.metadata()
self.redpanda.logger.info(f"metadata: {md}")
for topic in md['topics']:
for p in topic['partitions']:
for r in p['replicas']:
id = r['id']
if id not in node_replicas:
node_replicas[id] = 0
node_replicas[id] += 1
return node_replicas
def restart_node(node_id, cleanup=True):
self.logger.info(f"restarting node: {node_id}")
self.redpanda.stop_node(self.redpanda.nodes[node_id - 1])
if cleanup:
self.redpanda.clean_node(self.redpanda.nodes[node_id - 1])
self.redpanda.start_node(self.redpanda.nodes[node_id - 1])
admin = Admin(self.redpanda)
admin.set_log_level("cluster", "trace")
def has_new_replicas():
per_node = replicas_per_node()
self.logger.info(f"replicas per node: {per_node}")
return node_id in per_node
wait_until(has_new_replicas, timeout_sec=240, backoff_sec=2)
admin = Admin(self.redpanda)
admin.set_log_level("cluster", "trace")
work = self.generate_random_workload(10, skip_nodes=set())
self.redpanda.logger.info(f"node operations to execute: {work}")
for op in work:
op_type = op[0]
self.logger.info(f"executing - {op}")
if op_type == ADD:
id = op[1]
restart_node(id)
if op_type == DECOMMISSION:
id = op[1]
decommission(id)
elif op_type == ADD_TOPIC:
spec = TopicSpec(name=op[1],
replication_factor=op[2],
partition_count=op[3])
self.redpanda.create_topic(spec)
elif op_type == DELETE_TOPIC:
self.redpanda.delete_topic(op[1])
self.run_validation(enable_idempotence=False, consumer_timeout_sec=180)
def test_fetch_after_committed_offset_was_removed(self,
transactions_enabled):
"""
Test fetching when consumer offset was deleted by retention
"""
segment_size = 1048576
self.redpanda = RedpandaService(self.test_context,
3,
KafkaCliTools,
extra_rp_conf={
"enable_transactions":
transactions_enabled,
"enable_idempotence":
transactions_enabled,
"log_compaction_interval_ms": 5000,
"log_segment_size": segment_size,
"enable_leader_balancer": False,
})
self.redpanda.start()
topic = TopicSpec(partition_count=1,
replication_factor=3,
cleanup_policy=TopicSpec.CLEANUP_DELETE)
self.redpanda.create_topic(topic)
self.topic = topic.name
kafka_tools = KafkaCliTools(self.redpanda)
# produce until segments have been compacted
produce_until_segments(
self.redpanda,
topic=self.topic,
partition_idx=0,
count=10,
)
consumer_group = 'test'
rpk = RpkTool(self.redpanda)
def consume(n=1):
out = rpk.consume(self.topic, group=consumer_group, n=n)
split = out.split('}')
split = filter(lambda s: "{" in s, split)
return map(lambda s: json.loads(s + "}"), split)
#consume from the beggining
msgs = consume(10)
last = list(msgs).pop()
offset = last['offset']
# change retention time
kafka_tools.alter_topic_config(
self.topic, {
TopicSpec.PROPERTY_RETENTION_BYTES: 2 * segment_size,
})
wait_for_segments_removal(self.redpanda,
self.topic,
partition_idx=0,
count=5)
partitions = list(rpk.describe_topic(self.topic))
p = partitions[0]
assert p.start_offset > offset
# consume from the offset that doesn't exists,
# the one that was committed previously was already removed
out = list(consume(1))
assert out[0]['offset'] == p.start_offset
def test_cluster_is_available_during_upgrade_without_group_topic(self):
'''
Validates that cluster is available and healthy during
upgrade when `kafka_internal::group` topic is not present
'''
# set redpanda logical version to value without __consumer_offsets support
self.redpanda = RedpandaService(
self.test_context,
5,
extra_rp_conf={
"group_topic_partitions": 16,
"default_topic_replications": 3,
},
environment={"__REDPANDA_LOGICAL_VERSION": 1})
self.redpanda.start()
self._client = DefaultClient(self.redpanda)
spec = TopicSpec(partition_count=6, replication_factor=3)
self.client().create_topic(spec)
self.topic = spec.name
def cluster_is_stable():
admin = Admin(self.redpanda)
brokers = admin.get_brokers()
if len(brokers) < 3:
return False
for b in brokers:
self.logger.debug(f"broker: {b}")
if not (b['is_alive'] and 'disk_space' in b):
return False
return True
def node_stopped(node_id):
admin = Admin(self.redpanda)
brokers = admin.get_brokers()
for b in brokers:
self.logger.debug(f"broker: {b}")
if b['node_id'] == node_id:
return b['is_alive'] == False
return False
kcl = KCL(self.redpanda)
# check that consumer offsets topic is not present
topics = set(kcl.list_topics())
assert "__consumer_offsets" not in topics
# enable consumer offsets support
self.redpanda.set_environment({"__REDPANDA_LOGICAL_VERSION": 2})
def get_raft0_follower():
ctrl = self.redpanda.controller
node = random.choice(self.redpanda.nodes)
while self.redpanda.idx(node) == self.redpanda.idx(ctrl):
node = random.choice(self.redpanda.nodes)
return node
# restart node that is not controller
n = get_raft0_follower()
self.logger.info(f"restarting node {n.account.hostname}")
self.redpanda.stop_node(n, timeout=60)
# wait for leader balancer to start evening out leadership
wait_until(lambda: node_stopped(self.redpanda.idx(n)),
90,
backoff_sec=2)
self.redpanda.start_node(n)
wait_until(cluster_is_stable, 90, backoff_sec=2)
class TestMirrorMakerService(EndToEndTest):
kafka_source = "kafka"
redpanda_source = "redpanda"
def __init__(self, test_context):
super(TestMirrorMakerService, self).__init__(test_context)
self.topic = TopicSpec(replication_factor=3)
# create single zookeeper node for Kafka
self.zk = ZookeeperService(self.test_context,
num_nodes=1,
version=V_3_0_0)
self.source_broker = None
def setUp(self):
self.zk.start()
def tearDown(self):
# ducktape handle service teardown automatically, but it is hard
# to tell what went wrong if one of the services hangs. Do it
# explicitly here with some logging, to enable debugging issues
# like https://github.com/redpanda-data/redpanda/issues/4270
if self.source_broker is not None:
self.logger.info(
f"Stopping source broker ({self.source_broker.__class__.__name__})..."
