openstack/deb-python-kafka
Code Issues Proposed changes

Separate consumers/producers/partitioners

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This commit is contained in:
Dana Powers
2014-09-09 16:42:23 -07:00
parent f806c24d84
commit 27e812e66b
17 changed files with 950 additions and 873 deletions
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698
kafka/consumer.py
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from __future__ import absolute_import
try:
from itertools import zip_longest as izip_longest, repeat # pylint: disable-msg=E0611
except ImportError: # python 2
from itertools import izip_longest as izip_longest, repeat
import logging
import time
import numbers
from threading import Lock
from multiprocessing import Process, Queue as MPQueue, Event, Value
import six
try:
from Queue import Empty, Queue
except ImportError: # python 2
from queue import Empty, Queue
import kafka.common
from kafka.common import (
FetchRequest, OffsetRequest,
OffsetCommitRequest, OffsetFetchRequest,
ConsumerFetchSizeTooSmall, ConsumerNoMoreData,
UnknownTopicOrPartitionError
)
from kafka.util import ReentrantTimer
log = logging.getLogger("kafka")
AUTO_COMMIT_MSG_COUNT = 100
AUTO_COMMIT_INTERVAL = 5000
FETCH_DEFAULT_BLOCK_TIMEOUT = 1
FETCH_MAX_WAIT_TIME = 100
FETCH_MIN_BYTES = 4096
FETCH_BUFFER_SIZE_BYTES = 4096
MAX_FETCH_BUFFER_SIZE_BYTES = FETCH_BUFFER_SIZE_BYTES * 8
ITER_TIMEOUT_SECONDS = 60
NO_MESSAGES_WAIT_TIME_SECONDS = 0.1
class FetchContext(object):
"""
Class for managing the state of a consumer during fetch
"""
def __init__(self, consumer, block, timeout):
self.consumer = consumer
self.block = block
if block:
if not timeout:
timeout = FETCH_DEFAULT_BLOCK_TIMEOUT
self.timeout = timeout * 1000
def __enter__(self):
"""Set fetch values based on blocking status"""
self.orig_fetch_max_wait_time = self.consumer.fetch_max_wait_time
self.orig_fetch_min_bytes = self.consumer.fetch_min_bytes
if self.block:
self.consumer.fetch_max_wait_time = self.timeout
self.consumer.fetch_min_bytes = 1
else:
self.consumer.fetch_min_bytes = 0
def __exit__(self, type, value, traceback):
"""Reset values"""
self.consumer.fetch_max_wait_time = self.orig_fetch_max_wait_time
self.consumer.fetch_min_bytes = self.orig_fetch_min_bytes
class Consumer(object):
"""
Base class to be used by other consumers. Not to be used directly
This base class provides logic for
* initialization and fetching metadata of partitions
* Auto-commit logic
* APIs for fetching pending message count
"""
def __init__(self, client, group, topic, partitions=None, auto_commit=True,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL):
self.client = client
self.topic = topic
self.group = group
self.client.load_metadata_for_topics(topic)
self.offsets = {}
if not partitions:
partitions = self.client.get_partition_ids_for_topic(topic)
else:
assert all(isinstance(x, numbers.Integral) for x in partitions)
# Variables for handling offset commits
self.commit_lock = Lock()
self.commit_timer = None
self.count_since_commit = 0
self.auto_commit = auto_commit
self.auto_commit_every_n = auto_commit_every_n
self.auto_commit_every_t = auto_commit_every_t
# Set up the auto-commit timer
if auto_commit is True and auto_commit_every_t is not None:
self.commit_timer = ReentrantTimer(auto_commit_every_t,
self.commit)
self.commit_timer.start()
if auto_commit:
self.fetch_last_known_offsets(partitions)
else:
for partition in partitions:
self.offsets[partition] = 0
def fetch_last_known_offsets(self, partitions=None):
if not partitions:
partitions = self.client.get_partition_ids_for_topic(self.topic)
def get_or_init_offset(resp):
try:
kafka.common.check_error(resp)
return resp.offset
except UnknownTopicOrPartitionError:
return 0
for partition in partitions:
req = OffsetFetchRequest(self.topic, partition)
(resp,) = self.client.send_offset_fetch_request(self.group, [req],
fail_on_error=False)
self.offsets[partition] = get_or_init_offset(resp)
self.fetch_offsets = self.offsets.copy()
def commit(self, partitions=None):
"""
Commit offsets for this consumer
partitions: list of partitions to commit, default is to commit
all of them
"""
# short circuit if nothing happened. This check is kept outside
# to prevent un-necessarily acquiring a lock for checking the state
if self.count_since_commit == 0:
return
with self.commit_lock:
# Do this check again, just in case the state has changed
# during the lock acquiring timeout
if self.count_since_commit == 0:
return
reqs = []
if not partitions: # commit all partitions
partitions = self.offsets.keys()
for partition in partitions:
offset = self.offsets[partition]
log.debug("Commit offset %d in SimpleConsumer: "
"group=%s, topic=%s, partition=%s" %
(offset, self.group, self.topic, partition))
reqs.append(OffsetCommitRequest(self.topic, partition,
offset, None))
resps = self.client.send_offset_commit_request(self.group, reqs)
for resp in resps:
kafka.common.check_error(resp)
self.count_since_commit = 0
def _auto_commit(self):
"""
Check if we have to commit based on number of messages and commit
"""
# Check if we are supposed to do an auto-commit
if not self.auto_commit or self.auto_commit_every_n is None:
return
if self.count_since_commit >= self.auto_commit_every_n:
self.commit()
def stop(self):
if self.commit_timer is not None:
self.commit_timer.stop()
self.commit()
def pending(self, partitions=None):
"""
Gets the pending message count
partitions: list of partitions to check for, default is to check all
"""
if not partitions:
partitions = self.offsets.keys()
total = 0
reqs = []
for partition in partitions:
reqs.append(OffsetRequest(self.topic, partition, -1, 1))
resps = self.client.send_offset_request(reqs)
for resp in resps:
partition = resp.partition
pending = resp.offsets[0]
offset = self.offsets[partition]
total += pending - offset - (1 if offset > 0 else 0)
return total
class SimpleConsumer(Consumer):
"""
A simple consumer implementation that consumes all/specified partitions
for a topic
client: a connected KafkaClient
group: a name for this consumer, used for offset storage and must be unique
topic: the topic to consume
partitions: An optional list of partitions to consume the data from
auto_commit: default True. Whether or not to auto commit the offsets
auto_commit_every_n: default 100. How many messages to consume
before a commit
auto_commit_every_t: default 5000. How much time (in milliseconds) to
wait before commit
fetch_size_bytes: number of bytes to request in a FetchRequest
buffer_size: default 4K. Initial number of bytes to tell kafka we
have available. This will double as needed.
max_buffer_size: default 16K. Max number of bytes to tell kafka we have
available. None means no limit.
iter_timeout: default None. How much time (in seconds) to wait for a
message in the iterator before exiting. None means no
