def cli(mocker, conn):
mocker.patch('kafka.cluster.dns_lookup',
return_value=[(socket.AF_INET, None, None, None, ('localhost',
9092))])
client = KafkaClient(api_version=(0, 9))
client.poll(future=client.cluster.request_update())
return client
def _create_kafka_client(self):
kafka_conn_str = self.instance.get('kafka_connect_str')
if not isinstance(kafka_conn_str, (string_types, list)):
raise ConfigurationError('kafka_connect_str should be string or list of strings')
kafka_version = self.instance.get('kafka_client_api_version')
if isinstance(kafka_version, str):
kafka_version = tuple(map(int, kafka_version.split(".")))
kafka_client = KafkaClient(
bootstrap_servers=kafka_conn_str,
client_id='dd-agent',
request_timeout_ms=self.init_config.get('kafka_timeout', DEFAULT_KAFKA_TIMEOUT) * 1000,
# if `kafka_client_api_version` is not set, then kafka-python automatically probes the cluster for broker
# version during the bootstrapping process. Note that probing randomly picks a broker to probe, so in a
# mixed-version cluster probing returns a non-deterministic result.
api_version=kafka_version,
# While we check for SSL params, if not present they will default to the kafka-python values for plaintext
# connections
security_protocol=self.instance.get('security_protocol', 'PLAINTEXT'),
sasl_mechanism=self.instance.get('sasl_mechanism'),
sasl_plain_username=self.instance.get('sasl_plain_username'),
sasl_plain_password=self.instance.get('sasl_plain_password'),
sasl_kerberos_service_name=self.instance.get('sasl_kerberos_service_name', 'kafka'),
sasl_kerberos_domain_name=self.instance.get('sasl_kerberos_domain_name'),
ssl_cafile=self.instance.get('ssl_cafile'),
ssl_check_hostname=self.instance.get('ssl_check_hostname', True),
ssl_certfile=self.instance.get('ssl_certfile'),
ssl_keyfile=self.instance.get('ssl_keyfile'),
ssl_crlfile=self.instance.get('ssl_crlfile'),
ssl_password=self.instance.get('ssl_password'),
)
# Force initial population of the local cluster metadata cache
kafka_client.poll(future=kafka_client.cluster.request_update())
if kafka_client.cluster.topics(exclude_internal_topics=False) is None:
raise RuntimeError("Local cluster metadata cache did not populate.")
return kafka_client
def setup(topic_name):
# First, check if the topic already exists in kafka
kafka_client = KafkaClient(bootstrap_servers=KAFKA_SERVER,
api_version=(2, 5, 0))
future = kafka_client.cluster.request_update()
kafka_client.poll(future=future)
metadata = kafka_client.cluster
current_topics = metadata.topics()
kafka_client.close()
print('Active topics:', current_topics)
if topic_name not in current_topics:
print(f'Creating topic {topic_name}...')
kafka_admin_client = KafkaAdminClient(bootstrap_servers=KAFKA_SERVER,
api_version=(2, 5, 0))
topic_list = [
NewTopic(name=topic_name, num_partitions=1, replication_factor=1)
]
kafka_admin_client.create_topics(new_topics=topic_list,
validate_only=False)
kafka_admin_client.close()
else:
print(f'Topic {topic_name} exists')
def delete_topic():
"""
Delete the specified topic
"""
if ARG.server:
ARG.server += ':9092'
else:
ARG.server = ','.join(SERVER['Kafka']['broker_list'])
client = KafkaClient(bootstrap_servers=ARG.server)
try:
topic_req = admin.DeleteTopicsRequest_v1(topics=[ARG.topic],
timeout=1000)
future = client.send(client.least_loaded_node(), topic_req)
client.poll(timeout_ms=100, future=future)
result = future.value
LOGGER.debug(result)
error_code = result.topic_error_codes[0][1]
if error_code:
LOGGER.critical('Could not delete topic %s, error code=%d',
ARG.topic, error_code)
sys.exit(error_code)
else:
print("Deleted topic %s" % (ARG.topic))
except KafkaError:
LOGGER.critical("Could not delete topic %s", ARG.topic)
def test_maybe_refresh_metadata_ttl(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 1234
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(1.234, sleep=True)
def test_poll(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
metadata = mocker.patch.object(KafkaClient, '_maybe_refresh_metadata')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(api_version=(0, 9))
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
# metadata timeout wins
metadata.return_value = 1000
tasks.return_value = 2
cli.poll()
_poll.assert_called_with(1.0, sleep=True)
# user timeout wins
cli.poll(250)
_poll.assert_called_with(0.25, sleep=True)
# tasks timeout wins
tasks.return_value = 0
cli.poll(250)
_poll.assert_called_with(0, sleep=True)
# default is request_timeout_ms
metadata.return_value = 1000000
tasks.return_value = 10000
cli.poll()
_poll.assert_called_with(cli.config['request_timeout_ms'] / 1000.0,
sleep=True)
def test_maybe_refresh_metadata_ttl(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 1234
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(1.234, sleep=True)
def test_poll(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
metadata = mocker.patch.object(KafkaClient, '_maybe_refresh_metadata')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient()
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
# metadata timeout wins
metadata.return_value = 1000
tasks.return_value = 2
cli.poll()
_poll.assert_called_with(1.0, sleep=True)
# user timeout wins
cli.poll(250)
_poll.assert_called_with(0.25, sleep=True)
# tasks timeout wins
tasks.return_value = 0
cli.poll(250)
_poll.assert_called_with(0, sleep=True)
# default is request_timeout_ms
metadata.return_value = 1000000
tasks.return_value = 10000
cli.poll()
_poll.assert_called_with(cli.config['request_timeout_ms'] / 1000.0,
sleep=True)
def test_poll(mocker):
metadata = mocker.patch.object(KafkaClient, '_maybe_refresh_metadata')
_poll = mocker.patch.object(KafkaClient, '_poll')
ifrs = mocker.patch.object(KafkaClient, 'in_flight_request_count')
ifrs.return_value = 1
cli = KafkaClient(api_version=(0, 9))
# metadata timeout wins
metadata.return_value = 1000
cli.poll()
_poll.assert_called_with(1.0)
# user timeout wins
cli.poll(250)
_poll.assert_called_with(0.25)
# default is request_timeout_ms
metadata.return_value = 1000000
cli.poll()
_poll.assert_called_with(cli.config['request_timeout_ms'] / 1000.0)
# If no in-flight-requests, drop timeout to retry_backoff_ms
ifrs.return_value = 0
cli.poll()
_poll.assert_called_with(cli.config['retry_backoff_ms'] / 1000.0)
def test_bootstrap(mocker, conn):
conn.state = ConnectionStates.CONNECTED
cli = KafkaClient(api_version=(0, 9))
mocker.patch.object(cli, '_selector')
future = cli.cluster.request_update()
cli.poll(future=future)
assert future.succeeded()
args, kwargs = conn.call_args
assert args == ('localhost', 9092, socket.AF_UNSPEC)
kwargs.pop('state_change_callback')
kwargs.pop('node_id')
assert kwargs == cli.config
conn.send.assert_called_once_with(MetadataRequest[0]([]), blocking=False)
assert cli._bootstrap_fails == 0
assert cli.cluster.brokers() == set([BrokerMetadata(0, 'foo', 12, None),
BrokerMetadata(1, 'bar', 34, None)])
def test_maybe_refresh_metadata_backoff(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
now = time.time()
t = mocker.patch('time.time')
t.return_value = now
cli._last_no_node_available_ms = now * 1000
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(2.222, sleep=True)
def test_maybe_refresh_metadata_backoff(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
now = time.time()
t = mocker.patch('time.time')
t.return_value = now
cli._last_no_node_available_ms = now * 1000
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(2.222, sleep=True)
def test_maybe_refresh_metadata_update(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
mocker.patch.object(cli, 'least_loaded_node', return_value='foobar')
mocker.patch.object(cli, '_can_send_request', return_value=True)
send = mocker.patch.object(cli, 'send')
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(0, sleep=True)
assert cli._metadata_refresh_in_progress
request = MetadataRequest[0]([])
send.assert_called_with('foobar', request)
def test_maybe_refresh_metadata_update(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
mocker.patch.object(cli, 'least_loaded_node', return_value='foobar')
mocker.patch.object(cli, '_can_send_request', return_value=True)
send = mocker.patch.object(cli, 'send')
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(0, sleep=True)
assert cli._metadata_refresh_in_progress
request = MetadataRequest[0]([])
send.assert_called_with('foobar', request)
def test_maybe_refresh_metadata_failure(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
mocker.patch.object(cli, 'least_loaded_node', return_value='foobar')
now = time.time()
t = mocker.patch('time.time')
t.return_value = now
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(0, sleep=True)
assert cli._last_no_node_available_ms == now * 1000
assert not cli._metadata_refresh_in_progress
def test_maybe_refresh_metadata_failure(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(request_timeout_ms=9999999, retry_backoff_ms=2222)
tasks = mocker.patch.object(cli._delayed_tasks, 'next_at')
tasks.return_value = 9999999
ttl = mocker.patch.object(cli.cluster, 'ttl')
ttl.return_value = 0
mocker.patch.object(cli, 'least_loaded_node', return_value='foobar')
now = time.time()
t = mocker.patch('time.time')
t.return_value = now
cli.poll(timeout_ms=9999999, sleep=True)
_poll.assert_called_with(0, sleep=True)
assert cli._last_no_node_available_ms == now * 1000
assert not cli._metadata_refresh_in_progress
def ensure_topic(topic,
num_partitions,
replication_factor,
logger,
timeout_ms=3000,
brokers='localhost'):
logger.debug('checking kafka for topic ' + topic)
client = KafkaClient(bootstrap_servers=brokers)
if topic not in client.cluster.topics(exclude_internal_topics=True):
logger.debug('creating kafka topic ' + topic)
request = admin.CreateTopicsRequest_v0(
create_topic_requests=[(
topic,
num_partitions,
replication_factor,
[], # Partition assignment
[], # Configs
)],
timeout=timeout_ms)
future = client.send(client.least_loaded_node(), request)
client.poll(timeout_ms=timeout_ms, future=future)
result = future.value
error_code = result.topic_errors[0][1]
# 0: success
# 36: already exists, check topic
if error_code == 0:
logger.debug('kafka topic ' + topic + ' created')
return
elif error_code != 36:
logger.error('error creating kafka topic ' + topic)
raise Exception(
'Unknown error code during creation of topic `{}`: {}'.format(
topic, error_code))
else:
logger.debug('kafka topic ' + topic + ' exists')
def test_poll(mocker):
metadata = mocker.patch.object(KafkaClient, '_maybe_refresh_metadata')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(api_version=(0, 9))
# metadata timeout wins
metadata.return_value = 1000
cli.poll()
_poll.assert_called_with(1.0)
# user timeout wins
cli.poll(250)
_poll.assert_called_with(0.25)
# default is request_timeout_ms
metadata.return_value = 1000000
cli.poll()
_poll.assert_called_with(cli.config['request_timeout_ms'] / 1000.0)
def test_poll(mocker):
mocker.patch.object(KafkaClient, '_bootstrap')
metadata = mocker.patch.object(KafkaClient, '_maybe_refresh_metadata')
_poll = mocker.patch.object(KafkaClient, '_poll')
cli = KafkaClient(api_version=(0, 9))
# metadata timeout wins
metadata.return_value = 1000
cli.poll()
_poll.assert_called_with(1.0)
# user timeout wins
cli.poll(250)
_poll.assert_called_with(0.25)
# default is request_timeout_ms
metadata.return_value = 1000000
cli.poll()
_poll.assert_called_with(cli.config['request_timeout_ms'] / 1000.0)
class KafkaConsumerLag:
def __init__(self, bootstrap_servers):
self.client = KafkaClient(bootstrap_servers=bootstrap_servers)
self.client.check_version()
def _send(self, broker_id, request, response_type=None):
f = self.client.send(broker_id, request)
response = self.client.poll(future=f)
if response_type:
if response and len(response) > 0:
for r in response:
if isinstance(r, response_type):
return r
else:
if response and len(response) > 0:
return response[0]
return None
def check(self, group_topics=None, discovery=None):
"""
{
"<group>": {
"state": <str>,
"topics": {
"<topic>": {
"consumer_lag": <int>,
"partitions": {
"<partition>": {
"offset_first": <int>,
"offset_consumed": <int>,
"offset_last": <int>,
"lag": <int>
}
}
}
}
}
}
:param persist_groups:
:return: consumer statistics
"""
cluster = self.client.cluster
brokers = cluster.brokers()
# Consumer group ID -> list(topics)
if group_topics is None:
group_topics = {}
if discovery is None:
discovery = True
else:
group_topics = copy.deepcopy(group_topics)
# Set of consumer group IDs
consumer_groups = set(group_topics.iterkeys())
# Set of all known topics
topics = set(itertools.chain(*group_topics.itervalues()))
# Consumer group ID -> coordinating broker
consumer_coordinator = {}
# Coordinating broker - > list(consumer group IDs)
coordinator_consumers = {}
results = {}
for consumer_group in group_topics.iterkeys():
results[consumer_group] = {'state': None, 'topics': {}}
# Ensure connections to all brokers
for broker in brokers:
while not self.client.is_ready(broker.nodeId):
self.client.ready(broker.nodeId)
# Collect all active consumer groups
if discovery:
for broker in brokers:
response = self._send(broker.nodeId, _ListGroupsRequest(), _ListGroupsResponse)
if response:
for group in response.groups:
consumer_groups.add(group[0])
# Identify which broker is coordinating each consumer group
for group in consumer_groups:
response = self._send(next(iter(brokers)).nodeId, _GroupCoordinatorRequest(group), _GroupCoordinatorResponse)
if response:
consumer_coordinator[group] = response.coordinator_id
if response.coordinator_id not in coordinator_consumers:
coordinator_consumers[response.coordinator_id] = []
coordinator_consumers[response.coordinator_id].append(group)
# Populate consumer groups into dict
for group in consumer_groups:
if group not in group_topics:
group_topics[group] = []
# Add groups to results dict
for group, topic_list in group_topics.iteritems():
results[group] = {'state': None, 'topics': {}}
# Identify group information and topics read by each consumer group
for coordinator, consumers in coordinator_consumers.iteritems():
response = self._send(coordinator, _DescribeGroupsRequest(consumers), _DescribeGroupsResponse)
for group in response.groups:
if group[1] in results:
results[group[1]]['state'] = group[2]
# TODO Also include member data?
if discovery:
members = group[5]
for member in members:
try:
assignment = MemberAssignment.decode(member[4])
if assignment:
for partition in assignment.partition_assignment:
topic = partition[0]
# Add topic to topic set
topics.add(topic)
# Add topic to group
group_topics[group[1]].append(topic)
except:
pass
# Add topics to groups in results dict
for group, topic_list in group_topics.iteritems():
for topic in topic_list:
results[group]['topics'][topic] = {'consumer_lag': 0, 'partitions': {}}
# For storing the latest offset for all partitions of all topics
# topic -> partition -> offset
start_offsets = {}
end_offsets = {}
# Identify all the topic partitions that each broker is leader for
# and request next new offset for each partition
for broker, partitions in cluster._broker_partitions.iteritems():
# topic -> List(partition, time, max_offsets)
request_partitions = {}
for tp in partitions:
if tp.topic in topics:
if tp.topic not in request_partitions:
request_partitions[tp.topic] = []
# Time value '-2' is to get the offset for first available message
request_partitions[tp.topic].append((tp.partition, -2, 1))
# List(topic, List(partition, time, max_offsets))
topic_partitions = []
for tp in request_partitions.iteritems():
topic_partitions.append(tp)
# Request partition start offsets
response = self._send(broker, _OffsetRequest(-1, topic_partitions), _OffsetResponse)
if response:
for offset in response.topics:
topic = offset[0]
if topic not in start_offsets:
start_offsets[topic] = {}
for p in offset[1]:
start_offsets[topic][p[0]] = p[2][0]
for tp in topic_partitions:
for i, ptm in enumerate(tp[1]):
# Time value '-1' is to get the offset for next new message
tp[1][i] = (ptm[0], -1, 1)
# Request partition end offsets
response = self._send(broker, _OffsetRequest(-1, topic_partitions), _OffsetResponse)
if response:
for offset in response.topics:
topic = offset[0]
if topic not in end_offsets:
end_offsets[topic] = {}
for p in offset[1]:
end_offsets[topic][p[0]] = p[2][0]
# Populate with offset values
for group, topics in group_topics.iteritems():
coordinator = consumer_coordinator[group]
# topic -> list(partition)
request_partitions = {}
for topic in topics:
results[group]['topics'][topic]['consumer_lag'] = 0
results[group]['topics'][topic]['partitions'] = {}
if topic in start_offsets:
for p in start_offsets[topic]:
results[group]['topics'][topic]['partitions'][p] = {
'offset_first': start_offsets[topic][p],
'offset_last': end_offsets[topic][p],
'offset_consumed': 0,
'lag' : 0}
if topic not in request_partitions:
request_partitions[topic] = []
request_partitions[topic].append(p)
# List(topic -> list(partition))
topic_partitions = []
for tp in request_partitions.iteritems():
topic_partitions.append(tp)
response = self._send(coordinator, _OffsetFetchRequest(group, topic_partitions), _OffsetFetchResponse)
if response:
for offset in response.topics:
topic = offset[0]
offsets = offset[1]
if topic not in results[group]['topics']:
continue
for p_offset in offsets:
partition = p_offset[0]
offset_consumed = p_offset[1]
p_results = results[group]['topics'][topic]['partitions'][partition]
if offset_consumed != -1:
p_results['offset_consumed'] = offset_consumed
p_results['lag'] = p_results['offset_last'] - offset_consumed
else:
p_results['offset_consumed'] = 0
p_results['lag'] = p_results['offset_last'] - p_results['offset_first']
results[group]['topics'][topic]['consumer_lag'] += p_results['lag']
return results
def close(self):
if self.client:
self.client.close()
class KafkaConsumer(six.Iterator):
"""Consume records from a Kafka cluster.
The consumer will transparently handle the failure of servers in the Kafka
cluster, and adapt as topic-partitions are created or migrate between
brokers. It also interacts with the assigned kafka Group Coordinator node
to allow multiple consumers to load balance consumption of topics (requires
kafka >= 0.9.0.0).
Arguments:
*topics (str): optional list of topics to subscribe to. If not set,
call subscribe() or assign() before consuming records.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
group_id (str): name of the consumer group to join for dynamic
partition assignment (if enabled), and to use for fetching and
committing offsets. Default: 'kafka-python-default-group'
key_deserializer (callable): Any callable that takes a
raw message key and returns a deserialized key.
value_deserializer (callable, optional): Any callable that takes a
raw message value and returns a deserialized value.
fetch_min_bytes (int): Minimum amount of data the server should
return for a fetch request, otherwise wait up to
fetch_max_wait_ms for more data to accumulate. Default: 1024.
fetch_max_wait_ms (int): The maximum amount of time in milliseconds
the server will block before answering the fetch request if
there isn't sufficient data to immediately satisfy the
requirement given by fetch_min_bytes. Default: 500.
max_partition_fetch_bytes (int): The maximum amount of data
per-partition the server will return. The maximum total memory
used for a request = #partitions * max_partition_fetch_bytes.
This size must be at least as large as the maximum message size
the server allows or else it is possible for the producer to
send messages larger than the consumer can fetch. If that
happens, the consumer can get stuck trying to fetch a large
message on a certain partition. Default: 1048576.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 40000.
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
auto_offset_reset (str): A policy for resetting offsets on
OffsetOutOfRange errors: 'earliest' will move to the oldest
available message, 'latest' will move to the most recent. Any
ofther value will raise the exception. Default: 'latest'.
enable_auto_commit (bool): If true the consumer's offset will be
periodically committed in the background. Default: True.
auto_commit_interval_ms (int): milliseconds between automatic
offset commits, if enable_auto_commit is True. Default: 5000.
default_offset_commit_callback (callable): called as
callback(offsets, response) response will be either an Exception
or a OffsetCommitResponse struct. This callback can be used to
trigger custom actions when a commit request completes.
check_crcs (bool): Automatically check the CRC32 of the records
consumed. This ensures no on-the-wire or on-disk corruption to
the messages occurred. This check adds some overhead, so it may
be disabled in cases seeking extreme performance. Default: True
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
partition_assignment_strategy (list): List of objects to use to
distribute partition ownership amongst consumer instances when
group management is used. Default: [RoundRobinPartitionAssignor]
heartbeat_interval_ms (int): The expected time in milliseconds
between heartbeats to the consumer coordinator when using
Kafka's group management feature. Heartbeats are used to ensure
that the consumer's session stays active and to facilitate
rebalancing when new consumers join or leave the group. The
value must be set lower than session_timeout_ms, but typically
should be set no higher than 1/3 of that value. It can be
adjusted even lower to control the expected time for normal
rebalances. Default: 3000
session_timeout_ms (int): The timeout used to detect failures when
using Kafka's group managementment facilities. Default: 30000
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: 131072
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: 32768
consumer_timeout_ms (int): number of millisecond to throw a timeout
exception to the consumer if no message is available for
consumption. Default: -1 (dont throw exception)
api_version (str): specify which kafka API version to use.
0.9 enables full group coordination features; 0.8.2 enables
kafka-storage offset commits; 0.8.1 enables zookeeper-storage
offset commits; 0.8.0 is what is left. If set to 'auto', will
attempt to infer the broker version by probing various APIs.
Default: auto
Note:
Configuration parameters are described in more detail at
https://kafka.apache.org/090/configuration.html#newconsumerconfigs
"""
DEFAULT_CONFIG = {
"bootstrap_servers": "localhost",
"client_id": "kafka-python-" + __version__,
"group_id": "kafka-python-default-group",
"key_deserializer": None,
"value_deserializer": None,
"fetch_max_wait_ms": 500,
"fetch_min_bytes": 1024,
"max_partition_fetch_bytes": 1 * 1024 * 1024,
"request_timeout_ms": 40 * 1000,
"retry_backoff_ms": 100,
"reconnect_backoff_ms": 50,
"max_in_flight_requests_per_connection": 5,
"auto_offset_reset": "latest",
"enable_auto_commit": True,
"auto_commit_interval_ms": 5000,
"check_crcs": True,
"metadata_max_age_ms": 5 * 60 * 1000,
"partition_assignment_strategy": (RoundRobinPartitionAssignor,),
"heartbeat_interval_ms": 3000,
"session_timeout_ms": 30000,
"send_buffer_bytes": 128 * 1024,
"receive_buffer_bytes": 32 * 1024,
"consumer_timeout_ms": -1,
"api_version": "auto",
"connections_max_idle_ms": 9 * 60 * 1000, # not implemented yet
#'metric_reporters': None,
#'metrics_num_samples': 2,
#'metrics_sample_window_ms': 30000,
}
def __init__(self, *topics, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
for key in self.config:
if key in configs:
self.config[key] = configs.pop(key)
# Only check for extra config keys in top-level class
assert not configs, "Unrecognized configs: %s" % configs
deprecated = {"smallest": "earliest", "largest": "latest"}
if self.config["auto_offset_reset"] in deprecated:
new_config = deprecated[self.config["auto_offset_reset"]]
log.warning("use auto_offset_reset=%s (%s is deprecated)", new_config, self.config["auto_offset_reset"])
self.config["auto_offset_reset"] = new_config
self._client = KafkaClient(**self.config)
# Check Broker Version if not set explicitly
if self.config["api_version"] == "auto":
self.config["api_version"] = self._client.check_version()
assert self.config["api_version"] in ("0.9", "0.8.2", "0.8.1", "0.8.0")
# Convert api_version config to tuple for easy comparisons
self.config["api_version"] = tuple(map(int, self.config["api_version"].split(".")))
self._subscription = SubscriptionState(self.config["auto_offset_reset"])
self._fetcher = Fetcher(self._client, self._subscription, **self.config)
self._coordinator = ConsumerCoordinator(
self._client, self._subscription, assignors=self.config["partition_assignment_strategy"], **self.config
)
self._closed = False
self._iterator = None
self._consumer_timeout = float("inf")
# self.metrics = None
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
Arguments:
partitions (list of TopicPartition): assignment for this instance.
Raises:
IllegalStateError: if consumer has already called subscribe()
Warning:
It is not possible to use both manual partition assignment with
assign() and group assignment with subscribe().
Note:
This interface does not support incremental assignment and will
replace the previous assignment (if there was one).
Note:
Manual topic assignment through this method does not use the
consumer's group management functionality. As such, there will be
no rebalance operation triggered when group membership or cluster
and topic metadata change.
"""
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
"""Get the TopicPartitions currently assigned to this consumer.
If partitions were directly assigned using assign(), then this will
simply return the same partitions that were previously assigned.
If topics were subscribed using subscribe(), then this will give the
set of topic partitions currently assigned to the consumer (which may
be none if the assignment hasn't happened yet, or if the partitions are
in the process of being reassigned).
Returns:
set: {TopicPartition, ...}
"""
return self._subscription.assigned_partitions()
def close(self):
"""Close the consumer, waiting indefinitely for any needed cleanup."""
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close()
# self.metrics.close()
self._client.close()
try:
self.config["key_deserializer"].close()
except AttributeError:
pass
try:
self.config["value_deserializer"].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
"""Commit offsets to kafka asynchronously, optionally firing callback
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used.
This is an asynchronous call and will not block. Any errors encountered
are either passed to the callback (if provided) or discarded.
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
callback (callable, optional): called as callback(offsets, response)
with response as either an Exception or a OffsetCommitResponse
struct. This callback can be used to trigger custom actions when
a commit request completes.
Returns:
kafka.future.Future
"""
assert self.config["api_version"] >= (0, 8, 1)
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(offsets, callback=callback)
return future
def commit(self, offsets=None):
"""Commit offsets to kafka, blocking until success or error
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used.
Blocks until either the commit succeeds or an unrecoverable error is
encountered (in which case it is thrown to the caller).
Currently only supports kafka-topic offset storage (not zookeeper)
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
"""
assert self.config["api_version"] >= (0, 8, 1)
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
"""Get the last committed offset for the given partition
This offset will be used as the position for the consumer
in the event of a failure.
This call may block to do a remote call if the partition in question
isn't assigned to this consumer or if the consumer hasn't yet
initialized its cache of committed offsets.
Arguments:
partition (TopicPartition): the partition to check
Returns:
The last committed offset, or None if there was no prior commit.
"""
assert self.config["api_version"] >= (0, 8, 1)
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
"""Get all topic metadata topics the user is authorized to view.
[Not Implemented Yet]
Returns:
{topic: [partition_info]}
"""
raise NotImplementedError("TODO")
def partitions_for_topic(self, topic):
"""Get metadata about the partitions for a given topic.
Arguments:
topic (str): topic to check
Returns:
set: partition ids
"""
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0):
"""Fetch data from assigned topics / partitions.
Records are fetched and returned in batches by topic-partition.
On each poll, consumer will try to use the last consumed offset as the
starting offset and fetch sequentially. The last consumed offset can be
manually set through seek(partition, offset) or automatically set as
the last committed offset for the subscribed list of partitions.
Incompatible with iterator interface -- use one or the other, not both.
Arguments:
timeout_ms (int, optional): milliseconds to spend waiting in poll if
data is not available. If 0, returns immediately with any
records that are available now. Must not be negative. Default: 0
Returns:
dict: topic to list of records since the last fetch for the
subscribed list of topics and partitions
"""
assert timeout_ms >= 0, "Timeout must not be negative"
assert self._iterator is None, "Incompatible with iterator interface"
# poll for new data until the timeout expires
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining)
if records:
# before returning the fetched records, we can send off the
# next round of fetches and avoid block waiting for their
# responses to enable pipelining while the user is handling the
# fetched records.
self._fetcher.init_fetches()
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms):
"""
Do one round of polling. In addition to checking for new data, this does
any needed heart-beating, auto-commits, and offset updates.
Arguments:
timeout_ms (int): The maximum time in milliseconds to block
Returns:
dict: map of topic to list of records (may be empty)
"""
if self.config["api_version"] >= (0, 8, 2):
# TODO: Sub-requests should take into account the poll timeout (KAFKA-1894)
self._coordinator.ensure_coordinator_known()
if self.config["api_version"] >= (0, 9):
# ensure we have partitions assigned if we expect to
if self._subscription.partitions_auto_assigned():
self._coordinator.ensure_active_group()
# fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(self._subscription.missing_fetch_positions())
# init any new fetches (won't resend pending fetches)
records = self._fetcher.fetched_records()
# if data is available already, e.g. from a previous network client
# poll() call to commit, then just return it immediately
if records:
return records
self._fetcher.init_fetches()
self._client.poll(timeout_ms)
return self._fetcher.fetched_records()
def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): partition to check
"""
assert self._subscription.is_assigned(partition)
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions(partition)
offset = self._subscription.assignment[partition].position
return offset
def pause(self, *partitions):
"""Suspend fetching from the requested partitions.
Future calls to poll() will not return any records from these partitions
until they have been resumed using resume(). Note that this method does
not affect partition subscription. In particular, it does not cause a
group rebalance when automatic assignment is used.
Arguments:
*partitions (TopicPartition): partitions to pause
"""
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def resume(self, *partitions):
"""Resume fetching from the specified (paused) partitions.
Arguments:
*partitions (TopicPartition): partitions to resume
"""
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
"""Manually specify the fetch offset for a TopicPartition.
Overrides the fetch offsets that the consumer will use on the next
poll(). If this API is invoked for the same partition more than once,
the latest offset will be used on the next poll(). Note that you may
lose data if this API is arbitrarily used in the middle of consumption,
to reset the fetch offsets.
Arguments:
partition (TopicPartition): partition for seek operation
offset (int): message offset in partition
"""
assert offset >= 0
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
"""Seek to the oldest available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
"""
if not partitions:
partitions = self._subscription.assigned_partitions()
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
"""Seek to the most recent available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
"""
if not partitions:
partitions = self._subscription.assigned_partitions()
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with assign()
Arguments:
topics (list): List of topics for subscription.
pattern (str): Pattern to match available topics. You must provide
either topics or pattern, but not both.
listener (ConsumerRebalanceListener): Optionally include listener
callback, which will be called before and after each rebalance
operation.
As part of group management, the consumer will keep track of the
list of consumers that belong to a particular group and will
trigger a rebalance operation if one of the following events
trigger:
* Number of partitions change for any of the subscribed topics
* Topic is created or deleted
* An existing member of the consumer group dies
* A new member is added to the consumer group
When any of these events are triggered, the provided listener
will be invoked first to indicate that the consumer's assignment
has been revoked, and then again when the new assignment has
been received. Note that this listener will immediately override
any listener set in a previous call to subscribe. It is
guaranteed, however, that the partitions revoked/assigned
through this interface are from topics subscribed in this call.
"""
if not topics:
self.unsubscribe()
else:
self._subscription.subscribe(topics=topics, pattern=pattern, listener=listener)
# regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_metadata_for_all = True
log.debug("Subscribed to topic pattern: %s", topics)
else:
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
"""Get the current topic subscription.