)
self.source_broker.stop()
self.logger.info(
f"Awaiting source broker ({self.source_broker.__class__.__name__})..."
)
self.logger.info("Stopping zookeeper...")
self.zk.stop()
self.logger.info("Awaiting zookeeper...")
def start_brokers(self, source_type=kafka_source):
if source_type == TestMirrorMakerService.redpanda_source:
self.source_broker = RedpandaService(self.test_context,
num_brokers=3)
else:
self.source_broker = KafkaServiceAdapter(
self.test_context,
KafkaService(self.test_context,
num_nodes=3,
zk=self.zk,
version=V_3_0_0))
self.redpanda = RedpandaService(self.test_context, num_brokers=3)
self.source_broker.start()
self.redpanda.start()
self.source_client = DefaultClient(self.source_broker)
self.topic.partition_count = 1000 if self.redpanda.dedicated_nodes else 1
self.source_client.create_topic(self.topic)
def start_workload(self):
self.consumer = VerifiableConsumer(
self.test_context,
num_nodes=1,
redpanda=self.redpanda,
topic=self.topic.name,
group_id='consumer_test_group',
on_record_consumed=self.on_record_consumed)
self.consumer.start()
self.producer = VerifiableProducer(
self.test_context,
num_nodes=1,
redpanda=self.source_broker,
topic=self.topic.name,
throughput=1000,
message_validator=is_int_with_prefix)
self.producer.start()
def wait_for_n_messages(self, n_messages=100):
"""Wait for a minimum number of messages to be successfully produced."""
wait_until(
lambda: self.producer.num_acked > n_messages,
timeout_sec=10,
err_msg=
"Producer failed to produce %d messages in a reasonable amount of time."
% n_messages)
@cluster(num_nodes=10)
@parametrize(source_type=kafka_source)
@parametrize(source_type=redpanda_source)
def test_simple_end_to_end(self, source_type):
# start brokers
self.start_brokers(source_type=source_type)
# start mirror maker
self.mirror_maker = MirrorMaker2(self.test_context,
num_nodes=1,
source_cluster=self.source_broker,
target_cluster=self.redpanda)
topics = []
for i in range(0, 10):
topics.append(
TopicSpec(partition_count=random.randint(1, 10),
retention_bytes=random.randint(100000000, 300000000),
retention_ms=random.randint(1 * 3600000,
10 * 3600000)))
self.source_client.create_topic(topics)
self.mirror_maker.start()
# start source producer & target consumer
self.start_workload()
self.run_validation(consumer_timeout_sec=120)
self.mirror_maker.stop()
target_client = DefaultClient(self.redpanda)
for t in topics:
desc = target_client.describe_topic(t.name)
self.logger.debug(f'source topic: {t}, target topic: {desc}')
assert len(desc.partitions) == t.partition_count
@cluster(num_nodes=9)
@parametrize(source_type=kafka_source)
@parametrize(source_type=redpanda_source)
def test_consumer_group_mirroring(self, source_type):
# start redpanda
self.start_brokers(source_type=source_type)
consumer_group = "test-group-1"
# start mirror maker
self.mirror_maker = MirrorMaker2(self.test_context,
num_nodes=1,
source_cluster=self.source_broker,
target_cluster=self.redpanda,
consumer_group_pattern=consumer_group,
log_level="TRACE")
self.mirror_maker.start()
msg_size = 512
msg_cnt = 1000000 if self.redpanda.dedicated_nodes else 100
# produce some messages to source redpanda
producer = RpkProducer(self.test_context,
self.source_broker,
self.topic.name,
msg_size,
msg_cnt,
acks=-1)
producer.start()
producer.wait()
producer.free()
# consume some messages from source redpanda
consumer = RpkConsumer(self.test_context,
self.source_broker,
self.topic.name,
ignore_errors=False,
retries=3,
group=consumer_group,
save_msgs=False,
num_msgs=int(msg_cnt / 5))
consumer.start()
consumer.wait()
consumer.stop()
source_messages = consumer.messages
self.logger.info(f"source message count: {len(source_messages)}")
consumer.free()
src_rpk = RpkTool(self.source_broker)
source_group = src_rpk.group_describe(consumer_group)
target_rpk = RpkTool(self.redpanda)
def target_group_equal():
try:
target_group = target_rpk.group_describe(consumer_group)
except RpkException as e:
# e.g. COORDINATOR_NOT_AVAILABLE
self.logger.info(f"Error describing target cluster group: {e}")
return False
self.logger.info(
f"source {source_group}, target_group: {target_group}")
return target_group.partitions == source_group.partitions and target_group.name == source_group.name