timeout, so it will wait forever.
Auto commit details:
If both auto_commit_every_n and auto_commit_every_t are set, they will
reset one another when one is triggered. These triggers simply call the
commit method on this class. A manual call to commit will also reset
these triggers
"""
def __init__(self, client, group, topic, auto_commit=True, partitions=None,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL,
fetch_size_bytes=FETCH_MIN_BYTES,
buffer_size=FETCH_BUFFER_SIZE_BYTES,
max_buffer_size=MAX_FETCH_BUFFER_SIZE_BYTES,
iter_timeout=None):
super(SimpleConsumer, self).__init__(
client, group, topic,
partitions=partitions,
auto_commit=auto_commit,
auto_commit_every_n=auto_commit_every_n,
auto_commit_every_t=auto_commit_every_t)
if max_buffer_size is not None and buffer_size > max_buffer_size:
raise ValueError("buffer_size (%d) is greater than "
"max_buffer_size (%d)" %
(buffer_size, max_buffer_size))
self.buffer_size = buffer_size
self.max_buffer_size = max_buffer_size
self.partition_info = False # Do not return partition info in msgs
self.fetch_max_wait_time = FETCH_MAX_WAIT_TIME
self.fetch_min_bytes = fetch_size_bytes
self.fetch_offsets = self.offsets.copy()
self.iter_timeout = iter_timeout
self.queue = Queue()
def __repr__(self):
return '<SimpleConsumer group=%s, topic=%s, partitions=%s>' % \
(self.group, self.topic, str(self.offsets.keys()))
def provide_partition_info(self):
"""
Indicates that partition info must be returned by the consumer
"""
self.partition_info = True
def seek(self, offset, whence):
"""
Alter the current offset in the consumer, similar to fseek
offset: how much to modify the offset
whence: where to modify it from
0 is relative to the earliest available offset (head)
1 is relative to the current offset
2 is relative to the latest known offset (tail)
"""
if whence == 1: # relative to current position
for partition, _offset in self.offsets.items():
self.offsets[partition] = _offset + offset
elif whence in (0, 2): # relative to beginning or end
# divide the request offset by number of partitions,
# distribute the remained evenly
(delta, rem) = divmod(offset, len(self.offsets))
deltas = {}
for partition, r in izip_longest(self.offsets.keys(),
repeat(1, rem), fillvalue=0):
deltas[partition] = delta + r
reqs = []
for partition in self.offsets.keys():
if whence == 0:
reqs.append(OffsetRequest(self.topic, partition, -2, 1))
elif whence == 2:
reqs.append(OffsetRequest(self.topic, partition, -1, 1))
else:
pass
resps = self.client.send_offset_request(reqs)
for resp in resps:
self.offsets[resp.partition] = \
resp.offsets[0] + deltas[resp.partition]
else:
raise ValueError("Unexpected value for `whence`, %d" % whence)
# Reset queue and fetch offsets since they are invalid
self.fetch_offsets = self.offsets.copy()
if self.auto_commit:
self.count_since_commit += 1
self.commit()
self.queue = Queue()
def get_messages(self, count=1, block=True, timeout=0.1):
"""
Fetch the specified number of messages
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till some messages are fetched.
timeout: If block is True, the function will block for the specified
time (in seconds) until count messages is fetched. If None,
it will block forever.
"""
messages = []
if timeout is not None:
max_time = time.time() + timeout
new_offsets = {}
while count > 0 and (timeout is None or timeout > 0):
result = self._get_message(block, timeout, get_partition_info=True,
update_offset=False)
if result:
partition, message = result
if self.partition_info:
messages.append(result)
else:
messages.append(message)
new_offsets[partition] = message.offset + 1
count -= 1
else:
# Ran out of messages for the last request.
if not block:
# If we're not blocking, break.
break
if timeout is not None:
# If we're blocking and have a timeout, reduce it to the
# appropriate value
timeout = max_time - time.time()
# Update and commit offsets if necessary
self.offsets.update(new_offsets)
self.count_since_commit += len(messages)
self._auto_commit()
return messages
def get_message(self, block=True, timeout=0.1, get_partition_info=None):
return self._get_message(block, timeout, get_partition_info)
def _get_message(self, block=True, timeout=0.1, get_partition_info=None,
update_offset=True):
"""
If no messages can be fetched, returns None.
If get_partition_info is None, it defaults to self.partition_info
If get_partition_info is True, returns (partition, message)
If get_partition_info is False, returns message
"""
if self.queue.empty():
# We're out of messages, go grab some more.
with FetchContext(self, block, timeout):
self._fetch()
try:
partition, message = self.queue.get_nowait()
if update_offset:
# Update partition offset
self.offsets[partition] = message.offset + 1
# Count, check and commit messages if necessary
self.count_since_commit += 1
self._auto_commit()
if get_partition_info is None:
get_partition_info = self.partition_info
if get_partition_info:
return partition, message
else:
return message
except Empty:
return None
def __iter__(self):
if self.iter_timeout is None:
timeout = ITER_TIMEOUT_SECONDS
else:
timeout = self.iter_timeout
while True:
message = self.get_message(True, timeout)
if message:
yield message
elif self.iter_timeout is None:
# We did not receive any message yet but we don't have a
# timeout, so give up the CPU for a while before trying again
time.sleep(NO_MESSAGES_WAIT_TIME_SECONDS)
else:
# Timed out waiting for a message
break
def _fetch(self):
# Create fetch request payloads for all the partitions
partitions = dict((p, self.buffer_size)
for p in self.fetch_offsets.keys())
while partitions:
requests = []
for partition, buffer_size in six.iteritems(partitions):
requests.append(FetchRequest(self.topic, partition,
self.fetch_offsets[partition],
buffer_size))
# Send request
responses = self.client.send_fetch_request(
requests,
max_wait_time=int(self.fetch_max_wait_time),
min_bytes=self.fetch_min_bytes)
retry_partitions = {}
for resp in responses:
partition = resp.partition
buffer_size = partitions[partition]
try:
for message in resp.messages:
# Put the message in our queue
self.queue.put((partition, message))
self.fetch_offsets[partition] = message.offset + 1
except ConsumerFetchSizeTooSmall:
if (self.max_buffer_size is not None and
buffer_size == self.max_buffer_size):
log.error("Max fetch size %d too small",
self.max_buffer_size)
raise
if self.max_buffer_size is None:
buffer_size *= 2
else:
buffer_size = max(buffer_size * 2,
self.max_buffer_size)
log.warn("Fetch size too small, increase to %d (2x) "
"and retry", buffer_size)
retry_partitions[partition] = buffer_size
except ConsumerNoMoreData as e:
log.debug("Iteration was ended by %r", e)
except StopIteration:
# Stop iterating through this partition
log.debug("Done iterating over partition %s" % partition)
