def _on_change_subscription(self):
"""This is `group rebalanced` signal handler for update fetch positions
of assigned partitions"""
# 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():
ensure_future(self._update_fetch_positions(
self._subscription.missing_fetch_positions()),
loop=self._loop)
def _on_change_subscription(self):
"""This is `group rebalanced` signal handler for update fetch positions
of assigned partitions"""
# 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():
ensure_future(self._update_fetch_positions(
self._subscription.missing_fetch_positions()),
loop=self._loop)
def connect(self):
loop = self._loop
self._closed_fut = asyncio.Future(loop=loop)
if self._secutity_protocol == "PLAINTEXT":
ssl = None
else:
assert self._secutity_protocol == "SSL"
assert self._ssl_context is not None
ssl = self._ssl_context
# Create streams same as `open_connection`, but using custom protocol
reader = asyncio.StreamReader(limit=READER_LIMIT, loop=loop)
protocol = AIOKafkaProtocol(self._closed_fut, reader, loop=loop)
transport, _ = yield from asyncio.wait_for(
loop.create_connection(lambda: protocol,
self.host,
self.port,
ssl=ssl),
loop=loop,
timeout=self._request_timeout)
writer = asyncio.StreamWriter(transport, protocol, reader, loop)
self._reader, self._writer, self._protocol = reader, writer, protocol
# Start reader task.
self._read_task = ensure_future(self._read(), loop=loop)
# Start idle checker
if self._max_idle_ms is not None:
self._idle_handle = self._loop.call_soon(self._idle_check)
return reader, writer
def bootstrap(self):
"""Try to to bootstrap initial cluster metadata"""
# using request v0 for bootstap (bcs api version is not detected yet)
metadata_request = MetadataRequest[0]([])
for host, port, _ in self.hosts:
log.debug("Attempting to bootstrap via node at %s:%s", host, port)
try:
bootstrap_conn = yield from create_conn(
host,
port,
loop=self._loop,
client_id=self._client_id,
request_timeout_ms=self._request_timeout_ms,
ssl_context=self._ssl_context,
security_protocol=self._security_protocol,
max_idle_ms=self._connections_max_idle_ms)
except (OSError, asyncio.TimeoutError) as err:
log.error('Unable connect to "%s:%s": %s', host, port, err)
continue
try:
metadata = yield from bootstrap_conn.send(metadata_request)
except KafkaError as err:
log.warning('Unable to request metadata from "%s:%s": %s',
host, port, err)
bootstrap_conn.close()
continue
self.cluster.update_metadata(metadata)
# A cluster with no topics can return no broker metadata...
# In that case, we should keep the bootstrap connection till
# we get a normal cluster layout.
if not len(self.cluster.brokers()):
self._conns['bootstrap'] = bootstrap_conn
else:
bootstrap_conn.close()
log.debug('Received cluster metadata: %s', self.cluster)
break
else:
raise ConnectionError('Unable to bootstrap from {}'.format(
self.hosts))
# detect api version if need
if self._api_version == 'auto':
self._api_version = yield from self.check_version()
if type(self._api_version) is not tuple:
self._api_version = tuple(map(int, self._api_version.split('.')))