# wait for consumer group sync
timeout = 600 if self.redpanda.dedicated_nodes else 60
wait_until(target_group_equal, timeout_sec=timeout, backoff_sec=5)
self.mirror_maker.stop()
class EndToEndTest(Test):
"""
Test for common pattern:
- Produce and consume in the background
- Perform some action (e.g. partition movement)
- Run validation
"""
def __init__(self, test_context, extra_rp_conf=None):
super(EndToEndTest, self).__init__(test_context=test_context)
if extra_rp_conf is None:
self._extra_rp_conf = {}
else:
self._extra_rp_conf = extra_rp_conf
self.records_consumed = []
self.last_consumed_offsets = {}
self.redpanda = None
self.topic = None
self._client = None
def start_redpanda(self, num_nodes=1, extra_rp_conf=None):
if extra_rp_conf is not None:
# merge both configurations, the extra_rp_conf passed in
# paramter takes the precedence
self._extra_rp_conf = {**self._extra_rp_conf, **extra_rp_conf}
assert self.redpanda is None
self.redpanda = RedpandaService(self.test_context,
num_nodes,
extra_rp_conf=self._extra_rp_conf)
self.redpanda.start()
self._client = DefaultClient(self.redpanda)
def client(self):
assert self._client is not None
return self._client
def start_consumer(self, num_nodes=1, group_id="test_group"):
assert self.redpanda
assert self.topic
self.consumer = VerifiableConsumer(
self.test_context,
num_nodes=num_nodes,
redpanda=self.redpanda,
topic=self.topic,
group_id=group_id,
on_record_consumed=self.on_record_consumed)
self.consumer.start()
def start_producer(self, num_nodes=1, throughput=1000):
assert self.redpanda
assert self.topic
self.producer = VerifiableProducer(
self.test_context,
num_nodes=num_nodes,
redpanda=self.redpanda,
topic=self.topic,
throughput=throughput,
message_validator=is_int_with_prefix)
self.producer.start()
def on_record_consumed(self, record, node):
partition = TopicPartition(record["topic"], record["partition"])
record_id = record["value"]
offset = record["offset"]
self.last_consumed_offsets[partition] = offset
self.records_consumed.append(record_id)
def await_consumed_offsets(self, last_acked_offsets, timeout_sec):
def has_finished_consuming():
for partition, offset in last_acked_offsets.items():
if not partition in self.last_consumed_offsets:
return False
last_commit = self.consumer.last_commit(partition)
if not last_commit or last_commit <= offset:
self.logger.debug(
f"waiting for partition {partition} offset {offset} to be committed, last committed offset: {last_commit}"
)
return False
return True
wait_until(has_finished_consuming,
timeout_sec=timeout_sec,
err_msg="Consumer failed to consume up to offsets %s after waiting %ds." %\
(str(last_acked_offsets), timeout_sec))
def _collect_all_logs(self):
for s in self.test_context.services:
self.mark_for_collect(s)
def await_startup(self, min_records=5, timeout_sec=30):
try:
wait_until(lambda: self.consumer.total_consumed() >= min_records,
timeout_sec=timeout_sec,
err_msg="Timed out after %ds while awaiting initial record delivery of %d records" %\
(timeout_sec, min_records))
except BaseException:
self._collect_all_logs()
raise
def run_validation(self,
min_records=5000,
producer_timeout_sec=30,
consumer_timeout_sec=30,
enable_idempotence=False):
try:
wait_until(lambda: self.producer.num_acked > min_records,
timeout_sec=producer_timeout_sec,
err_msg="Producer failed to produce messages for %ds." %\
producer_timeout_sec)
self.logger.info("Stopping producer after writing up to offsets %s" %\
str(self.producer.last_acked_offsets))
self.producer.stop()
self.await_consumed_offsets(self.producer.last_acked_offsets,
consumer_timeout_sec)
self.consumer.stop()
self.validate(enable_idempotence)
except BaseException:
self._collect_all_logs()
raise
def validate(self, enable_idempotence):
self.logger.info("Number of acked records: %d" %
len(self.producer.acked))
self.logger.info("Number of consumed records: %d" %
len(self.records_consumed))
success = True
msg = ""
# Correctness of the set difference operation depends on using equivalent
# message_validators in producer and consumer
missing = set(self.producer.acked) - set(self.records_consumed)
if len(missing) > 0:
success = False
msg = annotate_missing_msgs(missing, self.producer.acked,
self.records_consumed, msg)