partitions = retry_partitions
def _mp_consume(client, group, topic, chunk, queue, start, exit, pause, size):
"""
A child process worker which consumes messages based on the
notifications given by the controller process
NOTE: Ideally, this should have been a method inside the Consumer
class. However, multiprocessing module has issues in windows. The
functionality breaks unless this function is kept outside of a class
"""
# Make the child processes open separate socket connections
client.reinit()
# We will start consumers without auto-commit. Auto-commit will be
# done by the master controller process.
consumer = SimpleConsumer(client, group, topic,
partitions=chunk,
auto_commit=False,
auto_commit_every_n=None,
auto_commit_every_t=None)
# Ensure that the consumer provides the partition information
consumer.provide_partition_info()
while True:
# Wait till the controller indicates us to start consumption
start.wait()
# If we are asked to quit, do so
if exit.is_set():
break
# Consume messages and add them to the queue. If the controller
# indicates a specific number of messages, follow that advice
count = 0
message = consumer.get_message()
if message:
queue.put(message)
count += 1
# We have reached the required size. The controller might have
# more than what he needs. Wait for a while.
# Without this logic, it is possible that we run into a big
# loop consuming all available messages before the controller
# can reset the 'start' event
if count == size.value:
pause.wait()
else:
# In case we did not receive any message, give up the CPU for
# a while before we try again
time.sleep(NO_MESSAGES_WAIT_TIME_SECONDS)
consumer.stop()
class MultiProcessConsumer(Consumer):
"""
A consumer implementation that consumes partitions for a topic in
parallel using multiple processes
client: a connected KafkaClient
group: a name for this consumer, used for offset storage and must be unique
topic: the topic to consume
auto_commit: default True. Whether or not to auto commit the offsets
auto_commit_every_n: default 100. How many messages to consume
before a commit
auto_commit_every_t: default 5000. How much time (in milliseconds) to
wait before commit
num_procs: Number of processes to start for consuming messages.
The available partitions will be divided among these processes
partitions_per_proc: Number of partitions to be allocated per process
(overrides num_procs)
Auto commit details:
If both auto_commit_every_n and auto_commit_every_t are set, they will
reset one another when one is triggered. These triggers simply call the
commit method on this class. A manual call to commit will also reset
these triggers
"""
def __init__(self, client, group, topic, auto_commit=True,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL,
num_procs=1, partitions_per_proc=0):
# Initiate the base consumer class
super(MultiProcessConsumer, self).__init__(
client, group, topic,
partitions=None,
auto_commit=auto_commit,
auto_commit_every_n=auto_commit_every_n,
auto_commit_every_t=auto_commit_every_t)
# Variables for managing and controlling the data flow from
# consumer child process to master
self.queue = MPQueue(1024) # Child consumers dump messages into this
self.start = Event() # Indicates the consumers to start fetch
self.exit = Event() # Requests the consumers to shutdown
self.pause = Event() # Requests the consumers to pause fetch
self.size = Value('i', 0) # Indicator of number of messages to fetch
partitions = self.offsets.keys()
# If unspecified, start one consumer per partition
# The logic below ensures that
# * we do not cross the num_procs limit
# * we have an even distribution of partitions among processes
if not partitions_per_proc:
partitions_per_proc = round(len(partitions) * 1.0 / num_procs)
if partitions_per_proc < num_procs * 0.5:
partitions_per_proc += 1
# The final set of chunks
chunker = lambda *x: [] + list(x)
chunks = map(chunker, *[iter(partitions)] * int(partitions_per_proc))
self.procs = []
for chunk in chunks:
chunk = filter(lambda x: x is not None, chunk)
args = (client.copy(),
group, topic, list(chunk),
self.queue, self.start, self.exit,
self.pause, self.size)
proc = Process(target=_mp_consume, args=args)
proc.daemon = True
proc.start()
self.procs.append(proc)
def __repr__(self):
return '<MultiProcessConsumer group=%s, topic=%s, consumers=%d>' % \
(self.group, self.topic, len(self.procs))
def stop(self):
# Set exit and start off all waiting consumers
self.exit.set()
self.pause.set()
self.start.set()
for proc in self.procs:
proc.join()
proc.terminate()
super(MultiProcessConsumer, self).stop()
def __iter__(self):
"""
Iterator to consume the messages available on this consumer
"""
# Trigger the consumer procs to start off.
# We will iterate till there are no more messages available
self.size.value = 0
self.pause.set()
while True:
self.start.set()
try:
# We will block for a small while so that the consumers get
# a chance to run and put some messages in the queue
# TODO: This is a hack and will make the consumer block for
# at least one second. Need to find a better way of doing this
partition, message = self.queue.get(block=True, timeout=1)
except Empty:
break
# Count, check and commit messages if necessary
self.offsets[partition] = message.offset + 1
self.start.clear()
self.count_since_commit += 1
self._auto_commit()
yield message
self.start.clear()
def get_messages(self, count=1, block=True, timeout=10):
"""
Fetch the specified number of messages
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till some messages are fetched.
timeout: If block is True, the function will block for the specified
time (in seconds) until count messages is fetched. If None,
it will block forever.
"""
messages = []
# Give a size hint to the consumers. Each consumer process will fetch
# a maximum of "count" messages. This will fetch more messages than
# necessary, but these will not be committed to kafka. Also, the extra
# messages can be provided in subsequent runs
self.size.value = count
self.pause.clear()
if timeout is not None:
max_time = time.time() + timeout
new_offsets = {}
while count > 0 and (timeout is None or timeout > 0):
# Trigger consumption only if the queue is empty
# By doing this, we will ensure that consumers do not
# go into overdrive and keep consuming thousands of
# messages when the user might need only a few
if self.queue.empty():
self.start.set()
try:
partition, message = self.queue.get(block, timeout)
except Empty:
break
messages.append(message)
new_offsets[partition] = message.offset + 1
count -= 1
if timeout is not None:
timeout = max_time - time.time()
self.size.value = 0
self.start.clear()
self.pause.set()
# Update and commit offsets if necessary
self.offsets.update(new_offsets)
self.count_since_commit += len(messages)
self._auto_commit()
return messages