if self._sync_task is None:
# starting metadata synchronizer task
self._sync_task = ensure_future(self._md_synchronizer(),
loop=self._loop)
def start(self):
"""Connect to Kafka cluster and check server version"""
log.debug("Starting the Kafka producer") # trace
yield from self.client.bootstrap()
if self._compression_type == 'lz4':
assert self.client.api_version >= (0, 8, 2), \
'LZ4 Requires >= Kafka 0.8.2 Brokers'
self._sender_task = ensure_future(self._sender_routine(),
loop=self._loop)
self._message_accumulator.set_api_version(self.client.api_version)
log.debug("Kafka producer started")
def bootstrap(self):
"""Try to to bootstrap initial cluster metadata"""
metadata_request = MetadataRequest([])
for host, port, _ in self.hosts:
log.debug("Attempting to bootstrap via node at %s:%s", host, port)
try:
bootstrap_conn = yield from create_conn(
host,
port,
loop=self._loop,
client_id=self._client_id,
request_timeout_ms=self._request_timeout_ms)
except (OSError, asyncio.TimeoutError) as err:
log.error('Unable connect to "%s:%s": %s', host, port, err)
continue
try:
metadata = yield from bootstrap_conn.send(metadata_request)
except KafkaError as err:
log.warning('Unable to request metadata from "%s:%s": %s',
host, port, err)
bootstrap_conn.close()
continue
self.cluster.update_metadata(metadata)
# A cluster with no topics can return no broker metadata
# in that case, we should keep the bootstrap connection
if not len(self.cluster.brokers()):
self._conns['bootstrap'] = bootstrap_conn
else:
bootstrap_conn.close()
log.debug('Received cluster metadata: %s', self.cluster)
break
else:
raise ConnectionError('Unable to bootstrap from {}'.format(
self.hosts))
if self._sync_task is None:
# starting metadata synchronizer task
self._sync_task = ensure_future(self._md_synchronizer(),
loop=self._loop)
def start(self):
"""Connect to Kafka cluster and check server version"""
log.debug("Starting the Kafka producer") # trace
yield from self.client.bootstrap()
# Check Broker Version if not set explicitly
if self._api_version == 'auto':
self._api_version = yield from self.client.check_version()
# Convert api_version config to tuple for easy comparisons
self._api_version = tuple(map(int, self._api_version.split('.')))
if self._compression_type == 'lz4':
assert self._api_version >= (0, 8, 2), \
'LZ4 Requires >= Kafka 0.8.2 Brokers'
self._sender_task = ensure_future(self._sender_routine(),
loop=self._loop)
log.debug("Kafka producer started")
def start(self):
"""Connect to Kafka cluster and check server version"""
log.debug("Starting the Kafka producer") # trace
yield from self.client.bootstrap()
# Check Broker Version if not set explicitly
if self._api_version == 'auto':
self._api_version = yield from self.client.check_version()
# Convert api_version config to tuple for easy comparisons
self._api_version = tuple(
map(int, self._api_version.split('.')))
if self._compression_type == 'lz4':
assert self._api_version >= (0, 8, 2), \
'LZ4 Requires >= Kafka 0.8.2 Brokers'
self._sender_task = ensure_future(
self._sender_routine(), loop=self._loop)
log.debug("Kafka producer started")
def _sender_routine(self):
"""backgroud task that sends message batches to Kafka brokers"""
tasks = set()
try:
while True:
batches, unknown_leaders_exist = \
self._message_accumulator.drain_by_nodes(
ignore_nodes=self._in_flight)
# create produce task for every batch
for node_id, batches in batches.items():
task = ensure_future(
self._send_produce_req(node_id, batches),
loop=self._loop)
tasks.add(task)
if unknown_leaders_exist:
# we have at least one unknown partition's leader,
# try to update cluster metadata and wait backoff time
self.client.force_metadata_update()
# Just to have at least 1 future in wait() call
fut = asyncio.sleep(self._retry_backoff, loop=self._loop)
waiters = tasks.union([fut])
else:
fut = self._message_accumulator.data_waiter()
waiters = tasks.union([fut])
# wait when:
# * At least one of produce task is finished
# * Data for new partition arrived
# * Metadata update if partition leader unknown
done, _ = yield from asyncio.wait(
waiters,
return_when=asyncio.FIRST_COMPLETED,
loop=self._loop)
tasks -= done
except asyncio.CancelledError:
pass
except Exception: # pragma: no cover
log.error("Unexpected error in sender routine", exc_info=True)
def bootstrap(self):
"""Try to to bootstrap initial cluster metadata"""
metadata_request = MetadataRequest([])
for host, port, _ in self.hosts:
log.debug("Attempting to bootstrap via node at %s:%s", host, port)
try:
bootstrap_conn = yield from create_conn(
host, port, loop=self._loop, client_id=self._client_id,
request_timeout_ms=self._request_timeout_ms)
except (OSError, asyncio.TimeoutError) as err:
log.error('Unable connect to "%s:%s": %s', host, port, err)
continue
try:
metadata = yield from bootstrap_conn.send(metadata_request)
except KafkaError as err:
log.warning('Unable to request metadata from "%s:%s": %s',
host, port, err)
bootstrap_conn.close()
continue
self.cluster.update_metadata(metadata)
# A cluster with no topics can return no broker metadata
# in that case, we should keep the bootstrap connection
if not len(self.cluster.brokers()):
self._conns['bootstrap'] = bootstrap_conn
else:
bootstrap_conn.close()
log.debug('Received cluster metadata: %s', self.cluster)
break
else:
raise ConnectionError(
'Unable to bootstrap from {}'.format(self.hosts))
if self._sync_task is None:
# starting metadata synchronizer task
self._sync_task = ensure_future(
self._md_synchronizer(), loop=self._loop)
def _sender_routine(self):
"""backgroud task that sends message batches to Kafka brokers"""
tasks = set()
try:
while True:
batches, unknown_leaders_exist = \
self._message_accumulator.drain_by_nodes(
ignore_nodes=self._in_flight)
# create produce task for every batch
for node_id, batches in batches.items():
task = ensure_future(
self._send_produce_req(node_id, batches),
loop=self._loop)
tasks.add(task)
if unknown_leaders_exist:
# we have at least one unknown partition's leader,
# try to update cluster metadata and wait backoff time
self.client.force_metadata_update()
# Just to have at least 1 future in wait() call
fut = asyncio.sleep(self._retry_backoff, loop=self._loop)
waiters = tasks.union([fut])
else:
fut = self._message_accumulator.data_waiter()
waiters = tasks.union([fut])
# wait when:
# * At least one of produce task is finished
# * Data for new partition arrived
done, _ = yield from asyncio.wait(
waiters,
return_when=asyncio.FIRST_COMPLETED,
loop=self._loop)
tasks -= done
except asyncio.CancelledError:
pass
except Exception: # noqa
log.error("Unexpected error in sender routine", exc_info=True)
def _on_change_subscription(self):
""" This is `group rebalanced` signal handler used to update fetch
positions of assigned partitions
"""
if self._closed: # pragma: no cover
return
# 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():
task = ensure_future(
self._update_fetch_positions(
self._subscription.missing_fetch_positions()),
loop=self._loop
)
self._pending_position_fetches.add(task)
def on_done(fut, tasks=self._pending_position_fetches):
tasks.discard(fut)
try:
fut.result()
except Exception as err: # pragma: no cover
log.error("Failed to update fetch positions: %r", err)
task.add_done_callback(on_done)
def __init__(self, client, subscriptions, *, loop,
key_deserializer=None,
value_deserializer=None,
fetch_min_bytes=1,
fetch_max_wait_ms=500,
max_partition_fetch_bytes=1048576,
check_crcs=True,
fetcher_timeout=0.2,
prefetch_backoff=0.1):
"""Initialize a Kafka Message Fetcher.
Parameters:
client (AIOKafkaClient): kafka client
subscription (SubscriptionState): instance of SubscriptionState
located in kafka.consumer.subscription_state
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.
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
fetcher_timeout (float): Maximum polling interval in the background
fetching routine. Default: 0.2
prefetch_backoff (float): number of seconds to wait until
consumption of partition is paused. Paused partitions will not
request new data from Kafka server (will not be included in
next poll request).
"""
self._client = client
self._loop = loop
self._key_deserializer = key_deserializer
self._value_deserializer = value_deserializer
self._fetch_min_bytes = fetch_min_bytes
self._fetch_max_wait_ms = fetch_max_wait_ms
self._max_partition_fetch_bytes = max_partition_fetch_bytes
self._check_crcs = check_crcs
self._fetcher_timeout = fetcher_timeout
self._prefetch_backoff = prefetch_backoff
self._subscriptions = subscriptions
self._records = collections.OrderedDict()
self._in_flight = set()
self._fetch_tasks = set()
self._wait_consume_future = None
self._wait_empty_future = None
self._fetch_task = ensure_future(
self._fetch_requests_routine(), loop=loop)
def _fetch_requests_routine(self):
""" Background task, that always prefetches next result page.