# Are there duplicates?
if len(set(self.records_consumed)) != len(self.records_consumed):
num_duplicates = abs(
len(set(self.records_consumed)) - len(self.records_consumed))
if enable_idempotence:
success = False
msg += "Detected %d duplicates even though idempotence was enabled.\n" % num_duplicates
else:
msg += "(There are also %d duplicate messages in the log - but that is an acceptable outcome)\n" % num_duplicates
# Collect all logs if validation fails
if not success:
self._collect_all_logs()
assert success, msg
class EndToEndShadowIndexingTest(EndToEndTest):
segment_size = 1048576 # 1 Mb
s3_host_name = "minio-s3"
s3_access_key = "panda-user"
s3_secret_key = "panda-secret"
s3_region = "panda-region"
s3_topic_name = "panda-topic"
topics = (TopicSpec(
name=s3_topic_name,
partition_count=1,
replication_factor=3,
), )
def __init__(self, test_context):
super(EndToEndShadowIndexingTest,
self).__init__(test_context=test_context)
self.s3_bucket_name = f"panda-bucket-{uuid.uuid1()}"
self.topic = EndToEndShadowIndexingTest.s3_topic_name
self._extra_rp_conf = dict(
cloud_storage_enabled=True,
cloud_storage_enable_remote_read=True,
cloud_storage_enable_remote_write=True,
cloud_storage_access_key=EndToEndShadowIndexingTest.s3_access_key,
cloud_storage_secret_key=EndToEndShadowIndexingTest.s3_secret_key,
cloud_storage_region=EndToEndShadowIndexingTest.s3_region,
cloud_storage_bucket=self.s3_bucket_name,
cloud_storage_disable_tls=True,
cloud_storage_api_endpoint=EndToEndShadowIndexingTest.s3_host_name,
cloud_storage_api_endpoint_port=9000,
cloud_storage_reconciliation_interval_ms=500,
cloud_storage_max_connections=5,
log_segment_size=EndToEndShadowIndexingTest.segment_size, # 1MB
)
self.scale = Scale(test_context)
self.redpanda = RedpandaService(
context=test_context,
num_brokers=3,
extra_rp_conf=self._extra_rp_conf,
)
self.kafka_tools = KafkaCliTools(self.redpanda)
self.s3_client = S3Client(
region=EndToEndShadowIndexingTest.s3_region,
access_key=EndToEndShadowIndexingTest.s3_access_key,
secret_key=EndToEndShadowIndexingTest.s3_secret_key,
endpoint=f"http://{EndToEndShadowIndexingTest.s3_host_name}:9000",
logger=self.logger,
)
def setUp(self):
self.s3_client.empty_bucket(self.s3_bucket_name)
self.s3_client.create_bucket(self.s3_bucket_name)
self.redpanda.start()
for topic in EndToEndShadowIndexingTest.topics:
self.kafka_tools.create_topic(topic)
def tearDown(self):
self.s3_client.empty_bucket(self.s3_bucket_name)
@cluster(num_nodes=5)
def test_write(self):
"""Write at least 10 segments, set retention policy to leave only 5
segments, wait for segments removal, consume data and run validation,
that everything that is acked is consumed."""
self.start_producer()
produce_until_segments(
redpanda=self.redpanda,
topic=self.topic,
partition_idx=0,
count=10,
)
self.kafka_tools.alter_topic_config(
self.topic,
{
TopicSpec.PROPERTY_RETENTION_BYTES:
5 * EndToEndShadowIndexingTest.segment_size,
},
)
wait_for_segments_removal(redpanda=self.redpanda,
topic=self.topic,
partition_idx=0,
count=6)
self.start_consumer()
self.run_validation()
def test_view_changes_on_add(self):
self.redpanda = RedpandaService(self.test_context, 3)
# start single node cluster
self.redpanda.start(nodes=[self.redpanda.nodes[0]])
admin = Admin(self.redpanda)
seed = None
def rp1_started():
nonlocal seed
try:
#{"version": 0, "brokers": [{"node_id": 1, "num_cores": 3, "membership_status": "active", "is_alive": true}]}
seed = admin.get_cluster_view(self.redpanda.nodes[0])
self.redpanda.logger.info(
f"view from {self.redpanda.nodes[0]}: {json.dumps(seed)}")
return len(seed["brokers"]) == 1
except requests.exceptions.RequestException as e:
self.redpanda.logger.debug(f"admin API isn't available ({e})")
return False
wait_until(
rp1_started,
timeout_sec=30,
backoff_sec=1,
err_msg="Cant get cluster view from {self.redpanda.nodes[0]}")