6
kafka/consumer/__init__.py Normal file
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@@ -0,0 +1,6 @@
from .simple import SimpleConsumer
from .multiprocess import MultiProcessConsumer
__all__ = [
'SimpleConsumer', 'MultiProcessConsumer'
]

169
kafka/consumer/base.py Normal file
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@@ -0,0 +1,169 @@
from __future__ import absolute_import
import logging
import numbers
from threading import Lock
import kafka.common
from kafka.common import (
OffsetRequest, OffsetCommitRequest, OffsetFetchRequest,
UnknownTopicOrPartitionError
)
from kafka.util import ReentrantTimer
log = logging.getLogger("kafka")
AUTO_COMMIT_MSG_COUNT = 100
AUTO_COMMIT_INTERVAL = 5000
FETCH_DEFAULT_BLOCK_TIMEOUT = 1
FETCH_MAX_WAIT_TIME = 100
FETCH_MIN_BYTES = 4096
FETCH_BUFFER_SIZE_BYTES = 4096
MAX_FETCH_BUFFER_SIZE_BYTES = FETCH_BUFFER_SIZE_BYTES * 8
ITER_TIMEOUT_SECONDS = 60
NO_MESSAGES_WAIT_TIME_SECONDS = 0.1
class Consumer(object):
"""
Base class to be used by other consumers. Not to be used directly
This base class provides logic for
* initialization and fetching metadata of partitions
* Auto-commit logic
* APIs for fetching pending message count
"""
def __init__(self, client, group, topic, partitions=None, auto_commit=True,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL):
self.client = client
self.topic = topic
self.group = group
self.client.load_metadata_for_topics(topic)
self.offsets = {}
if not partitions:
partitions = self.client.get_partition_ids_for_topic(topic)
else:
assert all(isinstance(x, numbers.Integral) for x in partitions)
# Variables for handling offset commits
self.commit_lock = Lock()
self.commit_timer = None
self.count_since_commit = 0
self.auto_commit = auto_commit
self.auto_commit_every_n = auto_commit_every_n
self.auto_commit_every_t = auto_commit_every_t
# Set up the auto-commit timer
if auto_commit is True and auto_commit_every_t is not None:
self.commit_timer = ReentrantTimer(auto_commit_every_t,
self.commit)
self.commit_timer.start()
if auto_commit:
self.fetch_last_known_offsets(partitions)
else:
for partition in partitions:
self.offsets[partition] = 0
def fetch_last_known_offsets(self, partitions=None):
if not partitions:
partitions = self.client.get_partition_ids_for_topic(self.topic)
def get_or_init_offset(resp):
try:
kafka.common.check_error(resp)
return resp.offset
except UnknownTopicOrPartitionError:
return 0
for partition in partitions:
req = OffsetFetchRequest(self.topic, partition)
(resp,) = self.client.send_offset_fetch_request(self.group, [req],
fail_on_error=False)
self.offsets[partition] = get_or_init_offset(resp)
self.fetch_offsets = self.offsets.copy()
def commit(self, partitions=None):
"""
Commit offsets for this consumer
partitions: list of partitions to commit, default is to commit
all of them
"""
# short circuit if nothing happened. This check is kept outside
# to prevent un-necessarily acquiring a lock for checking the state
if self.count_since_commit == 0:
return
with self.commit_lock:
# Do this check again, just in case the state has changed
# during the lock acquiring timeout
if self.count_since_commit == 0:
return
reqs = []
if not partitions: # commit all partitions
partitions = self.offsets.keys()
for partition in partitions:
offset = self.offsets[partition]
log.debug("Commit offset %d in SimpleConsumer: "
"group=%s, topic=%s, partition=%s" %
(offset, self.group, self.topic, partition))
reqs.append(OffsetCommitRequest(self.topic, partition,
offset, None))
resps = self.client.send_offset_commit_request(self.group, reqs)
for resp in resps:
kafka.common.check_error(resp)
self.count_since_commit = 0
def _auto_commit(self):
"""
Check if we have to commit based on number of messages and commit
"""
# Check if we are supposed to do an auto-commit
if not self.auto_commit or self.auto_commit_every_n is None:
return
if self.count_since_commit >= self.auto_commit_every_n:
self.commit()
def stop(self):
if self.commit_timer is not None:
self.commit_timer.stop()
self.commit()
def pending(self, partitions=None):
"""
Gets the pending message count
partitions: list of partitions to check for, default is to check all
"""
if not partitions:
partitions = self.offsets.keys()
total = 0
reqs = []
for partition in partitions:
reqs.append(OffsetRequest(self.topic, partition, -1, 1))
resps = self.client.send_offset_request(reqs)
for resp in resps:
partition = resp.partition
pending = resp.offsets[0]
offset = self.offsets[partition]
total += pending - offset - (1 if offset > 0 else 0)
return total