Returns:
set: {topic, ...}
"""
return self._subscription.subscription
def unsubscribe(self):
"""Unsubscribe from all topics and clear all assigned partitions."""
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_metadata_for_all_topics = False
log.debug("Unsubscribed all topics or patterns and assigned partitions")
def _update_fetch_positions(self, partitions):
"""
Set the fetch position to the committed position (if there is one)
or reset it using the offset reset policy the user has configured.
Arguments:
partitions (List[TopicPartition]): The partitions that need
updating fetch positions
Raises:
NoOffsetForPartitionError: If no offset is stored for a given
partition and no offset reset policy is defined
"""
if self.config["api_version"] >= (0, 8, 1):
# refresh commits for all assigned partitions
self._coordinator.refresh_committed_offsets_if_needed()
# then do any offset lookups in case some positions are not known
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription() is not None
while time.time() < self._consumer_timeout:
if self.config["api_version"] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
if self.config["api_version"] >= (0, 9):
# ensure we have partitions assigned if we expect to
if self._subscription.partitions_auto_assigned():
self._coordinator.ensure_active_group()
# fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
# We need to make sure we at least keep up with scheduled tasks,
# like heartbeats, auto-commits, and metadata refreshes
timeout_at = min(
self._consumer_timeout,
self._client._delayed_tasks.next_at() + time.time(),
self._client.cluster.ttl() / 1000.0 + time.time(),
)
if self.config["api_version"] >= (0, 9):
if not self.assignment():
sleep_time = time.time() - timeout_at
log.debug("No partitions assigned; sleeping for %s", sleep_time)
time.sleep(sleep_time)
continue
poll_ms = 1000 * (time.time() - self._consumer_timeout)
# Dont bother blocking if there are no fetches
if not self._fetcher.in_flight_fetches():
poll_ms = 0
self._client.poll(poll_ms)
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
# an else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# and we assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.init_fetches()
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config["consumer_timeout_ms"] >= 0:
self._consumer_timeout = time.time() + (self.config["consumer_timeout_ms"] / 1000.0)
# old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError("deprecated -- initialize a new consumer")
def set_topic_partitions(self, *topics):
raise NotImplementedError("deprecated -- use subscribe() or assign()")
def fetch_messages(self):
raise NotImplementedError("deprecated -- use poll() or iterator interface")
def get_partition_offsets(self, topic, partition, request_time_ms, max_num_offsets):
raise NotImplementedError("deprecated -- send an OffsetRequest with KafkaClient")
def offsets(self, group=None):
raise NotImplementedError("deprecated -- use committed(partition)")
def task_done(self, message):
raise NotImplementedError("deprecated -- commit offsets manually if needed")
class KafkaManager:
"""
A class used to interact with Kafka and Zookeeper
and easily retrive useful information
"""
MAX_RETRY = 10
MAX_POLL_RETRIES = 3
MAX_ZK_RETRIES = 5
TOPIC_RESOURCE_ID = 2
DEFAULT_TIMEOUT = 15000
SUCCESS_CODE = 0
ZK_REASSIGN_NODE = '/admin/reassign_partitions'
ZK_TOPIC_PARTITION_NODE = '/brokers/topics/'
ZK_TOPIC_CONFIGURATION_NODE = '/config/topics/'
# Not used yet.
ZK_TOPIC_DELETION_NODE = '/admin/delete_topics/'
def __init__(self, module, **configs):
self.module = module
self.zk_client = None
self.client = KafkaClient(**configs)
self.refresh()
def init_zk_client(self, **configs):
"""
Zookeeper client initialization
"""
self.zk_client = KazooClient(**configs)
self.zk_client.start()
def close_zk_client(self):
"""
Closes Zookeeper client
"""
self.zk_client.stop()
def close(self):
"""
Closes Kafka client
"""
self.client.close()
def refresh(self):
"""
Refresh topics state
"""
fut = self.client.cluster.request_update()
self.client.poll(future=fut)
if not fut.succeeded():
self.close()
self.module.fail_json(
msg='Error while updating topic state from Kafka server: %s.' %
fut.exception)
def create_topic(self,
name,
partitions,
replica_factor,
replica_assignment=[],
config_entries=[],
timeout=None):
"""
Creates a topic
Usable for Kafka version >= 0.10.1
"""
if timeout is None:
timeout = self.DEFAULT_TIMEOUT
request = CreateTopicsRequest_v0(create_topic_requests=[
(name, partitions, replica_factor, replica_assignment,
config_entries)
],
timeout=timeout)
response = self.send_request_and_get_response(request)
for topic, error_code in response.topic_errors:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while creating topic %s. '
'Error key is %s, %s.' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
def delete_topic(self, name, timeout=None):
"""
Deletes a topic
Usable for Kafka version >= 0.10.1
Need to know which broker is controller for topic
"""
if timeout is None:
timeout = self.DEFAULT_TIMEOUT
request = DeleteTopicsRequest_v0(topics=[name], timeout=timeout)
response = self.send_request_and_get_response(request)
for topic, error_code in response.topic_error_codes:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while deleting topic %s. '
'Error key is: %s, %s. '
'Is option \'delete.topic.enable\' set to true on '
' your Kafka server?' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
@staticmethod
def _convert_create_acls_resource_request_v0(acl_resource):
if acl_resource.operation == ACLOperation.ANY:
raise IllegalArgumentError("operation must not be ANY")
if acl_resource.permission_type == ACLPermissionType.ANY:
raise IllegalArgumentError("permission_type must not be ANY")
return (acl_resource.resource_type, acl_resource.name,
acl_resource.principal, acl_resource.host,
acl_resource.operation, acl_resource.permission_type)
@staticmethod
def _convert_create_acls_resource_request_v1(acl_resource):
if acl_resource.operation == ACLOperation.ANY:
raise IllegalArgumentError("operation must not be ANY")
if acl_resource.permission_type == ACLPermissionType.ANY:
raise IllegalArgumentError("permission_type must not be ANY")
return (acl_resource.resource_type, acl_resource.name,
acl_resource.pattern_type, acl_resource.principal,
acl_resource.host, acl_resource.operation,
acl_resource.permission_type)
@staticmethod
def _convert_delete_acls_resource_request_v0(acl_resource):
return (acl_resource.resource_type, acl_resource.name,
acl_resource.principal, acl_resource.host,
acl_resource.operation, acl_resource.permission_type)
@staticmethod
def _convert_delete_acls_resource_request_v1(acl_resource):
return (acl_resource.resource_type, acl_resource.name,
acl_resource.pattern_type, acl_resource.principal,
acl_resource.host, acl_resource.operation,
acl_resource.permission_type)
def describe_acls(self, acl_resource, api_version):
"""Describe a set of ACLs
"""
if api_version < parse_version('2.0.0'):
request = DescribeAclsRequest_v0(
resource_type=acl_resource.resource_type,
resource_name=acl_resource.name,
principal=acl_resource.principal,
host=acl_resource.host,
operation=acl_resource.operation,
permission_type=acl_resource.permission_type)
else:
request = DescribeAclsRequest_v1(
resource_type=acl_resource.resource_type,
resource_name=acl_resource.name,
resource_pattern_type_filter=acl_resource.pattern_type,
principal=acl_resource.principal,
host=acl_resource.host,
operation=acl_resource.operation,
permission_type=acl_resource.permission_type)
response = self.send_request_and_get_response(request)
if response.error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while describing ACL %s. '
'Error %s: %s.' %
(acl_resource, response.error_code, response.error_message))
return response.resources
def create_acls(self, acl_resources, api_version):
"""Create a set of ACLs"""
if api_version < parse_version('2.0.0'):
request = CreateAclsRequest_v0(creations=[
self._convert_create_acls_resource_request_v0(acl_resource)
for acl_resource in acl_resources
])
else:
request = CreateAclsRequest_v1(creations=[
self._convert_create_acls_resource_request_v1(acl_resource)
for acl_resource in acl_resources
])
response = self.send_request_and_get_response(request)
for error_code, error_message in response.creation_responses:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while creating ACL %s. '
'Error %s: %s.' %
(acl_resources, error_code, error_message))
def delete_acls(self, acl_resources, api_version):
"""Delete a set of ACLSs"""
if api_version < parse_version('2.0.0'):
request = DeleteAclsRequest_v0(filters=[
self._convert_delete_acls_resource_request_v0(acl_resource)
for acl_resource in acl_resources
])
else:
request = DeleteAclsRequest_v1(filters=[
self._convert_delete_acls_resource_request_v1(acl_resource)
for acl_resource in acl_resources
])
response = self.send_request_and_get_response(request)
for error_code, error_message, _ in response.filter_responses:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while deleting ACL %s. '
'Error %s: %s.' %
(acl_resources, error_code, error_message))
def send_request_and_get_response(self, request):
"""
Sends a Kafka protocol request and returns
the associated response
"""
try:
node_id = self.get_controller()
except UndefinedController:
self.module.fail_json(
msg='Cannot determine a controller for your current Kafka '
'server. Is your Kafka server running and available on '
'\'%s\' with security protocol \'%s\'?' %
(self.client.config['bootstrap_servers'],
self.client.config['security_protocol']))
except Exception as e:
self.module.fail_json(
msg='Cannot determine a controller for your current Kafka '
'server. Is your Kafka server running and available on '
'\'%s\' with security protocol \'%s\'? Are you using the '
'library versions from given \'requirements.txt\'? '
'Exception was: %s' %
(self.client.config['bootstrap_servers'],
self.client.config['security_protocol'], e))
if self.connection_check(node_id):
future = self.client.send(node_id, request)
self.client.poll(future=future)
if future.succeeded():
return future.value
else:
self.close()
self.module.fail_json(
msg='Error while sending request %s to Kafka server: %s.' %
(request, future.exception))
else:
self.close()
self.module.fail_json(
msg='Connection is not ready, please check your client '
'and server configurations.')
def get_controller(self):
"""
Returns the current controller
"""
if self.client.cluster.controller is not None:
node_id, _host, _port, _rack = self.client.cluster.controller
return node_id
else:
raise UndefinedController(
'Cant get a controller for this cluster.')
def get_controller_id_for_topic(self, topic_name):
"""
Returns current controller for topic
"""
request = MetadataRequest_v1(topics=[topic_name])
response = self.send_request_and_get_response(request)
return response.controller_id
def get_config_for_topic(self, topic_name, config_names):
"""
Returns responses with configuration
Usable with Kafka version >= 0.11.0
"""
request = DescribeConfigsRequest_v0(resources=[(self.TOPIC_RESOURCE_ID,
topic_name,
config_names)])
return self.send_request_and_get_response(request)
def get_responses_from_client(self, connection_sleep=1):
"""
Obtains response from server using poll()
It may need some times to get the response, so we had some retries
"""
retries = 0
if self.get_awaiting_request() > 0:
while retries < self.MAX_POLL_RETRIES:
resp = self.client.poll()
if resp:
return resp
time.sleep(connection_sleep)
retries += 1
self.close()
self.module.fail_json(
msg='Error while getting responses : no response to request '
'was obtained, please check your client and server '
'configurations.')
else:
self.close()
self.module.fail_json(
msg='No pending request, please check your client and server '
'configurations.')
def get_topics(self):
"""
Returns the topics list
"""
return self.client.cluster.topics()
def get_total_partitions_for_topic(self, topic):
"""
Returns the number of partitions for topic
"""
return len(self.client.cluster.partitions_for_topic(topic))
def get_partitions_for_topic(self, topic):
"""
Returns all partitions for topic, with information
TODO do not use private property anymore
"""
return self.client.cluster._partitions[topic]
def get_total_brokers(self):
"""
Returns number of brokers available
"""
return len(self.client.cluster.brokers())
def get_brokers(self):
"""
Returns all brokers
"""
return self.client.cluster.brokers()
def get_api_version(self):
"""
Returns Kafka server version
"""
major, minor, patch = self.client.config['api_version']
return '%s.%s.%s' % (major, minor, patch)
def get_awaiting_request(self):
"""
Returns the number of requests currently in the queue
"""
return self.client.in_flight_request_count()
def connection_check(self, node_id, connection_sleep=0.1):
"""
Checks that connection with broker is OK and that it is possible to
send requests
Since the maybe_connect() function used in ready() is 'async', we
need to manually call the poll() function to establish the connection
to the node
"""
retries = 0
if not self.client.ready(node_id):
while retries < self.MAX_RETRY:
self.client.poll()
if self.client.ready(node_id):
return True
time.sleep(connection_sleep)
retries += 1
return False
return True
def is_topic_configuration_need_update(self, topic_name, topic_conf):
"""
Checks whether topic's options need to be updated or not.
Since the DescribeConfigsRequest does not give all current
configuration entries for a topic, we need to use Zookeeper.
Requires zk connection.
"""
current_config, _zk_stats = self.zk_client.get(
self.ZK_TOPIC_CONFIGURATION_NODE + topic_name)
current_config = json.loads(current_config)['config']
if len(topic_conf) != len(current_config.keys()):
return True
else:
for conf_name, conf_value in topic_conf:
if (conf_name not in current_config.keys()
or str(conf_value) != str(current_config[conf_name])):
return True
return False
def is_topic_partitions_need_update(self, topic_name, partitions):
"""
Checks whether topic's partitions need to be updated or not.
"""
total_partitions = self.get_total_partitions_for_topic(topic_name)
need_update = False
if partitions != total_partitions:
if partitions > total_partitions:
# increasing partition number
need_update = True
else:
# decreasing partition number, which is not possible
self.close()
self.module.fail_json(
msg='Can\'t update \'%s\' topic partition from %s to %s :'
'only increase is possible.' %
(topic_name, total_partitions, partitions))
return need_update
def is_topic_replication_need_update(self, topic_name, replica_factor):
"""
Checks whether a topic replica needs to be updated or not.
"""
need_update = False
for _id, part in self.get_partitions_for_topic(topic_name).items():
_topic, _partition, _leader, replicas, _isr, _error = part
if len(replicas) != replica_factor:
need_update = True
return need_update
def update_topic_partitions(self, topic_name, partitions):
"""
Updates the topic partitions
Usable for Kafka version >= 1.0.0
Requires to be the sended to the current controller of the Kafka
cluster.
The request requires to precise the total number of partitions and
broker assignment for each new partition without forgeting replica.
See NewPartitions class for explanations
apache/kafka/clients/admin/NewPartitions.java#L53
"""
brokers = []
for node_id, _, _, _ in self.get_brokers():
brokers.append(int(node_id))
brokers_iterator = itertools.cycle(brokers)
topic, _, _, replicas, _, _ = (
self.get_partitions_for_topic(topic_name)[0])
total_replica = len(replicas)
old_partition = self.get_total_partitions_for_topic(topic_name)
assignments = []
for _new_partition in range(partitions - old_partition):
assignment = []
for _replica in range(total_replica):
assignment.append(next(brokers_iterator))
assignments.append(assignment)
request = CreatePartitionsRequest_v0(topic_partitions=[
(topic_name, (partitions, assignments))
],
timeout=self.DEFAULT_TIMEOUT,
validate_only=False)
response = self.send_request_and_get_response(request)
for topic, error_code, _error_message in response.topic_errors:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while updating topic \'%s\' partitions. '
'Error key is %s, %s. Request was %s.' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description,
str(request)))
self.refresh()
def update_topic_configuration(self, topic_name, topic_conf):
"""
Updates the topic configuration
Usable for Kafka version >= 0.11.0
Requires to be the sended to the current controller of the Kafka
cluster.
"""
request = AlterConfigsRequest_v0(resources=[(self.TOPIC_RESOURCE_ID,
topic_name, topic_conf)],
validate_only=False)
response = self.send_request_and_get_response(request)
for error_code, _, _, resource_name in response.resources:
if error_code != self.SUCCESS_CODE:
self.close()
self.module.fail_json(
msg='Error while updating topic \'%s\' configuration. '
'Error key is %s, %s' %
(resource_name, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
self.refresh()
def get_assignment_for_replica_factor_update(self, topic_name,
replica_factor):
"""
Generates a json assignment based on replica_factor given to update
replicas for a topic.
Uses all brokers available and distributes them as replicas using
a round robin method.
"""
all_replicas = []
assign = {'partitions': [], 'version': 1}
if replica_factor > self.get_total_brokers():
self.close()
self.close_zk_client()
self.module.fail_json(
msg='Error while updating topic \'%s\' replication factor : '
'replication factor \'%s\' is more than available brokers '
'\'%s\'' %
(topic_name, replica_factor, self.get_total_brokers()))
else:
for node_id, _, _, _ in self.get_brokers():
all_replicas.append(node_id)
brokers_iterator = itertools.cycle(all_replicas)
for _, part in self.get_partitions_for_topic(topic_name).items():
_, partition, _, _, _, _ = part
assign_tmp = {
'topic': topic_name,
'partition': partition,
'replicas': []
}
for _i in range(replica_factor):
assign_tmp['replicas'].append(next(brokers_iterator))
assign['partitions'].append(assign_tmp)
return bytes(str(json.dumps(assign)).encode('ascii'))
def get_assignment_for_partition_update(self, topic_name, partitions):
"""
Generates a json assignment based on number of partitions given to
update partitions for a topic.
Uses all brokers available and distributes them among partitions
using a round robin method.
"""
all_brokers = []
assign = {'partitions': {}, 'version': 1}
_, _, _, replicas, _, _ = self.get_partitions_for_topic(topic_name)[0]
total_replica = len(replicas)
for node_id, _host, _port, _rack in self.get_brokers():
all_brokers.append(node_id)
brokers_iterator = itertools.cycle(all_brokers)
for i in range(partitions):
assign_tmp = []
for _j in range(total_replica):
assign_tmp.append(next(brokers_iterator))
assign['partitions'][str(i)] = assign_tmp
return bytes(str(json.dumps(assign)).encode('ascii'))
def wait_for_znode_assignment(self, zk_sleep_time, zk_max_retries):
"""
Wait for the reassignment znode to be consumed by Kafka.
Raises `ReassignPartitionsTimeout` if `zk_max_retries` is reached.
"""
retries = 0
while (self.zk_client.exists(self.ZK_REASSIGN_NODE)
and retries < zk_max_retries):
retries += 1
time.sleep(zk_sleep_time)
if retries >= zk_max_retries:
raise ReassignPartitionsTimeout(
'The znode %s, is still present after %s tries, giving up.'
'Consider increasing your `zookeeper_max_retries` and/or '
'`zookeeper_sleep_time` parameters and check your cluster.',
self.ZK_REASSIGN_NODE, retries)
def update_admin_assignment(self, json_assignment, zk_sleep_time,
zk_max_retries):
"""
Updates the topic replica factor using a json assignment
Cf core/src/main/scala/kafka/admin/ReassignPartitionsCommand.scala#L580
1 - Send AlterReplicaLogDirsRequest to allow broker to create replica in
the right log dir later if the replica has not been created yet.
2 - Create reassignment znode so that controller will send
LeaderAndIsrRequest to create replica in the broker
def path = "/admin/reassign_partitions" ->
zk.create("/admin/reassign_partitions", b"a value")
case class ReplicaAssignment(
@BeanProperty @JsonProperty("topic") topic: String,
@BeanProperty @JsonProperty("partition") partition: Int,
@BeanProperty @JsonProperty("replicas") replicas: java.util.List[Int])
3 - Send AlterReplicaLogDirsRequest again to make sure broker will start
to move replica to the specified log directory.
It may take some time for controller to create replica in the broker
Retry if the replica has not been created.
It may be possible that the node '/admin/reassign_partitions' is already
there for another topic. That's why we need to check for its existence
and wait for its consumption if it is already present.
Requires zk connection.
"""
try:
self.wait_for_znode_assignment(zk_sleep_time, zk_max_retries)
self.zk_client.create(self.ZK_REASSIGN_NODE, json_assignment)
self.wait_for_znode_assignment(zk_sleep_time, zk_max_retries)
except ReassignPartitionsTimeout as e:
self.close()
self.close_zk_client()
self.module.fail_json(msg=str(e))
self.refresh()
def update_topic_assignment(self, json_assignment, zknode):
"""
Updates the topic partition assignment using a json assignment
Used when Kafka version < 1.0.0
Requires zk connection.
"""
if not self.zk_client.exists(zknode):
self.close()
self.close_zk_client()
self.module.fail_json(
msg='Error while updating assignment: zk node %s missing. '
'Is the topic name correct?' % (zknode))
self.zk_client.set(zknode, json_assignment)
self.refresh()
class KafkaManager:
"""
A class used to interact with Kafka and Zookeeper
and easily retrive useful information
"""
MAX_RETRY = 10
MAX_POLL_RETRIES = 3
MAX_ZK_RETRIES = 5
TOPIC_RESOURCE_ID = 2
DEFAULT_TIMEOUT = 15000
SUCCESS_CODE = 0
ZK_REASSIGN_NODE = '/admin/reassign_partitions'
ZK_TOPIC_PARTITION_NODE = '/brokers/topics/'
ZK_TOPIC_CONFIGURATION_NODE = '/config/topics/'
# Not used yet.
ZK_TOPIC_DELETION_NODE = '/admin/delete_topics/'
def __init__(self, **configs):
self.zk_client = None
self.zk_configuration = None
self.zookeeper_sleep_time = 5
self.zookeeper_max_retries = 5
self.kafka_sleep_time = 5
self.kafka_max_retries = 5
self.client = KafkaClient(**configs)
self.refresh()
def init_zk_client(self):
"""
Zookeeper client initialization
"""
if (self.zk_configuration is None
or self.zk_configuration['hosts'] == ''):
raise MissingConfiguration(
'\'zookeeper\', parameter is needed when '
'parameter \'state\' is \'present\' for resource '
'\'topic\'.')
try:
self.zk_client = KazooClient(**self.zk_configuration)
self.zk_client.start()
except Exception as e:
raise ZookeeperBroken(
msg='Error while initializing Zookeeper client : '
'%s. Is your Zookeeper server available and '
'running on \'%s\'?' % (e, self.zk_configuration['hosts']))
def close_zk_client(self):
"""
Closes Zookeeper client
"""
self.zk_client.stop()
def close_kafka_client(self):
"""
Closes Kafka client
"""
self.client.close()
def close(self):
"""
Closes any available client
"""
self.close_kafka_client()
if self.zk_client is not None:
self.close_zk_client()
def refresh(self):
"""
Refresh topics state
"""
fut = self.client.cluster.request_update()
self.client.poll(future=fut)
if not fut.succeeded():
raise UnableToRefreshState(
'Error while updating topic state from Kafka server: %s.' %
fut.exception)
def create_topic(self,
name,
partitions,
replica_factor,
replica_assignment=[],
config_entries=[],
timeout=None):
"""
Creates a topic
Usable for Kafka version >= 0.10.1
"""
if timeout is None:
timeout = self.DEFAULT_TIMEOUT
request = CreateTopicsRequest_v0(create_topic_requests=[
(name, partitions, replica_factor, replica_assignment,
config_entries)
],
timeout=timeout)
response = self.send_request_and_get_response(request)
for topic, error_code in response.topic_errors:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while creating topic %s. '
'Error key is %s, %s.' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
def delete_topic(self, name, timeout=None):
"""
Deletes a topic
Usable for Kafka version >= 0.10.1
Need to know which broker is controller for topic
"""
if timeout is None:
timeout = self.DEFAULT_TIMEOUT
request = DeleteTopicsRequest_v0(topics=[name], timeout=timeout)
response = self.send_request_and_get_response(request)
for topic, error_code in response.topic_error_codes:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while deleting topic %s. Error key is: %s, %s. '
'Is option \'delete.topic.enable\' set to true on '
' your Kafka server?' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
@staticmethod
def _convert_create_acls_resource_request_v0(acl_resource):
if acl_resource.operation == ACLOperation.ANY:
raise IllegalArgumentError("operation must not be ANY")
if acl_resource.permission_type == ACLPermissionType.ANY:
raise IllegalArgumentError("permission_type must not be ANY")
return (acl_resource.resource_type, acl_resource.name,
acl_resource.principal, acl_resource.host,
acl_resource.operation, acl_resource.permission_type)
@staticmethod
def _convert_create_acls_resource_request_v1(acl_resource):
if acl_resource.operation == ACLOperation.ANY:
raise IllegalArgumentError("operation must not be ANY")
if acl_resource.permission_type == ACLPermissionType.ANY:
raise IllegalArgumentError("permission_type must not be ANY")
return (acl_resource.resource_type, acl_resource.name,
acl_resource.pattern_type, acl_resource.principal,
acl_resource.host, acl_resource.operation,
acl_resource.permission_type)
@staticmethod
def _convert_delete_acls_resource_request_v0(acl_resource):
return (acl_resource.resource_type, acl_resource.name,
acl_resource.principal, acl_resource.host,
acl_resource.operation, acl_resource.permission_type)
@staticmethod
def _convert_delete_acls_resource_request_v1(acl_resource):
return (acl_resource.resource_type, acl_resource.name,
acl_resource.pattern_type, acl_resource.principal,
acl_resource.host, acl_resource.operation,
acl_resource.permission_type)
def describe_acls(self, acl_resource, api_version):
"""Describe a set of ACLs
"""
if api_version < parse_version('2.0.0'):
request = DescribeAclsRequest_v0(
resource_type=acl_resource.resource_type,
resource_name=acl_resource.name,
principal=acl_resource.principal,
host=acl_resource.host,
operation=acl_resource.operation,
permission_type=acl_resource.permission_type)
else:
request = DescribeAclsRequest_v1(
resource_type=acl_resource.resource_type,
resource_name=acl_resource.name,
resource_pattern_type_filter=acl_resource.pattern_type,
principal=acl_resource.principal,
host=acl_resource.host,
operation=acl_resource.operation,
permission_type=acl_resource.permission_type)
response = self.send_request_and_get_response(request)
if response.error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while describing ACL %s. Error %s: %s.' %
(acl_resource, response.error_code, response.error_message))
return response.resources
def create_acls(self, acl_resources, api_version):
"""Create a set of ACLs"""
if api_version < parse_version('2.0.0'):
request = CreateAclsRequest_v0(creations=[
self._convert_create_acls_resource_request_v0(acl_resource)
for acl_resource in acl_resources
])
else:
request = CreateAclsRequest_v1(creations=[
self._convert_create_acls_resource_request_v1(acl_resource)
for acl_resource in acl_resources
])
response = self.send_request_and_get_response(request)
for error_code, error_message in response.creation_responses:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while creating ACL %s. Error %s: %s.' %
(acl_resources, error_code, error_message))
def delete_acls(self, acl_resources, api_version):
"""Delete a set of ACLSs"""
if api_version < parse_version('2.0.0'):
request = DeleteAclsRequest_v0(filters=[
self._convert_delete_acls_resource_request_v0(acl_resource)
for acl_resource in acl_resources
])
else:
request = DeleteAclsRequest_v1(filters=[
self._convert_delete_acls_resource_request_v1(acl_resource)
for acl_resource in acl_resources
])
response = self.send_request_and_get_response(request)
for error_code, error_message, _ in response.filter_responses:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while deleting ACL %s. Error %s: %s.' %
(acl_resources, error_code, error_message))
def send_request_and_get_response(self, request, node_id=None):
"""
Sends a Kafka protocol request and returns
the associated response
"""
if node_id is None:
try:
node_id = self.get_controller()
except UndefinedController:
raise
except Exception as e:
raise KafkaManagerError(
'Cannot determine a controller for your current Kafka '
'server. Is your Kafka server running and available on '
'\'%s\' with security protocol \'%s\'? Are you using the '
'library versions from given \'requirements.txt\'? '
'Exception was: %s' %
(self.client.config['bootstrap_servers'],
self.client.config['security_protocol'], e))
if self.connection_check(node_id):
future = self.client.send(node_id, request)
self.client.poll(future=future)
if future.succeeded():
return future.value
else:
raise KafkaManagerError(
'Error while sending request %s to Kafka server: %s.' %
(request, future.exception))
else:
raise KafkaManagerError(
'Connection is not ready, please check your client '
'and server configurations.')
def get_controller(self):
"""
Returns the current controller
"""
if self.client.cluster.controller is not None:
node_id, _host, _port, _rack = self.client.cluster.controller
return node_id
else:
raise UndefinedController(
'Cannot determine a controller for your current Kafka '
'server. Is your Kafka server running and available on '
'\'%s\' with security protocol \'%s\'?' %
(self.client.config['bootstrap_servers'],
self.client.config['security_protocol']))
def get_config_for_topic(self, topic_name, config_names):
"""
Returns responses with configuration
Usable with Kafka version >= 0.11.0
"""
current_config = {}
if parse_version(self.get_api_version()) < parse_version('1.1.0'):
request = DescribeConfigsRequest_v0(
resources=[(self.TOPIC_RESOURCE_ID, topic_name, config_names)])
kafka_config = self.send_request_and_get_response(request)
for error_code, _, _, _, config_entries in kafka_config.resources:
for (config_names, config_values, _, is_default,
_) in config_entries:
if not is_default:
current_config[config_names] = config_values
else:
request = DescribeConfigsRequest_v1(
resources=[(self.TOPIC_RESOURCE_ID, topic_name, config_names)])
kafka_config = self.send_request_and_get_response(request)
for error_code, _, _, _, config_entries in kafka_config.resources:
for (config_names, config_values, _, config_source, _,
_) in config_entries:
# Dynamic topic config
if config_source == 1:
current_config[config_names] = config_values
return current_config
def get_topics(self):
"""
Returns the topics list
"""
return self.client.cluster.topics()
def get_total_partitions_for_topic(self, topic):
"""
Returns the number of partitions for topic
"""
return len(self.client.cluster.partitions_for_topic(topic))
def get_partitions_for_topic(self, topic):
"""
Returns all partitions for topic, with information
TODO do not use private property anymore
"""
return self.client.cluster._partitions[topic]
def get_total_brokers(self):
"""
Returns number of brokers available
"""
return len(self.client.cluster.brokers())
def get_brokers(self):
"""
Returns all brokers
"""
return self.client.cluster.brokers()
def get_api_version(self):
"""
Returns Kafka server version
"""
major, minor, patch = self.client.config['api_version']
return '%s.%s.%s' % (major, minor, patch)
def connection_check(self, node_id, connection_sleep=0.1):
"""
Checks that connection with broker is OK and that it is possible to
send requests
Since the maybe_connect() function used in ready() is 'async', we
need to manually call the poll() function to establish the connection
to the node
"""
retries = 0
if not self.client.ready(node_id):
while retries < self.MAX_RETRY:
self.client.poll()
if self.client.ready(node_id):
return True
time.sleep(connection_sleep)
retries += 1
return False
return True
def is_topic_configuration_need_update(self, topic_name, topic_conf):
"""
Checks whether topic's options need to be updated or not.
Since the DescribeConfigsRequest does not give all current
configuration entries for a topic, we need to use Zookeeper.
Requires zk connection.
"""
current_config = self.get_config_for_topic(topic_name, None)
if len(topic_conf) != len(current_config.keys()):
return True
else:
for conf_name, conf_value in topic_conf:
if (conf_name not in current_config.keys()
or str(conf_value) != str(current_config[conf_name])):
return True
return False
def is_topic_partitions_need_update(self, topic_name, partitions):
"""
Checks whether topic's partitions need to be updated or not.