self.redpanda.start_node(self.redpanda.nodes[1])
self.redpanda.start_node(self.redpanda.nodes[2])
def rest_started():
try:
last = None
ids = None
for i in range(0, 3):
view = admin.get_cluster_view(self.redpanda.nodes[i])
self.redpanda.logger.info(
f"view from {self.redpanda.nodes[i]}: {json.dumps(view)}"
)
if view["version"] <= seed["version"]:
return False
if len(view["brokers"]) != 3:
return False
if last == None:
last = view
ids = set(
map(lambda broker: broker["node_id"],
view["brokers"]))
if last["version"] != view["version"]:
return False
if not ids.issubset(
map(lambda broker: broker["node_id"],
view["brokers"])):
return False
return True
except requests.exceptions.RequestException as e:
self.redpanda.logger.debug(f"admin API isn't available ({e})")
return False
wait_until(rest_started,
timeout_sec=30,
backoff_sec=1,
err_msg="Cant get cluster view from {self.redpanda.nodes}")
def test_migrating_consume_offsets(self, failures, cpus):
'''
Validates correctness while executing consumer offsets migration
'''
# set redpanda logical version to value without __consumer_offsets support
self.redpanda = RedpandaService(
self.test_context,
5,
resource_settings=ResourceSettings(num_cpus=cpus),
extra_rp_conf={
"group_topic_partitions": 16,
"default_topic_replications": 3,
},
environment={"__REDPANDA_LOGICAL_VERSION": 1})
self.redpanda.start()
self._client = DefaultClient(self.redpanda)
# set of failure suppressed nodes - required to make restarts deterministic
suppressed = set()
def failure_injector_loop():
f_injector = FailureInjector(self.redpanda)
while failures:
f_type = random.choice(FailureSpec.FAILURE_TYPES)
length = 0
node = random.choice(self.redpanda.nodes)
while self.redpanda.idx(node) in suppressed:
node = random.choice(self.redpanda.nodes)
# allow suspending any node
if f_type == FailureSpec.FAILURE_SUSPEND:
length = random.randint(
1,
ConsumerOffsetsMigrationTest.max_suspend_duration_sec)
f_injector.inject_failure(
FailureSpec(node=node, type=f_type, length=length))
delay = random.randint(
ConsumerOffsetsMigrationTest.min_inter_failure_time_sec,
ConsumerOffsetsMigrationTest.max_inter_failure_time_sec)
self.redpanda.logger.info(
f"waiting {delay} seconds before next failure")
time.sleep(delay)
if failures:
finjector_thread = threading.Thread(target=failure_injector_loop,
args=())
finjector_thread.daemon = True
finjector_thread.start()
spec = TopicSpec(partition_count=6, replication_factor=3)
self.client().create_topic(spec)
self.topic = spec.name
self.start_producer(1, throughput=5000)
self.start_consumer(1)
self.await_startup()
def cluster_is_stable():
admin = Admin(self.redpanda)
brokers = admin.get_brokers()
if len(brokers) < 3:
return False
for b in brokers:
self.logger.debug(f"broker: {b}")
if not (b['is_alive'] and 'disk_space' in b):
return False
return True
kcl = KCL(self.redpanda)
def _group_present():
return len(kcl.list_groups().splitlines()) > 1
# make sure that group is there
wait_until(_group_present, 10, 1)
# check that consumer offsets topic is not present
topics = set(kcl.list_topics())
assert "__consumer_offsets" not in topics
# enable consumer offsets support
self.redpanda.set_environment({"__REDPANDA_LOGICAL_VERSION": 2})
for n in self.redpanda.nodes:
id = self.redpanda.idx(n)
suppressed.add(id)
self.redpanda.restart_nodes(n, stop_timeout=60)
suppressed.remove(id)
# wait for leader balancer to start evening out leadership
wait_until(cluster_is_stable, 90, backoff_sec=2)
def _consumer_offsets_present():
try:
partitions = list(
self.client().describe_topic("__consumer_offsets"))
return len(partitions) > 0
except:
return False
wait_until(_consumer_offsets_present, timeout_sec=90, backoff_sec=3)
self.run_validation(min_records=100000,
producer_timeout_sec=300,
consumer_timeout_sec=180)
class LibrdkafkaTest(Test):
"""
Execute the librdkafka test suite against redpanda.