248
kafka/consumer/multiprocess.py Normal file
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@@ -0,0 +1,248 @@
from __future__ import absolute_import
import logging
import time
from multiprocessing import Process, Queue as MPQueue, Event, Value
try:
from Queue import Empty
except ImportError: # python 2
from queue import Empty
from .base import (
AUTO_COMMIT_MSG_COUNT, AUTO_COMMIT_INTERVAL,
NO_MESSAGES_WAIT_TIME_SECONDS
)
from .simple import Consumer, SimpleConsumer
log = logging.getLogger("kafka")
def _mp_consume(client, group, topic, chunk, queue, start, exit, pause, size):
"""
A child process worker which consumes messages based on the
notifications given by the controller process
NOTE: Ideally, this should have been a method inside the Consumer
class. However, multiprocessing module has issues in windows. The
functionality breaks unless this function is kept outside of a class
"""
# Make the child processes open separate socket connections
client.reinit()
# We will start consumers without auto-commit. Auto-commit will be
# done by the master controller process.
consumer = SimpleConsumer(client, group, topic,
partitions=chunk,
auto_commit=False,
auto_commit_every_n=None,
auto_commit_every_t=None)
# Ensure that the consumer provides the partition information
consumer.provide_partition_info()
while True:
# Wait till the controller indicates us to start consumption
start.wait()
# If we are asked to quit, do so
if exit.is_set():
break
# Consume messages and add them to the queue. If the controller
# indicates a specific number of messages, follow that advice
count = 0
message = consumer.get_message()
if message:
queue.put(message)
count += 1
# We have reached the required size. The controller might have
# more than what he needs. Wait for a while.
# Without this logic, it is possible that we run into a big
# loop consuming all available messages before the controller
# can reset the 'start' event
if count == size.value:
pause.wait()
else:
# In case we did not receive any message, give up the CPU for
# a while before we try again
time.sleep(NO_MESSAGES_WAIT_TIME_SECONDS)
consumer.stop()
class MultiProcessConsumer(Consumer):
"""
A consumer implementation that consumes partitions for a topic in
parallel using multiple processes
client: a connected KafkaClient
group: a name for this consumer, used for offset storage and must be unique
topic: the topic to consume
auto_commit: default True. Whether or not to auto commit the offsets
auto_commit_every_n: default 100. How many messages to consume
before a commit
auto_commit_every_t: default 5000. How much time (in milliseconds) to
wait before commit
num_procs: Number of processes to start for consuming messages.
The available partitions will be divided among these processes
partitions_per_proc: Number of partitions to be allocated per process
(overrides num_procs)
Auto commit details:
If both auto_commit_every_n and auto_commit_every_t are set, they will
reset one another when one is triggered. These triggers simply call the
commit method on this class. A manual call to commit will also reset
these triggers
"""
def __init__(self, client, group, topic, auto_commit=True,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL,
num_procs=1, partitions_per_proc=0):
# Initiate the base consumer class
super(MultiProcessConsumer, self).__init__(
client, group, topic,
partitions=None,
auto_commit=auto_commit,
auto_commit_every_n=auto_commit_every_n,
auto_commit_every_t=auto_commit_every_t)
# Variables for managing and controlling the data flow from
# consumer child process to master
self.queue = MPQueue(1024) # Child consumers dump messages into this
self.start = Event() # Indicates the consumers to start fetch
self.exit = Event() # Requests the consumers to shutdown
self.pause = Event() # Requests the consumers to pause fetch
self.size = Value('i', 0) # Indicator of number of messages to fetch
partitions = self.offsets.keys()
# If unspecified, start one consumer per partition
# The logic below ensures that
# * we do not cross the num_procs limit
# * we have an even distribution of partitions among processes
if not partitions_per_proc:
partitions_per_proc = round(len(partitions) * 1.0 / num_procs)
if partitions_per_proc < num_procs * 0.5:
partitions_per_proc += 1
# The final set of chunks
chunker = lambda *x: [] + list(x)
chunks = map(chunker, *[iter(partitions)] * int(partitions_per_proc))
self.procs = []
for chunk in chunks:
chunk = filter(lambda x: x is not None, chunk)
args = (client.copy(),
group, topic, list(chunk),
self.queue, self.start, self.exit,
self.pause, self.size)
proc = Process(target=_mp_consume, args=args)
proc.daemon = True
proc.start()
self.procs.append(proc)
def __repr__(self):
return '<MultiProcessConsumer group=%s, topic=%s, consumers=%d>' % \
(self.group, self.topic, len(self.procs))
def stop(self):
# Set exit and start off all waiting consumers
self.exit.set()
self.pause.set()
self.start.set()
for proc in self.procs:
proc.join()
proc.terminate()
super(MultiProcessConsumer, self).stop()
def __iter__(self):
"""
Iterator to consume the messages available on this consumer
"""
# Trigger the consumer procs to start off.
# We will iterate till there are no more messages available
self.size.value = 0
self.pause.set()
while True:
self.start.set()
try:
# We will block for a small while so that the consumers get
# a chance to run and put some messages in the queue
# TODO: This is a hack and will make the consumer block for
# at least one second. Need to find a better way of doing this
partition, message = self.queue.get(block=True, timeout=1)
except Empty:
break
# Count, check and commit messages if necessary
self.offsets[partition] = message.offset + 1
self.start.clear()
self.count_since_commit += 1
self._auto_commit()
yield message
self.start.clear()
def get_messages(self, count=1, block=True, timeout=10):
"""
Fetch the specified number of messages
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till some messages are fetched.
timeout: If block is True, the function will block for the specified
time (in seconds) until count messages is fetched. If None,
it will block forever.
"""
messages = []
# Give a size hint to the consumers. Each consumer process will fetch
# a maximum of "count" messages. This will fetch more messages than
# necessary, but these will not be committed to kafka. Also, the extra
# messages can be provided in subsequent runs
self.size.value = count
self.pause.clear()
if timeout is not None:
max_time = time.time() + timeout
new_offsets = {}
while count > 0 and (timeout is None or timeout > 0):
# Trigger consumption only if the queue is empty
# By doing this, we will ensure that consumers do not
# go into overdrive and keep consuming thousands of
# messages when the user might need only a few
if self.queue.empty():
self.start.set()
try:
partition, message = self.queue.get(block, timeout)
except Empty:
break
messages.append(message)
new_offsets[partition] = message.offset + 1
count -= 1
if timeout is not None:
timeout = max_time - time.time()
self.size.value = 0
self.start.clear()
self.pause.set()
# Update and commit offsets if necessary
self.offsets.update(new_offsets)
self.count_since_commit += len(messages)
self._auto_commit()
return messages