"""
total_partitions = self.get_total_partitions_for_topic(topic_name)
need_update = False
if partitions != total_partitions:
if partitions > total_partitions:
# increasing partition number
need_update = True
else:
# decreasing partition number, which is not possible
raise KafkaManagerError(
'Can\'t update \'%s\' topic partition from %s to %s :'
'only increase is possible.' %
(topic_name, total_partitions, partitions))
return need_update
def is_topic_replication_need_update(self, topic_name, replica_factor):
"""
Checks whether a topic replica needs to be updated or not.
"""
need_update = False
for _id, part in self.get_partitions_for_topic(topic_name).items():
_topic, _partition, _leader, replicas, _isr, _error = part
if len(replicas) != replica_factor:
need_update = True
return need_update
def update_topic_partitions(self, topic_name, partitions):
"""
Updates the topic partitions
Usable for Kafka version >= 1.0.0
Requires to be the sended to the current controller of the Kafka
cluster.
The request requires to precise the total number of partitions and
broker assignment for each new partition without forgeting replica.
See NewPartitions class for explanations
apache/kafka/clients/admin/NewPartitions.java#L53
"""
brokers = []
for node_id, _, _, _ in self.get_brokers():
brokers.append(int(node_id))
brokers_iterator = itertools.cycle(brokers)
topic, _, _, replicas, _, _ = (
self.get_partitions_for_topic(topic_name)[0])
total_replica = len(replicas)
old_partition = self.get_total_partitions_for_topic(topic_name)
assignments = []
for _new_partition in range(partitions - old_partition):
assignment = []
for _replica in range(total_replica):
assignment.append(next(brokers_iterator))
assignments.append(assignment)
request = CreatePartitionsRequest_v0(topic_partitions=[
(topic_name, (partitions, assignments))
],
timeout=self.DEFAULT_TIMEOUT,
validate_only=False)
response = self.send_request_and_get_response(request)
for topic, error_code, _error_message in response.topic_errors:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while updating topic \'%s\' partitions. '
'Error key is %s, %s. Request was %s.' %
(topic, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description, request))
self.refresh()
def update_topic_configuration(self, topic_name, topic_conf):
"""
Updates the topic configuration
Usable for Kafka version >= 0.11.0
Requires to be the sended to the current controller of the Kafka
cluster.
"""
request = AlterConfigsRequest_v0(resources=[(self.TOPIC_RESOURCE_ID,
topic_name, topic_conf)],
validate_only=False)
response = self.send_request_and_get_response(request)
for error_code, _, _, resource_name in response.resources:
if error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while updating topic \'%s\' configuration. '
'Error key is %s, %s' %
(resource_name, kafka.errors.for_code(error_code).message,
kafka.errors.for_code(error_code).description))
self.refresh()
def get_assignment_for_replica_factor_update(self, topic_name,
replica_factor):
"""
Generates a json assignment based on replica_factor given to update
replicas for a topic.
Uses all brokers available and distributes them as replicas using
a round robin method.
"""
all_replicas = []
partitions = []
if replica_factor > self.get_total_brokers():
raise KafkaManagerError(
'Error while updating topic \'%s\' replication factor : '
'replication factor \'%s\' is more than available brokers '
'\'%s\'' %
(topic_name, replica_factor, self.get_total_brokers()))
else:
for node_id, _, _, _ in self.get_brokers():
all_replicas.append(node_id)
brokers_iterator = itertools.cycle(all_replicas)
for _, part in self.get_partitions_for_topic(topic_name).items():
_, partition, _, _, _, _ = part
replicas = []
for _i in range(replica_factor):
replicas.append(next(brokers_iterator))
assign_tmp = (partition, replicas, {})
partitions.append(assign_tmp)
return [(topic_name, partitions, {})]
def get_assignment_for_replica_factor_update_with_zk(
self, topic_name, replica_factor):
"""
Generates a json assignment based on replica_factor given to update
replicas for a topic.
Uses all brokers available and distributes them as replicas using
a round robin method.
"""
all_replicas = []
assign = {'partitions': [], 'version': 1}
if replica_factor > self.get_total_brokers():
raise KafkaManagerError(
'Error while updating topic \'%s\' replication factor : '
'replication factor \'%s\' is more than available brokers '
'\'%s\'' %
(topic_name, replica_factor, self.get_total_brokers()))
else:
for node_id, _, _, _ in self.get_brokers():
all_replicas.append(node_id)
brokers_iterator = itertools.cycle(all_replicas)
for _, part in self.get_partitions_for_topic(topic_name).items():
_, partition, _, _, _, _ = part
assign_tmp = {
'topic': topic_name,
'partition': partition,
'replicas': []
}
for _i in range(replica_factor):
assign_tmp['replicas'].append(next(brokers_iterator))
assign['partitions'].append(assign_tmp)
return bytes(str(json.dumps(assign)).encode('ascii'))
def get_assignment_for_partition_update(self, topic_name, partitions):
"""
Generates a json assignment based on number of partitions given to
update partitions for a topic.
Uses all brokers available and distributes them among partitions
using a round robin method.
"""
all_brokers = []
assign = {'partitions': {}, 'version': 1}
_, _, _, replicas, _, _ = self.get_partitions_for_topic(topic_name)[0]
total_replica = len(replicas)
for node_id, _host, _port, _rack in self.get_brokers():
all_brokers.append(node_id)
brokers_iterator = itertools.cycle(all_brokers)
for i in range(partitions):
assign_tmp = []
for _j in range(total_replica):
assign_tmp.append(next(brokers_iterator))
assign['partitions'][str(i)] = assign_tmp
return bytes(str(json.dumps(assign)).encode('ascii'))
def wait_for_partition_assignement(self):
"""
wait until all assignements is done.
"""
retries = 0
assignement_done = False
while (not assignement_done and retries < self.kafka_max_retries):
request = ListPartitionReassignmentsRequest_v0(timeout_ms=60000,
topics=None,
tags={})
response = self.send_request_and_get_response(request)
if len(response.topics) == 0:
break
retries += 1
time.sleep(self.kafka_sleep_time)
if retries >= self.kafka_max_retries:
raise ReassignPartitionsTimeout(
'Reassignement, is still in progress after %s tries,'
'giving up. Consider increasing your `kafka_max_retries`'
'and/or `kafka_sleep_time` parameters and check your'
'cluster.', retries)
def wait_for_znode_assignment(self):
"""
Wait for the reassignment znode to be consumed by Kafka.
Raises `ReassignPartitionsTimeout` if `zk_max_retries` is reached.
"""
retries = 0
while (self.zk_client.exists(self.ZK_REASSIGN_NODE)
and retries < self.zookeeper_max_retries):
retries += 1
time.sleep(self.zookeeper_sleep_time)
if retries >= self.zookeeper_max_retries:
raise ReassignPartitionsTimeout(
'The znode %s, is still present after %s tries, giving up.'
'Consider increasing your `zookeeper_max_retries` and/or '
'`zookeeper_sleep_time` parameters and check your cluster.',
self.ZK_REASSIGN_NODE, retries)
def update_admin_assignment(self, name, replica_factor):
"""
Updates the topic replica factor using a json assignment
Cf core/src/main/scala/kafka/admin/ReassignPartitionsCommand.scala#L580
1 - Send AlterReplicaLogDirsRequest to allow broker to create replica in
the right log dir later if the replica has not been created yet.
2 - Create reassignment znode so that controller will send
LeaderAndIsrRequest to create replica in the broker
def path = "/admin/reassign_partitions" ->
zk.create("/admin/reassign_partitions", b"a value")
case class ReplicaAssignment(
@BeanProperty @JsonProperty("topic") topic: String,
@BeanProperty @JsonProperty("partition") partition: Int,
@BeanProperty @JsonProperty("replicas") replicas: java.util.List[Int])
3 - Send AlterReplicaLogDirsRequest again to make sure broker will start
to move replica to the specified log directory.
It may take some time for controller to create replica in the broker
Retry if the replica has not been created.
It may be possible that the node '/admin/reassign_partitions' is already
there for another topic. That's why we need to check for its existence
and wait for its consumption if it is already present.
Requires zk connection.
"""
if (parse_version(self.get_api_version()) >= parse_version('2.4.0')):
assign = self.get_assignment_for_replica_factor_update(
name, replica_factor)
request = AlterPartitionReassignmentsRequest_v0(timeout_ms=60000,
topics=assign,
tags={})
self.wait_for_partition_assignement()
self.send_request_and_get_response(request)
self.wait_for_partition_assignement()
elif self.zk_configuration is not None:
try:
json_assignment = (
self.get_assignment_for_replica_factor_update_with_zk(
name, replica_factor))
self.init_zk_client()
self.wait_for_znode_assignment()
self.zk_client.create(self.ZK_REASSIGN_NODE, json_assignment)
self.wait_for_znode_assignment()
finally:
self.close_zk_client()
else:
raise KafkaManagerError(
'Zookeeper is mandatory for partition assignment when \
using Kafka <= 2.4.0.')
self.refresh()
def update_topic_assignment(self, json_assignment, zknode):
"""
Updates the topic partition assignment using a json assignment
Used when Kafka version < 1.0.0
Requires zk connection.
"""
try:
self.init_zk_client()
if not self.zk_client.exists(zknode):
raise KafkaManagerError(
'Error while updating assignment: zk node %s missing. '
'Is the topic name correct?' % (zknode))
self.zk_client.set(zknode, json_assignment)
self.refresh()
finally:
self.close_zk_client()
@staticmethod
def generate_consumer_groups_for_broker(broker, response):
"""
From a `broker` and `response` generate a list of consumer groups
"""
consumer_groups = {}
for err, gid, gstate, prot_type, prot, _members in response.groups:
members = {}
for mid, cid, chost, mdata, assign in _members:
mdata = ProtocolMetadata.decode(mdata)
assign = MemberAssignment.decode(assign)
assignment = {}
for t, p in assign.assignment:
assignment[t] = p
members[mid] = {
'client_id': cid,
'client_host': chost,
'member_metadata': {
'version': mdata.version,
'subscription': mdata.subscription,
'user_data': mdata.user_data.decode('utf-8')
},
'member_assignment': {
'version': assign.version,
'assignment': assignment,
'user_data': assign.user_data.decode('utf-8')
}
}
group = {
'error_code': err,
'group_state': gstate,
'members': members,
'protocol_type': prot_type,
'protocol': prot,
'coordinator': {
'host': broker.host,
'nodeId': broker.nodeId,
'port': broker.port,
'rack': broker.rack
}
}
consumer_groups[gid] = group
return consumer_groups
def get_consumer_groups_resource(self):
"""
Return a dict object containing information about consumer groups and
following this structure:
{
"AWESOME_consumer_group_1607465801": {
"coordinator": {
"host": "172.17.0.9",
"nodeId": 1001,
"port": 9092,
"rack": null
},
"error_code": 0,
"group_state": "Empty",
"members": {},
"protocol": "",
"protocol_type": "consumer"
},
"AWESOME_consumer_group_1607466258": {
"coordinator": {
"host": "172.17.0.10",
"nodeId": 1002,
"port": 9092,
"rack": null
},
"error_code": 0,
"group_state": "Stable",
"members": {
"kafka-python-2.0.1-e5500fee-8df9-4f37-bcd7-788522a1c382": {
"client_host": "/172.17.0.1",
"client_id": "kafka-python-2.0.1",
"member_assignment": {
"assignment": {
"test_1607465755": [
0
]
},
"user_data": "",
"version": 0
},
"member_metadata": {
"subscription": [
"test_1607465755"
],
"user_data": "",
"version": 0
}
}
},
"protocol": "range",
"protocol_type": "consumer"
}
}
"""
consumer_groups = {}
for broker in self.get_brokers():
request = ListGroupsRequest_v2()
response = self.send_request_and_get_response(
request, node_id=broker.nodeId)
if response.error_code != self.SUCCESS_CODE:
raise KafkaManagerError(
'Error while list consumer groups of %s. '
'Error key is %s, %s.' %
(broker.nodeId, kafka.errors.for_code(
response.error_code).message,
kafka.errors.for_code(response.error_code).description))
if response.groups:
request = DescribeGroupsRequest_v0(groups=tuple(
[group for group, protocol in response.groups]))
response = self.send_request_and_get_response(
request, node_id=broker.nodeId)
consumer_groups.update(
self.generate_consumer_groups_for_broker(broker, response))
return consumer_groups
def get_brokers_resource(self):
"""
Return a dict object containing information about brokers and
following this structure:
{
"1001": {
"host": "172.17.0.9",
"nodeId": 1001,
"port": 9092,
"rack": null
},
"1002": {
"host": "172.17.0.10",
"nodeId": 1002,
"port": 9092,
"rack": null
}
}
"""
brokers = {}
for broker in self.get_brokers():
brokers[broker.nodeId] = broker._asdict()
return brokers
def get_topics_resource(self):
"""
Return a dict object containing information about topics and partitions,
and following this structure:
{
"test_1600378061": {
"0": {
"isr": [
1002
],
"leader": 1002,
"replicas": [
1002
]
}
}
}
"""
topics = {}
for topic in self.get_topics():
topics[topic] = {}
partitions = self.get_partitions_for_topic(topic)
for partition, metadata in partitions.items():
_, _, leader, replicas, isr, _ = metadata
topics[topic][partition] = {
'leader': leader,
'replicas': replicas,
'isr': isr
}
return topics
@property
def resource_to_func(self):
return {
'topic': self.get_topics_resource,
'broker': self.get_brokers_resource,
'consumer_group': self.get_consumer_groups_resource
}
def get_resource(self, resource):
if resource not in self.resource_to_func:
raise ValueError('Unexpected resource "%s"' % resource)
return self.resource_to_func[resource]()
def ensure_topic(self, name, options, partitions, replica_factor):
changed = False
warn = None
if self.is_topic_configuration_need_update(name, options):
self.update_topic_configuration(name, options)
changed = True
if partitions > 0 and replica_factor > 0:
# partitions and replica_factor are set
if self.is_topic_replication_need_update(name, replica_factor):
self.update_admin_assignment(name, replica_factor)
changed = True
if self.is_topic_partitions_need_update(name, partitions):
cur_version = parse_version(self.get_api_version())
if cur_version < parse_version('1.0.0'):
json_assignment = (
self.get_assignment_for_partition_update(
name, partitions))
zknode = '/brokers/topics/%s' % name
self.update_topic_assignment(json_assignment, zknode)
else:
self.update_topic_partitions(name, partitions)
changed = True
else:
# 0 or "default" (-1)
warn = ("Current values of 'partitions' (%s) and "
"'replica_factor' (%s) does not let this lib to "
"perform any action related to partitions and "
"replication. SKIPPING." % (partitions, replica_factor))
return changed, warn
class KafkaConsumer(six.Iterator):
"""Consume records from a Kafka cluster.
The consumer will transparently handle the failure of servers in the Kafka
cluster, and adapt as topic-partitions are created or migrate between
brokers. It also interacts with the assigned kafka Group Coordinator node
to allow multiple consumers to load balance consumption of topics (requires
kafka >= 0.9.0.0).
Arguments:
*topics (str): optional list of topics to subscribe to. If not set,
call subscribe() or assign() before consuming records.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): A name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
group_id (str or None): The name of the consumer group to join for dynamic
partition assignment (if enabled), and to use for fetching and
committing offsets. If None, auto-partition assignment (via
group coordinator) and offset commits are disabled.
Default: 'kafka-python-default-group'
key_deserializer (callable): Any callable that takes a
raw message key and returns a deserialized key.
value_deserializer (callable): Any callable that takes a
raw message value and returns a deserialized value.
fetch_min_bytes (int): Minimum amount of data the server should
return for a fetch request, otherwise wait up to
fetch_max_wait_ms for more data to accumulate. Default: 1.
fetch_max_wait_ms (int): The maximum amount of time in milliseconds
the server will block before answering the fetch request if
there isn't sufficient data to immediately satisfy the
requirement given by fetch_min_bytes. Default: 500.
max_partition_fetch_bytes (int): The maximum amount of data
per-partition the server will return. The maximum total memory
used for a request = #partitions * max_partition_fetch_bytes.
This size must be at least as large as the maximum message size
the server allows or else it is possible for the producer to
send messages larger than the consumer can fetch. If that
happens, the consumer can get stuck trying to fetch a large
message on a certain partition. Default: 1048576.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 40000.
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
auto_offset_reset (str): A policy for resetting offsets on
OffsetOutOfRange errors: 'earliest' will move to the oldest
available message, 'latest' will move to the most recent. Any
other value will raise the exception. Default: 'latest'.
enable_auto_commit (bool): If True , the consumer's offset will be
periodically committed in the background. Default: True.
auto_commit_interval_ms (int): Number of milliseconds between automatic
offset commits, if enable_auto_commit is True. Default: 5000.
default_offset_commit_callback (callable): Called as
callback(offsets, response) response will be either an Exception
or an OffsetCommitResponse struct. This callback can be used to
trigger custom actions when a commit request completes.
check_crcs (bool): Automatically check the CRC32 of the records
consumed. This ensures no on-the-wire or on-disk corruption to
the messages occurred. This check adds some overhead, so it may
be disabled in cases seeking extreme performance. Default: True
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata, even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
partition_assignment_strategy (list): List of objects to use to
distribute partition ownership amongst consumer instances when
group management is used.
Default: [RangePartitionAssignor, RoundRobinPartitionAssignor]
heartbeat_interval_ms (int): The expected time in milliseconds
between heartbeats to the consumer coordinator when using
Kafka's group management feature. Heartbeats are used to ensure
that the consumer's session stays active and to facilitate
rebalancing when new consumers join or leave the group. The
value must be set lower than session_timeout_ms, but typically
should be set no higher than 1/3 of that value. It can be
adjusted even lower to control the expected time for normal
rebalances. Default: 3000
session_timeout_ms (int): The timeout used to detect failures when
using Kafka's group management facilities. Default: 30000
max_poll_records (int): The maximum number of records returned in a
single call to poll(). Default: 500
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). The java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). The java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
consumer_timeout_ms (int): number of milliseconds to block during
message iteration before raising StopIteration (i.e., ending the
iterator). Default block forever [float('inf')].
skip_double_compressed_messages (bool): A bug in KafkaProducer <= 1.2.4
caused some messages to be corrupted via double-compression.
By default, the fetcher will return these messages as a compressed
blob of bytes with a single offset, i.e. how the message was
actually published to the cluster. If you prefer to have the
fetcher automatically detect corrupt messages and skip them,
set this option to True. Default: False.
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): Flag to configure whether ssl handshake
should verify that the certificate matches the brokers hostname.
Default: True.
ssl_cafile (str): Optional filename of ca file to use in certificate
verification. Default: None.
ssl_certfile (str): Optional filename of file in pem format containing
the client certificate, as well as any ca certificates needed to
establish the certificate's authenticity. Default: None.
ssl_keyfile (str): Optional filename containing the client private key.
Default: None.
ssl_password (str): Optional password to be used when loading the
certificate chain. Default: None.
ssl_crlfile (str): Optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
Default: None.
api_version (tuple): Specify which kafka API version to use.
If set to None, the client will attempt to infer the broker version
by probing various APIs. Default: None
Examples:
(0, 9) enables full group coordination features with automatic
partition assignment and rebalancing,
(0, 8, 2) enables kafka-storage offset commits with manual
partition assignment only,
(0, 8, 1) enables zookeeper-storage offset commits with manual
partition assignment only,
(0, 8, 0) enables basic functionality but requires manual
partition assignment and offset management.
For a full list of supported versions, see KafkaClient.API_VERSIONS
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version set to 'auto'
metric_reporters (list): A list of classes to use as metrics reporters.
Implementing the AbstractMetricsReporter interface allows plugging
in classes that will be notified of new metric creation. Default: []
metrics_num_samples (int): The number of samples maintained to compute
metrics. Default: 2
metrics_sample_window_ms (int): The maximum age in milliseconds of
samples used to compute metrics. Default: 30000
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
exclude_internal_topics (bool): Whether records from internal topics
(such as offsets) should be exposed to the consumer. If set to True
the only way to receive records from an internal topic is
subscribing to it. Requires 0.10+ Default: True
sasl_mechanism (str): String picking sasl mechanism when security_protocol
is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported.
Default: None
sasl_plain_username (str): Username for sasl PLAIN authentication.
Default: None
sasl_plain_password (str): Password for sasl PLAIN authentication.
Default: None
Note:
Configuration parameters are described in more detail at
https://kafka.apache.org/0100/configuration.html#newconsumerconfigs
"""
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'group_id': 'kafka-python-default-group',
'key_deserializer': None,
'value_deserializer': None,
'fetch_max_wait_ms': 500,
'fetch_min_bytes': 1,
'max_partition_fetch_bytes': 1 * 1024 * 1024,
'request_timeout_ms': 40 * 1000,
'retry_backoff_ms': 100,
'reconnect_backoff_ms': 50,
'max_in_flight_requests_per_connection': 5,
'auto_offset_reset': 'latest',
'enable_auto_commit': True,
'auto_commit_interval_ms': 5000,
'default_offset_commit_callback': lambda offsets, response: True,
'check_crcs': True,
'metadata_max_age_ms': 5 * 60 * 1000,
'partition_assignment_strategy': (RangePartitionAssignor, RoundRobinPartitionAssignor),
'heartbeat_interval_ms': 3000,
'session_timeout_ms': 30000,
'max_poll_records': 500,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'consumer_timeout_ms': float('inf'),
'skip_double_compressed_messages': False,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_crlfile': None,
'ssl_password': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'connections_max_idle_ms': 9 * 60 * 1000, # Not implemented yet
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
'metric_group_prefix': 'consumer',
'selector': selectors.DefaultSelector,
'exclude_internal_topics': True,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
}
def __init__(self, *topics, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
for key in self.config:
if key in configs:
self.config[key] = configs.pop(key)
# Only check for extra config keys in top-level class
assert not configs, 'Unrecognized configs: %s' % configs
deprecated = {'smallest': 'earliest', 'largest': 'latest'}
if self.config['auto_offset_reset'] in deprecated:
new_config = deprecated[self.config['auto_offset_reset']]
log.warning('use auto_offset_reset=%s (%s is deprecated)',
new_config, self.config['auto_offset_reset'])
self.config['auto_offset_reset'] = new_config
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [reporter() for reporter in self.config['metric_reporters']]
self._metrics = Metrics(metric_config, reporters)
# TODO _metrics likely needs to be passed to KafkaClient, etc.
# api_version was previously a str. Accept old format for now
if isinstance(self.config['api_version'], str):
str_version = self.config['api_version']
if str_version == 'auto':
self.config['api_version'] = None
else:
self.config['api_version'] = tuple(map(int, str_version.split('.')))
log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',
str(self.config['api_version']), str_version)
self._client = KafkaClient(metrics=self._metrics, **self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
self._subscription = SubscriptionState(self.config['auto_offset_reset'])
self._fetcher = Fetcher(
self._client, self._subscription, self._metrics, **self.config)
self._coordinator = ConsumerCoordinator(
self._client, self._subscription, self._metrics,
assignors=self.config['partition_assignment_strategy'],
**self.config)
self._closed = False
self._iterator = None
self._consumer_timeout = float('inf')
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
Arguments:
partitions (list of TopicPartition): Assignment for this instance.
Raises:
IllegalStateError: If consumer has already called subscribe()
Warning:
It is not possible to use both manual partition assignment with
assign() and group assignment with subscribe().
Note:
This interface does not support incremental assignment and will
replace the previous assignment (if there was one).
Note:
Manual topic assignment through this method does not use the
consumer's group management functionality. As such, there will be
no rebalance operation triggered when group membership or cluster
and topic metadata change.
"""
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
"""Get the TopicPartitions currently assigned to this consumer.
If partitions were directly assigned using assign(), then this will
simply return the same partitions that were previously assigned.
If topics were subscribed using subscribe(), then this will give the
set of topic partitions currently assigned to the consumer (which may
be None if the assignment hasn't happened yet, or if the partitions are
in the process of being reassigned).
Returns:
set: {TopicPartition, ...}
"""
return self._subscription.assigned_partitions()
def close(self):
"""Close the consumer, waiting indefinitely for any needed cleanup."""
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close()
self._metrics.close()
self._client.close()
try:
self.config['key_deserializer'].close()
except AttributeError:
pass
try:
self.config['value_deserializer'].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
"""Commit offsets to kafka asynchronously, optionally firing callback.
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
This is an asynchronous call and will not block. Any errors encountered
are either passed to the callback (if provided) or discarded.
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to currently
consumed offsets for all subscribed partitions.
callback (callable, optional): Called as callback(offsets, response)
with response as either an Exception or an OffsetCommitResponse
struct. This callback can be used to trigger custom actions when
a commit request completes.
Returns:
kafka.future.Future
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(
offsets, callback=callback)
return future
def commit(self, offsets=None):
"""Commit offsets to kafka, blocking until success or error.
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
Blocks until either the commit succeeds or an unrecoverable error is
encountered (in which case it is thrown to the caller).
Currently only supports kafka-topic offset storage (not zookeeper).
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to currently
consumed offsets for all subscribed partitions.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
"""Get the last committed offset for the given partition.
This offset will be used as the position for the consumer
in the event of a failure.
This call may block to do a remote call if the partition in question
isn't assigned to this consumer or if the consumer hasn't yet
initialized its cache of committed offsets.
Arguments:
partition (TopicPartition): The partition to check.
Returns:
The last committed offset, or None if there was no prior commit.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
"""Get all topics the user is authorized to view.
Returns:
set: topics
"""
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
return cluster.topics()
def partitions_for_topic(self, topic):
"""Get metadata about the partitions for a given topic.
Arguments:
topic (str): Topic to check.
Returns:
set: Partition ids
"""
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0, max_records=None):
"""Fetch data from assigned topics / partitions.
Records are fetched and returned in batches by topic-partition.
On each poll, consumer will try to use the last consumed offset as the
starting offset and fetch sequentially. The last consumed offset can be
manually set through seek(partition, offset) or automatically set as
the last committed offset for the subscribed list of partitions.
Incompatible with iterator interface -- use one or the other, not both.
Arguments:
timeout_ms (int, optional): Milliseconds spent waiting in poll if
data is not available in the buffer. If 0, returns immediately
with any records that are available currently in the buffer,
else returns empty. Must not be negative. Default: 0
max_records (int, optional): The maximum number of records returned
in a single call to :meth:`poll`. Default: Inherit value from
max_poll_records.
Returns:
dict: Topic to list of records since the last fetch for the
subscribed list of topics and partitions.
"""
assert timeout_ms >= 0, 'Timeout must not be negative'
if max_records is None:
max_records = self.config['max_poll_records']
# Poll for new data until the timeout expires
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining, max_records)
if records:
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms, max_records):
"""Do one round of polling. In addition to checking for new data, this does
any needed heart-beating, auto-commits, and offset updates.
Arguments:
timeout_ms (int): The maximum time in milliseconds to block.
Returns:
dict: Map of topic to list of records (may be empty).
"""
if self._use_consumer_group():
self._coordinator.ensure_coordinator_known()
self._coordinator.ensure_active_group()
# 0.8.2 brokers support kafka-backed offset storage via group coordinator
elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
# Fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(self._subscription.missing_fetch_positions())
# If data is available already, e.g. from a previous network client
# poll() call to commit, then just return it immediately
records, partial = self._fetcher.fetched_records(max_records)
if records:
# Before returning the fetched records, we can send off the
# next round of fetches and avoid block waiting for their
# responses to enable pipelining while the user is handling the
# fetched records.
if not partial:
self._fetcher.send_fetches()
return records
# Send any new fetches (won't resend pending fetches)
self._fetcher.send_fetches()
self._client.poll(timeout_ms=timeout_ms, sleep=True)
records, _ = self._fetcher.fetched_records(max_records)
return records
def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): Partition to check
Returns:
int: Offset
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions([partition])
offset = self._subscription.assignment[partition].position
return offset
def highwater(self, partition):
"""Last known highwater offset for a partition.
A highwater offset is the offset that will be assigned to the next
message that is produced. It may be useful for calculating lag, by
comparing with the reported position. Note that both position and
highwater refer to the *next* offset -- i.e., highwater offset is
one greater than the newest available message.
Highwater offsets are returned in FetchResponse messages, so will
not be available if no FetchRequests have been sent for this partition
yet.
Arguments:
partition (TopicPartition): Partition to check
Returns:
int or None: Offset if available
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
return self._subscription.assignment[partition].highwater
def pause(self, *partitions):
"""Suspend fetching from the requested partitions.
Future calls to poll() will not return any records from these partitions
until they have been resumed using resume(). Note that this method does
not affect partition subscription. In particular, it does not cause a
group rebalance when automatic assignment is used.
Arguments:
*partitions (TopicPartition): Partitions to pause.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def paused(self):
"""Get the partitions that were previously paused by a call to pause().
Returns:
set: {partition (TopicPartition), ...}
"""
return self._subscription.paused_partitions()
def resume(self, *partitions):
"""Resume fetching from the specified (paused) partitions.
Arguments:
*partitions (TopicPartition): Partitions to resume.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
"""Manually specify the fetch offset for a TopicPartition.
Overrides the fetch offsets that the consumer will use on the next
poll(). If this API is invoked for the same partition more than once,
the latest offset will be used on the next poll(). Note that you may
lose data if this API is arbitrarily used in the middle of consumption,
to reset the fetch offsets.
Arguments:
partition (TopicPartition): Partition for seek operation
offset (int): Message offset in partition
Raises:
AssertionError: If offset is not an int >= 0; or if partition is not
currently assigned.
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0'
assert partition in self._subscription.assigned_partitions(), 'Unassigned partition'
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
"""Seek to the oldest available offset for partitions.
Arguments:
*partitions: Optionally provide specific TopicPartitions, otherwise
default to all assigned partitions.
Raises:
AssertionError: If any partition is not currently assigned, or if
no partitions are assigned.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
"""Seek to the most recent available offset for partitions.
Arguments:
*partitions: Optionally provide specific TopicPartitions, otherwise
default to all assigned partitions.
Raises:
AssertionError: If any partition is not currently assigned, or if
no partitions are assigned.
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern.
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with assign().
Arguments:
topics (list): List of topics for subscription.
pattern (str): Pattern to match available topics. You must provide
either topics or pattern, but not both.
listener (ConsumerRebalanceListener): Optionally include listener
callback, which will be called before and after each rebalance
operation.
As part of group management, the consumer will keep track of the
list of consumers that belong to a particular group and will
trigger a rebalance operation if one of the following events
trigger:
* Number of partitions change for any of the subscribed topics
* Topic is created or deleted
* An existing member of the consumer group dies
* A new member is added to the consumer group
When any of these events are triggered, the provided listener
will be invoked first to indicate that the consumer's assignment
has been revoked, and then again when the new assignment has
been received. Note that this listener will immediately override
any listener set in a previous call to subscribe. It is
guaranteed, however, that the partitions revoked/assigned
through this interface are from topics subscribed in this call.
Raises:
IllegalStateError: If called after previously calling assign().
AssertionError: If neither topics or pattern is provided.
TypeError: If listener is not a ConsumerRebalanceListener.