"""
TESTS_DIR = "/opt/librdkafka/tests"
CONF_FILE = os.path.join(TESTS_DIR, "test.conf")
def __init__(self, context):
super(LibrdkafkaTest, self).__init__(context)
self._context = context
self._extra_rp_conf = dict(
auto_create_topics_enabled=True,
default_topic_partitions=4,
)
self._redpanda = None
def _start_redpanda(self, num_brokers):
self._redpanda = RedpandaService(self._context,
num_brokers,
extra_rp_conf=self._extra_rp_conf)
self._redpanda.start()
with open(LibrdkafkaTest.CONF_FILE, "w") as f:
brokers = self._redpanda.brokers()
f.write("metadata.broker.list={}".format(brokers))
def teardown(self):
self._redpanda.stop()
os.remove(LibrdkafkaTest.CONF_FILE)
# yapf: disable
@cluster(num_nodes=3)
@ignore(test_num=19, num_brokers=1) # segfault in group membership https://app.clubhouse.io/vectorized/story/963/dereferencing-null-pointer-in-kafka-group-membership
@ignore(test_num=52, num_brokers=1) # https://app.clubhouse.io/vectorized/story/997/librdkafka-tests-failing-due-to-consumer-out-of-range-timestamps
@ignore(test_num=54, num_brokers=1) # timequery issues: https://app.clubhouse.io/vectorized/story/995/librdkafka-offset-time-query
@ignore(test_num=63, num_brokers=1) # cluster-id: https://app.clubhouse.io/vectorized/story/939/generate-cluster-id-uuid-on-bootstrap-and-expose-through-metadata-request
@ignore(test_num=67, num_brokers=1) # empty topic offset edge case: https://app.clubhouse.io/vectorized/story/940/consuming-from-empty-topic-should-return-eof
@ignore(test_num=77, num_brokers=1) # topic compaction settings: https://app.clubhouse.io/vectorized/story/999/support-create-topic-configurations-for-compaction-retention-policies
@ignore(test_num=92, num_brokers=1) # no support for v2 -> v1 message version conversion in the broker
@ignore(test_num=44, num_brokers=1) # we do not support runtime changes to topic partition count
@ignore(test_num=69, num_brokers=1) # we do not support runtime changes to topic partition count
@ignore(test_num=81, num_brokers=1) # we do not support replica assignment
@ignore(test_num=61, num_brokers=1) # transactions
@ignore(test_num=76, num_brokers=1) # idempotent producer
@ignore(test_num=86, num_brokers=1) # idempotent producer
@ignore(test_num=90, num_brokers=1) # idempotent producer
@ignore(test_num=94, num_brokers=1) # idempotent producer
@ignore(test_num=98, num_brokers=1) # transactions
# @ignore appears to not be quite smart enough to handle the partial
# parameterization so we repeat them here with num_brokers=3. this would be
# a nice enhancement that we could upstream.
@ignore(test_num=19, num_brokers=3)
@ignore(test_num=52, num_brokers=3)
@ignore(test_num=54, num_brokers=3)
@ignore(test_num=63, num_brokers=3)
@ignore(test_num=67, num_brokers=3)
@ignore(test_num=77, num_brokers=3)
@ignore(test_num=92, num_brokers=3)
@ignore(test_num=44, num_brokers=3)
@ignore(test_num=69, num_brokers=3)
@ignore(test_num=81, num_brokers=3)
@ignore(test_num=61, num_brokers=3)
@ignore(test_num=76, num_brokers=3)
@ignore(test_num=86, num_brokers=3)
@ignore(test_num=90, num_brokers=3)
@ignore(test_num=94, num_brokers=3)
@ignore(test_num=98, num_brokers=3)
@matrix(test_num=range(101), num_brokers=[1, 3])
# yapf: enable
def test_librdkafka(self, test_num, num_brokers):
self._start_redpanda(num_brokers)
p = subprocess.Popen(["make"],
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
shell=True,
cwd=LibrdkafkaTest.TESTS_DIR,
env=dict(TESTS="%04d" % test_num, **os.environ))
for line in iter(p.stdout.readline, b''):
self.logger.debug(line.rstrip())
p.wait()
if p.returncode != 0:
raise RuntimeError("librdkafka test failed {}".format(
p.returncode))
class NodeOperationFuzzyTest(EndToEndTest):
max_suspend_duration_seconds = 10
min_inter_failure_time = 30
max_inter_failure_time = 60
def generate_random_workload(self, count, skip_nodes, available_nodes):
op_types = [ADD, DECOMMISSION]
tp_op_types = [ADD_TOPIC, DELETE_TOPIC]
# current state
active_nodes = list(available_nodes)
decommissioned_nodes = []
operations = []
topics = []
def eligible_active_nodes():
return list(
filter(lambda n: not (n == 1 or n in skip_nodes),
active_nodes))
def decommission(id):
active_nodes.remove(id)
decommissioned_nodes.append(id)
def add(id):
active_nodes.append(id)
decommissioned_nodes.remove(id)
for _ in range(0, count):
if len(decommissioned_nodes) == 2:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
elif len(decommissioned_nodes) == 0:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
else:
op = random.choice(op_types)
if op == DECOMMISSION:
id = random.choice(eligible_active_nodes())
operations.append((DECOMMISSION, id))
decommission(id)
elif op == ADD:
id = random.choice(decommissioned_nodes)
operations.append((ADD, id))
add(id)
# topic operation
if len(topics) == 0:
op = ADD_TOPIC
else:
op = random.choice(tp_op_types)
if op == ADD_TOPIC:
operations.append((
ADD_TOPIC,
f"test-topic-{random.randint(0,2000)}-{round(time.time()*1000000)}",
random.choice(ALLOWED_REPLICATION), 3))
else:
operations.append((DELETE_TOPIC, random.choice(topics)))
return operations
def _create_random_topics(self, count):
max_partitions = 10
topics = []
for i in range(0, count):
name = f"topic-{i}"
spec = TopicSpec(
name=name,
partition_count=random.randint(1, max_partitions),
replication_factor=random.choice(ALLOWED_REPLICATION))
topics.append(spec)
for spec in topics:
DefaultClient(self.redpanda).create_topic(spec)
return topics
"""
Adding nodes to the cluster should result in partition reallocations to new
nodes
"""
@cluster(num_nodes=7, log_allow_list=CHAOS_LOG_ALLOW_LIST)
@parametrize(enable_failures=True)
@parametrize(enable_failures=False)
def test_node_operations(self, enable_failures):
# allocate 5 nodes for the cluster
self.redpanda = RedpandaService(
self.test_context,
5,
extra_rp_conf={
"enable_auto_rebalance_on_node_add": True,
"group_topic_partitions": 3,
"default_topic_replications": 3,
})
self.redpanda.start()
# create some topics
topics = self._create_random_topics(10)
self.redpanda.logger.info(f"using topics: {topics}")
# select one of the topics to use in consumer/producer
self.topic = random.choice(topics).name
self.start_producer(1, throughput=100)
self.start_consumer(1)
self.await_startup()
self.active_nodes = set(
[self.redpanda.idx(n) for n in self.redpanda.nodes])
# collect current mapping
self.ids_mapping = {}
for n in self.redpanda.nodes:
self.ids_mapping[self.redpanda.idx(n)] = self.redpanda.idx(n)
self.next_id = sorted(list(self.ids_mapping.keys()))[-1] + 1
self.redpanda.logger.info(f"Initial ids mapping: {self.ids_mapping}")
NODE_OP_TIMEOUT = 360
def get_next_id():
id = self.next_id
self.next_id += 1
return id
def failure_injector_loop():
f_injector = FailureInjector(self.redpanda)
while enable_failures:
f_type = random.choice(FailureSpec.FAILURE_TYPES)
length = 0
# allow suspending any node
if f_type == FailureSpec.FAILURE_SUSPEND:
length = random.randint(
1, NodeOperationFuzzyTest.max_suspend_duration_seconds)
node = random.choice(self.redpanda.nodes)
else:
#kill/termianate only active nodes (not to influence the test outcome)
idx = random.choice(list(self.active_nodes))
node = self.redpanda.get_node(idx)
f_injector.inject_failure(
FailureSpec(node=node, type=f_type, length=length))
delay = random.randint(
NodeOperationFuzzyTest.min_inter_failure_time,
NodeOperationFuzzyTest.max_inter_failure_time)
self.redpanda.logger.info(
f"waiting {delay} seconds before next failure")
time.sleep(delay)
if enable_failures:
finjector_thread = threading.Thread(target=failure_injector_loop,
args=())
finjector_thread.daemon = True
finjector_thread.start()
def decommission(idx):
node_id = self.ids_mapping[idx]
self.logger.info(f"decommissioning node: {idx} with id: {node_id}")
def decommissioned():
try:
admin = Admin(self.redpanda)
# if broker is already draining, it is suceess
brokers = admin.get_brokers()
for b in brokers:
if b['node_id'] == node_id and b[
'membership_status'] == 'draining':
return True
r = admin.decommission_broker(id=node_id)
return r.status_code == 200
except requests.exceptions.RetryError:
return False
except requests.exceptions.ConnectionError:
return False
except requests.exceptions.HTTPError:
return False
wait_until(decommissioned,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
admin = Admin(self.redpanda)
def is_node_removed(idx_to_query, node_id):
try:
brokers = admin.get_brokers(
self.redpanda.get_node(idx_to_query))
ids = map(lambda broker: broker['node_id'], brokers)
return not node_id in ids
except:
return False
def node_removed():
node_removed_cnt = 0
for idx in self.active_nodes:
if is_node_removed(idx, node_id):
node_removed_cnt += 1
node_count = len(self.redpanda.nodes)
majority = int(node_count / 2) + 1
self.redpanda.logger.debug(
f"node {node_id} removed on {node_removed_cnt} nodes, majority: {majority}"
)
return node_removed_cnt >= majority
wait_until(node_removed,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
self.redpanda.stop_node(self.redpanda.get_node(idx))
kafkacat = KafkaCat(self.redpanda)
def replicas_per_node():
node_replicas = {}
md = kafkacat.metadata()
self.redpanda.logger.info(f"metadata: {md}")
for topic in md['topics']:
for p in topic['partitions']:
for r in p['replicas']:
id = r['id']
if id not in node_replicas:
node_replicas[id] = 0
node_replicas[id] += 1
return node_replicas
def seed_servers_for(idx):
seeds = map(
lambda n: {
"address": n.account.hostname,
"port": 33145
}, self.redpanda.nodes)
return list(
filter(
lambda n: n['address'] != self.redpanda.get_node(idx).