318
kafka/consumer/simple.py Normal file
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from __future__ import absolute_import
try:
from itertools import zip_longest as izip_longest, repeat # pylint: disable-msg=E0611
except ImportError: # python 2
from itertools import izip_longest as izip_longest, repeat
import logging
import time
import six
try:
from Queue import Empty, Queue
except ImportError: # python 2
from queue import Empty, Queue
from kafka.common import (
FetchRequest, OffsetRequest,
ConsumerFetchSizeTooSmall, ConsumerNoMoreData
)
from .base import (
Consumer,
FETCH_DEFAULT_BLOCK_TIMEOUT,
AUTO_COMMIT_MSG_COUNT,
AUTO_COMMIT_INTERVAL,
FETCH_MIN_BYTES,
FETCH_BUFFER_SIZE_BYTES,
MAX_FETCH_BUFFER_SIZE_BYTES,
FETCH_MAX_WAIT_TIME,
ITER_TIMEOUT_SECONDS,
NO_MESSAGES_WAIT_TIME_SECONDS
)
log = logging.getLogger("kafka")
class FetchContext(object):
"""
Class for managing the state of a consumer during fetch
"""
def __init__(self, consumer, block, timeout):
self.consumer = consumer
self.block = block
if block:
if not timeout:
timeout = FETCH_DEFAULT_BLOCK_TIMEOUT
self.timeout = timeout * 1000
def __enter__(self):
"""Set fetch values based on blocking status"""
self.orig_fetch_max_wait_time = self.consumer.fetch_max_wait_time
self.orig_fetch_min_bytes = self.consumer.fetch_min_bytes
if self.block:
self.consumer.fetch_max_wait_time = self.timeout
self.consumer.fetch_min_bytes = 1
else:
self.consumer.fetch_min_bytes = 0
def __exit__(self, type, value, traceback):
"""Reset values"""
self.consumer.fetch_max_wait_time = self.orig_fetch_max_wait_time
self.consumer.fetch_min_bytes = self.orig_fetch_min_bytes
class SimpleConsumer(Consumer):
"""
A simple consumer implementation that consumes all/specified partitions
for a topic
client: a connected KafkaClient
group: a name for this consumer, used for offset storage and must be unique
topic: the topic to consume
partitions: An optional list of partitions to consume the data from
auto_commit: default True. Whether or not to auto commit the offsets
auto_commit_every_n: default 100. How many messages to consume
before a commit
auto_commit_every_t: default 5000. How much time (in milliseconds) to
wait before commit
fetch_size_bytes: number of bytes to request in a FetchRequest
buffer_size: default 4K. Initial number of bytes to tell kafka we
have available. This will double as needed.
max_buffer_size: default 16K. Max number of bytes to tell kafka we have
available. None means no limit.
iter_timeout: default None. How much time (in seconds) to wait for a
message in the iterator before exiting. None means no
timeout, so it will wait forever.
Auto commit details:
If both auto_commit_every_n and auto_commit_every_t are set, they will
reset one another when one is triggered. These triggers simply call the
commit method on this class. A manual call to commit will also reset
these triggers
"""
def __init__(self, client, group, topic, auto_commit=True, partitions=None,
auto_commit_every_n=AUTO_COMMIT_MSG_COUNT,
auto_commit_every_t=AUTO_COMMIT_INTERVAL,
fetch_size_bytes=FETCH_MIN_BYTES,
buffer_size=FETCH_BUFFER_SIZE_BYTES,
max_buffer_size=MAX_FETCH_BUFFER_SIZE_BYTES,
iter_timeout=None):
super(SimpleConsumer, self).__init__(
client, group, topic,
partitions=partitions,
auto_commit=auto_commit,
auto_commit_every_n=auto_commit_every_n,
auto_commit_every_t=auto_commit_every_t)
if max_buffer_size is not None and buffer_size > max_buffer_size:
raise ValueError("buffer_size (%d) is greater than "
"max_buffer_size (%d)" %
(buffer_size, max_buffer_size))
self.buffer_size = buffer_size
self.max_buffer_size = max_buffer_size
self.partition_info = False # Do not return partition info in msgs
self.fetch_max_wait_time = FETCH_MAX_WAIT_TIME
self.fetch_min_bytes = fetch_size_bytes
self.fetch_offsets = self.offsets.copy()
self.iter_timeout = iter_timeout
self.queue = Queue()
def __repr__(self):
return '<SimpleConsumer group=%s, topic=%s, partitions=%s>' % \
(self.group, self.topic, str(self.offsets.keys()))
def provide_partition_info(self):
"""
Indicates that partition info must be returned by the consumer
"""
self.partition_info = True
def seek(self, offset, whence):
"""
Alter the current offset in the consumer, similar to fseek
offset: how much to modify the offset
whence: where to modify it from
0 is relative to the earliest available offset (head)
1 is relative to the current offset
2 is relative to the latest known offset (tail)
"""
if whence == 1: # relative to current position
for partition, _offset in self.offsets.items():
self.offsets[partition] = _offset + offset
elif whence in (0, 2): # relative to beginning or end
# divide the request offset by number of partitions,
# distribute the remained evenly
(delta, rem) = divmod(offset, len(self.offsets))
deltas = {}
for partition, r in izip_longest(self.offsets.keys(),
repeat(1, rem), fillvalue=0):
deltas[partition] = delta + r
reqs = []
for partition in self.offsets.keys():
if whence == 0:
reqs.append(OffsetRequest(self.topic, partition, -2, 1))
elif whence == 2:
reqs.append(OffsetRequest(self.topic, partition, -1, 1))
else:
pass
resps = self.client.send_offset_request(reqs)
for resp in resps:
self.offsets[resp.partition] = \
resp.offsets[0] + deltas[resp.partition]
else:
raise ValueError("Unexpected value for `whence`, %d" % whence)
# Reset queue and fetch offsets since they are invalid
self.fetch_offsets = self.offsets.copy()
if self.auto_commit:
self.count_since_commit += 1
self.commit()
self.queue = Queue()
def get_messages(self, count=1, block=True, timeout=0.1):
"""
Fetch the specified number of messages
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till some messages are fetched.
timeout: If block is True, the function will block for the specified
time (in seconds) until count messages is fetched. If None,
it will block forever.
"""
messages = []
if timeout is not None:
max_time = time.time() + timeout
new_offsets = {}
while count > 0 and (timeout is None or timeout > 0):
result = self._get_message(block, timeout, get_partition_info=True,
update_offset=False)
if result:
partition, message = result
if self.partition_info:
messages.append(result)
else:
messages.append(message)
new_offsets[partition] = message.offset + 1
count -= 1
else:
# Ran out of messages for the last request.
if not block:
# If we're not blocking, break.
break
if timeout is not None:
# If we're blocking and have a timeout, reduce it to the
# appropriate value
timeout = max_time - time.time()
# Update and commit offsets if necessary
self.offsets.update(new_offsets)
self.count_since_commit += len(messages)
self._auto_commit()
return messages
def get_message(self, block=True, timeout=0.1, get_partition_info=None):
return self._get_message(block, timeout, get_partition_info)
def _get_message(self, block=True, timeout=0.1, get_partition_info=None,
update_offset=True):
"""
If no messages can be fetched, returns None.
If get_partition_info is None, it defaults to self.partition_info
If get_partition_info is True, returns (partition, message)
If get_partition_info is False, returns message
"""
if self.queue.empty():
# We're out of messages, go grab some more.
with FetchContext(self, block, timeout):
self._fetch()
try:
partition, message = self.queue.get_nowait()
if update_offset:
# Update partition offset
self.offsets[partition] = message.offset + 1
# Count, check and commit messages if necessary
self.count_since_commit += 1
self._auto_commit()
if get_partition_info is None:
get_partition_info = self.partition_info
if get_partition_info:
return partition, message
else:
return message
except Empty:
return None
def __iter__(self):
if self.iter_timeout is None:
timeout = ITER_TIMEOUT_SECONDS
else:
timeout = self.iter_timeout
while True:
message = self.get_message(True, timeout)
if message:
yield message
elif self.iter_timeout is None:
# We did not receive any message yet but we don't have a
# timeout, so give up the CPU for a while before trying again
time.sleep(NO_MESSAGES_WAIT_TIME_SECONDS)
else:
# Timed out waiting for a message
break
def _fetch(self):
# Create fetch request payloads for all the partitions
partitions = dict((p, self.buffer_size)
for p in self.fetch_offsets.keys())
while partitions:
requests = []
for partition, buffer_size in six.iteritems(partitions):
requests.append(FetchRequest(self.topic, partition,
self.fetch_offsets[partition],
buffer_size))
# Send request
responses = self.client.send_fetch_request(
requests,
max_wait_time=int(self.fetch_max_wait_time),
min_bytes=self.fetch_min_bytes)
retry_partitions = {}
for resp in responses:
partition = resp.partition
buffer_size = partitions[partition]
try:
for message in resp.messages:
# Put the message in our queue
self.queue.put((partition, message))
self.fetch_offsets[partition] = message.offset + 1
except ConsumerFetchSizeTooSmall:
if (self.max_buffer_size is not None and
buffer_size == self.max_buffer_size):
log.error("Max fetch size %d too small",
self.max_buffer_size)
raise
if self.max_buffer_size is None:
buffer_size *= 2
else:
buffer_size = max(buffer_size * 2,
self.max_buffer_size)
log.warn("Fetch size too small, increase to %d (2x) "
"and retry", buffer_size)
retry_partitions[partition] = buffer_size
except ConsumerNoMoreData as e:
log.debug("Iteration was ended by %r", e)
except StopIteration:
# Stop iterating through this partition
log.debug("Done iterating over partition %s" % partition)
partitions = retry_partitions