"""
# SubscriptionState handles error checking
self._subscription.subscribe(topics=topics,
pattern=pattern,
listener=listener)
# Regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_all_topic_metadata = True
self._client.set_topics([])
self._client.cluster.request_update()
log.debug("Subscribed to topic pattern: %s", pattern)
else:
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
"""Get the current topic subscription.
Returns:
set: {topic, ...}
"""
return self._subscription.subscription
def unsubscribe(self):
"""Unsubscribe from all topics and clear all assigned partitions."""
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics([])
log.debug("Unsubscribed all topics or patterns and assigned partitions")
def metrics(self, raw=False):
"""Warning: this is an unstable interface.
It may change in future releases without warning"""
if raw:
return self._metrics.metrics
metrics = {}
for k, v in self._metrics.metrics.items():
if k.group not in metrics:
metrics[k.group] = {}
if k.name not in metrics[k.group]:
metrics[k.group][k.name] = {}
metrics[k.group][k.name] = v.value()
return metrics
def _use_consumer_group(self):
"""Return True iff this consumer can/should join a broker-coordinated group."""
if self.config['api_version'] < (0, 9):
return False
elif self.config['group_id'] is None:
return False
elif not self._subscription.partitions_auto_assigned():
return False
return True
def _update_fetch_positions(self, partitions):
"""Set the fetch position to the committed position (if there is one)
or reset it using the offset reset policy the user has configured.
Arguments:
partitions (List[TopicPartition]): The partitions that need
updating fetch positions.
Raises:
NoOffsetForPartitionError: If no offset is stored for a given
partition and no offset reset policy is defined.
"""
if (self.config['api_version'] >= (0, 8, 1)
and self.config['group_id'] is not None):
# Refresh commits for all assigned partitions
self._coordinator.refresh_committed_offsets_if_needed()
# Then, do any offset lookups in case some positions are not known
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription() is not None, 'No topic subscription or manual partition assignment'
while time.time() < self._consumer_timeout:
if self._use_consumer_group():
self._coordinator.ensure_coordinator_known()
self._coordinator.ensure_active_group()
# 0.8.2 brokers support kafka-backed offset storage via group coordinator
elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
# Fetch offsets for any subscribed partitions that we arent tracking yet
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
poll_ms = 1000 * (self._consumer_timeout - time.time())
if not self._fetcher.in_flight_fetches():
poll_ms = 0
self._client.poll(timeout_ms=poll_ms, sleep=True)
# We need to make sure we at least keep up with scheduled tasks,
# like heartbeats, auto-commits, and metadata refreshes
timeout_at = self._next_timeout()
# Because the consumer client poll does not sleep unless blocking on
# network IO, we need to explicitly sleep when we know we are idle
# because we haven't been assigned any partitions to fetch / consume
if self._use_consumer_group() and not self.assignment():
sleep_time = max(timeout_at - time.time(), 0)
if sleep_time > 0 and not self._client.in_flight_request_count():
log.debug('No partitions assigned; sleeping for %s', sleep_time)
time.sleep(sleep_time)
continue
# Short-circuit the fetch iterator if we are already timed out
# to avoid any unintentional interaction with fetcher setup
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
# An else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# We assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.send_fetches()
def _next_timeout(self):
timeout = min(self._consumer_timeout,
self._client._delayed_tasks.next_at() + time.time(),
self._client.cluster.ttl() / 1000.0 + time.time())
# Although the delayed_tasks timeout above should cover processing
# HeartbeatRequests, it is still possible that HeartbeatResponses
# are left unprocessed during a long _fetcher iteration without
# an intermediate poll(). And because tasks are responsible for
# rescheduling themselves, an unprocessed response will prevent
# the next heartbeat from being sent. This check should help
# avoid that.
if self._use_consumer_group():
heartbeat = time.time() + self._coordinator.heartbeat.ttl()
timeout = min(timeout, heartbeat)
return timeout
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config['consumer_timeout_ms'] >= 0:
self._consumer_timeout = time.time() + (
self.config['consumer_timeout_ms'] / 1000.0)
# Old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError(
'deprecated -- initialize a new consumer')
def set_topic_partitions(self, *topics):
raise NotImplementedError(
'deprecated -- use subscribe() or assign()')
def fetch_messages(self):
raise NotImplementedError(
'deprecated -- use poll() or iterator interface')
def get_partition_offsets(self, topic, partition,
request_time_ms, max_num_offsets):
raise NotImplementedError(
'deprecated -- send an OffsetRequest with KafkaClient')
def offsets(self, group=None):
raise NotImplementedError('deprecated -- use committed(partition)')
def task_done(self, message):
raise NotImplementedError(
'deprecated -- commit offsets manually if needed')
class KafkaAdminClient(object):
"""A class for administering the Kafka cluster.
Warning:
This is an unstable interface that was recently added and is subject to
change without warning. In particular, many methods currently return
raw protocol tuples. In future releases, we plan to make these into
nicer, more pythonic objects. Unfortunately, this will likely break
those interfaces.
The KafkaAdminClient class will negotiate for the latest version of each message
protocol format supported by both the kafka-python client library and the
Kafka broker. Usage of optional fields from protocol versions that are not
supported by the broker will result in IncompatibleBrokerVersion exceptions.
Use of this class requires a minimum broker version >= 0.10.0.0.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
reconnect_backoff_max_ms (int): The maximum amount of time in
milliseconds to wait when reconnecting to a broker that has
repeatedly failed to connect. If provided, the backoff per host
will increase exponentially for each consecutive connection
failure, up to this maximum. To avoid connection storms, a
randomization factor of 0.2 will be applied to the backoff
resulting in a random range between 20% below and 20% above
the computed value. Default: 1000.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 30000.
connections_max_idle_ms: Close idle connections after the number of
milliseconds specified by this config. The broker closes idle
connections after connections.max.idle.ms, so this avoids hitting
unexpected socket disconnected errors on the client.
Default: 540000
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). Java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). Java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): Flag to configure whether SSL handshake
should verify that the certificate matches the broker's hostname.
Default: True.
ssl_cafile (str): Optional filename of CA file to use in certificate
veriication. Default: None.
ssl_certfile (str): Optional filename of file in PEM format containing
the client certificate, as well as any CA certificates needed to
establish the certificate's authenticity. Default: None.
ssl_keyfile (str): Optional filename containing the client private key.
Default: None.
ssl_password (str): Optional password to be used when loading the
certificate chain. Default: None.
ssl_crlfile (str): Optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
Default: None.
api_version (tuple): Specify which Kafka API version to use. If set
to None, KafkaClient will attempt to infer the broker version by
probing various APIs. Example: (0, 10, 2). Default: None
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version is None
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
metrics (kafka.metrics.Metrics): Optionally provide a metrics
instance for capturing network IO stats. Default: None.
metric_group_prefix (str): Prefix for metric names. Default: ''
sasl_mechanism (str): Authentication mechanism when security_protocol
is configured for SASL_PLAINTEXT or SASL_SSL. Valid values are:
PLAIN, GSSAPI, OAUTHBEARER.
sasl_plain_username (str): username for sasl PLAIN authentication.
Required if sasl_mechanism is PLAIN.
sasl_plain_password (str): password for sasl PLAIN authentication.
Required if sasl_mechanism is PLAIN.
sasl_kerberos_service_name (str): Service name to include in GSSAPI
sasl mechanism handshake. Default: 'kafka'
sasl_oauth_token_provider (AbstractTokenProvider): OAuthBearer token provider
instance. (See kafka.oauth.abstract). Default: None
"""
DEFAULT_CONFIG = {
# client configs
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'request_timeout_ms': 30000,
'connections_max_idle_ms': 9 * 60 * 1000,
'reconnect_backoff_ms': 50,
'reconnect_backoff_max_ms': 1000,
'max_in_flight_requests_per_connection': 5,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'sock_chunk_bytes': 4096, # undocumented experimental option
'sock_chunk_buffer_count': 1000, # undocumented experimental option
'retry_backoff_ms': 100,
'metadata_max_age_ms': 300000,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_password': None,
'ssl_crlfile': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'selector': selectors.DefaultSelector,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
'sasl_kerberos_service_name': 'kafka',
'sasl_oauth_token_provider': None,
# metrics configs
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
}
def __init__(self, **configs):
log.debug("Starting KafkaAdminClient with configuration: %s", configs)
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError(
"Unrecognized configs: {}".format(extra_configs))
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
# Configure metrics
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(
samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [
reporter() for reporter in self.config['metric_reporters']
]
self._metrics = Metrics(metric_config, reporters)
self._client = KafkaClient(metrics=self._metrics,
metric_group_prefix='admin',
**self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
self._closed = False
self._refresh_controller_id()
log.debug("KafkaAdminClient started.")
def close(self):
"""Close the KafkaAdminClient connection to the Kafka broker."""
if not hasattr(self, '_closed') or self._closed:
log.info("KafkaAdminClient already closed.")
return
self._metrics.close()
self._client.close()
self._closed = True
log.debug("KafkaAdminClient is now closed.")
def _matching_api_version(self, operation):
"""Find the latest version of the protocol operation supported by both
this library and the broker.
This resolves to the lesser of either the latest api version this
library supports, or the max version supported by the broker.
:param operation: A list of protocol operation versions from kafka.protocol.
:return: The max matching version number between client and broker.
"""
version = min(
len(operation) - 1,
self._client.get_api_versions()[operation[0].API_KEY][1])
if version < self._client.get_api_versions()[operation[0].API_KEY][0]:
# max library version is less than min broker version. Currently,
# no Kafka versions specify a min msg version. Maybe in the future?
raise IncompatibleBrokerVersion(
"No version of the '{}' Kafka protocol is supported by both the client and broker."
.format(operation.__name__))
return version
def _validate_timeout(self, timeout_ms):
"""Validate the timeout is set or use the configuration default.
:param timeout_ms: The timeout provided by api call, in milliseconds.
:return: The timeout to use for the operation.
"""
return timeout_ms or self.config['request_timeout_ms']
def _refresh_controller_id(self):
"""Determine the Kafka cluster controller."""
version = self._matching_api_version(MetadataRequest)
if 1 <= version <= 6:
request = MetadataRequest[version]()
future = self._send_request_to_node(
self._client.least_loaded_node(), request)
self._wait_for_futures([future])
response = future.value
controller_id = response.controller_id
# verify the controller is new enough to support our requests
controller_version = self._client.check_version(controller_id)
if controller_version < (0, 10, 0):
raise IncompatibleBrokerVersion(
"The controller appears to be running Kafka {}. KafkaAdminClient requires brokers >= 0.10.0.0."
.format(controller_version))
self._controller_id = controller_id
else:
raise UnrecognizedBrokerVersion(
"Kafka Admin interface cannot determine the controller using MetadataRequest_v{}."
.format(version))
def _find_group_coordinator_id(self, group_id):
"""Find the broker node_id of the coordinator of the given group.
Sends a FindCoordinatorRequest message to the cluster. Will block until
the FindCoordinatorResponse is received. Any errors are immediately
raised.
:param group_id: The consumer group ID. This is typically the group
name as a string.
:return: The node_id of the broker that is the coordinator.
"""
# Note: Java may change how this is implemented in KAFKA-6791.
#
# TODO add support for dynamically picking version of
# GroupCoordinatorRequest which was renamed to FindCoordinatorRequest.
# When I experimented with this, GroupCoordinatorResponse_v1 didn't
# match GroupCoordinatorResponse_v0 and I couldn't figure out why.
gc_request = GroupCoordinatorRequest[0](group_id)
future = self._send_request_to_node(self._client.least_loaded_node(),
gc_request)
self._wait_for_futures([future])
gc_response = future.value
# use the extra error checking in add_group_coordinator() rather than
# immediately returning the group coordinator.
success = self._client.cluster.add_group_coordinator(
group_id, gc_response)
if not success:
error_type = Errors.for_code(gc_response.error_code)
assert error_type is not Errors.NoError
# Note: When error_type.retriable, Java will retry... see
# KafkaAdminClient's handleFindCoordinatorError method
raise error_type(
"Could not identify group coordinator for group_id '{}' from response '{}'."
.format(group_id, gc_response))
group_coordinator = self._client.cluster.coordinator_for_group(
group_id)
# will be None if the coordinator was never populated, which should never happen here
assert group_coordinator is not None
# will be -1 if add_group_coordinator() failed... but by this point the
# error should have been raised.
assert group_coordinator != -1
return group_coordinator
def _send_request_to_node(self, node_id, request):
"""Send a Kafka protocol message to a specific broker.
Returns a future that may be polled for status and results.
:param node_id: The broker id to which to send the message.
:param request: The message to send.
:return: A future object that may be polled for status and results.
:exception: The exception if the message could not be sent.
"""
while not self._client.ready(node_id):
# poll until the connection to broker is ready, otherwise send()
# will fail with NodeNotReadyError
self._client.poll()
return self._client.send(node_id, request)
def _send_request_to_controller(self, request):
"""Send a Kafka protocol message to the cluster controller.
Will block until the message result is received.
:param request: The message to send.
:return: The Kafka protocol response for the message.
"""
tries = 2 # in case our cached self._controller_id is outdated
while tries:
tries -= 1
future = self._send_request_to_node(self._controller_id, request)
self._wait_for_futures([future])
response = future.value
# In Java, the error fieldname is inconsistent:
# - CreateTopicsResponse / CreatePartitionsResponse uses topic_errors
# - DeleteTopicsResponse uses topic_error_codes
# So this is a little brittle in that it assumes all responses have
# one of these attributes and that they always unpack into
# (topic, error_code) tuples.
topic_error_tuples = (response.topic_errors if hasattr(
response, 'topic_errors') else response.topic_error_codes)
# Also small py2/py3 compatibility -- py3 can ignore extra values
# during unpack via: for x, y, *rest in list_of_values. py2 cannot.
# So for now we have to map across the list and explicitly drop any
# extra values (usually the error_message)
for topic, error_code in map(lambda e: e[:2], topic_error_tuples):
error_type = Errors.for_code(error_code)
if tries and error_type is NotControllerError:
# No need to inspect the rest of the errors for
# non-retriable errors because NotControllerError should
# either be thrown for all errors or no errors.
self._refresh_controller_id()
break
elif error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'.".format(
request, response))
else:
return response
raise RuntimeError(
"This should never happen, please file a bug with full stacktrace if encountered"
)
@staticmethod
def _convert_new_topic_request(new_topic):
return (
new_topic.name, new_topic.num_partitions,
new_topic.replication_factor, [
(partition_id, replicas) for partition_id, replicas in
new_topic.replica_assignments.items()
],
[(config_key, config_value)
for config_key, config_value in new_topic.topic_configs.items()])
def create_topics(self, new_topics, timeout_ms=None, validate_only=False):
"""Create new topics in the cluster.
:param new_topics: A list of NewTopic objects.
:param timeout_ms: Milliseconds to wait for new topics to be created
before the broker returns.
:param validate_only: If True, don't actually create new topics.
Not supported by all versions. Default: False
:return: Appropriate version of CreateTopicResponse class.
"""
version = self._matching_api_version(CreateTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version == 0:
if validate_only:
raise IncompatibleBrokerVersion(
"validate_only requires CreateTopicsRequest >= v1, which is not supported by Kafka {}."
.format(self.config['api_version']))
request = CreateTopicsRequest[version](create_topic_requests=[
self._convert_new_topic_request(new_topic)
for new_topic in new_topics
],
timeout=timeout_ms)
elif version <= 2:
request = CreateTopicsRequest[version](create_topic_requests=[
self._convert_new_topic_request(new_topic)
for new_topic in new_topics
],
timeout=timeout_ms,
validate_only=validate_only)
else:
raise NotImplementedError(
"Support for CreateTopics v{} has not yet been added to KafkaAdminClient."
.format(version))
# TODO convert structs to a more pythonic interface
# TODO raise exceptions if errors
return self._send_request_to_controller(request)
def delete_topics(self, topics, timeout_ms=None):
"""Delete topics from the cluster.
:param topics: A list of topic name strings.
:param timeout_ms: Milliseconds to wait for topics to be deleted
before the broker returns.
:return: Appropriate version of DeleteTopicsResponse class.
"""
version = self._matching_api_version(DeleteTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version <= 1:
request = DeleteTopicsRequest[version](topics=topics,
timeout=timeout_ms)
response = self._send_request_to_controller(request)
else:
raise NotImplementedError(
"Support for DeleteTopics v{} has not yet been added to KafkaAdminClient."
.format(version))
return response
# list topics functionality is in ClusterMetadata
# Note: if implemented here, send the request to the least_loaded_node()
# describe topics functionality is in ClusterMetadata
# Note: if implemented here, send the request to the controller
# describe cluster functionality is in ClusterMetadata
# Note: if implemented here, send the request to the least_loaded_node()
# describe_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
# create_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
# delete_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
@staticmethod
def _convert_describe_config_resource_request(config_resource):
return (config_resource.resource_type, config_resource.name, [
config_key
for config_key, config_value in config_resource.configs.items()
] if config_resource.configs else None)
def describe_configs(self, config_resources, include_synonyms=False):
"""Fetch configuration parameters for one or more Kafka resources.
:param config_resources: An list of ConfigResource objects.
Any keys in ConfigResource.configs dict will be used to filter the
result. Setting the configs dict to None will get all values. An
empty dict will get zero values (as per Kafka protocol).
:param include_synonyms: If True, return synonyms in response. Not
supported by all versions. Default: False.
:return: Appropriate version of DescribeConfigsResponse class.
"""
version = self._matching_api_version(DescribeConfigsRequest)
if version == 0:
if include_synonyms:
raise IncompatibleBrokerVersion(
"include_synonyms requires DescribeConfigsRequest >= v1, which is not supported by Kafka {}."
.format(self.config['api_version']))
request = DescribeConfigsRequest[version](resources=[
self._convert_describe_config_resource_request(config_resource)
for config_resource in config_resources
])
elif version == 1:
request = DescribeConfigsRequest[version](
resources=[
self._convert_describe_config_resource_request(
config_resource)
for config_resource in config_resources
],
include_synonyms=include_synonyms)
else:
raise NotImplementedError(
"Support for DescribeConfigs v{} has not yet been added to KafkaAdminClient."
.format(version))
future = self._send_request_to_node(self._client.least_loaded_node(),
request)
self._wait_for_futures([future])
return future.value
@staticmethod
def _convert_alter_config_resource_request(config_resource):
return (config_resource.resource_type, config_resource.name, [
(config_key, config_value)
for config_key, config_value in config_resource.configs.items()
])
def alter_configs(self, config_resources):
"""Alter configuration parameters of one or more Kafka resources.
Warning:
This is currently broken for BROKER resources because those must be
sent to that specific broker, versus this always picks the
least-loaded node. See the comment in the source code for details.
We would happily accept a PR fixing this.
:param config_resources: A list of ConfigResource objects.
:return: Appropriate version of AlterConfigsResponse class.
"""
version = self._matching_api_version(AlterConfigsRequest)
if version == 0:
request = AlterConfigsRequest[version](resources=[
self._convert_alter_config_resource_request(config_resource)
for config_resource in config_resources
])
else:
raise NotImplementedError(
"Support for AlterConfigs v{} has not yet been added to KafkaAdminClient."
.format(version))
# TODO the Java client has the note:
# // We must make a separate AlterConfigs request for every BROKER resource we want to alter
# // and send the request to that specific broker. Other resources are grouped together into
# // a single request that may be sent to any broker.
#
# So this is currently broken as it always sends to the least_loaded_node()
future = self._send_request_to_node(self._client.least_loaded_node(),
request)
self._wait_for_futures([future])
return future.value
# alter replica logs dir protocol not yet implemented
# Note: have to lookup the broker with the replica assignment and send the request to that broker
# describe log dirs protocol not yet implemented
# Note: have to lookup the broker with the replica assignment and send the request to that broker
@staticmethod
def _convert_create_partitions_request(topic_name, new_partitions):
return (topic_name, (new_partitions.total_count,
new_partitions.new_assignments))
def create_partitions(self,
topic_partitions,
timeout_ms=None,
validate_only=False):
"""Create additional partitions for an existing topic.
:param topic_partitions: A map of topic name strings to NewPartition objects.
:param timeout_ms: Milliseconds to wait for new partitions to be
created before the broker returns.
:param validate_only: If True, don't actually create new partitions.
Default: False
:return: Appropriate version of CreatePartitionsResponse class.
"""
version = self._matching_api_version(CreatePartitionsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version == 0:
request = CreatePartitionsRequest[version](
topic_partitions=[
self._convert_create_partitions_request(
topic_name, new_partitions)
for topic_name, new_partitions in topic_partitions.items()
],
timeout=timeout_ms,
validate_only=validate_only)
else:
raise NotImplementedError(
"Support for CreatePartitions v{} has not yet been added to KafkaAdminClient."
.format(version))
return self._send_request_to_controller(request)
# delete records protocol not yet implemented
# Note: send the request to the partition leaders
# create delegation token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# renew delegation token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# expire delegation_token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# describe delegation_token protocol not yet implemented
# Note: send the request to the least_loaded_node()
def describe_consumer_groups(self, group_ids, group_coordinator_id=None):
"""Describe a set of consumer groups.
Any errors are immediately raised.
:param group_ids: A list of consumer group IDs. These are typically the
group names as strings.
:param group_coordinator_id: The node_id of the groups' coordinator
broker. If set to None, it will query the cluster for each group to
find that group's coordinator. Explicitly specifying this can be
useful for avoiding extra network round trips if you already know
the group coordinator. This is only useful when all the group_ids
have the same coordinator, otherwise it will error. Default: None.
:return: A list of group descriptions. For now the group descriptions
are the raw results from the DescribeGroupsResponse. Long-term, we
plan to change this to return namedtuples as well as decoding the
partition assignments.
"""
group_descriptions = []
futures = []
version = self._matching_api_version(DescribeGroupsRequest)
for group_id in group_ids:
if group_coordinator_id is not None:
this_groups_coordinator_id = group_coordinator_id
else:
this_groups_coordinator_id = self._find_group_coordinator_id(
group_id)
if version <= 1:
# Note: KAFKA-6788 A potential optimization is to group the
# request per coordinator and send one request with a list of
# all consumer groups. Java still hasn't implemented this
# because the error checking is hard to get right when some
# groups error and others don't.
request = DescribeGroupsRequest[version](groups=(group_id, ))
futures.append(
self._send_request_to_node(this_groups_coordinator_id,
request))
else:
raise NotImplementedError(
"Support for DescribeGroups v{} has not yet been added to KafkaAdminClient."
.format(version))
self._wait_for_futures(futures)
for future in futures:
response = future.value
assert len(response.groups) == 1
# TODO need to implement converting the response tuple into
# a more accessible interface like a namedtuple and then stop
# hardcoding tuple indices here. Several Java examples,
# including KafkaAdminClient.java
group_description = response.groups[0]
error_code = group_description[0]
error_type = Errors.for_code(error_code)
# Java has the note: KAFKA-6789, we can retry based on the error code
if error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'.".format(
request, response))
# TODO Java checks the group protocol type, and if consumer
# (ConsumerProtocol.PROTOCOL_TYPE) or empty string, it decodes
# the members' partition assignments... that hasn't yet been
# implemented here so just return the raw struct results
group_descriptions.append(group_description)
return group_descriptions
def list_consumer_groups(self, broker_ids=None):
"""List all consumer groups known to the cluster.
This returns a list of Consumer Group tuples. The tuples are
composed of the consumer group name and the consumer group protocol
type.
Only consumer groups that store their offsets in Kafka are returned.
The protocol type will be an empty string for groups created using
Kafka < 0.9 APIs because, although they store their offsets in Kafka,
they don't use Kafka for group coordination. For groups created using
Kafka >= 0.9, the protocol type will typically be "consumer".
As soon as any error is encountered, it is immediately raised.
:param broker_ids: A list of broker node_ids to query for consumer
groups. If set to None, will query all brokers in the cluster.
Explicitly specifying broker(s) can be useful for determining which
consumer groups are coordinated by those broker(s). Default: None
:return list: List of tuples of Consumer Groups.
:exception GroupCoordinatorNotAvailableError: The coordinator is not
available, so cannot process requests.
:exception GroupLoadInProgressError: The coordinator is loading and
hence can't process requests.
"""
# While we return a list, internally use a set to prevent duplicates
# because if a group coordinator fails after being queried, and its
# consumer groups move to new brokers that haven't yet been queried,
# then the same group could be returned by multiple brokers.
consumer_groups = set()
futures = []
if broker_ids is None:
broker_ids = [
broker.nodeId for broker in self._client.cluster.brokers()
]
version = self._matching_api_version(ListGroupsRequest)
if version <= 2:
request = ListGroupsRequest[version]()
for broker_id in broker_ids:
futures.append(self._send_request_to_node(broker_id, request))
self._wait_for_futures(futures)
for future in futures:
response = future.value
error_type = Errors.for_code(response.error_code)
if error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'.".format(
request, response))
consumer_groups.update(response.groups)
else:
raise NotImplementedError(
"Support for ListGroups v{} has not yet been added to KafkaAdminClient."
.format(version))
return list(consumer_groups)
def list_consumer_group_offsets(self,
group_id,
group_coordinator_id=None,
partitions=None):
"""Fetch Consumer Group Offsets.
Note:
This does not verify that the group_id or partitions actually exist
in the cluster.
As soon as any error is encountered, it is immediately raised.
:param group_id: The consumer group id name for which to fetch offsets.
:param group_coordinator_id: The node_id of the group's coordinator
broker. If set to None, will query the cluster to find the group
coordinator. Explicitly specifying this can be useful to prevent
that extra network round trip if you already know the group
coordinator. Default: None.
:param partitions: A list of TopicPartitions for which to fetch
offsets. On brokers >= 0.10.2, this can be set to None to fetch all
known offsets for the consumer group. Default: None.
:return dictionary: A dictionary with TopicPartition keys and
OffsetAndMetada values. Partitions that are not specified and for
which the group_id does not have a recorded offset are omitted. An
offset value of `-1` indicates the group_id has no offset for that
TopicPartition. A `-1` can only happen for partitions that are
explicitly specified.
"""
group_offsets_listing = {}
if group_coordinator_id is None:
group_coordinator_id = self._find_group_coordinator_id(group_id)
version = self._matching_api_version(OffsetFetchRequest)
if version <= 3:
if partitions is None:
if version <= 1:
raise ValueError(
"""OffsetFetchRequest_v{} requires specifying the
partitions for which to fetch offsets. Omitting the
partitions is only supported on brokers >= 0.10.2.
For details, see KIP-88.""".format(version))
topics_partitions = None
else:
# transform from [TopicPartition("t1", 1), TopicPartition("t1", 2)] to [("t1", [1, 2])]
topics_partitions_dict = defaultdict(set)
for topic, partition in partitions:
topics_partitions_dict[topic].add(partition)
topics_partitions = list(six.iteritems(topics_partitions_dict))
request = OffsetFetchRequest[version](group_id, topics_partitions)
future = self._send_request_to_node(group_coordinator_id, request)
self._wait_for_futures([future])
response = future.value
if version > 1: # OffsetFetchResponse_v1 lacks a top-level error_code
error_type = Errors.for_code(response.error_code)
if error_type is not Errors.NoError:
# optionally we could retry if error_type.retriable
raise error_type(
"Request '{}' failed with response '{}'.".format(
request, response))
# transform response into a dictionary with TopicPartition keys and
# OffsetAndMetada values--this is what the Java AdminClient returns
for topic, partitions in response.topics:
for partition, offset, metadata, error_code in partitions:
error_type = Errors.for_code(error_code)
if error_type is not Errors.NoError:
raise error_type(
"Unable to fetch offsets for group_id {}, topic {}, partition {}"
.format(group_id, topic, partition))
group_offsets_listing[TopicPartition(
topic,
partition)] = OffsetAndMetadata(offset, metadata)
else:
raise NotImplementedError(
"Support for OffsetFetch v{} has not yet been added to KafkaAdminClient."
.format(version))
return group_offsets_listing
# delete groups protocol not yet implemented
# Note: send the request to the group's coordinator.
def _wait_for_futures(self, futures):
while not all(future.succeeded() for future in futures):
for future in futures:
self._client.poll(future=future)
if future.failed():
raise future.exception # pylint: disable-msg=raising-bad-type
return None
payload['sequence'] = sequence_number
payload['data'] = processed_stream_data
# print(processed_stream_data)
return payload
if __name__ == '__main__':
# First, check if the topic already exists in kafka
kafka_client = KafkaClient(bootstrap_servers=KAFKA_SERVER,
api_version=(2, 5, 0))
version = kafka_client.check_version()
future = kafka_client.cluster.request_update()
kafka_client.poll(future=future)
metadata = kafka_client.cluster
current_topics = metadata.topics()
kafka_client.close()
print('Active topics:', current_topics)
if KAFKA_TOPIC not in current_topics:
print(f'Creating topic {KAFKA_TOPIC}...')
kafka_admin_client = KafkaAdminClient(bootstrap_servers=KAFKA_SERVER,
api_version=(2, 5, 0))
topic_list = [
NewTopic(name=KAFKA_TOPIC, num_partitions=1, replication_factor=1)
class OffsetsFetcherAsync(object):
DEFAULT_CONFIG = {
'session_timeout_ms': 30000,
'heartbeat_interval_ms': 3000,
'retry_backoff_ms': 100,
'api_version': (0, 9),
'metric_group_prefix': ''
}
def __init__(self, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
self._client = KafkaClient(**self.config)
self._coordinator_id = None
self.group_id = configs['group_id']
self.topic = configs['topic']
def _ensure_coordinator_known(self):
"""Block until the coordinator for this group is known
(and we have an active connection -- java client uses unsent queue).