account.hostname, seeds))
def add_node(idx, cleanup=True):
id = get_next_id()
self.logger.info(f"adding node: {idx} back with new id: {id}")
self.ids_mapping[idx] = id
self.redpanda.stop_node(self.redpanda.get_node(idx))
if cleanup:
self.redpanda.clean_node(self.redpanda.get_node(idx),
preserve_logs=True)
# we do not reuse previous node ids and override seed server list
self.redpanda.start_node(
self.redpanda.get_node(idx),
timeout=NodeOperationFuzzyTest.min_inter_failure_time +
NodeOperationFuzzyTest.max_suspend_duration_seconds + 30,
override_cfg_params={
"node_id": id,
"seed_servers": seed_servers_for(idx)
})
def has_new_replicas():
per_node = replicas_per_node()
self.logger.info(f"replicas per node: {per_node}")
return id in per_node
wait_until(has_new_replicas,
timeout_sec=NODE_OP_TIMEOUT,
backoff_sec=2)
def is_topic_present(name):
kcl = KCL(self.redpanda)
lines = kcl.list_topics().splitlines()
self.redpanda.logger.debug(
f"checking if topic {name} is present in {lines}")
for l in lines:
if l.startswith(name):
return True
return False
def create_topic(spec):
try:
DefaultClient(self.redpanda).create_topic(spec)
except Exception as e:
self.redpanda.logger.warn(
f"error creating topic {spec.name} - {e}")
try:
return is_topic_present(spec.name)
except Exception as e:
self.redpanda.logger.warn(f"error while listing topics - {e}")
return False
def delete_topic(name):
try:
DefaultClient(self.redpanda).delete_topic(name)
except Exception as e:
self.redpanda.logger.warn(f"error deleting topic {name} - {e}")
try:
return not is_topic_present(name)
except Exception as e:
self.redpanda.logger.warn(f"error while listing topics - {e}")
return False
work = self.generate_random_workload(10,
skip_nodes=set(),
available_nodes=self.active_nodes)
self.redpanda.logger.info(f"node operations to execute: {work}")
for op in work:
op_type = op[0]
self.logger.info(
f"executing - {op} - current ids: {self.ids_mapping}")
if op_type == ADD:
idx = op[1]
self.active_nodes.add(idx)
add_node(idx)
if op_type == DECOMMISSION:
idx = op[1]
self.active_nodes.remove(idx)
decommission(idx)
elif op_type == ADD_TOPIC:
spec = TopicSpec(name=op[1],
replication_factor=op[2],
partition_count=op[3])
wait_until(lambda: create_topic(spec) == True,
timeout_sec=180,
backoff_sec=2)
elif op_type == DELETE_TOPIC:
wait_until(lambda: delete_topic(op[1]) == True,
timeout_sec=180,
backoff_sec=2)
enable_failures = False
self.run_validation(enable_idempotence=False,
producer_timeout_sec=60,
consumer_timeout_sec=180)
class RedpandaTest(Test):
"""
Base class for tests that use the Redpanda service.
"""
# List of topics to be created automatically when the cluster starts. Each
# topic is defined by an instance of a TopicSpec.
topics: Sequence[TopicSpec] = []
def __init__(self,
test_context,
num_brokers=None,
extra_rp_conf=dict(),
enable_pp=False,
enable_sr=False,
si_settings=None,
**kwargs):
"""
Any trailing keyword arguments are passed through to the
RedpandaService constructor.
"""
super(RedpandaTest, self).__init__(test_context)
self.scale = Scale(test_context)
self.si_settings = si_settings
if num_brokers is None:
# Default to a 3 node cluster if sufficient nodes are available, else
# a single node cluster. This is just a default: tests are welcome
# to override constructor to pass an explicit size. This logic makes
# it convenient to mix 3 node and 1 node cases in the same class, by
# just modifying the @cluster node count per test.
if test_context.cluster.available().size() >= 3:
num_brokers = 3
else:
num_brokers = 1
if self.si_settings:
self.si_settings.load_context(self.logger, test_context)
self.redpanda = RedpandaService(test_context,
num_brokers,
extra_rp_conf=extra_rp_conf,
enable_pp=enable_pp,
enable_sr=enable_sr,
si_settings=self.si_settings,
**kwargs)
self._client = DefaultClient(self.redpanda)
@property
def topic(self):
"""
Return the name of the auto-created initial topic. Accessing this
property requires exactly one initial topic be configured.
"""
assert len(self.topics) == 1
return self.topics[0].name
@property
def debug_mode(self):
"""
Useful for tests that want to change behaviour when running on
the much slower debug builds of redpanda, which generally cannot
keep up with significant quantities of data or partition counts.
"""
return os.environ.get('BUILD_TYPE', None) == 'debug'
@property
def ci_mode(self):
"""
Useful for tests that want to dynamically degrade/disable on low-resource
developer environments (e.g. laptops) but apply stricter checks in CI.
"""
return os.environ.get('CI', None) != 'false'
@property
def s3_client(self):
return self.redpanda.s3_client
def setUp(self):
self.redpanda.start()
self._create_initial_topics()
def client(self):
return self._client
def _create_initial_topics(self):
config = self.redpanda.security_config()
user = config.get("sasl_plain_username")
passwd = config.get("sasl_plain_password")
client = KafkaCliTools(self.redpanda, user=user, passwd=passwd)
for spec in self.topics:
self.logger.debug(f"Creating initial topic {spec}")
client.create_topic(spec)