The algorithm:
* Group partitions per node, which is the leader for it.
* If all partitions for this node need prefetch - do it right alway
* If any partition has some data (in `self._records`) wait up till
`prefetch_backoff` so application can consume data from it.
* If data in `self._records` is not consumed up to
`prefetch_backoff` just request data for other partitions from this
node.
We request data in such manner cause Kafka blocks the connection if
we perform a FetchRequest and we don't have enough data. This means
we must perform a FetchRequest to as many partitions as we can in a
node.
Original java Kafka client processes data differently, as it only
prefetches data if all messages were given to application (i.e. if
`self._records` are empty). We don't use this method, cause we allow
to process partitions separately (by passing `partitions` list to
`getall()` call of the consumer), which can end up in a long wait
if some partitions (or topics) are processed slower, than others.
"""
try:
while True:
# Reset consuming signal future.
self._wait_consume_future = asyncio.Future(loop=self._loop)
# Create and send fetch requests
requests, timeout = self._create_fetch_requests()
for node_id, request in requests:
node_ready = yield from self._client.ready(node_id)
if not node_ready:
# We will request it on next routine
continue
log.debug("Sending FetchRequest to node %s", node_id)
task = ensure_future(
self._proc_fetch_request(node_id, request),
loop=self._loop)
self._fetch_tasks.add(task)
self._in_flight.add(node_id)
done_set, _ = yield from asyncio.wait(
chain(self._fetch_tasks, [self._wait_consume_future]),
loop=self._loop,
timeout=timeout,
return_when=asyncio.FIRST_COMPLETED)
# Process fetch tasks results if any
done_fetches = self._fetch_tasks.intersection(done_set)
if done_fetches:
has_new_data = any(fut.result() for fut in done_fetches)
if has_new_data:
# we added some messages to self._records,
# wake up getters
self._notify(self._wait_empty_future)
self._fetch_tasks -= done_fetches
except asyncio.CancelledError:
pass
except Exception: # noqa
log.error("Unexpected error in fetcher routine", exc_info=True)
def _sender_routine(self):
""" Background task, that sends pending batches to leader nodes for
batch's partition. This incapsulates same logic as Java's `Sender`
background thread. Because we use asyncio this is more event based
loop, rather than counting timeout till next possible even like in
Java.
The procedure:
* Group pending batches by partition leaders (write nodes)
* Ignore not ready (disconnected) and nodes, that already have a
pending request.
* If we have unknown leaders for partitions, we request a metadata
update.
* Wait for any event, that can change the above procedure, like
new metadata or pending send is finished and a new one can be
done.
"""
tasks = set()
try:
while True:
batches, unknown_leaders_exist = \
self._message_accumulator.drain_by_nodes(
ignore_nodes=self._in_flight)
# create produce task for every batch
for node_id, batches in batches.items():
task = ensure_future(self._send_produce_req(
node_id, batches),
loop=self._loop)
self._in_flight.add(node_id)
tasks.add(task)
if unknown_leaders_exist:
# we have at least one unknown partition's leader,
# try to update cluster metadata and wait backoff time
fut = self.client.force_metadata_update()
waiters = tasks.union([fut])
else:
fut = self._message_accumulator.data_waiter()
waiters = tasks.union([fut])
# wait when:
# * At least one of produce task is finished
# * Data for new partition arrived
# * Metadata update if partition leader unknown
done, _ = yield from asyncio.wait(
waiters,
return_when=asyncio.FIRST_COMPLETED,
loop=self._loop)
# done tasks should never produce errors, if they are it's a
# bug
for task in done:
task.result()
tasks -= done
except asyncio.CancelledError:
pass
except Exception: # pragma: no cover
log.error("Unexpected error in sender routine", exc_info=True)
def connect(self):
future = asyncio.open_connection(self.host, self.port, loop=self._loop)
self._reader, self._writer = yield from asyncio.wait_for(
future, self._request_timeout, loop=self._loop)
self._read_task = ensure_future(self._read(), loop=self._loop)
def __init__(self,
client,
subscription,
*,
loop,
group_id='aiokafka-default-group',
session_timeout_ms=30000,
heartbeat_interval_ms=3000,
retry_backoff_ms=100,
enable_auto_commit=True,
auto_commit_interval_ms=5000,
assignors=(RoundRobinPartitionAssignor, )):
"""Initialize the coordination manager.