"""
while self._coordinator_unknown():
# Prior to 0.8.2 there was no group coordinator
# so we will just pick a node at random and treat
# it as the "coordinator"
if self.config['api_version'] < (0, 8, 2):
self._coordinator_id = self._client.least_loaded_node()
self._client.ready(self._coordinator_id)
continue
future = self._send_group_coordinator_request()
self._client.poll(future=future)
if future.failed():
if isinstance(future.exception,
Errors.GroupCoordinatorNotAvailableError):
continue
elif future.retriable():
metadata_update = self._client.cluster.request_update()
self._client.poll(future=metadata_update)
else:
raise future.exception # pylint: disable-msg=raising-bad-type
def _coordinator_unknown(self):
"""Check if we know who the coordinator is and have an active connection
Side-effect: reset _coordinator_id to None if connection failed
Returns:
bool: True if the coordinator is unknown
"""
if self._coordinator_id is None:
return True
if self._client.is_disconnected(self._coordinator_id):
self._coordinator_dead()
return True
return False
def _coordinator_dead(self, error=None):
"""Mark the current coordinator as dead."""
if self._coordinator_id is not None:
log.warning("Marking the coordinator dead (node %s) for group %s: %s.",
self._coordinator_id, self.group_id, error)
self._coordinator_id = None
def _send_group_coordinator_request(self):
"""Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator
"""
node_id = self._client.least_loaded_node()
if node_id is None:
return Future().failure(Errors.NoBrokersAvailable())
log.debug("Sending group coordinator request for group %s to broker %s",
self.group_id, node_id)
request = GroupCoordinatorRequest[0](self.group_id)
future = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_group_coordinator_response, future)
_f.add_errback(self._failed_request, node_id, request, future)
return future
def _handle_group_coordinator_response(self, future, response):
log.debug("Received group coordinator response %s", response)
if not self._coordinator_unknown():
# We already found the coordinator, so ignore the request
log.debug("Coordinator already known -- ignoring metadata response")
future.success(self._coordinator_id)
return
error_type = Errors.for_code(response.error_code)
if error_type is Errors.NoError:
ok = self._client.cluster.add_group_coordinator(self.group_id, response)
if not ok:
# This could happen if coordinator metadata is different
# than broker metadata
future.failure(Errors.IllegalStateError())
return
self._coordinator_id = response.coordinator_id
log.info("Discovered coordinator %s for group %s",
self._coordinator_id, self.group_id)
self._client.ready(self._coordinator_id)
future.success(self._coordinator_id)
elif error_type is Errors.GroupCoordinatorNotAvailableError:
log.debug("Group Coordinator Not Available; retry")
future.failure(error_type())
elif error_type is Errors.GroupAuthorizationFailedError:
error = error_type(self.group_id)
log.error("Group Coordinator Request failed: %s", error)
future.failure(error)
else:
error = error_type()
log.error("Unrecognized failure in Group Coordinator Request: %s",
error)
future.failure(error)
def _failed_request(self, node_id, request, future, error):
log.error('Error sending %s to node %s [%s]',
request.__class__.__name__, node_id, error)
# Marking coordinator dead
# unless the error is caused by internal client pipelining
if not isinstance(error, (Errors.NodeNotReadyError,
Errors.TooManyInFlightRequests)):
self._coordinator_dead()
future.failure(error)
def offsets(self, partitions, timestamp):
"""Fetch a single offset before the given timestamp for the set of partitions.
Blocks until offset is obtained, or a non-retriable exception is raised
Arguments:
partitions (iterable of TopicPartition) The partition that needs fetching offset.
timestamp (int): timestamp for fetching offset. -1 for the latest
available, -2 for the earliest available. Otherwise timestamp
is treated as epoch seconds.
Returns:
dict: TopicPartition and message offsets
"""
retries = 3
while retries > 0:
offsets = {}
for future in self._send_offset_request(partitions, timestamp):
self._client.poll(future=future)
if future.succeeded():
for tp, offset in future.value:
offsets[tp] = offset
continue
if not future.retriable():
raise future.exception # pylint: disable-msg=raising-bad-type
if future.exception.invalid_metadata:
refresh_future = self._client.cluster.request_update()
self._client.poll(future=refresh_future, sleep=True)
log.warning("Got exception %s and kept the loop", future.exception)
if offsets:
return offsets
retries -= 1
log.warning("Retrying the offsets fetch loop (%d retries left)", retries)
log.error("Unsuccessful offsets retrieval")
return {}
def _send_offset_request(self, partitions, timestamp):
"""Fetch a single offset before the given timestamp for the partition.
Arguments:
partitions iterable of TopicPartition: partitions that needs fetching offset
timestamp (int): timestamp for fetching offset
Returns:
list of Future: resolves to the corresponding offset
"""
topic = partitions[0].topic
nodes_per_partitions = {}
for partition in partitions:
node_id = self._client.cluster.leader_for_partition(partition)
if node_id is None:
log.debug("Partition %s is unknown for fetching offset,"
" wait for metadata refresh", partition)
return [Future().failure(Errors.StaleMetadata(partition))]
elif node_id == -1:
log.debug("Leader for partition %s unavailable for fetching offset,"
" wait for metadata refresh", partition)
return [Future().failure(Errors.LeaderNotAvailableError(partition))]
nodes_per_partitions.setdefault(node_id, []).append(partition)
# Client returns a future that only fails on network issues
# so create a separate future and attach a callback to update it
# based on response error codes
futures = []
for node_id, partitions in six.iteritems(nodes_per_partitions):
request = OffsetRequest[0](
-1, [(topic, [(partition.partition, timestamp, 1) for partition in partitions])]
)
future_request = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_offset_response, partitions, future_request)
def errback(e):
log.error("Offset request errback error %s", e)
future_request.failure(e)
_f.add_errback(errback)
futures.append(future_request)
return futures
def _handle_offset_response(self, partitions, future, response):
"""Callback for the response of the list offset call above.
Arguments:
partition (TopicPartition): The partition that was fetched
future (Future): the future to update based on response
response (OffsetResponse): response from the server
Raises:
AssertionError: if response does not match partition
"""
topic, partition_info = response.topics[0]
assert len(response.topics) == 1, (
'OffsetResponse should only be for a single topic')
partition_ids = set([part.partition for part in partitions])
result = []
for pi in partition_info:
part, error_code, offsets = pi
assert topic == partitions[0].topic and part in partition_ids, (
'OffsetResponse partition does not match OffsetRequest partition')
error_type = Errors.for_code(error_code)
if error_type is Errors.NoError:
assert len(offsets) == 1, 'Expected OffsetResponse with one offset'
log.debug("Fetched offset %s for partition %d", offsets[0], part)
result.append((TopicPartition(topic, part), offsets[0]))
elif error_type in (Errors.NotLeaderForPartitionError,
Errors.UnknownTopicOrPartitionError):
log.debug("Attempt to fetch offsets for partition %s failed due"
" to obsolete leadership information, retrying.",
str(partitions))
future.failure(error_type(partitions))
else:
log.warning("Attempt to fetch offsets for partition %s failed due to:"
" %s", partitions, error_type)
future.failure(error_type(partitions))
future.success(result)
def fetch_committed_offsets(self, partitions):
"""Fetch the current committed offsets for specified partitions
Arguments:
partitions (list of TopicPartition): partitions to fetch
Returns:
dict: {TopicPartition: OffsetAndMetadata}
"""
if not partitions:
return {}
while True:
self._ensure_coordinator_known()
# contact coordinator to fetch committed offsets
future = self._send_offset_fetch_request(partitions)
self._client.poll(future=future)
if future.succeeded():
return future.value
if not future.retriable():
raise future.exception # pylint: disable-msg=raising-bad-type
time.sleep(self.config['retry_backoff_ms'] / 1000.0)
def _send_offset_fetch_request(self, partitions):
"""Fetch the committed offsets for a set of partitions.
This is a non-blocking call. The returned future can be polled to get
the actual offsets returned from the broker.
Arguments:
partitions (list of TopicPartition): the partitions to fetch
Returns:
Future: resolves to dict of offsets: {TopicPartition: int}
"""
assert self.config['api_version'] >= (0, 8, 1), 'Unsupported Broker API'
assert all(map(lambda k: isinstance(k, TopicPartition), partitions))
if not partitions:
return Future().success({})
elif self._coordinator_unknown():
return Future().failure(Errors.GroupCoordinatorNotAvailableError)
node_id = self._coordinator_id
# Verify node is ready
if not self._client.ready(node_id):
log.debug("Node %s not ready -- failing offset fetch request",
node_id)
return Future().failure(Errors.NodeNotReadyError)
log.debug("Group %s fetching committed offsets for partitions: %s",
self.group_id, partitions)
# construct the request
topic_partitions = collections.defaultdict(set)
for tp in partitions:
topic_partitions[tp.topic].add(tp.partition)
if self.config['api_version'] >= (0, 8, 2):
request = OffsetFetchRequest[1](
self.group_id,
list(topic_partitions.items())
)
else:
request = OffsetFetchRequest[0](
self.group_id,
list(topic_partitions.items())
)
# send the request with a callback
future = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_offset_fetch_response, future)
_f.add_errback(self._failed_request, node_id, request, future)
return future
def _handle_offset_fetch_response(self, future, response):
offsets = {}
for topic, partitions in response.topics:
for partition, offset, metadata, error_code in partitions:
tp = TopicPartition(topic, partition)
error_type = Errors.for_code(error_code)
if error_type is not Errors.NoError:
error = error_type()
log.debug("Group %s failed to fetch offset for partition"
" %s: %s", self.group_id, tp, error)
if error_type is Errors.GroupLoadInProgressError:
# just retry
future.failure(error)
elif error_type is Errors.NotCoordinatorForGroupError:
# re-discover the coordinator and retry
self._coordinator_dead()
future.failure(error)
elif error_type in (Errors.UnknownMemberIdError,
Errors.IllegalGenerationError):
future.failure(error)
elif error_type is Errors.UnknownTopicOrPartitionError:
log.warning("OffsetFetchRequest -- unknown topic %s"
" (have you committed any offsets yet?)",
topic)
continue
else:
log.error("Unknown error fetching offsets for %s: %s",
tp, error)
future.failure(error)
return
elif offset >= 0:
# record the position with the offset
# (-1 indicates no committed offset to fetch)
offsets[tp] = OffsetAndMetadata(offset, metadata)
else:
log.debug("Group %s has no committed offset for partition"
" %s", self.group_id, tp)
future.success(offsets)
def get(self):
topic_partitions = self._client.cluster.partitions_for_topic(self.topic)
if not topic_partitions:
future = self._client.cluster.request_update()
log.info("No partitions available, performing metadata update.")
self._client.poll(future=future)
return {}
partitions = [TopicPartition(self.topic, partition_id) for partition_id in topic_partitions]
offsets = self.offsets(partitions, -1)
committed = self.fetch_committed_offsets(partitions)
lags = {}
for tp, offset in six.iteritems(offsets):
commit_offset = committed[tp] if tp in committed else 0
numerical = commit_offset if isinstance(commit_offset, int) else commit_offset.offset
lag = offset - numerical
pid = tp.partition if isinstance(tp, TopicPartition) else tp
log.debug("Lag for %s (%s): %s, %s, %s", self.topic, pid, offset, commit_offset, lag)
lags[pid] = lag
return lags
class KafkaManager(object):
"""
Easier access to Kafka information
"""
TOPIC_RESOURCE_ID = 2
MAX_POLL_RETRIES = 3
MAX_RETRY = 10
SUCCESS_CODE = 0
def __init__(self, **configs):
self.client = KafkaClient(**configs)
self.refresh()
def refresh(self):
"""
Refresh topics state
"""
fut = self.client.cluster.request_update()
self.client.poll(future=fut)
if not fut.succeeded():
self.close()
self.module.fail_json(
msg='Error while updating topic state from Kafka server: %s.' %
fut.exception)
def close(self):
"""
Closes the client. Must be called once
the client is not used anymore.
"""
self.client.close()
def get_controller(self):
"""
Return the current controller for cluster.
"""
node_id, _host, _port, _rack = self.client.cluster.controller
return node_id
def get_topics(self):
"""
Returns the topics list
"""
return self.client.cluster.topics()
def get_total_partitions_for_topic(self, topic):
"""
Returns the number of partitions for topic
"""
return len(self.client.cluster.partitions_for_topic(topic))
def get_partitions_metadata_for_topic(self, topic):
"""
Returns set of partition for topic
"""
return self.client.cluster._partitions[topic]
def get_config_for_topic(self, topic_name, config_name):
"""
Returns value for config_name topic option
"""
request = DescribeConfigsRequestV0(resources=[(self.TOPIC_RESOURCE_ID,
topic_name,
config_name)])
response = self.send_request_and_get_response(request)
for err_code, err_message, _, _, config_entries in response.resources:
if err_code != self.SUCCESS_CODE:
raise Exception(err_message)
for _, value, _, _, _ in config_entries:
return value
@staticmethod
def _map_to_quota_resources(entries):
return [{
'entity': [{
'entity_type': entity['entity_type'],
'entity_name': entity['entity_name']
} for entity in entry['entity']],
'quotas':
{quota['name']: quota['value']
for quota in entry['values']}
} for entry in entries]
def describe_quotas(self):
request = DescribeClientQuotasRequest_v0(components=[])
response = self.send_request_and_get_response(request)
if response.error_code != 0:
raise []
return self._map_to_quota_resources(response.to_object()['entries'])
def describe_acls(self, acl_resource):
"""Describe a set of ACLs
"""
request = DescribeAclsRequest_v0(
resource_type=acl_resource['resource_type'],
resource_name=acl_resource['name'],
principal=acl_resource['principal'],
host=acl_resource['host'],
operation=acl_resource['operation'],
permission_type=acl_resource['permission_type'])
response = self.send_request_and_get_response(request)
if response.error_code == self.SUCCESS_CODE:
return response.resources
return None
def connection_check(self, node_id, connection_sleep=1):
"""
Checks that connection with broker is OK and that it is possible
to send requests
Since the _maybe_connect() function used in ready() is 'async',
we need to manually call it several time to make the connection
"""
retries = 0
if not self.client.ready(node_id):
while retries < self.MAX_RETRY:
self.client.poll()
if self.client.ready(node_id):
return True
time.sleep(connection_sleep)
retries += 1
return False
return True
def send_request_and_get_response(self, request):
"""
Send requet and get associated response
"""
try:
node_id = self.get_controller()
except Exception:
raise
if self.connection_check(node_id):
future = self.client.send(node_id, request)
self.client.poll(future=future)
if future.succeeded():
return future.value
else:
raise future.exception
return None
def cli(mocker, conn):
client = KafkaClient(api_version=(0, 9))
client.poll(future=client.cluster.request_update())
return client
class OffsetsFetcherAsync(object):
DEFAULT_CONFIG = {
'session_timeout_ms': 30000,
'heartbeat_interval_ms': 3000,
'retry_backoff_ms': 100,
'api_version': (0, 9),
'metric_group_prefix': ''
}
def __init__(self, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
self._client = KafkaClient(**self.config)
self._coordinator_id = None
self.group_id = configs['group_id']
self.topic = configs['topic']
def _ensure_coordinator_known(self):
"""Block until the coordinator for this group is known
(and we have an active connection -- java client uses unsent queue).
"""
while self._coordinator_unknown():
# Prior to 0.8.2 there was no group coordinator
# so we will just pick a node at random and treat
# it as the "coordinator"
if self.config['api_version'] < (0, 8, 2):
self._coordinator_id = self._client.least_loaded_node()
self._client.ready(self._coordinator_id)
continue
future = self._send_group_coordinator_request()
self._client.poll(future=future)
if future.failed():
if isinstance(future.exception,
Errors.GroupCoordinatorNotAvailableError):
continue
elif future.retriable():
metadata_update = self._client.cluster.request_update()
self._client.poll(future=metadata_update)
else:
raise future.exception # pylint: disable-msg=raising-bad-type
def _coordinator_unknown(self):
"""Check if we know who the coordinator is and have an active connection
Side-effect: reset _coordinator_id to None if connection failed
Returns:
bool: True if the coordinator is unknown
"""
if self._coordinator_id is None:
return True
if self._client.is_disconnected(self._coordinator_id):
self._coordinator_dead()
return True
return False
def _coordinator_dead(self, error=None):
"""Mark the current coordinator as dead."""
if self._coordinator_id is not None:
log.warning("Marking the coordinator dead (node %s) for group %s: %s.",
self._coordinator_id, self.group_id, error)
self._coordinator_id = None
def _send_group_coordinator_request(self):
"""Discover the current coordinator for the group.
Returns:
Future: resolves to the node id of the coordinator
"""
node_id = self._client.least_loaded_node()
if node_id is None:
return Future().failure(Errors.NoBrokersAvailable())
log.debug("Sending group coordinator request for group %s to broker %s",
self.group_id, node_id)
request = GroupCoordinatorRequest[0](self.group_id)
future = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_group_coordinator_response, future)
_f.add_errback(self._failed_request, node_id, request, future)
return future
def _handle_group_coordinator_response(self, future, response):
log.debug("Received group coordinator response %s", response)
if not self._coordinator_unknown():
# We already found the coordinator, so ignore the request
log.debug("Coordinator already known -- ignoring metadata response")
future.success(self._coordinator_id)
return
error_type = Errors.for_code(response.error_code)
if error_type is Errors.NoError:
ok = self._client.cluster.add_group_coordinator(self.group_id, response)
if not ok:
# This could happen if coordinator metadata is different
# than broker metadata
future.failure(Errors.IllegalStateError())
return
self._coordinator_id = response.coordinator_id
log.info("Discovered coordinator %s for group %s",
self._coordinator_id, self.group_id)
self._client.ready(self._coordinator_id)
future.success(self._coordinator_id)
elif error_type is Errors.GroupCoordinatorNotAvailableError:
log.debug("Group Coordinator Not Available; retry")
future.failure(error_type())
elif error_type is Errors.GroupAuthorizationFailedError:
error = error_type(self.group_id)
log.error("Group Coordinator Request failed: %s", error)
future.failure(error)
else:
error = error_type()
log.error("Unrecognized failure in Group Coordinator Request: %s",
error)
future.failure(error)
def _failed_request(self, node_id, request, future, error):
log.error('Error sending %s to node %s [%s]',
request.__class__.__name__, node_id, error)
# Marking coordinator dead
# unless the error is caused by internal client pipelining
if not isinstance(error, (Errors.NodeNotReadyError,
Errors.TooManyInFlightRequests)):
self._coordinator_dead()
future.failure(error)
def offsets(self, partitions, timestamp):
"""Fetch a single offset before the given timestamp for the set of partitions.
Blocks until offset is obtained, or a non-retriable exception is raised
Arguments:
partitions (iterable of TopicPartition) The partition that needs fetching offset.
timestamp (int): timestamp for fetching offset. -1 for the latest
available, -2 for the earliest available. Otherwise timestamp
is treated as epoch seconds.
Returns:
dict: TopicPartition and message offsets
"""
retries = 3
while retries > 0:
offsets = {}
for future in self._send_offset_request(partitions, timestamp):
self._client.poll(future=future)
if future.succeeded():
for tp, offset in future.value:
offsets[tp] = offset
continue
if not future.retriable():
raise future.exception # pylint: disable-msg=raising-bad-type
if future.exception.invalid_metadata:
refresh_future = self._client.cluster.request_update()
self._client.poll(future=refresh_future)
log.warning("Got exception %s and kept the loop", future.exception)
if offsets:
return offsets
retries -= 1
log.warning("Retrying the offsets fetch loop (%d retries left)", retries)
log.error("Unsuccessful offsets retrieval")
return {}
def _send_offset_request(self, partitions, timestamp):
"""Fetch a single offset before the given timestamp for the partition.
Arguments:
partitions iterable of TopicPartition: partitions that needs fetching offset
timestamp (int): timestamp for fetching offset
Returns:
list of Future: resolves to the corresponding offset
"""
topic = partitions[0].topic
nodes_per_partitions = {}
for partition in partitions:
node_id = self._client.cluster.leader_for_partition(partition)
if node_id is None:
log.debug("Partition %s is unknown for fetching offset,"
" wait for metadata refresh", partition)
return [Future().failure(Errors.StaleMetadata(partition))]
elif node_id == -1:
log.debug("Leader for partition %s unavailable for fetching offset,"
" wait for metadata refresh", partition)
return [Future().failure(Errors.LeaderNotAvailableError(partition))]
nodes_per_partitions.setdefault(node_id, []).append(partition)
# Client returns a future that only fails on network issues
# so create a separate future and attach a callback to update it
# based on response error codes
futures = []
for node_id, partitions in six.iteritems(nodes_per_partitions):
request = OffsetRequest[0](
-1, [(topic, [(partition.partition, timestamp, 1) for partition in partitions])]
)
future_request = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_offset_response, partitions, future_request)
def errback(e):
log.error("Offset request errback error %s", e)
future_request.failure(e)
_f.add_errback(errback)
futures.append(future_request)
return futures
def _handle_offset_response(self, partitions, future, response):
"""Callback for the response of the list offset call above.
Arguments:
partition (TopicPartition): The partition that was fetched
future (Future): the future to update based on response
response (OffsetResponse): response from the server
Raises:
AssertionError: if response does not match partition
"""
topic, partition_info = response.topics[0]
assert len(response.topics) == 1, (
'OffsetResponse should only be for a single topic')
partition_ids = set([part.partition for part in partitions])
result = []
for pi in partition_info:
part, error_code, offsets = pi
assert topic == partitions[0].topic and part in partition_ids, (
'OffsetResponse partition does not match OffsetRequest partition')
error_type = Errors.for_code(error_code)
if error_type is Errors.NoError:
assert len(offsets) == 1, 'Expected OffsetResponse with one offset'
log.debug("Fetched offset %s for partition %d", offsets[0], part)
result.append((TopicPartition(topic, part), offsets[0]))
elif error_type in (Errors.NotLeaderForPartitionError,
Errors.UnknownTopicOrPartitionError):
log.debug("Attempt to fetch offsets for partition %s failed due"
" to obsolete leadership information, retrying.",
str(partitions))
future.failure(error_type(partitions))
else:
log.warning("Attempt to fetch offsets for partition %s failed due to:"
" %s", partitions, error_type)
future.failure(error_type(partitions))
future.success(result)
def fetch_committed_offsets(self, partitions):
"""Fetch the current committed offsets for specified partitions
Arguments:
partitions (list of TopicPartition): partitions to fetch
Returns:
dict: {TopicPartition: OffsetAndMetadata}
"""
if not partitions:
return {}
while True:
self._ensure_coordinator_known()
# contact coordinator to fetch committed offsets
future = self._send_offset_fetch_request(partitions)
self._client.poll(future=future)
if future.succeeded():
return future.value
if not future.retriable():
raise future.exception # pylint: disable-msg=raising-bad-type
time.sleep(self.config['retry_backoff_ms'] / 1000.0)
def _send_offset_fetch_request(self, partitions):
"""Fetch the committed offsets for a set of partitions.
This is a non-blocking call. The returned future can be polled to get
the actual offsets returned from the broker.
Arguments:
partitions (list of TopicPartition): the partitions to fetch
Returns:
Future: resolves to dict of offsets: {TopicPartition: int}
"""
assert self.config['api_version'] >= (0, 8, 1), 'Unsupported Broker API'
assert all(map(lambda k: isinstance(k, TopicPartition), partitions))
if not partitions:
return Future().success({})
elif self._coordinator_unknown():
return Future().failure(Errors.GroupCoordinatorNotAvailableError)
node_id = self._coordinator_id
# Verify node is ready
if not self._client.ready(node_id):
log.debug("Node %s not ready -- failing offset fetch request",
node_id)
return Future().failure(Errors.NodeNotReadyError)
log.debug("Group %s fetching committed offsets for partitions: %s",
self.group_id, partitions)
# construct the request
topic_partitions = collections.defaultdict(set)
for tp in partitions:
topic_partitions[tp.topic].add(tp.partition)
if self.config['api_version'] >= (0, 8, 2):
request = OffsetFetchRequest[1](
self.group_id,
list(topic_partitions.items())
)
else:
request = OffsetFetchRequest[0](
self.group_id,
list(topic_partitions.items())
)
# send the request with a callback
future = Future()
_f = self._client.send(node_id, request)
_f.add_callback(self._handle_offset_fetch_response, future)
_f.add_errback(self._failed_request, node_id, request, future)
return future
def _handle_offset_fetch_response(self, future, response):
offsets = {}
for topic, partitions in response.topics:
for partition, offset, metadata, error_code in partitions:
tp = TopicPartition(topic, partition)
error_type = Errors.for_code(error_code)
if error_type is not Errors.NoError:
error = error_type()
log.debug("Group %s failed to fetch offset for partition"
" %s: %s", self.group_id, tp, error)
if error_type is Errors.GroupLoadInProgressError:
# just retry
future.failure(error)
elif error_type is Errors.NotCoordinatorForGroupError:
# re-discover the coordinator and retry
self._coordinator_dead()
future.failure(error)
elif error_type in (Errors.UnknownMemberIdError,
Errors.IllegalGenerationError):
future.failure(error)
elif error_type is Errors.UnknownTopicOrPartitionError:
log.warning("OffsetFetchRequest -- unknown topic %s"
" (have you committed any offsets yet?)",
topic)
continue
else:
log.error("Unknown error fetching offsets for %s: %s",
tp, error)
future.failure(error)
return
elif offset >= 0:
# record the position with the offset
# (-1 indicates no committed offset to fetch)
offsets[tp] = OffsetAndMetadata(offset, metadata)
else:
log.debug("Group %s has no committed offset for partition"
" %s", self.group_id, tp)
future.success(offsets)
def get(self):
topic_partitions = self._client.cluster.partitions_for_topic(self.topic)
if not topic_partitions:
future = self._client.cluster.request_update()
log.info("No partitions available, performing metadata update.")
self._client.poll(future=future)
return {}
partitions = [TopicPartition(self.topic, partition_id) for partition_id in topic_partitions]
offsets = self.offsets(partitions, -1)
committed = self.fetch_committed_offsets(partitions)
lags = {}
for tp, offset in six.iteritems(offsets):
commit_offset = committed[tp] if tp in committed else 0
numerical = commit_offset if isinstance(commit_offset, int) else commit_offset.offset
lag = offset - numerical
pid = tp.partition if isinstance(tp, TopicPartition) else tp
log.debug("Lag for %s (%s): %s, %s, %s", self.topic, pid, offset, commit_offset, lag)
lags[pid] = lag
return lags
class AdminClient(object):
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
}
DELETE_TIMEOUT = 1000
CREATE_TIMEOUT = 1000
ALTER_TIMEOUT = 1000
def __init__(self, **configs):
# Only check for extra config keys in top-level class
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError("Unrecognized configs: %s" %
extra_configs)
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
self._client = KafkaClient(**self.config)
def _get_controller_id(self):
"""Get the cluster controller
"""
node_id = self._client.least_loaded_node()
m = MetadataRequest[1](topics=[])
future = self._client.send(node_id, m)
self._client.poll(future=future)
response = future.value
return response.controller_id
def brokers(self):
""" Get all brokers """
return self._client.cluster.brokers()
def topics(self, exclude_internal_topics=True):
"""Get all topics the user is authorized to view.
Returns:
dict: {topic (str): [PartitionMetadata]}
"""
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
_topics = []
# FIXME: this should be part of ClusterMetadata class
for topic, partitions in cluster._partitions.items():
if exclude_internal_topics and topic in cluster.internal_topics:
continue
_topics.append(Topic(topic, partitions.values()))
return _topics
def describe_topic(self, topic):
""" Get all details about a topic, like current offsets for its partitions
Returns: Topic or None
"""
cluster = self._client.cluster
if topic not in cluster.topics():
# Refresh metadata
self.topics()
if topic in cluster.topics():
topic = Topic(topic, cluster._partitions[topic].values())
else:
return None
# Describe offsets
for partition in topic.partitions:
offsets = self.partition_offset(partition)
partition.set_offset(offsets)
return topic
def partition_offset(self, partition):
"""
Get the latest offset for a given topic partition
"""
partition_timestamp = (partition.partition, -1)
request = OffsetRequest[1](replica_id=-1,
topics=[
(partition.topic, [partition_timestamp
]),
])
future = self._client.send(partition.leader, request)
self._client.poll(future=future)
response = future.value
topic = response.topics[0]
return topic[-1][0][-1]
def consumer_groups(self):
"""Get all consumer groups known to the cluster
Returns:
list: [Group]
"""
groups = {}
for broker in self._client.cluster.brokers():
request = ListGroupsRequest[0]()
future = self._client.send(broker.nodeId, request)
self._client.poll(future=future)
response = future.value
if response:
for g in response.groups:
group = g[0]
groups[group] = Group(group, broker.nodeId)
else:
log.error("No response for ListGroupsRequest")
return list(groups.values())
def describe_consumer_group(self, group):
"""
Describe a consumer group
Returns: Group
"""
groups = self.consumer_groups()
if group not in groups:
return None
group = groups[group]
request = DescribeGroupsRequest[-1](groups=[group])
future = self._client.send(group.coordinator_id, request)
self._client.poll(future=future)
response = future.value
group.set_metadata(response.groups[0])
return group
def consumer_offset_info(self, consumer):
""" Fetch and configure the consumed offset information for a given consumer group
"""
all_topics = self.topics()
topics_request = [(topic.name, [p.partition for p in topic.partitions])
for topic in all_topics]
o = OffsetFetchRequest[1](consumer_group=consumer.name,
topics=topics_request)
future = self._client.send(consumer.coordinator_id, o)
self._client.poll(future=future)
response = future.value
for r in response.topics:
topic_name, offsets = r
if any((o[1] != -1) for o in offsets):
topic_info = self.describe_topic(topic_name)
for o in offsets:
if o[1] == -1:
continue
partition = o[0]
offset = o[1]
consumer.set_offset(topic_name, partition, offset,
topic_info.get_offset(partition))
return consumer
def create_topic(self, name, partitions, replication_factor):
""" Create a new topic
"""
node_id = self._get_controller_id()
cc = CreateTopicsRequest[0](
create_topic_requests=[
(name, partitions, replication_factor, [], []),
],
timeout=self.CREATE_TIMEOUT,
)
future = self._client.send(node_id, cc)
self._client.poll(future=future)
response = future.value
error = response.topic_error_codes and response.topic_error_codes[
0] and response.topic_error_codes[0][1]
if error == 0:
return True
else:
log.error('controler: {} create topic error: {}'.format(
node_id, error))
return False
def delete_topic(self, name):
""" Delete a topic """
node_id = self._get_controller_id()
d = DeleteTopicsRequest[0](topics=[name], timeout=self.DELETE_TIMEOUT)
future = self._client.send(node_id, d)
self._client.poll(future=future)
response = future.value
error = response.topic_error_codes and response.topic_error_codes[
0] and response.topic_error_codes[0][1]
if error == 0:
return True
else:
log.error('controler: {} delete topic error: {}'.format(
node_id, error))
return False
def alter_topic(self, topic_name, partitions):
""" Add partitions """
node_id = self._get_controller_id()
a = CreatePartitionsRequest[0](
topic_partitions=[(topic_name, (partitions, None))],
timeout=self.ALTER_TIMEOUT,
validate_only=False,
)
future = self._client.send(node_id, a)
self._client.poll(future=future)
response = future.value
error = response.topic_errors and response.topic_errors[
0] and response.topic_errors[0][1]
if error == 0:
return True
else:
log.error('controler: {} alter topic error: {}'.format(
node_id, error))
return False
def cli(mocker, conn):
client = KafkaClient(api_version=(0, 9))
mocker.patch.object(client, '_selector')
client.poll(future=client.cluster.request_update())
return client
class KafkaConsumer(six.Iterator):
"""Consume records from a Kafka cluster.
The consumer will transparently handle the failure of servers in the Kafka
cluster, and adapt as topic-partitions are created or migrate between
brokers. It also interacts with the assigned kafka Group Coordinator node
to allow multiple consumers to load balance consumption of topics (requires
kafka >= 0.9.0.0).
Arguments:
*topics (str): optional list of topics to subscribe to. If not set,
call subscribe() or assign() before consuming records.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
group_id (str or None): name of the consumer group to join for dynamic
partition assignment (if enabled), and to use for fetching and
committing offsets. If None, auto-partition assignment (via
group coordinator) and offset commits are disabled.