58
kafka/partitioner.py
View File

@@ -1,58 +0,0 @@
from itertools import cycle
class Partitioner(object):
"""
Base class for a partitioner
"""
def __init__(self, partitions):
"""
Initialize the partitioner
partitions - A list of available partitions (during startup)
"""
self.partitions = partitions
def partition(self, key, partitions):
"""
Takes a string key and num_partitions as argument and returns
a partition to be used for the message
partitions - The list of partitions is passed in every call. This
may look like an overhead, but it will be useful
(in future) when we handle cases like rebalancing
"""
raise NotImplementedError('partition function has to be implemented')
class RoundRobinPartitioner(Partitioner):
"""
Implements a round robin partitioner which sends data to partitions
in a round robin fashion
"""
def __init__(self, partitions):
super(RoundRobinPartitioner, self).__init__(partitions)
self.iterpart = cycle(partitions)
def _set_partitions(self, partitions):
self.partitions = partitions
self.iterpart = cycle(partitions)
def partition(self, key, partitions):
# Refresh the partition list if necessary
if self.partitions != partitions:
self._set_partitions(partitions)
return next(self.iterpart)
class HashedPartitioner(Partitioner):
"""
Implements a partitioner which selects the target partition based on
the hash of the key
"""
def partition(self, key, partitions):
size = len(partitions)
idx = hash(key) % size
return partitions[idx]

6
kafka/partitioner/__init__.py Normal file
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@@ -0,0 +1,6 @@
from .roundrobin import RoundRobinPartitioner
from .hashed import HashedPartitioner
__all__ = [
'RoundRobinPartitioner', 'HashedPartitioner'
]

23
kafka/partitioner/base.py Normal file
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@@ -0,0 +1,23 @@
class Partitioner(object):
"""
Base class for a partitioner
"""
def __init__(self, partitions):
"""
Initialize the partitioner
partitions - A list of available partitions (during startup)
"""
self.partitions = partitions
def partition(self, key, partitions):
"""
Takes a string key and num_partitions as argument and returns
a partition to be used for the message
partitions - The list of partitions is passed in every call. This
may look like an overhead, but it will be useful
(in future) when we handle cases like rebalancing
"""
raise NotImplementedError('partition function has to be implemented')

12
kafka/partitioner/hashed.py Normal file
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@@ -0,0 +1,12 @@
from .base import Partitioner
class HashedPartitioner(Partitioner):
"""
Implements a partitioner which selects the target partition based on
the hash of the key
"""
def partition(self, key, partitions):
size = len(partitions)
idx = hash(key) % size
return partitions[idx]

23
kafka/partitioner/roundrobin.py Normal file
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@@ -0,0 +1,23 @@
from itertools import cycle
from .base import Partitioner
class RoundRobinPartitioner(Partitioner):
"""
Implements a round robin partitioner which sends data to partitions
in a round robin fashion
"""
def __init__(self, partitions):
super(RoundRobinPartitioner, self).__init__(partitions)
self.iterpart = cycle(partitions)
def _set_partitions(self, partitions):
self.partitions = partitions
self.iterpart = cycle(partitions)
def partition(self, key, partitions):
# Refresh the partition list if necessary
if self.partitions != partitions:
self._set_partitions(partitions)
return next(self.iterpart)

6
kafka/producer/__init__.py Normal file
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@@ -0,0 +1,6 @@
from .simple import SimpleProducer
from .keyed import KeyedProducer
__all__ = [
'SimpleProducer', 'KeyedProducer'
]

115
kafka/producer.py → kafka/producer/base.py
View File

@@ -2,23 +2,19 @@ from __future__ import absolute_import
import logging
import time
import random
try:
from queue import Empty
except ImportError:
from Queue import Empty
from collections import defaultdict
from itertools import cycle
from multiprocessing import Queue, Process
import six
from six.moves import xrange
from kafka.common import (
ProduceRequest, TopicAndPartition, UnsupportedCodecError, UnknownTopicOrPartitionError
ProduceRequest, TopicAndPartition, UnsupportedCodecError
)
from kafka.partitioner import HashedPartitioner
from kafka.protocol import CODEC_NONE, ALL_CODECS, create_message_set
log = logging.getLogger("kafka")
@@ -208,112 +204,3 @@ class Producer(object):
if self.proc.is_alive():
self.proc.terminate()
class SimpleProducer(Producer):
"""
A simple, round-robin producer. Each message goes to exactly one partition
Params:
client - The Kafka client instance to use
async - If True, the messages are sent asynchronously via another
thread (process). We will not wait for a response to these
req_acks - A value indicating the acknowledgements that the server must
receive before responding to the request
ack_timeout - Value (in milliseconds) indicating a timeout for waiting
for an acknowledgement
batch_send - If True, messages are send in batches
batch_send_every_n - If set, messages are send in batches of this size
batch_send_every_t - If set, messages are send after this timeout
random_start - If true, randomize the initial partition which the
the first message block will be published to, otherwise
if false, the first message block will always publish
to partition 0 before cycling through each partition
"""
def __init__(self, client, async=False,
req_acks=Producer.ACK_AFTER_LOCAL_WRITE,
ack_timeout=Producer.DEFAULT_ACK_TIMEOUT,
codec=None,
batch_send=False,
batch_send_every_n=BATCH_SEND_MSG_COUNT,
batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL,
random_start=False):
self.partition_cycles = {}
self.random_start = random_start
super(SimpleProducer, self).__init__(client, async, req_acks,
ack_timeout, codec, batch_send,
batch_send_every_n,
batch_send_every_t)
def _next_partition(self, topic):
if topic not in self.partition_cycles:
if not self.client.has_metadata_for_topic(topic):
self.client.load_metadata_for_topics(topic)
self.partition_cycles[topic] = cycle(self.client.get_partition_ids_for_topic(topic))
# Randomize the initial partition that is returned
if self.random_start:
num_partitions = len(self.client.get_partition_ids_for_topic(topic))
for _ in xrange(random.randint(0, num_partitions-1)):
next(self.partition_cycles[topic])
return next(self.partition_cycles[topic])
def send_messages(self, topic, *msg):
partition = self._next_partition(topic)
return super(SimpleProducer, self).send_messages(topic, partition, *msg)
def __repr__(self):
return '<SimpleProducer batch=%s>' % self.async
class KeyedProducer(Producer):
"""
A producer which distributes messages to partitions based on the key
Args:
client - The kafka client instance
partitioner - A partitioner class that will be used to get the partition
to send the message to. Must be derived from Partitioner
async - If True, the messages are sent asynchronously via another
thread (process). We will not wait for a response to these
ack_timeout - Value (in milliseconds) indicating a timeout for waiting
for an acknowledgement
batch_send - If True, messages are send in batches
batch_send_every_n - If set, messages are send in batches of this size
batch_send_every_t - If set, messages are send after this timeout
"""
def __init__(self, client, partitioner=None, async=False,
req_acks=Producer.ACK_AFTER_LOCAL_WRITE,
ack_timeout=Producer.DEFAULT_ACK_TIMEOUT,
codec=None,
batch_send=False,
batch_send_every_n=BATCH_SEND_MSG_COUNT,
batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL):
if not partitioner:
partitioner = HashedPartitioner
self.partitioner_class = partitioner
self.partitioners = {}
super(KeyedProducer, self).__init__(client, async, req_acks,
ack_timeout, codec, batch_send,
batch_send_every_n,
batch_send_every_t)
def _next_partition(self, topic, key):
if topic not in self.partitioners:
if not self.client.has_metadata_for_topic(topic):
self.client.load_metadata_for_topics(topic)
self.partitioners[topic] = self.partitioner_class(self.client.get_partition_ids_for_topic(topic))
partitioner = self.partitioners[topic]
return partitioner.partition(key, self.client.get_partition_ids_for_topic(topic))
def send(self, topic, key, msg):
partition = self._next_partition(topic, key)
return self.send_messages(topic, partition, msg)
def __repr__(self):
return '<KeyedProducer batch=%s>' % self.async