Parameters:
client (AIOKafkaClient): kafka client
subscription (SubscriptionState): instance of SubscriptionState
located in kafka.consumer.subscription_state
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: 'aiokafka-default-group'
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.
assignors (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
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
"""
self._client = client
self._session_timeout_ms = session_timeout_ms
self._heartbeat_interval_ms = heartbeat_interval_ms
self._retry_backoff_ms = retry_backoff_ms
self.generation = OffsetCommitRequest.DEFAULT_GENERATION_ID
self.member_id = JoinGroupRequest.UNKNOWN_MEMBER_ID
self.group_id = group_id
self.coordinator_id = None
self.rejoin_needed = True
self.needs_join_prepare = True
self.loop = loop
# rejoin group can be called in parallel
# (from consumer and from heartbeat task), so we need lock
self._rejoin_lock = asyncio.Lock(loop=loop)
self._enable_auto_commit = enable_auto_commit
self._auto_commit_interval_ms = auto_commit_interval_ms
self._assignors = assignors
self._subscription = subscription
self._partitions_per_topic = {}
self._cluster = client.cluster
self._auto_commit_task = None
# _closing future used as a signal to heartbeat task for finish ASAP
self._closing = asyncio.Future(loop=loop)
# update subscribe state usint currently known metadata
self._handle_metadata_update(client.cluster)
self._cluster.add_listener(self._handle_metadata_update)
self._group_rebalanced_callback = None
self.heartbeat_task = ensure_future(self._heartbeat_task_routine(),
loop=loop)
if self._enable_auto_commit:
interval = self._auto_commit_interval_ms / 1000
self._auto_commit_task = ensure_future(
self.auto_commit_routine(interval), loop=loop)
def _fetch_requests_routine(self):
""" Background task, that always prefetches next result page.
The algorithm:
* Group partitions per node, which is the leader for it.
* If all partitions for this node need prefetch - do it right away
* If any partition has some data (in `self._records`) wait up till
`prefetch_backoff` so application can consume data from it.
* If data in `self._records` is not consumed up to
`prefetch_backoff` just request data for other partitions from this
node.
We request data in such manner cause Kafka blocks the connection if
we perform a FetchRequest and we don't have enough data. This means
we must perform a FetchRequest to as many partitions as we can in a
node.
Original Java Kafka client processes data differently, as it only
prefetches data if all messages were given to application (i.e. if
`self._records` are empty). We don't use this method, cause we allow
to process partitions separately (by passing `partitions` list to
`getall()` call of the consumer), which can end up in a long wait
if some partitions (or topics) are processed slower, than others.
"""
try:
while True:
# Reset consuming signal future.
self._wait_consume_future = asyncio.Future(loop=self._loop)
# Create and send fetch requests
requests, timeout = self._create_fetch_requests()
for node_id, request in requests:
node_ready = yield from self._client.ready(node_id)
if not node_ready:
# We will request it on next routine
continue
log.debug("Sending FetchRequest to node %s", node_id)
task = ensure_future(self._proc_fetch_request(
node_id, request),
loop=self._loop)
self._fetch_tasks.add(task)
self._in_flight.add(node_id)
done_set, _ = yield from asyncio.wait(
chain(self._fetch_tasks, [self._wait_consume_future]),
loop=self._loop,
timeout=timeout,
return_when=asyncio.FIRST_COMPLETED)
# Process fetch tasks results if any
done_fetches = self._fetch_tasks.intersection(done_set)
if done_fetches:
has_new_data = any(fut.result() for fut in done_fetches)
if has_new_data:
# we added some messages to self._records,
# wake up getters
self._notify(self._wait_empty_future)
self._fetch_tasks -= done_fetches
except asyncio.CancelledError:
pass
except Exception: # pragma: no cover
log.error("Unexpected error in fetcher routine", exc_info=True)
def __init__(self,
client,
subscriptions,
*,
loop,
key_deserializer=None,
value_deserializer=None,
fetch_min_bytes=1,
fetch_max_wait_ms=500,
max_partition_fetch_bytes=1048576,
check_crcs=True,
fetcher_timeout=0.2,
prefetch_backoff=0.1,
retry_backoff_ms=100):
"""Initialize a Kafka Message Fetcher.