Default: 'kafka-python-default-group'
key_deserializer (callable): Any callable that takes a
raw message key and returns a deserialized key.
value_deserializer (callable): Any callable that takes a
raw message value and returns a deserialized value.
fetch_min_bytes (int): Minimum amount of data the server should
return for a fetch request, otherwise wait up to
fetch_max_wait_ms for more data to accumulate. Default: 1.
fetch_max_wait_ms (int): The maximum amount of time in milliseconds
the server will block before answering the fetch request if
there isn't sufficient data to immediately satisfy the
requirement given by fetch_min_bytes. Default: 500.
max_partition_fetch_bytes (int): The maximum amount of data
per-partition the server will return. The maximum total memory
used for a request = #partitions * max_partition_fetch_bytes.
This size must be at least as large as the maximum message size
the server allows or else it is possible for the producer to
send messages larger than the consumer can fetch. If that
happens, the consumer can get stuck trying to fetch a large
message on a certain partition. Default: 1048576.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 40000.
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
auto_offset_reset (str): A policy for resetting offsets on
OffsetOutOfRange errors: 'earliest' will move to the oldest
available message, 'latest' will move to the most recent. Any
other value will raise the exception. Default: 'latest'.
enable_auto_commit (bool): If true the consumer's offset will be
periodically committed in the background. Default: True.
auto_commit_interval_ms (int): milliseconds between automatic
offset commits, if enable_auto_commit is True. Default: 5000.
default_offset_commit_callback (callable): called as
callback(offsets, response) response will be either an Exception
or a OffsetCommitResponse struct. This callback can be used to
trigger custom actions when a commit request completes.
check_crcs (bool): Automatically check the CRC32 of the records
consumed. This ensures no on-the-wire or on-disk corruption to
the messages occurred. This check adds some overhead, so it may
be disabled in cases seeking extreme performance. Default: True
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
partition_assignment_strategy (list): List of objects to use to
distribute partition ownership amongst consumer instances when
group management is used.
Default: [RangePartitionAssignor, RoundRobinPartitionAssignor]
heartbeat_interval_ms (int): The expected time in milliseconds
between heartbeats to the consumer coordinator when using
Kafka's group management feature. Heartbeats are used to ensure
that the consumer's session stays active and to facilitate
rebalancing when new consumers join or leave the group. The
value must be set lower than session_timeout_ms, but typically
should be set no higher than 1/3 of that value. It can be
adjusted even lower to control the expected time for normal
rebalances. Default: 3000
session_timeout_ms (int): The timeout used to detect failures when
using Kafka's group managementment facilities. Default: 30000
max_poll_records (int): The maximum number of records returned in a
single call to poll().
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). The java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). The java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
consumer_timeout_ms (int): number of milliseconds to block during
message iteration before raising StopIteration (i.e., ending the
iterator). Default block forever [float('inf')].
skip_double_compressed_messages (bool): A bug in KafkaProducer <= 1.2.4
caused some messages to be corrupted via double-compression.
By default, the fetcher will return these messages as a compressed
blob of bytes with a single offset, i.e. how the message was
actually published to the cluster. If you prefer to have the
fetcher automatically detect corrupt messages and skip them,
set this option to True. Default: False.
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): flag to configure whether ssl handshake
should verify that the certificate matches the brokers hostname.
default: true.
ssl_cafile (str): optional filename of ca file to use in certificate
verification. default: none.
ssl_certfile (str): optional filename of file in pem format containing
the client certificate, as well as any ca certificates needed to
establish the certificate's authenticity. default: none.
ssl_keyfile (str): optional filename containing the client private key.
default: none.
ssl_password (str): optional password to be used when loading the
certificate chain. default: None.
ssl_crlfile (str): optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
default: none.
api_version (tuple): specify which kafka API version to use.
If set to None, the client will attempt to infer the broker version
by probing various APIs. Default: None
Examples:
(0, 9) enables full group coordination features with automatic
partition assignment and rebalancing,
(0, 8, 2) enables kafka-storage offset commits with manual
partition assignment only,
(0, 8, 1) enables zookeeper-storage offset commits with manual
partition assignment only,
(0, 8, 0) enables basic functionality but requires manual
partition assignment and offset management.
For a full list of supported versions, see KafkaClient.API_VERSIONS
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version set to 'auto'
metric_reporters (list): A list of classes to use as metrics reporters.
Implementing the AbstractMetricsReporter interface allows plugging
in classes that will be notified of new metric creation. Default: []
metrics_num_samples (int): The number of samples maintained to compute
metrics. Default: 2
metrics_sample_window_ms (int): The maximum age in milliseconds of
samples used to compute metrics. Default: 30000
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
exclude_internal_topics (bool): Whether records from internal topics
(such as offsets) should be exposed to the consumer. If set to True
the only way to receive records from an internal topic is
subscribing to it. Requires 0.10+ Default: True
sasl_mechanism (str): string picking sasl mechanism when security_protocol
is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported.
Default: None
sasl_plain_username (str): username for sasl PLAIN authentication.
Default: None
sasl_plain_password (str): password for sasl PLAIN authentication.
Default: None
Note:
Configuration parameters are described in more detail at
https://kafka.apache.org/0100/configuration.html#newconsumerconfigs
"""
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'group_id': 'kafka-python-default-group',
'key_deserializer': None,
'value_deserializer': None,
'fetch_max_wait_ms': 500,
'fetch_min_bytes': 1,
'max_partition_fetch_bytes': 1 * 1024 * 1024,
'request_timeout_ms': 40 * 1000,
'retry_backoff_ms': 100,
'reconnect_backoff_ms': 50,
'max_in_flight_requests_per_connection': 5,
'auto_offset_reset': 'latest',
'enable_auto_commit': True,
'auto_commit_interval_ms': 5000,
'default_offset_commit_callback': lambda offsets, response: True,
'check_crcs': True,
'metadata_max_age_ms': 5 * 60 * 1000,
'partition_assignment_strategy': (RangePartitionAssignor, RoundRobinPartitionAssignor),
'heartbeat_interval_ms': 3000,
'session_timeout_ms': 30000,
'max_poll_records': sys.maxsize,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'consumer_timeout_ms': float('inf'),
'skip_double_compressed_messages': False,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_crlfile': None,
'ssl_password': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'connections_max_idle_ms': 9 * 60 * 1000, # not implemented yet
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
'metric_group_prefix': 'consumer',
'selector': selectors.DefaultSelector,
'exclude_internal_topics': True,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
}
def __init__(self, *topics, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
for key in self.config:
if key in configs:
self.config[key] = configs.pop(key)
# Only check for extra config keys in top-level class
assert not configs, 'Unrecognized configs: %s' % configs
deprecated = {'smallest': 'earliest', 'largest': 'latest'}
if self.config['auto_offset_reset'] in deprecated:
new_config = deprecated[self.config['auto_offset_reset']]
log.warning('use auto_offset_reset=%s (%s is deprecated)',
new_config, self.config['auto_offset_reset'])
self.config['auto_offset_reset'] = new_config
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [reporter() for reporter in self.config['metric_reporters']]
self._metrics = Metrics(metric_config, reporters)
# TODO _metrics likely needs to be passed to KafkaClient, etc.
# api_version was previously a str. accept old format for now
if isinstance(self.config['api_version'], str):
str_version = self.config['api_version']
if str_version == 'auto':
self.config['api_version'] = None
else:
self.config['api_version'] = tuple(map(int, str_version.split('.')))
log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',
str(self.config['api_version']), str_version)
self._client = KafkaClient(metrics=self._metrics, **self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
self._subscription = SubscriptionState(self.config['auto_offset_reset'])
self._fetcher = Fetcher(
self._client, self._subscription, self._metrics, **self.config)
self._coordinator = ConsumerCoordinator(
self._client, self._subscription, self._metrics,
assignors=self.config['partition_assignment_strategy'],
**self.config)
self._closed = False
self._iterator = None
self._consumer_timeout = float('inf')
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
Arguments:
partitions (list of TopicPartition): assignment for this instance.
Raises:
IllegalStateError: if consumer has already called subscribe()
Warning:
It is not possible to use both manual partition assignment with
assign() and group assignment with subscribe().
Note:
This interface does not support incremental assignment and will
replace the previous assignment (if there was one).
Note:
Manual topic assignment through this method does not use the
consumer's group management functionality. As such, there will be
no rebalance operation triggered when group membership or cluster
and topic metadata change.
"""
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
"""Get the TopicPartitions currently assigned to this consumer.
If partitions were directly assigned using assign(), then this will
simply return the same partitions that were previously assigned.
If topics were subscribed using subscribe(), then this will give the
set of topic partitions currently assigned to the consumer (which may
be none if the assignment hasn't happened yet, or if the partitions are
in the process of being reassigned).
Returns:
set: {TopicPartition, ...}
"""
return self._subscription.assigned_partitions()
def close(self):
"""Close the consumer, waiting indefinitely for any needed cleanup."""
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close()
self._metrics.close()
self._client.close()
try:
self.config['key_deserializer'].close()
except AttributeError:
pass
try:
self.config['value_deserializer'].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
"""Commit offsets to kafka asynchronously, optionally firing callback
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
This is an asynchronous call and will not block. Any errors encountered
are either passed to the callback (if provided) or discarded.
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
callback (callable, optional): called as callback(offsets, response)
with response as either an Exception or a OffsetCommitResponse
struct. This callback can be used to trigger custom actions when
a commit request completes.
Returns:
kafka.future.Future
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(
offsets, callback=callback)
return future
def commit(self, offsets=None):
"""Commit offsets to kafka, blocking until success or error
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used. To avoid re-processing the last
message read if a consumer is restarted, the committed offset should be
the next message your application should consume, i.e.: last_offset + 1.
Blocks until either the commit succeeds or an unrecoverable error is
encountered (in which case it is thrown to the caller).
Currently only supports kafka-topic offset storage (not zookeeper)
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
"""Get the last committed offset for the given partition
This offset will be used as the position for the consumer
in the event of a failure.
This call may block to do a remote call if the partition in question
isn't assigned to this consumer or if the consumer hasn't yet
initialized its cache of committed offsets.
Arguments:
partition (TopicPartition): the partition to check
Returns:
The last committed offset, or None if there was no prior commit.
"""
assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
"""Get all topics the user is authorized to view.
Returns:
set: topics
"""
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
return cluster.topics()
def partitions_for_topic(self, topic):
"""Get metadata about the partitions for a given topic.
Arguments:
topic (str): topic to check
Returns:
set: partition ids
"""
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0, max_records=None):
"""Fetch data from assigned topics / partitions.
Records are fetched and returned in batches by topic-partition.
On each poll, consumer will try to use the last consumed offset as the
starting offset and fetch sequentially. The last consumed offset can be
manually set through seek(partition, offset) or automatically set as
the last committed offset for the subscribed list of partitions.
Incompatible with iterator interface -- use one or the other, not both.
Arguments:
timeout_ms (int, optional): milliseconds spent waiting in poll if
data is not available in the buffer. If 0, returns immediately
with any records that are available currently in the buffer,
else returns empty. Must not be negative. Default: 0
max_records (int, optional): The maximum number of records returned
in a single call to :meth:`poll`. Default: Inherit value from
max_poll_records.
Returns:
dict: topic to list of records since the last fetch for the
subscribed list of topics and partitions
"""
assert timeout_ms >= 0, 'Timeout must not be negative'
if max_records is None:
max_records = self.config['max_poll_records']
# poll for new data until the timeout expires
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining, max_records)
if records:
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms, max_records):
"""
Do one round of polling. In addition to checking for new data, this does
any needed heart-beating, auto-commits, and offset updates.
Arguments:
timeout_ms (int): The maximum time in milliseconds to block
Returns:
dict: map of topic to list of records (may be empty)
"""
if self._use_consumer_group():
self._coordinator.ensure_coordinator_known()
self._coordinator.ensure_active_group()
# 0.8.2 brokers support kafka-backed offset storage via group coordinator
elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
# fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(self._subscription.missing_fetch_positions())
# if data is available already, e.g. from a previous network client
# poll() call to commit, then just return it immediately
records, partial = self._fetcher.fetched_records(max_records)
if records:
# before returning the fetched records, we can send off the
# next round of fetches and avoid block waiting for their
# responses to enable pipelining while the user is handling the
# fetched records.
if not partial:
self._fetcher.send_fetches()
return records
# send any new fetches (won't resend pending fetches)
self._fetcher.send_fetches()
self._client.poll(timeout_ms=timeout_ms, sleep=True)
records, _ = self._fetcher.fetched_records(max_records)
return records
def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): partition to check
Returns:
int: offset
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions([partition])
offset = self._subscription.assignment[partition].position
return offset
def highwater(self, partition):
"""Last known highwater offset for a partition
A highwater offset is the offset that will be assigned to the next
message that is produced. It may be useful for calculating lag, by
comparing with the reported position. Note that both position and
highwater refer to the *next* offset -- i.e., highwater offset is
one greater than the newest available message.
Highwater offsets are returned in FetchResponse messages, so will
not be available if no FetchRequests have been sent for this partition
yet.
Arguments:
partition (TopicPartition): partition to check
Returns:
int or None: offset if available
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert self._subscription.is_assigned(partition), 'Partition is not assigned'
return self._subscription.assignment[partition].highwater
def pause(self, *partitions):
"""Suspend fetching from the requested partitions.
Future calls to poll() will not return any records from these partitions
until they have been resumed using resume(). Note that this method does
not affect partition subscription. In particular, it does not cause a
group rebalance when automatic assignment is used.
Arguments:
*partitions (TopicPartition): partitions to pause
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def paused(self):
"""Get the partitions that were previously paused by a call to pause().
Returns:
set: {partition (TopicPartition), ...}
"""
return self._subscription.paused_partitions()
def resume(self, *partitions):
"""Resume fetching from the specified (paused) partitions.
Arguments:
*partitions (TopicPartition): partitions to resume
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
"""Manually specify the fetch offset for a TopicPartition.
Overrides the fetch offsets that the consumer will use on the next
poll(). If this API is invoked for the same partition more than once,
the latest offset will be used on the next poll(). Note that you may
lose data if this API is arbitrarily used in the middle of consumption,
to reset the fetch offsets.
Arguments:
partition (TopicPartition): partition for seek operation
offset (int): message offset in partition
Raises:
AssertionError: if offset is not an int >= 0; or if partition is not
currently assigned.
"""
if not isinstance(partition, TopicPartition):
raise TypeError('partition must be a TopicPartition namedtuple')
assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0'
assert partition in self._subscription.assigned_partitions(), 'Unassigned partition'
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
"""Seek to the oldest available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
Raises:
AssertionError: if any partition is not currently assigned, or if
no partitions are assigned
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
"""Seek to the most recent available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
Raises:
AssertionError: if any partition is not currently assigned, or if
no partitions are assigned
"""
if not all([isinstance(p, TopicPartition) for p in partitions]):
raise TypeError('partitions must be TopicPartition namedtuples')
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with assign()
Arguments:
topics (list): List of topics for subscription.
pattern (str): Pattern to match available topics. You must provide
either topics or pattern, but not both.
listener (ConsumerRebalanceListener): Optionally include listener
callback, which will be called before and after each rebalance
operation.
As part of group management, the consumer will keep track of the
list of consumers that belong to a particular group and will
trigger a rebalance operation if one of the following events
trigger:
* Number of partitions change for any of the subscribed topics
* Topic is created or deleted
* An existing member of the consumer group dies
* A new member is added to the consumer group
When any of these events are triggered, the provided listener
will be invoked first to indicate that the consumer's assignment
has been revoked, and then again when the new assignment has
been received. Note that this listener will immediately override
any listener set in a previous call to subscribe. It is
guaranteed, however, that the partitions revoked/assigned
through this interface are from topics subscribed in this call.
Raises:
IllegalStateError: if called after previously calling assign()
AssertionError: if neither topics or pattern is provided
TypeError: if listener is not a ConsumerRebalanceListener
"""
# SubscriptionState handles error checking
self._subscription.subscribe(topics=topics,
pattern=pattern,
listener=listener)
# regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_all_topic_metadata = True
self._client.set_topics([])
log.debug("Subscribed to topic pattern: %s", pattern)
else:
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
"""Get the current topic subscription.
Returns:
set: {topic, ...}
"""
return self._subscription.subscription
def unsubscribe(self):
"""Unsubscribe from all topics and clear all assigned partitions."""
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics([])
log.debug("Unsubscribed all topics or patterns and assigned partitions")
def metrics(self, raw=False):
"""Warning: this is an unstable interface.
It may change in future releases without warning"""
if raw:
return self._metrics.metrics
metrics = {}
for k, v in self._metrics.metrics.items():
if k.group not in metrics:
metrics[k.group] = {}
if k.name not in metrics[k.group]:
metrics[k.group][k.name] = {}
metrics[k.group][k.name] = v.value()
return metrics
def _use_consumer_group(self):
"""Return True iff this consumer can/should join a broker-coordinated group."""
if self.config['api_version'] < (0, 9):
return False
elif self.config['group_id'] is None:
return False
elif not self._subscription.partitions_auto_assigned():
return False
return True
def _update_fetch_positions(self, partitions):
"""
Set the fetch position to the committed position (if there is one)
or reset it using the offset reset policy the user has configured.
Arguments:
partitions (List[TopicPartition]): The partitions that need
updating fetch positions
Raises:
NoOffsetForPartitionError: If no offset is stored for a given
partition and no offset reset policy is defined
"""
if (self.config['api_version'] >= (0, 8, 1)
and self.config['group_id'] is not None):
# refresh commits for all assigned partitions
self._coordinator.refresh_committed_offsets_if_needed()
# then do any offset lookups in case some positions are not known
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription() is not None, 'No topic subscription or manual partition assignment'
while time.time() < self._consumer_timeout:
if self._use_consumer_group():
self._coordinator.ensure_coordinator_known()
self._coordinator.ensure_active_group()
# 0.8.2 brokers support kafka-backed offset storage via group coordinator
elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
# fetch offsets for any subscribed partitions that we arent tracking yet
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
poll_ms = 1000 * (self._consumer_timeout - time.time())
if not self._fetcher.in_flight_fetches():
poll_ms = 0
self._client.poll(timeout_ms=poll_ms, sleep=True)
# We need to make sure we at least keep up with scheduled tasks,
# like heartbeats, auto-commits, and metadata refreshes
timeout_at = self._next_timeout()
# Because the consumer client poll does not sleep unless blocking on
# network IO, we need to explicitly sleep when we know we are idle
# because we haven't been assigned any partitions to fetch / consume
if self._use_consumer_group() and not self.assignment():
sleep_time = max(timeout_at - time.time(), 0)
if sleep_time > 0 and not self._client.in_flight_request_count():
log.debug('No partitions assigned; sleeping for %s', sleep_time)
time.sleep(sleep_time)
continue
# Short-circuit the fetch iterator if we are already timed out
# to avoid any unintentional interaction with fetcher setup
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
# an else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# and we assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.send_fetches()
def _next_timeout(self):
timeout = min(self._consumer_timeout,
self._client._delayed_tasks.next_at() + time.time(),
self._client.cluster.ttl() / 1000.0 + time.time())
# Although the delayed_tasks timeout above should cover processing
# HeartbeatRequests, it is still possible that HeartbeatResponses
# are left unprocessed during a long _fetcher iteration without
# an intermediate poll(). And because tasks are responsible for
# rescheduling themselves, an unprocessed response will prevent
# the next heartbeat from being sent. This check should help
# avoid that.
if self._use_consumer_group():
heartbeat = time.time() + self._coordinator.heartbeat.ttl()
timeout = min(timeout, heartbeat)
return timeout
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config['consumer_timeout_ms'] >= 0:
self._consumer_timeout = time.time() + (
self.config['consumer_timeout_ms'] / 1000.0)
# old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError(
'deprecated -- initialize a new consumer')
def set_topic_partitions(self, *topics):
raise NotImplementedError(
'deprecated -- use subscribe() or assign()')
def fetch_messages(self):
raise NotImplementedError(
'deprecated -- use poll() or iterator interface')
def get_partition_offsets(self, topic, partition,
request_time_ms, max_num_offsets):
raise NotImplementedError(
'deprecated -- send an OffsetRequest with KafkaClient')
def offsets(self, group=None):
raise NotImplementedError('deprecated -- use committed(partition)')
def task_done(self, message):
raise NotImplementedError(
'deprecated -- commit offsets manually if needed')
class KafkaConsumerLag:
def __init__(self, bootstrap_servers):
self.client = KafkaClient(bootstrap_servers=bootstrap_servers)
self.client.check_version()
def _send(self, broker_id, request, response_type=None):
f = self.client.send(broker_id, request)
response = self.client.poll(future=f)
if response_type:
if response and len(response) > 0:
for r in response:
if isinstance(r, response_type):
return r
else:
if response and len(response) > 0:
return response[0]
return None
def check(self, group_topics=None, discovery=None):
"""
{
"<group>": {
"state": <str>,
"topics": {
"<topic>": {
"consumer_lag": <int>,
"partitions": {
"<partition>": {
"offset_first": <int>,
"offset_consumed": <int>,
"offset_last": <int>,
"lag": <int>
}
}
}
}
}
}
:param persist_groups:
:return: consumer statistics
"""
cluster = self.client.cluster
brokers = cluster.brokers()
# Consumer group ID -> list(topics)
if group_topics is None:
group_topics = {}
if discovery is None:
discovery = True
else:
group_topics = copy.deepcopy(group_topics)
# Set of consumer group IDs
consumer_groups = set(group_topics.iterkeys())
# Set of all known topics
topics = set(itertools.chain(*group_topics.itervalues()))
# Consumer group ID -> coordinating broker
consumer_coordinator = {}
# Coordinating broker - > list(consumer group IDs)
coordinator_consumers = {}
results = {}
for consumer_group in group_topics.iterkeys():
results[consumer_group] = {'state': None, 'topics': {}}
# Ensure connections to all brokers
for broker in brokers:
while not self.client.is_ready(broker.nodeId):
self.client.ready(broker.nodeId)
# Collect all active consumer groups
if discovery:
for broker in brokers:
response = self._send(broker.nodeId, _ListGroupsRequest(),
_ListGroupsResponse)
if response:
for group in response.groups:
consumer_groups.add(group[0])
# Identify which broker is coordinating each consumer group
for group in consumer_groups:
response = self._send(
next(iter(brokers)).nodeId, _GroupCoordinatorRequest(group),
_GroupCoordinatorResponse)
if response:
consumer_coordinator[group] = response.coordinator_id
if response.coordinator_id not in coordinator_consumers:
coordinator_consumers[response.coordinator_id] = []
coordinator_consumers[response.coordinator_id].append(group)
# Populate consumer groups into dict
for group in consumer_groups:
if group not in group_topics:
group_topics[group] = []
# Add groups to results dict
for group, topic_list in group_topics.iteritems():
results[group] = {'state': None, 'topics': {}}
# Identify group information and topics read by each consumer group
for coordinator, consumers in coordinator_consumers.iteritems():
response = self._send(coordinator,
_DescribeGroupsRequest(consumers),
_DescribeGroupsResponse)
for group in response.groups:
if group[1] in results:
results[group[1]]['state'] = group[2]
# TODO Also include member data?
if discovery:
members = group[5]
for member in members:
try:
assignment = MemberAssignment.decode(member[4])
if assignment:
for partition in assignment.partition_assignment:
topic = partition[0]
# Add topic to topic set
topics.add(topic)
# Add topic to group
group_topics[group[1]].append(topic)
except:
pass
# Add topics to groups in results dict
for group, topic_list in group_topics.iteritems():
for topic in topic_list:
results[group]['topics'][topic] = {
'consumer_lag': 0,
'partitions': {}
}
# For storing the latest offset for all partitions of all topics
# topic -> partition -> offset
start_offsets = {}
end_offsets = {}
# Identify all the topic partitions that each broker is leader for
# and request next new offset for each partition
for broker, partitions in cluster._broker_partitions.iteritems():
# topic -> List(partition, time, max_offsets)
request_partitions = {}
for tp in partitions:
if tp.topic in topics:
if tp.topic not in request_partitions:
request_partitions[tp.topic] = []
# Time value '-2' is to get the offset for first available message
request_partitions[tp.topic].append((tp.partition, -2, 1))
# List(topic, List(partition, time, max_offsets))
topic_partitions = []
for tp in request_partitions.iteritems():
topic_partitions.append(tp)
# Request partition start offsets
response = self._send(broker, _OffsetRequest(-1, topic_partitions),
_OffsetResponse)
if response:
for offset in response.topics:
topic = offset[0]
if topic not in start_offsets:
start_offsets[topic] = {}
for p in offset[1]:
start_offsets[topic][p[0]] = p[2][0]
for tp in topic_partitions:
for i, ptm in enumerate(tp[1]):
# Time value '-1' is to get the offset for next new message
tp[1][i] = (ptm[0], -1, 1)
# Request partition end offsets
response = self._send(broker, _OffsetRequest(-1, topic_partitions),
_OffsetResponse)
if response:
for offset in response.topics:
topic = offset[0]
if topic not in end_offsets:
end_offsets[topic] = {}
for p in offset[1]:
end_offsets[topic][p[0]] = p[2][0]
# Populate with offset values
for group, topics in group_topics.iteritems():
coordinator = consumer_coordinator[group]
# topic -> list(partition)
request_partitions = {}
for topic in topics:
results[group]['topics'][topic]['consumer_lag'] = 0
results[group]['topics'][topic]['partitions'] = {}
if topic in start_offsets:
for p in start_offsets[topic]:
results[group]['topics'][topic]['partitions'][p] = {
'offset_first': start_offsets[topic][p],
'offset_last': end_offsets[topic][p],
'offset_consumed': 0,
'lag': 0
}
if topic not in request_partitions:
request_partitions[topic] = []
request_partitions[topic].append(p)
# List(topic -> list(partition))
topic_partitions = []
for tp in request_partitions.iteritems():
topic_partitions.append(tp)
response = self._send(coordinator,
_OffsetFetchRequest(group, topic_partitions),
_OffsetFetchResponse)
if response:
for offset in response.topics:
topic = offset[0]
offsets = offset[1]
if topic not in results[group]['topics']:
continue
for p_offset in offsets:
partition = p_offset[0]
offset_consumed = p_offset[1]
p_results = results[group]['topics'][topic][
'partitions'][partition]
if offset_consumed != -1:
p_results['offset_consumed'] = offset_consumed
p_results['lag'] = p_results[
'offset_last'] - offset_consumed
else:
p_results['offset_consumed'] = 0
p_results['lag'] = p_results[
'offset_last'] - p_results['offset_first']
results[group]['topics'][topic][
'consumer_lag'] += p_results['lag']
return results
def close(self):
if self.client:
self.client.close()
class KafkaAdmin(object):
"""An class for administering the kafka cluster.
The KafkaAdmin class will negotiate for the latest version of each message protocol format supported
by both the kafka-python client library and the kafka broker. Usage of optional fields from protocol
versions that are not supported by the broker will result in UnsupportedVersionError exceptions.
Use of this class requires a minimum broker version >= 0.10.0.0.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
reconnect_backoff_max_ms (int): The maximum amount of time in
milliseconds to wait when reconnecting to a broker that has
repeatedly failed to connect. If provided, the backoff per host
will increase exponentially for each consecutive connection
failure, up to this maximum. To avoid connection storms, a
randomization factor of 0.2 will be applied to the backoff
resulting in a random range between 20% below and 20% above
the computed value. Default: 1000.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 30000.
connections_max_idle_ms: Close idle connections after the number of
milliseconds specified by this config. The broker closes idle
connections after connections.max.idle.ms, so this avoids hitting
unexpected socket disconnected errors on the client.
Default: 540000
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). Java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). Java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): Flag to configure whether SSL handshake
should verify that the certificate matches the broker's hostname.
Default: True.
ssl_cafile (str): Optional filename of CA file to use in certificate
veriication. Default: None.
ssl_certfile (str): Optional filename of file in PEM format containing
the client certificate, as well as any CA certificates needed to
establish the certificate's authenticity. Default: None.
ssl_keyfile (str): Optional filename containing the client private key.
Default: None.
ssl_password (str): Optional password to be used when loading the
certificate chain. Default: None.
ssl_crlfile (str): Optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
Default: None.
api_version (tuple): Specify which Kafka API version to use. If set
to None, KafkaClient will attempt to infer the broker version by
probing various APIs. Example: (0, 10, 2). Default: None
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version is None
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
metrics (kafka.metrics.Metrics): Optionally provide a metrics
instance for capturing network IO stats. Default: None.
metric_group_prefix (str): Prefix for metric names. Default: ''
sasl_mechanism (str): string picking sasl mechanism when security_protocol
is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported.
Default: None
sasl_plain_username (str): username for sasl PLAIN authentication.
Default: None
sasl_plain_password (str): password for sasl PLAIN authentication.
Default: None
sasl_kerberos_service_name (str): Service name to include in GSSAPI
sasl mechanism handshake. Default: 'kafka'
"""
DEFAULT_CONFIG = {
# client configs
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'request_timeout_ms': 30000,
'connections_max_idle_ms': 9 * 60 * 1000,
'reconnect_backoff_ms': 50,
'reconnect_backoff_max_ms': 1000,
'max_in_flight_requests_per_connection': 5,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'sock_chunk_bytes': 4096, # undocumented experimental option
'sock_chunk_buffer_count': 1000, # undocumented experimental option
'retry_backoff_ms': 100,
'metadata_max_age_ms': 300000,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_password': None,
'ssl_crlfile': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'selector': selectors.DefaultSelector,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
'sasl_kerberos_service_name': 'kafka',
# metrics configs
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
}
def __init__(self, **configs):
log.debug("Starting Kafka administration interface")
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError("Unrecognized configs: %s" %
extra_configs)
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
# api_version was previously a str. accept old format for now
if isinstance(self.config['api_version'], str):
deprecated = self.config['api_version']
if deprecated == 'auto':
self.config['api_version'] = None
else:
self.config['api_version'] = tuple(
map(int, deprecated.split('.')))
log.warning(
'use api_version=%s [tuple] -- "%s" as str is deprecated',
str(self.config['api_version']), deprecated)
# Configure metrics
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(
samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [
reporter() for reporter in self.config['metric_reporters']
]
self._metrics = Metrics(metric_config, reporters)
self._client = KafkaClient(metrics=self._metrics,
metric_group_prefix='admin',
**self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
self._closed = False
self._refresh_controller_id()
log.debug('Kafka administration interface started')
def close(self):
"""Close the administration connection to the kafka broker"""
if not hasattr(self, '_closed') or self._closed:
log.info('Kafka administration interface already closed')
return
self._metrics.close()
self._client.close()
self._closed = True
log.debug('Kafka administartion interface has closed')
def _matching_api_version(self, operation):
"""Find matching api version, the lesser of either the latest api version the library supports, or
the max version supported by the broker
:param operation: An operation array from kafka.protocol
:return: The max matching version number between client and broker
"""
version = min(
len(operation) - 1,
self._client.get_api_versions()[operation[0].API_KEY][1])
if version < self._client.get_api_versions()[operation[0].API_KEY][0]:
# max library version is less than min broker version. Not sure any brokers
# actually set a min version greater than 0 right now, tho. But maybe in the future?
raise UnsupportedVersionError(
"Could not find matching protocol version for {}".format(
operation.__name__))
return version
def _validate_timeout(self, timeout_ms):
"""Validate the timeout is set or use the configuration default
:param timeout_ms: The timeout provided by api call, in milliseconds
:return: The timeout to use for the operation
"""
return timeout_ms or self.config['request_timeout_ms']
def _refresh_controller_id(self):
"""Determine the kafka cluster controller
"""
response = self._send_request_to_node(self._client.least_loaded_node(),
MetadataRequest[1]([]))
self._controller_id = response.controller_id
version = self._client.check_version(self._controller_id)
if version < (0, 10, 0):
raise UnsupportedVersionError(
"Kafka Admin interface not supported for cluster controller version {} < 0.10.0.0"
.format(version))
def _send_request_to_node(self, node, request):
"""Send a kafka protocol message to a specific broker. Will block until the message result is received.