62
kafka/producer/keyed.py Normal file
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@@ -0,0 +1,62 @@
from __future__ import absolute_import
import logging
from kafka.partitioner import HashedPartitioner
from .base import (
Producer, BATCH_SEND_DEFAULT_INTERVAL,
BATCH_SEND_MSG_COUNT
)
log = logging.getLogger("kafka")
class KeyedProducer(Producer):
"""
A producer which distributes messages to partitions based on the key
Args:
client - The kafka client instance
partitioner - A partitioner class that will be used to get the partition
to send the message to. Must be derived from Partitioner
async - If True, the messages are sent asynchronously via another
thread (process). We will not wait for a response to these
ack_timeout - Value (in milliseconds) indicating a timeout for waiting
for an acknowledgement
batch_send - If True, messages are send in batches
batch_send_every_n - If set, messages are send in batches of this size
batch_send_every_t - If set, messages are send after this timeout
"""
def __init__(self, client, partitioner=None, async=False,
req_acks=Producer.ACK_AFTER_LOCAL_WRITE,
ack_timeout=Producer.DEFAULT_ACK_TIMEOUT,
codec=None,
batch_send=False,
batch_send_every_n=BATCH_SEND_MSG_COUNT,
batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL):
if not partitioner:
partitioner = HashedPartitioner
self.partitioner_class = partitioner
self.partitioners = {}
super(KeyedProducer, self).__init__(client, async, req_acks,
ack_timeout, codec, batch_send,
batch_send_every_n,
batch_send_every_t)
def _next_partition(self, topic, key):
if topic not in self.partitioners:
if not self.client.has_metadata_for_topic(topic):
self.client.load_metadata_for_topics(topic)
self.partitioners[topic] = self.partitioner_class(self.client.get_partition_ids_for_topic(topic))
partitioner = self.partitioners[topic]
return partitioner.partition(key, self.client.get_partition_ids_for_topic(topic))
def send(self, topic, key, msg):
partition = self._next_partition(topic, key)
return self.send_messages(topic, partition, msg)
def __repr__(self):
return '<KeyedProducer batch=%s>' % self.async

73
kafka/producer/simple.py Normal file
View File

@@ -0,0 +1,73 @@
from __future__ import absolute_import
import logging
import random
from itertools import cycle
from six.moves import xrange
from .base import (
Producer, BATCH_SEND_DEFAULT_INTERVAL,
BATCH_SEND_MSG_COUNT
)
log = logging.getLogger("kafka")
class SimpleProducer(Producer):
"""
A simple, round-robin producer. Each message goes to exactly one partition
Params:
client - The Kafka client instance to use
async - If True, the messages are sent asynchronously via another
thread (process). We will not wait for a response to these
req_acks - A value indicating the acknowledgements that the server must
receive before responding to the request
ack_timeout - Value (in milliseconds) indicating a timeout for waiting
for an acknowledgement
batch_send - If True, messages are send in batches
batch_send_every_n - If set, messages are send in batches of this size
batch_send_every_t - If set, messages are send after this timeout
random_start - If true, randomize the initial partition which the
the first message block will be published to, otherwise
if false, the first message block will always publish
to partition 0 before cycling through each partition
"""
def __init__(self, client, async=False,
req_acks=Producer.ACK_AFTER_LOCAL_WRITE,
ack_timeout=Producer.DEFAULT_ACK_TIMEOUT,
codec=None,
batch_send=False,
batch_send_every_n=BATCH_SEND_MSG_COUNT,
batch_send_every_t=BATCH_SEND_DEFAULT_INTERVAL,
random_start=False):
self.partition_cycles = {}
self.random_start = random_start
super(SimpleProducer, self).__init__(client, async, req_acks,
ack_timeout, codec, batch_send,
batch_send_every_n,
batch_send_every_t)
def _next_partition(self, topic):
if topic not in self.partition_cycles:
if not self.client.has_metadata_for_topic(topic):
self.client.load_metadata_for_topics(topic)
self.partition_cycles[topic] = cycle(self.client.get_partition_ids_for_topic(topic))
# Randomize the initial partition that is returned
if self.random_start:
num_partitions = len(self.client.get_partition_ids_for_topic(topic))
for _ in xrange(random.randint(0, num_partitions-1)):
next(self.partition_cycles[topic])
return next(self.partition_cycles[topic])
def send_messages(self, topic, *msg):
partition = self._next_partition(topic)
return super(SimpleProducer, self).send_messages(topic, partition, *msg)
def __repr__(self):
return '<SimpleProducer batch=%s>' % self.async

2
test/test_consumer_integration.py
View File

@@ -4,7 +4,7 @@ from six.moves import xrange
from kafka import SimpleConsumer, MultiProcessConsumer, create_message
from kafka.common import ProduceRequest, ConsumerFetchSizeTooSmall
from kafka.consumer import MAX_FETCH_BUFFER_SIZE_BYTES
from kafka.consumer.base import MAX_FETCH_BUFFER_SIZE_BYTES
from test.fixtures import ZookeeperFixture, KafkaFixture
from test.testutil import (

2
test/test_failover_integration.py
View File

@@ -6,7 +6,7 @@ from . import unittest
from kafka import KafkaClient, SimpleConsumer
from kafka.common import TopicAndPartition, FailedPayloadsError, ConnectionError
from kafka.producer import Producer
from kafka.producer.base import Producer
from test.fixtures import ZookeeperFixture, KafkaFixture
from test.testutil import (

2
test/test_producer.py
View File

@@ -5,7 +5,7 @@ import logging
from mock import MagicMock
from . import unittest
from kafka.producer import Producer
from kafka.producer.base import Producer
class TestKafkaProducer(unittest.TestCase):
def test_producer_message_types(self):