Parameters:
client (AIOKafkaClient): kafka client
subscription (SubscriptionState): instance of SubscriptionState
located in kafka.consumer.subscription_state
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.
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
fetcher_timeout (float): Maximum polling interval in the background
fetching routine. Default: 0.2
prefetch_backoff (float): number of seconds to wait until
consumption of partition is paused. Paused partitions will not
request new data from Kafka server (will not be included in
next poll request).
"""
self._client = client
self._loop = loop
self._key_deserializer = key_deserializer
self._value_deserializer = value_deserializer
self._fetch_min_bytes = fetch_min_bytes
self._fetch_max_wait_ms = fetch_max_wait_ms
self._max_partition_fetch_bytes = max_partition_fetch_bytes
self._check_crcs = check_crcs
self._fetcher_timeout = fetcher_timeout
self._prefetch_backoff = prefetch_backoff
self._retry_backoff = retry_backoff_ms / 1000
self._subscriptions = subscriptions
self._records = collections.OrderedDict()
self._in_flight = set()
self._fetch_tasks = set()
self._wait_consume_future = None
self._wait_empty_future = None
req_version = 2 if client.api_version >= (0, 10) else 1
self._fetch_request_class = FetchRequest[req_version]
self._fetch_task = ensure_future(self._fetch_requests_routine(),
loop=loop)
def __init__(self, client, subscription, *, loop,
group_id='aiokafka-default-group',
session_timeout_ms=30000, heartbeat_interval_ms=3000,
retry_backoff_ms=100,
enable_auto_commit=True, auto_commit_interval_ms=5000,
assignors=(RoundRobinPartitionAssignor,)
):
"""Initialize the coordination manager.
Parameters:
client (AIOKafkaClient): kafka client
subscription (SubscriptionState): instance of SubscriptionState
located in kafka.consumer.subscription_state
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: 'aiokafka-default-group'
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.
assignors (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
retry_backoff_ms (int): Milliseconds to backoff when retrying on
errors. Default: 100.
"""
self._client = client
self._session_timeout_ms = session_timeout_ms
self._heartbeat_interval_ms = heartbeat_interval_ms
self._retry_backoff_ms = retry_backoff_ms
self.generation = OffsetCommitRequest.DEFAULT_GENERATION_ID
self.member_id = JoinGroupRequest.UNKNOWN_MEMBER_ID
self.group_id = group_id
self.coordinator_id = None
self.rejoin_needed = True
self.needs_join_prepare = True
self.loop = loop
# rejoin group can be called in parallel
# (from consumer and from heartbeat task), so we need lock
self._rejoin_lock = asyncio.Lock(loop=loop)
self._enable_auto_commit = enable_auto_commit
self._auto_commit_interval_ms = auto_commit_interval_ms
self._assignors = assignors
self._subscription = subscription
self._partitions_per_topic = {}
self._cluster = client.cluster
self._auto_commit_task = None
# _closing future used as a signal to heartbeat task for finish ASAP
self._closing = asyncio.Future(loop=loop)
# update subscribe state usint currently known metadata
self._handle_metadata_update(client.cluster)
self._cluster.add_listener(self._handle_metadata_update)
self._group_rebalanced_callback = None
self.heartbeat_task = ensure_future(
self._heartbeat_task_routine(), loop=loop)
if self._enable_auto_commit:
interval = self._auto_commit_interval_ms / 1000
self._auto_commit_task = ensure_future(
self.auto_commit_routine(interval), loop=loop)
def connect(self):
future = asyncio.open_connection(self.host, self.port, loop=self._loop)
self._reader, self._writer = yield from asyncio.wait_for(
future, self._request_timeout, loop=self._loop)
self._read_task = ensure_future(self._read(), loop=self._loop)