:param node: The broker id to which to send the message
:param request: The message to send
:return: The kafka protocol response for the message
:exception: The exception if the message could not be sent
"""
while not self._client.ready(node):
# connection to broker not ready, poll until it is or send will fail with NodeNotReadyError
self._client.poll()
future = self._client.send(node, request)
self._client.poll(future=future)
if future.succeeded():
return future.value
else:
raise future.exception # pylint: disable-msg=raising-bad-type
def _send(self, request):
"""Send a kafka protocol message to the cluster controller. Will block until the message result is received.
:param request: The message to send
:return The kafka protocol response for the message
:exception NodeNotReadyError: If the controller connection can't be established
"""
remaining_tries = 2
while remaining_tries > 0:
remaining_tries = remaining_tries - 1
try:
return self._send_request_to_node(self._controller_id, request)
except (NotControllerError, KafkaConnectionError) as e:
# controller changed? refresh it
self._refresh_controller_id()
raise NodeNotReadyError(self._controller_id)
@staticmethod
def _convert_new_topic_request(new_topic):
return (
new_topic.name, new_topic.num_partitions,
new_topic.replication_factor, [
(partition_id, replicas) for partition_id, replicas in
new_topic.replica_assignments.items()
],
[(config_key, config_value)
for config_key, config_value in new_topic.topic_configs.items()])
def create_topics(self, new_topics, timeout_ms=None, validate_only=None):
"""Create new topics in the cluster.
:param new_topics: Array of NewTopic objects
:param timeout_ms: Milliseconds to wait for new topics to be created before broker returns
:param validate_only: If True, don't actually create new topics. Not supported by all versions.
:return: Appropriate version of CreateTopicResponse class
"""
version = self._matching_api_version(CreateTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version == 0:
if validate_only:
raise UnsupportedVersionError(
"validate_only not supported on cluster version {}".format(
self.config['api_version']))
request = CreateTopicsRequest[version](create_topic_requests=[
self._convert_new_topic_request(new_topic)
for new_topic in new_topics
],
timeout=timeout_ms)
elif version <= 2:
validate_only = validate_only or False
request = CreateTopicsRequest[version](create_topic_requests=[
self._convert_new_topic_request(new_topic)
for new_topic in new_topics
],
timeout=timeout_ms,
validate_only=validate_only)
else:
raise UnsupportedVersionError(
"missing implementation of CreateTopics for library supported version {}"
.format(version))
return self._send(request)
def delete_topics(self, topics, timeout_ms=None):
"""Delete topics from the cluster
:param topics: Array of topic name strings
:param timeout_ms: Milliseconds to wait for topics to be deleted before broker returns
:return: Appropriate version of DeleteTopicsResponse class
"""
version = self._matching_api_version(DeleteTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version <= 1:
request = DeleteTopicsRequest[version](topics=topics,
timeout=timeout_ms)
else:
raise UnsupportedVersionError(
"missing implementation of DeleteTopics for library supported version {}"
.format(version))
return self._send(request)
# list topics functionality is in ClusterMetadata
# describe topics functionality is in ClusterMetadata
# describe cluster functionality is in ClusterMetadata
# describe_acls protocol not implemented
# create_acls protocol not implemented
# delete_acls protocol not implemented
@staticmethod
def _convert_describe_config_resource_request(config_resource):
return (config_resource.resource_type, config_resource.name, [
config_key
for config_key, config_value in config_resource.configs.items()
] if config_resource.configs else None)
def describe_configs(self, config_resources, include_synonyms=None):
"""Fetch configuration parameters for one or more kafka resources.
:param config_resources: An array of ConfigResource objects.
Any keys in ConfigResource.configs dict will be used to filter the result. The configs dict should be None
to get all values. An empty dict will get zero values (as per kafka protocol).
:param include_synonyms: If True, return synonyms in response. Not supported by all versions.
:return: Appropriate version of DescribeConfigsResponse class
"""
version = self._matching_api_version(DescribeConfigsRequest)
if version == 0:
if include_synonyms:
raise UnsupportedVersionError(
"include_synonyms not supported on cluster version {}".
format(self.config['api_version']))
request = DescribeConfigsRequest[version](resources=[
self._convert_describe_config_resource_request(config_resource)
for config_resource in config_resources
])
elif version <= 1:
include_synonyms = include_synonyms or False
request = DescribeConfigsRequest[version](
resources=[
self._convert_describe_config_resource_request(
config_resource)
for config_resource in config_resources
],
include_synonyms=include_synonyms)
else:
raise UnsupportedVersionError(
"missing implementation of DescribeConfigs for library supported version {}"
.format(version))
return self._send(request)
@staticmethod
def _convert_alter_config_resource_request(config_resource):
return (config_resource.resource_type, config_resource.name, [
(config_key, config_value)
for config_key, config_value in config_resource.configs.items()
])
def alter_configs(self, config_resources):
"""Alter configuration parameters of one or more kafka resources.
:param config_resources: An array of ConfigResource objects.
:return: Appropriate version of AlterConfigsResponse class
"""
version = self._matching_api_version(AlterConfigsRequest)
if version == 0:
request = AlterConfigsRequest[version](resources=[
self._convert_alter_config_resource_request(config_resource)
for config_resource in config_resources
])
else:
raise UnsupportedVersionError(
"missing implementation of AlterConfigs for library supported version {}"
.format(version))
return self._send(request)
# alter replica logs dir protocol not implemented
# describe log dirs protocol not implemented
@staticmethod
def _convert_create_partitions_request(topic_name, new_partitions):
return (topic_name, (new_partitions.total_count,
new_partitions.new_assignments))
def create_partitions(self,
topic_partitions,
timeout_ms=None,
validate_only=None):
"""Create additional partitions for an existing topic.
:param topic_partitions: A map of topic name strings to NewPartition objects
:param timeout_ms: Milliseconds to wait for new partitions to be created before broker returns
:param validate_only: If True, don't actually create new partitions.
:return: Appropriate version of CreatePartitionsResponse class
"""
version = self._matching_api_version(CreatePartitionsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
validate_only = validate_only or False
if version == 0:
request = CreatePartitionsRequest[version](
topic_partitions=[
self._convert_create_partitions_request(
topic_name, new_partitions)
for topic_name, new_partitions in topic_partitions.items()
],
timeout=timeout_ms,
validate_only=validate_only)
else:
raise UnsupportedVersionError(
"missing implementation of CreatePartitions for library supported version {}"
.format(version))
return self._send(request)
# delete records protocol not implemented
# create delegation token protocol not implemented
# renew delegation token protocol not implemented
# expire delegation_token protocol not implemented
# describe delegation_token protocol not implemented
def describe_consumer_groups(self, group_ids):
"""Describe a set of consumer groups.
:param group_ids: A list of consumer group id names
:return: Appropriate version of DescribeGroupsResponse class
"""
version = self._matching_api_version(DescribeGroupsRequest)
if version <= 1:
request = DescribeGroupsRequest[version](groups=group_ids)
else:
raise UnsupportedVersionError(
"missing implementation of DescribeGroups for library supported version {}"
.format(version))
return self._send(request)
def list_consumer_groups(self):
"""List all consumer groups known to the cluster.
:return: Appropriate version of ListGroupsResponse class
"""
version = self._matching_api_version(ListGroupsRequest)
if version <= 1:
request = ListGroupsRequest[version]()
else:
raise UnsupportedVersionError(
"missing implementation of ListGroups for library supported version {}"
.format(version))
return self._send(request)
class KafkaAdminClient(object):
"""A class for administering the Kafka cluster.
Warning:
This is an unstable interface that was recently added and is subject to
change without warning. In particular, many methods currently return
raw protocol tuples. In future releases, we plan to make these into
nicer, more pythonic objects. Unfortunately, this will likely break
those interfaces.
The KafkaAdminClient class will negotiate for the latest version of each message
protocol format supported by both the kafka-python client library and the
Kafka broker. Usage of optional fields from protocol versions that are not
supported by the broker will result in IncompatibleBrokerVersion exceptions.
Use of this class requires a minimum broker version >= 0.10.0.0.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
reconnect_backoff_max_ms (int): The maximum amount of time in
milliseconds to wait when reconnecting to a broker that has
repeatedly failed to connect. If provided, the backoff per host
will increase exponentially for each consecutive connection
failure, up to this maximum. To avoid connection storms, a
randomization factor of 0.2 will be applied to the backoff
resulting in a random range between 20% below and 20% above
the computed value. Default: 1000.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 30000.
connections_max_idle_ms: Close idle connections after the number of
milliseconds specified by this config. The broker closes idle
connections after connections.max.idle.ms, so this avoids hitting
unexpected socket disconnected errors on the client.
Default: 540000
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: None (relies on
system defaults). Java client defaults to 32768.
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: None (relies on
system defaults). Java client defaults to 131072.
socket_options (list): List of tuple-arguments to socket.setsockopt
to apply to broker connection sockets. Default:
[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
security_protocol (str): Protocol used to communicate with brokers.
Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT.
ssl_context (ssl.SSLContext): Pre-configured SSLContext for wrapping
socket connections. If provided, all other ssl_* configurations
will be ignored. Default: None.
ssl_check_hostname (bool): Flag to configure whether SSL handshake
should verify that the certificate matches the broker's hostname.
Default: True.
ssl_cafile (str): Optional filename of CA file to use in certificate
veriication. Default: None.
ssl_certfile (str): Optional filename of file in PEM format containing
the client certificate, as well as any CA certificates needed to
establish the certificate's authenticity. Default: None.
ssl_keyfile (str): Optional filename containing the client private key.
Default: None.
ssl_password (str): Optional password to be used when loading the
certificate chain. Default: None.
ssl_crlfile (str): Optional filename containing the CRL to check for
certificate expiration. By default, no CRL check is done. When
providing a file, only the leaf certificate will be checked against
this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
Default: None.
api_version (tuple): Specify which Kafka API version to use. If set
to None, KafkaClient will attempt to infer the broker version by
probing various APIs. Example: (0, 10, 2). Default: None
api_version_auto_timeout_ms (int): number of milliseconds to throw a
timeout exception from the constructor when checking the broker
api version. Only applies if api_version is None
selector (selectors.BaseSelector): Provide a specific selector
implementation to use for I/O multiplexing.
Default: selectors.DefaultSelector
metrics (kafka.metrics.Metrics): Optionally provide a metrics
instance for capturing network IO stats. Default: None.
metric_group_prefix (str): Prefix for metric names. Default: ''
sasl_mechanism (str): Authentication mechanism when security_protocol
is configured for SASL_PLAINTEXT or SASL_SSL. Valid values are:
PLAIN, GSSAPI, OAUTHBEARER.
sasl_plain_username (str): username for sasl PLAIN authentication.
Required if sasl_mechanism is PLAIN.
sasl_plain_password (str): password for sasl PLAIN authentication.
Required if sasl_mechanism is PLAIN.
sasl_kerberos_service_name (str): Service name to include in GSSAPI
sasl mechanism handshake. Default: 'kafka'
sasl_oauth_token_provider (AbstractTokenProvider): OAuthBearer token provider
instance. (See kafka.oauth.abstract). Default: None
"""
DEFAULT_CONFIG = {
# client configs
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'request_timeout_ms': 30000,
'connections_max_idle_ms': 9 * 60 * 1000,
'reconnect_backoff_ms': 50,
'reconnect_backoff_max_ms': 1000,
'max_in_flight_requests_per_connection': 5,
'receive_buffer_bytes': None,
'send_buffer_bytes': None,
'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
'sock_chunk_bytes': 4096, # undocumented experimental option
'sock_chunk_buffer_count': 1000, # undocumented experimental option
'retry_backoff_ms': 100,
'metadata_max_age_ms': 300000,
'security_protocol': 'PLAINTEXT',
'ssl_context': None,
'ssl_check_hostname': True,
'ssl_cafile': None,
'ssl_certfile': None,
'ssl_keyfile': None,
'ssl_password': None,
'ssl_crlfile': None,
'api_version': None,
'api_version_auto_timeout_ms': 2000,
'selector': selectors.DefaultSelector,
'sasl_mechanism': None,
'sasl_plain_username': None,
'sasl_plain_password': None,
'sasl_kerberos_service_name': 'kafka',
'sasl_oauth_token_provider': None,
# metrics configs
'metric_reporters': [],
'metrics_num_samples': 2,
'metrics_sample_window_ms': 30000,
}
def __init__(self, **configs):
log.debug("Starting KafkaAdminClient with configuration: %s", configs)
extra_configs = set(configs).difference(self.DEFAULT_CONFIG)
if extra_configs:
raise KafkaConfigurationError("Unrecognized configs: {}".format(extra_configs))
self.config = copy.copy(self.DEFAULT_CONFIG)
self.config.update(configs)
# Configure metrics
metrics_tags = {'client-id': self.config['client_id']}
metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
time_window_ms=self.config['metrics_sample_window_ms'],
tags=metrics_tags)
reporters = [reporter() for reporter in self.config['metric_reporters']]
self._metrics = Metrics(metric_config, reporters)
self._client = KafkaClient(metrics=self._metrics,
metric_group_prefix='admin',
**self.config)
# Get auto-discovered version from client if necessary
if self.config['api_version'] is None:
self.config['api_version'] = self._client.config['api_version']
self._closed = False
self._refresh_controller_id()
log.debug("KafkaAdminClient started.")
def close(self):
"""Close the KafkaAdminClient connection to the Kafka broker."""
if not hasattr(self, '_closed') or self._closed:
log.info("KafkaAdminClient already closed.")
return
self._metrics.close()
self._client.close()
self._closed = True
log.debug("KafkaAdminClient is now closed.")
def _matching_api_version(self, operation):
"""Find the latest version of the protocol operation supported by both
this library and the broker.
This resolves to the lesser of either the latest api version this
library supports, or the max version supported by the broker.
:param operation: A list of protocol operation versions from kafka.protocol.
:return: The max matching version number between client and broker.
"""
version = min(len(operation) - 1,
self._client.get_api_versions()[operation[0].API_KEY][1])
if version < self._client.get_api_versions()[operation[0].API_KEY][0]:
# max library version is less than min broker version. Currently,
# no Kafka versions specify a min msg version. Maybe in the future?
raise IncompatibleBrokerVersion(
"No version of the '{}' Kafka protocol is supported by both the client and broker."
.format(operation.__name__))
return version
def _validate_timeout(self, timeout_ms):
"""Validate the timeout is set or use the configuration default.
:param timeout_ms: The timeout provided by api call, in milliseconds.
:return: The timeout to use for the operation.
"""
return timeout_ms or self.config['request_timeout_ms']
def _refresh_controller_id(self):
"""Determine the Kafka cluster controller."""
version = self._matching_api_version(MetadataRequest)
if 1 <= version <= 6:
request = MetadataRequest[version]()
response = self._send_request_to_node(self._client.least_loaded_node(), request)
controller_id = response.controller_id
# verify the controller is new enough to support our requests
controller_version = self._client.check_version(controller_id)
if controller_version < (0, 10, 0):
raise IncompatibleBrokerVersion(
"The controller appears to be running Kafka {}. KafkaAdminClient requires brokers >= 0.10.0.0."
.format(controller_version))
self._controller_id = controller_id
else:
raise UnrecognizedBrokerVersion(
"Kafka Admin interface cannot determine the controller using MetadataRequest_v{}."
.format(version))
def _find_group_coordinator_id(self, group_id):
"""Find the broker node_id of the coordinator of the given group.
Sends a FindCoordinatorRequest message to the cluster. Will block until
the FindCoordinatorResponse is received. Any errors are immediately
raised.
:param group_id: The consumer group ID. This is typically the group
name as a string.
:return: The node_id of the broker that is the coordinator.
"""
# Note: Java may change how this is implemented in KAFKA-6791.
#
# TODO add support for dynamically picking version of
# GroupCoordinatorRequest which was renamed to FindCoordinatorRequest.
# When I experimented with this, GroupCoordinatorResponse_v1 didn't
# match GroupCoordinatorResponse_v0 and I couldn't figure out why.
gc_request = GroupCoordinatorRequest[0](group_id)
gc_response = self._send_request_to_node(self._client.least_loaded_node(), gc_request)
# use the extra error checking in add_group_coordinator() rather than
# immediately returning the group coordinator.
success = self._client.cluster.add_group_coordinator(group_id, gc_response)
if not success:
error_type = Errors.for_code(gc_response.error_code)
assert error_type is not Errors.NoError
# Note: When error_type.retriable, Java will retry... see
# KafkaAdminClient's handleFindCoordinatorError method
raise error_type(
"Could not identify group coordinator for group_id '{}' from response '{}'."
.format(group_id, gc_response))
group_coordinator = self._client.cluster.coordinator_for_group(group_id)
# will be None if the coordinator was never populated, which should never happen here
assert group_coordinator is not None
# will be -1 if add_group_coordinator() failed... but by this point the
# error should have been raised.
assert group_coordinator != -1
return group_coordinator
def _send_request_to_node(self, node_id, request):
"""Send a Kafka protocol message to a specific broker.
Will block until the message result is received.
:param node_id: The broker id to which to send the message.
:param request: The message to send.
:return: The Kafka protocol response for the message.
:exception: The exception if the message could not be sent.
"""
while not self._client.ready(node_id):
# poll until the connection to broker is ready, otherwise send()
# will fail with NodeNotReadyError
self._client.poll()
future = self._client.send(node_id, request)
self._client.poll(future=future)
if future.succeeded():
return future.value
else:
raise future.exception # pylint: disable-msg=raising-bad-type
def _send_request_to_controller(self, request):
"""Send a Kafka protocol message to the cluster controller.
Will block until the message result is received.
:param request: The message to send.
:return: The Kafka protocol response for the message.
"""
tries = 2 # in case our cached self._controller_id is outdated
while tries:
tries -= 1
response = self._send_request_to_node(self._controller_id, request)
# In Java, the error fieldname is inconsistent:
# - CreateTopicsResponse / CreatePartitionsResponse uses topic_errors
# - DeleteTopicsResponse uses topic_error_codes
# So this is a little brittle in that it assumes all responses have
# one of these attributes and that they always unpack into
# (topic, error_code) tuples.
topic_error_tuples = (response.topic_errors if hasattr(response, 'topic_errors')
else response.topic_error_codes)
# Also small py2/py3 compatibility -- py3 can ignore extra values
# during unpack via: for x, y, *rest in list_of_values. py2 cannot.
# So for now we have to map across the list and explicitly drop any
# extra values (usually the error_message)
for topic, error_code in map(lambda e: e[:2], topic_error_tuples):
error_type = Errors.for_code(error_code)
if tries and error_type is NotControllerError:
# No need to inspect the rest of the errors for
# non-retriable errors because NotControllerError should
# either be thrown for all errors or no errors.
self._refresh_controller_id()
break
elif error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'."
.format(request, response))
else:
return response
raise RuntimeError("This should never happen, please file a bug with full stacktrace if encountered")
@staticmethod
def _convert_new_topic_request(new_topic):
return (
new_topic.name,
new_topic.num_partitions,
new_topic.replication_factor,
[
(partition_id, replicas) for partition_id, replicas in new_topic.replica_assignments.items()
],
[
(config_key, config_value) for config_key, config_value in new_topic.topic_configs.items()
]
)
def create_topics(self, new_topics, timeout_ms=None, validate_only=False):
"""Create new topics in the cluster.
:param new_topics: A list of NewTopic objects.
:param timeout_ms: Milliseconds to wait for new topics to be created
before the broker returns.
:param validate_only: If True, don't actually create new topics.
Not supported by all versions. Default: False
:return: Appropriate version of CreateTopicResponse class.
"""
version = self._matching_api_version(CreateTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version == 0:
if validate_only:
raise IncompatibleBrokerVersion(
"validate_only requires CreateTopicsRequest >= v1, which is not supported by Kafka {}."
.format(self.config['api_version']))
request = CreateTopicsRequest[version](
create_topic_requests=[self._convert_new_topic_request(new_topic) for new_topic in new_topics],
timeout=timeout_ms
)
elif version <= 2:
request = CreateTopicsRequest[version](
create_topic_requests=[self._convert_new_topic_request(new_topic) for new_topic in new_topics],
timeout=timeout_ms,
validate_only=validate_only
)
else:
raise NotImplementedError(
"Support for CreateTopics v{} has not yet been added to KafkaAdminClient."
.format(version))
# TODO convert structs to a more pythonic interface
# TODO raise exceptions if errors
return self._send_request_to_controller(request)
def delete_topics(self, topics, timeout_ms=None):
"""Delete topics from the cluster.
:param topics: A list of topic name strings.
:param timeout_ms: Milliseconds to wait for topics to be deleted
before the broker returns.
:return: Appropriate version of DeleteTopicsResponse class.
"""
version = self._matching_api_version(DeleteTopicsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version <= 1:
request = DeleteTopicsRequest[version](
topics=topics,
timeout=timeout_ms
)
response = self._send_request_to_controller(request)
else:
raise NotImplementedError(
"Support for DeleteTopics v{} has not yet been added to KafkaAdminClient."
.format(version))
return response
# list topics functionality is in ClusterMetadata
# Note: if implemented here, send the request to the least_loaded_node()
# describe topics functionality is in ClusterMetadata
# Note: if implemented here, send the request to the controller
# describe cluster functionality is in ClusterMetadata
# Note: if implemented here, send the request to the least_loaded_node()
# describe_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
# create_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
# delete_acls protocol not yet implemented
# Note: send the request to the least_loaded_node()
@staticmethod
def _convert_describe_config_resource_request(config_resource):
return (
config_resource.resource_type,
config_resource.name,
[
config_key for config_key, config_value in config_resource.configs.items()
] if config_resource.configs else None
)
def describe_configs(self, config_resources, include_synonyms=False):
"""Fetch configuration parameters for one or more Kafka resources.
:param config_resources: An list of ConfigResource objects.
Any keys in ConfigResource.configs dict will be used to filter the
result. Setting the configs dict to None will get all values. An
empty dict will get zero values (as per Kafka protocol).
:param include_synonyms: If True, return synonyms in response. Not
supported by all versions. Default: False.
:return: Appropriate version of DescribeConfigsResponse class.
"""
version = self._matching_api_version(DescribeConfigsRequest)
if version == 0:
if include_synonyms:
raise IncompatibleBrokerVersion(
"include_synonyms requires DescribeConfigsRequest >= v1, which is not supported by Kafka {}."
.format(self.config['api_version']))
request = DescribeConfigsRequest[version](
resources=[self._convert_describe_config_resource_request(config_resource) for config_resource in config_resources]
)
elif version == 1:
request = DescribeConfigsRequest[version](
resources=[self._convert_describe_config_resource_request(config_resource) for config_resource in config_resources],
include_synonyms=include_synonyms
)
else:
raise NotImplementedError(
"Support for DescribeConfigs v{} has not yet been added to KafkaAdminClient."
.format(version))
return self._send_request_to_node(self._client.least_loaded_node(), request)
@staticmethod
def _convert_alter_config_resource_request(config_resource):
return (
config_resource.resource_type,
config_resource.name,
[
(config_key, config_value) for config_key, config_value in config_resource.configs.items()
]
)
def alter_configs(self, config_resources):
"""Alter configuration parameters of one or more Kafka resources.
Warning:
This is currently broken for BROKER resources because those must be
sent to that specific broker, versus this always picks the
least-loaded node. See the comment in the source code for details.
We would happily accept a PR fixing this.
:param config_resources: A list of ConfigResource objects.
:return: Appropriate version of AlterConfigsResponse class.
"""
version = self._matching_api_version(AlterConfigsRequest)
if version == 0:
request = AlterConfigsRequest[version](
resources=[self._convert_alter_config_resource_request(config_resource) for config_resource in config_resources]
)
else:
raise NotImplementedError(
"Support for AlterConfigs v{} has not yet been added to KafkaAdminClient."
.format(version))
# TODO the Java client has the note:
# // We must make a separate AlterConfigs request for every BROKER resource we want to alter
# // and send the request to that specific broker. Other resources are grouped together into
# // a single request that may be sent to any broker.
#
# So this is currently broken as it always sends to the least_loaded_node()
return self._send_request_to_node(self._client.least_loaded_node(), request)
# alter replica logs dir protocol not yet implemented
# Note: have to lookup the broker with the replica assignment and send the request to that broker
# describe log dirs protocol not yet implemented
# Note: have to lookup the broker with the replica assignment and send the request to that broker
@staticmethod
def _convert_create_partitions_request(topic_name, new_partitions):
return (
topic_name,
(
new_partitions.total_count,
new_partitions.new_assignments
)
)
def create_partitions(self, topic_partitions, timeout_ms=None, validate_only=False):
"""Create additional partitions for an existing topic.
:param topic_partitions: A map of topic name strings to NewPartition objects.
:param timeout_ms: Milliseconds to wait for new partitions to be
created before the broker returns.
:param validate_only: If True, don't actually create new partitions.
Default: False
:return: Appropriate version of CreatePartitionsResponse class.
"""
version = self._matching_api_version(CreatePartitionsRequest)
timeout_ms = self._validate_timeout(timeout_ms)
if version == 0:
request = CreatePartitionsRequest[version](
topic_partitions=[self._convert_create_partitions_request(topic_name, new_partitions) for topic_name, new_partitions in topic_partitions.items()],
timeout=timeout_ms,
validate_only=validate_only
)
else:
raise NotImplementedError(
"Support for CreatePartitions v{} has not yet been added to KafkaAdminClient."
.format(version))
return self._send_request_to_controller(request)
# delete records protocol not yet implemented
# Note: send the request to the partition leaders
# create delegation token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# renew delegation token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# expire delegation_token protocol not yet implemented
# Note: send the request to the least_loaded_node()
# describe delegation_token protocol not yet implemented
# Note: send the request to the least_loaded_node()
def describe_consumer_groups(self, group_ids, group_coordinator_id=None):
"""Describe a set of consumer groups.
Any errors are immediately raised.
:param group_ids: A list of consumer group IDs. These are typically the
group names as strings.
:param group_coordinator_id: The node_id of the groups' coordinator
broker. If set to None, it will query the cluster for each group to
find that group's coordinator. Explicitly specifying this can be
useful for avoiding extra network round trips if you already know
the group coordinator. This is only useful when all the group_ids
have the same coordinator, otherwise it will error. Default: None.
:return: A list of group descriptions. For now the group descriptions
are the raw results from the DescribeGroupsResponse. Long-term, we
plan to change this to return namedtuples as well as decoding the
partition assignments.
"""
group_descriptions = []
version = self._matching_api_version(DescribeGroupsRequest)
for group_id in group_ids:
if group_coordinator_id is not None:
this_groups_coordinator_id = group_coordinator_id
else:
this_groups_coordinator_id = self._find_group_coordinator_id(group_id)
if version <= 1:
# Note: KAFKA-6788 A potential optimization is to group the
# request per coordinator and send one request with a list of
# all consumer groups. Java still hasn't implemented this
# because the error checking is hard to get right when some
# groups error and others don't.
request = DescribeGroupsRequest[version](groups=(group_id,))
response = self._send_request_to_node(this_groups_coordinator_id, request)
assert len(response.groups) == 1
# TODO need to implement converting the response tuple into
# a more accessible interface like a namedtuple and then stop
# hardcoding tuple indices here. Several Java examples,
# including KafkaAdminClient.java
group_description = response.groups[0]
error_code = group_description[0]
error_type = Errors.for_code(error_code)
# Java has the note: KAFKA-6789, we can retry based on the error code
if error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'."
.format(request, response))
# TODO Java checks the group protocol type, and if consumer
# (ConsumerProtocol.PROTOCOL_TYPE) or empty string, it decodes
# the members' partition assignments... that hasn't yet been
# implemented here so just return the raw struct results
group_descriptions.append(group_description)
else:
raise NotImplementedError(
"Support for DescribeGroups v{} has not yet been added to KafkaAdminClient."
.format(version))
return group_descriptions
def list_consumer_groups(self, broker_ids=None):
"""List all consumer groups known to the cluster.
This returns a list of Consumer Group tuples. The tuples are
composed of the consumer group name and the consumer group protocol
type.
Only consumer groups that store their offsets in Kafka are returned.
The protocol type will be an empty string for groups created using
Kafka < 0.9 APIs because, although they store their offsets in Kafka,
they don't use Kafka for group coordination. For groups created using
Kafka >= 0.9, the protocol type will typically be "consumer".
As soon as any error is encountered, it is immediately raised.
:param broker_ids: A list of broker node_ids to query for consumer
groups. If set to None, will query all brokers in the cluster.
Explicitly specifying broker(s) can be useful for determining which
consumer groups are coordinated by those broker(s). Default: None
:return list: List of tuples of Consumer Groups.
:exception GroupCoordinatorNotAvailableError: The coordinator is not
available, so cannot process requests.
:exception GroupLoadInProgressError: The coordinator is loading and
hence can't process requests.
"""
# While we return a list, internally use a set to prevent duplicates
# because if a group coordinator fails after being queried, and its
# consumer groups move to new brokers that haven't yet been queried,
# then the same group could be returned by multiple brokers.
consumer_groups = set()
if broker_ids is None:
broker_ids = [broker.nodeId for broker in self._client.cluster.brokers()]
version = self._matching_api_version(ListGroupsRequest)
if version <= 2:
request = ListGroupsRequest[version]()
for broker_id in broker_ids:
response = self._send_request_to_node(broker_id, request)
error_type = Errors.for_code(response.error_code)
if error_type is not Errors.NoError:
raise error_type(
"Request '{}' failed with response '{}'."
.format(request, response))
consumer_groups.update(response.groups)
else:
raise NotImplementedError(
"Support for ListGroups v{} has not yet been added to KafkaAdminClient."
.format(version))
return list(consumer_groups)
def list_consumer_group_offsets(self, group_id, group_coordinator_id=None,
partitions=None):
"""Fetch Consumer Group Offsets.
Note:
This does not verify that the group_id or partitions actually exist
in the cluster.
As soon as any error is encountered, it is immediately raised.
:param group_id: The consumer group id name for which to fetch offsets.
:param group_coordinator_id: The node_id of the group's coordinator
broker. If set to None, will query the cluster to find the group
coordinator. Explicitly specifying this can be useful to prevent
that extra network round trip if you already know the group
coordinator. Default: None.
:param partitions: A list of TopicPartitions for which to fetch
offsets. On brokers >= 0.10.2, this can be set to None to fetch all
known offsets for the consumer group. Default: None.
:return dictionary: A dictionary with TopicPartition keys and
OffsetAndMetada values. Partitions that are not specified and for
which the group_id does not have a recorded offset are omitted. An
offset value of `-1` indicates the group_id has no offset for that
TopicPartition. A `-1` can only happen for partitions that are
explicitly specified.
"""
group_offsets_listing = {}
if group_coordinator_id is None:
group_coordinator_id = self._find_group_coordinator_id(group_id)
version = self._matching_api_version(OffsetFetchRequest)
if version <= 3:
if partitions is None:
if version <= 1:
raise ValueError(
"""OffsetFetchRequest_v{} requires specifying the
partitions for which to fetch offsets. Omitting the
partitions is only supported on brokers >= 0.10.2.
For details, see KIP-88.""".format(version))
topics_partitions = None
else:
# transform from [TopicPartition("t1", 1), TopicPartition("t1", 2)] to [("t1", [1, 2])]
topics_partitions_dict = defaultdict(set)
for topic, partition in partitions:
topics_partitions_dict[topic].add(partition)
topics_partitions = list(six.iteritems(topics_partitions_dict))
request = OffsetFetchRequest[version](group_id, topics_partitions)
response = self._send_request_to_node(group_coordinator_id, request)
if version > 1: # OffsetFetchResponse_v1 lacks a top-level error_code
error_type = Errors.for_code(response.error_code)
if error_type is not Errors.NoError:
# optionally we could retry if error_type.retriable
raise error_type(
"Request '{}' failed with response '{}'."
.format(request, response))
# transform response into a dictionary with TopicPartition keys and
# OffsetAndMetada values--this is what the Java AdminClient returns
for topic, partitions in response.topics:
for partition, offset, metadata, error_code in partitions:
error_type = Errors.for_code(error_code)
if error_type is not Errors.NoError:
raise error_type(
"Unable to fetch offsets for group_id {}, topic {}, partition {}"
.format(group_id, topic, partition))
group_offsets_listing[TopicPartition(topic, partition)] = OffsetAndMetadata(offset, metadata)
else:
raise NotImplementedError(
"Support for OffsetFetch v{} has not yet been added to KafkaAdminClient."
.format(version))
return group_offsets_listing
class KafkaConsumer(six.Iterator):
"""Consume records from a Kafka cluster.
The consumer will transparently handle the failure of servers in the Kafka
cluster, and adapt as topic-partitions are created or migrate between
brokers. It also interacts with the assigned kafka Group Coordinator node
to allow multiple consumers to load balance consumption of topics (requires
kafka >= 0.9.0.0).
Arguments:
*topics (str): optional list of topics to subscribe to. If not set,
call subscribe() or assign() before consuming records.
Keyword Arguments:
bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
strings) that the consumer should contact to bootstrap initial
cluster metadata. This does not have to be the full node list.
It just needs to have at least one broker that will respond to a
Metadata API Request. Default port is 9092. If no servers are
specified, will default to localhost:9092.
client_id (str): a name for this client. This string is passed in
each request to servers and can be used to identify specific
server-side log entries that correspond to this client. Also
submitted to GroupCoordinator for logging with respect to
consumer group administration. Default: 'kafka-python-{version}'
group_id (str or None): name of the consumer group to join for dynamic
partition assignment (if enabled), and to use for fetching and
committing offsets. If None, auto-partition assignment (via
group coordinator) and offset commits are disabled.
Default: 'kafka-python-default-group'
key_deserializer (callable): Any callable that takes a
raw message key and returns a deserialized key.
value_deserializer (callable, optional): Any callable that takes a
raw message value and returns a deserialized value.
fetch_min_bytes (int): Minimum amount of data the server should
return for a fetch request, otherwise wait up to
fetch_max_wait_ms for more data to accumulate. Default: 1.
fetch_max_wait_ms (int): The maximum amount of time in milliseconds
the server will block before answering the fetch request if
there isn't sufficient data to immediately satisfy the
requirement given by fetch_min_bytes. Default: 500.
max_partition_fetch_bytes (int): The maximum amount of data
per-partition the server will return. The maximum total memory
used for a request = #partitions * max_partition_fetch_bytes.
This size must be at least as large as the maximum message size
the server allows or else it is possible for the producer to
send messages larger than the consumer can fetch. If that
happens, the consumer can get stuck trying to fetch a large
message on a certain partition. Default: 1048576.
request_timeout_ms (int): Client request timeout in milliseconds.
Default: 40000.
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
reconnect_backoff_ms (int): The amount of time in milliseconds to
wait before attempting to reconnect to a given host.
Default: 50.
max_in_flight_requests_per_connection (int): Requests are pipelined
to kafka brokers up to this number of maximum requests per
broker connection. Default: 5.
auto_offset_reset (str): A policy for resetting offsets on
OffsetOutOfRange errors: 'earliest' will move to the oldest
available message, 'latest' will move to the most recent. Any
ofther value will raise the exception. Default: 'latest'.
enable_auto_commit (bool): If true the consumer's offset will be
periodically committed in the background. Default: True.
auto_commit_interval_ms (int): milliseconds between automatic
offset commits, if enable_auto_commit is True. Default: 5000.
default_offset_commit_callback (callable): called as
callback(offsets, response) response will be either an Exception
or a OffsetCommitResponse struct. This callback can be used to
trigger custom actions when a commit request completes.
check_crcs (bool): Automatically check the CRC32 of the records
consumed. This ensures no on-the-wire or on-disk corruption to
the messages occurred. This check adds some overhead, so it may
be disabled in cases seeking extreme performance. Default: True
metadata_max_age_ms (int): The period of time in milliseconds after
which we force a refresh of metadata even if we haven't seen any
partition leadership changes to proactively discover any new
brokers or partitions. Default: 300000
partition_assignment_strategy (list): List of objects to use to
distribute partition ownership amongst consumer instances when
group management is used. Default: [RoundRobinPartitionAssignor]
heartbeat_interval_ms (int): The expected time in milliseconds
between heartbeats to the consumer coordinator when using
Kafka's group management feature. Heartbeats are used to ensure
that the consumer's session stays active and to facilitate
rebalancing when new consumers join or leave the group. The
value must be set lower than session_timeout_ms, but typically
should be set no higher than 1/3 of that value. It can be
adjusted even lower to control the expected time for normal
rebalances. Default: 3000
session_timeout_ms (int): The timeout used to detect failures when
using Kafka's group managementment facilities. Default: 30000
send_buffer_bytes (int): The size of the TCP send buffer
(SO_SNDBUF) to use when sending data. Default: 131072
receive_buffer_bytes (int): The size of the TCP receive buffer
(SO_RCVBUF) to use when reading data. Default: 32768
consumer_timeout_ms (int): number of millisecond to throw a timeout
exception to the consumer if no message is available for
consumption. Default: -1 (dont throw exception)
api_version (str): specify which kafka API version to use.
0.9 enables full group coordination features; 0.8.2 enables
kafka-storage offset commits; 0.8.1 enables zookeeper-storage
offset commits; 0.8.0 is what is left. If set to 'auto', will
attempt to infer the broker version by probing various APIs.
Default: auto
Note:
Configuration parameters are described in more detail at
https://kafka.apache.org/090/configuration.html#newconsumerconfigs
"""
DEFAULT_CONFIG = {
'bootstrap_servers': 'localhost',
'client_id': 'kafka-python-' + __version__,
'group_id': 'kafka-python-default-group',
'key_deserializer': None,
'value_deserializer': None,
'fetch_max_wait_ms': 500,
'fetch_min_bytes': 1,
'max_partition_fetch_bytes': 1 * 1024 * 1024,
'request_timeout_ms': 40 * 1000,
'retry_backoff_ms': 100,
'reconnect_backoff_ms': 50,
'max_in_flight_requests_per_connection': 5,
'auto_offset_reset': 'latest',
'enable_auto_commit': True,
'auto_commit_interval_ms': 5000,
'check_crcs': True,
'metadata_max_age_ms': 5 * 60 * 1000,
'partition_assignment_strategy': (RoundRobinPartitionAssignor, ),
'heartbeat_interval_ms': 3000,
'session_timeout_ms': 30000,
'send_buffer_bytes': 128 * 1024,
'receive_buffer_bytes': 32 * 1024,
'consumer_timeout_ms': -1,
'api_version': 'auto',
'connections_max_idle_ms': 9 * 60 * 1000, # not implemented yet
#'metric_reporters': None,
#'metrics_num_samples': 2,
#'metrics_sample_window_ms': 30000,
}
def __init__(self, *topics, **configs):
self.config = copy.copy(self.DEFAULT_CONFIG)
for key in self.config:
if key in configs:
self.config[key] = configs.pop(key)
# Only check for extra config keys in top-level class
assert not configs, 'Unrecognized configs: %s' % configs
deprecated = {'smallest': 'earliest', 'largest': 'latest'}
if self.config['auto_offset_reset'] in deprecated:
new_config = deprecated[self.config['auto_offset_reset']]
log.warning('use auto_offset_reset=%s (%s is deprecated)',
new_config, self.config['auto_offset_reset'])
self.config['auto_offset_reset'] = new_config
self._client = KafkaClient(**self.config)
# Check Broker Version if not set explicitly
if self.config['api_version'] == 'auto':
self.config['api_version'] = self._client.check_version()
assert self.config['api_version'] in (
'0.9', '0.8.2', '0.8.1', '0.8.0'), 'Unrecognized api version'
# Convert api_version config to tuple for easy comparisons
self.config['api_version'] = tuple(
map(int, self.config['api_version'].split('.')))
self._subscription = SubscriptionState(
self.config['auto_offset_reset'])
self._fetcher = Fetcher(self._client, self._subscription,
**self.config)
self._coordinator = ConsumerCoordinator(
self._client,
self._subscription,
assignors=self.config['partition_assignment_strategy'],
**self.config)
self._closed = False
self._iterator = None
self._consumer_timeout = float('inf')
#self.metrics = None
if topics:
self._subscription.subscribe(topics=topics)
self._client.set_topics(topics)
def assign(self, partitions):
"""Manually assign a list of TopicPartitions to this consumer.
Arguments:
partitions (list of TopicPartition): assignment for this instance.
Raises:
IllegalStateError: if consumer has already called subscribe()
Warning:
It is not possible to use both manual partition assignment with
assign() and group assignment with subscribe().
Note:
This interface does not support incremental assignment and will
replace the previous assignment (if there was one).
Note:
Manual topic assignment through this method does not use the
consumer's group management functionality. As such, there will be
no rebalance operation triggered when group membership or cluster
and topic metadata change.
"""
self._subscription.assign_from_user(partitions)
self._client.set_topics([tp.topic for tp in partitions])
def assignment(self):
"""Get the TopicPartitions currently assigned to this consumer.
If partitions were directly assigned using assign(), then this will
simply return the same partitions that were previously assigned.
If topics were subscribed using subscribe(), then this will give the
set of topic partitions currently assigned to the consumer (which may
be none if the assignment hasn't happened yet, or if the partitions are
in the process of being reassigned).
Returns:
set: {TopicPartition, ...}
"""
return self._subscription.assigned_partitions()
def close(self):
"""Close the consumer, waiting indefinitely for any needed cleanup."""
if self._closed:
return
log.debug("Closing the KafkaConsumer.")
self._closed = True
self._coordinator.close()
#self.metrics.close()
self._client.close()
try:
self.config['key_deserializer'].close()
except AttributeError:
pass
try:
self.config['value_deserializer'].close()
except AttributeError:
pass
log.debug("The KafkaConsumer has closed.")
def commit_async(self, offsets=None, callback=None):
"""Commit offsets to kafka asynchronously, optionally firing callback
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used.
This is an asynchronous call and will not block. Any errors encountered
are either passed to the callback (if provided) or discarded.
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
callback (callable, optional): called as callback(offsets, response)
with response as either an Exception or a OffsetCommitResponse
struct. This callback can be used to trigger custom actions when
a commit request completes.
Returns:
kafka.future.Future
"""
assert self.config['api_version'] >= (0, 8,
1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
log.debug("Committing offsets: %s", offsets)
future = self._coordinator.commit_offsets_async(offsets,
callback=callback)
return future
def commit(self, offsets=None):
"""Commit offsets to kafka, blocking until success or error
This commits offsets only to Kafka. The offsets committed using this API
will be used on the first fetch after every rebalance and also on
startup. As such, if you need to store offsets in anything other than
Kafka, this API should not be used.
Blocks until either the commit succeeds or an unrecoverable error is
encountered (in which case it is thrown to the caller).
Currently only supports kafka-topic offset storage (not zookeeper)
Arguments:
offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict
to commit with the configured group_id. Defaults to current
consumed offsets for all subscribed partitions.
"""
assert self.config['api_version'] >= (0, 8,
1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if offsets is None:
offsets = self._subscription.all_consumed_offsets()
self._coordinator.commit_offsets_sync(offsets)
def committed(self, partition):
"""Get the last committed offset for the given partition
This offset will be used as the position for the consumer
in the event of a failure.
This call may block to do a remote call if the partition in question
isn't assigned to this consumer or if the consumer hasn't yet
initialized its cache of committed offsets.
Arguments:
partition (TopicPartition): the partition to check
Returns:
The last committed offset, or None if there was no prior commit.
"""
assert self.config['api_version'] >= (0, 8,
1), 'Requires >= Kafka 0.8.1'
assert self.config['group_id'] is not None, 'Requires group_id'
if self._subscription.is_assigned(partition):
committed = self._subscription.assignment[partition].committed
if committed is None:
self._coordinator.refresh_committed_offsets_if_needed()
committed = self._subscription.assignment[partition].committed
else:
commit_map = self._coordinator.fetch_committed_offsets([partition])
if partition in commit_map:
committed = commit_map[partition].offset
else:
committed = None
return committed
def topics(self):
"""Get all topics the user is authorized to view.
Returns:
set: topics
"""
cluster = self._client.cluster
if self._client._metadata_refresh_in_progress and self._client._topics:
future = cluster.request_update()
self._client.poll(future=future)
stash = cluster.need_all_topic_metadata
cluster.need_all_topic_metadata = True
future = cluster.request_update()
self._client.poll(future=future)
cluster.need_all_topic_metadata = stash
return cluster.topics()
def partitions_for_topic(self, topic):
"""Get metadata about the partitions for a given topic.
Arguments:
topic (str): topic to check
Returns:
set: partition ids
"""
return self._client.cluster.partitions_for_topic(topic)
def poll(self, timeout_ms=0):
"""Fetch data from assigned topics / partitions.
Records are fetched and returned in batches by topic-partition.
On each poll, consumer will try to use the last consumed offset as the
starting offset and fetch sequentially. The last consumed offset can be
manually set through seek(partition, offset) or automatically set as
the last committed offset for the subscribed list of partitions.
Incompatible with iterator interface -- use one or the other, not both.
Arguments:
timeout_ms (int, optional): milliseconds to spend waiting in poll if
data is not available. If 0, returns immediately with any
records that are available now. Must not be negative. Default: 0
Returns:
dict: topic to list of records since the last fetch for the
subscribed list of topics and partitions
"""
assert timeout_ms >= 0, 'Timeout must not be negative'
assert self._iterator is None, 'Incompatible with iterator interface'
# poll for new data until the timeout expires
start = time.time()
remaining = timeout_ms
while True:
records = self._poll_once(remaining)
if records:
# before returning the fetched records, we can send off the
# next round of fetches and avoid block waiting for their
# responses to enable pipelining while the user is handling the
# fetched records.
self._fetcher.init_fetches()
return records
elapsed_ms = (time.time() - start) * 1000
remaining = timeout_ms - elapsed_ms
if remaining <= 0:
return {}
def _poll_once(self, timeout_ms):
"""
Do one round of polling. In addition to checking for new data, this does
any needed heart-beating, auto-commits, and offset updates.
Arguments:
timeout_ms (int): The maximum time in milliseconds to block
Returns:
dict: map of topic to list of records (may be empty)
"""
if self.config['group_id'] is not None:
if self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
if self.config['api_version'] >= (0, 9):
# ensure we have partitions assigned if we expect to
if self._subscription.partitions_auto_assigned():
self._coordinator.ensure_active_group()
# fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
self._update_fetch_positions(
self._subscription.missing_fetch_positions())
# init any new fetches (won't resend pending fetches)
records = self._fetcher.fetched_records()
# if data is available already, e.g. from a previous network client
# poll() call to commit, then just return it immediately
if records:
return records
self._fetcher.init_fetches()
self._client.poll(timeout_ms)
return self._fetcher.fetched_records()
def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): partition to check
Returns:
int: offset
"""
assert self._subscription.is_assigned(
partition), 'Partition is not assigned'
offset = self._subscription.assignment[partition].position
if offset is None:
self._update_fetch_positions(partition)
offset = self._subscription.assignment[partition].position
return offset
def highwater(self, partition):
"""Last known highwater offset for a partition
A highwater offset is the offset that will be assigned to the next
message that is produced. It may be useful for calculating lag, by
comparing with the reported position. Note that both position and
highwater refer to the *next* offset -- i.e., highwater offset is
one greater than the newest availabel message.
Highwater offsets are returned in FetchResponse messages, so will
not be available if not FetchRequests have been sent for this partition
yet.
Arguments:
partition (TopicPartition): partition to check
Returns:
int or None: offset if available
"""
assert self._subscription.is_assigned(
partition), 'Partition is not assigned'
return self._subscription.assignment[partition].highwater
def pause(self, *partitions):
"""Suspend fetching from the requested partitions.
Future calls to poll() will not return any records from these partitions
until they have been resumed using resume(). Note that this method does
not affect partition subscription. In particular, it does not cause a
group rebalance when automatic assignment is used.
Arguments:
*partitions (TopicPartition): partitions to pause
"""
for partition in partitions:
log.debug("Pausing partition %s", partition)
self._subscription.pause(partition)
def resume(self, *partitions):
"""Resume fetching from the specified (paused) partitions.
Arguments:
*partitions (TopicPartition): partitions to resume
"""
for partition in partitions:
log.debug("Resuming partition %s", partition)
self._subscription.resume(partition)
def seek(self, partition, offset):
"""Manually specify the fetch offset for a TopicPartition.
Overrides the fetch offsets that the consumer will use on the next
poll(). If this API is invoked for the same partition more than once,
the latest offset will be used on the next poll(). Note that you may
lose data if this API is arbitrarily used in the middle of consumption,
to reset the fetch offsets.
Arguments:
partition (TopicPartition): partition for seek operation
offset (int): message offset in partition
Raises:
AssertionError: if offset is not an int >= 0; or if partition is not
currently assigned.
"""
assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0'
assert partition in self._subscription.assigned_partitions(
), 'Unassigned partition'
log.debug("Seeking to offset %s for partition %s", offset, partition)
self._subscription.assignment[partition].seek(offset)
def seek_to_beginning(self, *partitions):
"""Seek to the oldest available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
Raises:
AssertionError: if any partition is not currently assigned, or if
no partitions are assigned
"""
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(
), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to beginning of partition %s", tp)
self._subscription.need_offset_reset(tp,
OffsetResetStrategy.EARLIEST)
def seek_to_end(self, *partitions):
"""Seek to the most recent available offset for partitions.
Arguments:
*partitions: optionally provide specific TopicPartitions, otherwise
default to all assigned partitions
Raises:
AssertionError: if any partition is not currently assigned, or if
no partitions are assigned
"""
if not partitions:
partitions = self._subscription.assigned_partitions()
assert partitions, 'No partitions are currently assigned'
else:
for p in partitions:
assert p in self._subscription.assigned_partitions(
), 'Unassigned partition'
for tp in partitions:
log.debug("Seeking to end of partition %s", tp)
self._subscription.need_offset_reset(tp,
OffsetResetStrategy.LATEST)
def subscribe(self, topics=(), pattern=None, listener=None):
"""Subscribe to a list of topics, or a topic regex pattern
Partitions will be dynamically assigned via a group coordinator.
Topic subscriptions are not incremental: this list will replace the
current assignment (if there is one).
This method is incompatible with assign()
Arguments:
topics (list): List of topics for subscription.
pattern (str): Pattern to match available topics. You must provide
either topics or pattern, but not both.
listener (ConsumerRebalanceListener): Optionally include listener
callback, which will be called before and after each rebalance
operation.
As part of group management, the consumer will keep track of the
list of consumers that belong to a particular group and will
trigger a rebalance operation if one of the following events
trigger:
* Number of partitions change for any of the subscribed topics
* Topic is created or deleted
* An existing member of the consumer group dies
* A new member is added to the consumer group
When any of these events are triggered, the provided listener
will be invoked first to indicate that the consumer's assignment
has been revoked, and then again when the new assignment has
been received. Note that this listener will immediately override
any listener set in a previous call to subscribe. It is
guaranteed, however, that the partitions revoked/assigned
through this interface are from topics subscribed in this call.
Raises:
IllegalStateError: if called after previously calling assign()
AssertionError: if neither topics or pattern is provided
TypeError: if listener is not a ConsumerRebalanceListener
"""
# SubscriptionState handles error checking
self._subscription.subscribe(topics=topics,
pattern=pattern,
listener=listener)
# regex will need all topic metadata
if pattern is not None:
self._client.cluster.need_all_topic_metadata = True
self._client.set_topics([])
log.debug("Subscribed to topic pattern: %s", pattern)
else:
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics(self._subscription.group_subscription())
log.debug("Subscribed to topic(s): %s", topics)
def subscription(self):
"""Get the current topic subscription.
Returns:
set: {topic, ...}
"""
return self._subscription.subscription
def unsubscribe(self):
"""Unsubscribe from all topics and clear all assigned partitions."""
self._subscription.unsubscribe()
self._coordinator.close()
self._client.cluster.need_all_topic_metadata = False
self._client.set_topics([])
log.debug(
"Unsubscribed all topics or patterns and assigned partitions")
def _update_fetch_positions(self, partitions):
"""
Set the fetch position to the committed position (if there is one)
or reset it using the offset reset policy the user has configured.
Arguments:
partitions (List[TopicPartition]): The partitions that need
updating fetch positions
Raises:
NoOffsetForPartitionError: If no offset is stored for a given
partition and no offset reset policy is defined
"""
if (self.config['api_version'] >= (0, 8, 1)
and self.config['group_id'] is not None):
# refresh commits for all assigned partitions
self._coordinator.refresh_committed_offsets_if_needed()
# then do any offset lookups in case some positions are not known
self._fetcher.update_fetch_positions(partitions)
def _message_generator(self):
assert self.assignment() or self.subscription(
) is not None, 'No topic subscription or manual partition assignment'
while time.time() < self._consumer_timeout:
if self.config['group_id'] is not None:
if self.config['api_version'] >= (0, 8, 2):
self._coordinator.ensure_coordinator_known()
if self.config['api_version'] >= (0, 9):
# ensure we have partitions assigned if we expect to
if self._subscription.partitions_auto_assigned():
self._coordinator.ensure_active_group()
# fetch positions if we have partitions we're subscribed to that we
# don't know the offset for
if not self._subscription.has_all_fetch_positions():
partitions = self._subscription.missing_fetch_positions()
self._update_fetch_positions(partitions)
poll_ms = 1000 * (self._consumer_timeout - time.time())
if not self._fetcher.in_flight_fetches():
poll_ms = 0
self._client.poll(poll_ms)
# We need to make sure we at least keep up with scheduled tasks,
# like heartbeats, auto-commits, and metadata refreshes
timeout_at = self._next_timeout()
if self.config['api_version'] >= (0, 9):
if self.config['group_id'] is not None and not self.assignment(
):
sleep_time = max(timeout_at - time.time(), 0)
if sleep_time > 0 and not self._client.in_flight_request_count(
):
log.debug('No partitions assigned; sleeping for %s',
sleep_time)
time.sleep(sleep_time)
continue
if time.time() > timeout_at:
continue
for msg in self._fetcher:
yield msg
if time.time() > timeout_at:
log.debug("internal iterator timeout - breaking for poll")
break
# an else block on a for loop only executes if there was no break
# so this should only be called on a StopIteration from the fetcher
# and we assume that it is safe to init_fetches when fetcher is done
# i.e., there are no more records stored internally
else:
self._fetcher.init_fetches()
def _next_timeout(self):
return min(self._consumer_timeout,
self._client._delayed_tasks.next_at() + time.time(),
self._client.cluster.ttl() / 1000.0 + time.time())
def __iter__(self): # pylint: disable=non-iterator-returned
return self
def __next__(self):
if not self._iterator:
self._iterator = self._message_generator()
self._set_consumer_timeout()
try:
return next(self._iterator)
except StopIteration:
self._iterator = None
raise
def _set_consumer_timeout(self):
# consumer_timeout_ms can be used to stop iteration early
if self.config['consumer_timeout_ms'] >= 0:
self._consumer_timeout = time.time() + (
self.config['consumer_timeout_ms'] / 1000.0)
# old KafkaConsumer methods are deprecated
def configure(self, **configs):
raise NotImplementedError('deprecated -- initialize a new consumer')
def set_topic_partitions(self, *topics):
raise NotImplementedError('deprecated -- use subscribe() or assign()')
def fetch_messages(self):
raise NotImplementedError(
'deprecated -- use poll() or iterator interface')
def get_partition_offsets(self, topic, partition, request_time_ms,
max_num_offsets):
raise NotImplementedError(
'deprecated -- send an OffsetRequest with KafkaClient')
def offsets(self, group=None):
raise NotImplementedError('deprecated -- use committed(partition)')
def task_done(self, message):
raise NotImplementedError(
'deprecated -- commit offsets manually if needed')
class KafkaConsumerGroups:
kafka_brokers = None
client = None
timeout = None
security_protocol = None
sasl_mechanism = None
sasl_plain_username = None
sasl_plain_password = None
ssl_certfile = None
ssl_keyfile = None
ssl_context = None
def __init__(self,
kafka_brokers,
security_protocol,
sasl_mechanism,
sasl_plain_username,
sasl_plain_password,
ssl_context,
timeout=5000):
self.kafka_brokers = kafka_brokers
self.security_protocol = security_protocol
self.sasl_mechanism = sasl_mechanism
self.sasl_plain_username = sasl_plain_username
self.sasl_plain_password = sasl_plain_password
self.ssl_context = ssl_context
self.timeout = timeout
self.client = KafkaClient(bootstrap_servers=kafka_brokers,
security_protocol=security_protocol,
sasl_mechanism=sasl_mechanism,
sasl_plain_username=sasl_plain_username,
sasl_plain_password=sasl_plain_password,
ssl_context=ssl_context,
timeout=timeout)
self.lag_topics_found = []
self.lag_total = 0
def list(self):
list_groups_request = admin.ListGroupsRequest_v0(timeout=self.timeout)
kafka_broker_ids = [
broker.nodeId for broker in self.client.cluster.brokers()
]
consumers_grp = {}
for broker_id in kafka_broker_ids:
current_of_tries = 0
max_of_tries = 5
data_from_node = False
while not data_from_node and current_of_tries <= max_of_tries:
future = self.client.send(broker_id, list_groups_request)
self.client.poll(timeout_ms=self.timeout, future=future)
if future.value is not None:
result = future.value.groups
for i in result:
consumers_grp.update({i[0]: broker_id})
data_from_node = True
else:
current_of_tries += 1
time.sleep(0.5)
return consumers_grp
def get_members(self, node_id, group_name):
describe_groups_request = admin.DescribeGroupsRequest_v0(
groups=[(group_name)])
future = self.client.send(node_id, describe_groups_request)
self.client.poll(timeout_ms=self.timeout, future=future)
(error_code, group_id, state, protocol_type, protocol,
members) = future.value.groups[0]
if error_code != 0:
print(
"Kafka API - RET admin.DescribeGroupsRequest, error_code={}, group_id={}, state={}, protocol_type={}, protocol={}, members_count={}"
.format(error_code, group_id, state, protocol_type, protocol,
len(members)))
exit(1)
lmembers = []
for member in members:
(member_id, client_id, client_host, member_metadata,
member_assignment) = member
lmembers.append({
'member_id': member_id,
'client_id': client_id,
'client_host': client_host
})
return lmembers
def describe(self, node_id, group_name):
describe_groups_request = admin.DescribeGroupsRequest_v0(
groups=[(group_name)])
future = self.client.send(node_id, describe_groups_request)
self.client.poll(timeout_ms=self.timeout, future=future)
(error_code, group_id, state, protocol_type, protocol,
members) = future.value.groups[0]
if error_code != 0:
print(
"Kafka API - RET admin.DescribeGroupsRequest, error_code={}, group_id={}, state={}, protocol_type={}, protocol={}, members_count={}"
.format(error_code, group_id, state, protocol_type, protocol,
len(members)))
exit(1)
metadata_consumer_group = {
'id': group_name,
'state': state,
'topics': [],
'lag': 0,
'members': []
}
if len(members) != 0:
for member in members:
(member_id, client_id, client_host, member_metadata,
member_assignment) = member
member_topics_assignment = []
for (topic, partitions) in MemberAssignment.decode(
member_assignment).assignment:
member_topics_assignment.append(topic)
metadata_consumer_group['members'].append({
'member_id':
member_id,
'client_id':
client_id,
'client_host':
client_host,
'topic':
member_topics_assignment
})
metadata_consumer_group['topics'] += member_topics_assignment
(lag_total, topics_found) = self.get_lag_by_topic_list(
group_name, topics=metadata_consumer_group['topics'])
metadata_consumer_group[
'lag'] = metadata_consumer_group['lag'] + lag_total
else:
all_topics = self.client.cluster.topics()
while '__consumer_offsets' in all_topics:
all_topics.remove('__consumer_offsets')
(lag_total,
topics_found) = self.get_lag_by_topic_list(group_name,
topics=all_topics)
metadata_consumer_group[
'lag'] = metadata_consumer_group['lag'] + lag_total
metadata_consumer_group['topics'] = topics_found
return metadata_consumer_group
def get_lag_by_topic_list(self, group_name, topics):
self.lag_topics_found = []
self.lag_total = 0
topics = list(topics)
no_threads = 16
batches = [
topics[i:i + no_threads] for i in range(0, len(topics), no_threads)
]
for batch_topics in batches:
threads = []
for topic in batch_topics:
t = threading.Thread(target=self.get_lag_by_topic,
args=(
group_name,
topic,
))
threads.append(t)
t.start()
for t in threads:
if t.isAlive():
t.join()
return self.lag_total, list(set(self.lag_topics_found))
def get_lag_by_topic(self, group_name, topic):
consumer = KafkaConsumer(bootstrap_servers=self.kafka_brokers,
group_id=group_name,
security_protocol=self.security_protocol,
sasl_mechanism=self.sasl_mechanism,
sasl_plain_username=self.sasl_plain_username,
sasl_plain_password=self.sasl_plain_password,
ssl_context=self.ssl_context)
partitions_per_topic = consumer.partitions_for_topic(topic)
for partition in partitions_per_topic:
tp = TopicPartition(topic, partition)
consumer.assign([tp])
committed = consumer.committed(tp)
consumer.seek_to_end(tp)
last_offset = consumer.position(tp)
if committed is not None and int(
committed) and last_offset is not None and int(
last_offset):
self.lag_topics_found.append(topic)
self.lag_total += (last_offset - committed)
consumer.close(autocommit=False)
return