def fetch(self, max_size=None):
log.debug("Fetch called on SimpleConsumer {0}".format(self.id))
bps_to_offsets = self._bps_to_next_offsets
# Do all the fetches we need to (this should get replaced with
# multifetch or performance is going to suck wind later)...
message_sets = []
# We only iterate over those broker partitions for which we have offsets
for bp in bps_to_offsets:
offset = bps_to_offsets[bp]
kafka = self._connections[bp.broker_id]
offsets_msgs, next_offset = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
msg_set = MessageSet(bp, offset, offsets_msgs)
msg_set.next_offset = next_offset
# fetches bytes messages max_fetch
old_stats = self._stats[bp]
self._stats[bp] = ConsumerStats(fetches=old_stats.fetches + 1,
bytes=old_stats.bytes + msg_set.size,
messages=old_stats.messages + len(msg_set),
max_fetch=max(old_stats.max_fetch, msg_set.size))
message_sets.append(msg_set)
if message_sets:
result = FetchResult(sorted(message_sets))
else:
result = []
# Filter out broker partitions whose end offsets we've exceeded
self._bps_to_next_offsets = {}
for msg_result in result:
bp = msg_result.broker_partition
next_offset = msg_result.next_offset
end_offset = self._end_broker_partitions.get(bp, None)
if end_offset is None or next_offset <= end_offset:
self._bps_to_next_offsets[bp] = next_offset
return result
def fetch(self, max_size=None, min_size=None, fetch_step=None):
log.debug("Fetch called on SimpleConsumer {0}".format(self.id))
bps_to_offsets = self._bps_to_next_offsets
# Do all the fetches we need to (this should get replaced with
# multifetch or performance is going to suck wind later)...
message_sets = []
# We only iterate over those broker partitions for which we have offsets
for bp in bps_to_offsets:
offset = bps_to_offsets[bp]
kafka = self._connections[bp.broker_id]
offsets_msgs = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
min_size=min_size,
max_size=max_size,
fetch_step=fetch_step)
msg_set = MessageSet(bp, offset, offsets_msgs)
# fetches bytes messages max_fetch
old_stats = self._stats[bp]
self._stats[bp] = ConsumerStats(
fetches=old_stats.fetches + 1,
bytes=old_stats.bytes + msg_set.size,
messages=old_stats.messages + len(msg_set),
max_fetch=max(old_stats.max_fetch, msg_set.size))
message_sets.append(msg_set)
if message_sets:
result = FetchResult(sorted(message_sets))
else:
result = FetchResult([])
# Filter out broker partitions whose end offsets we've exceeded
self._bps_to_next_offsets = {}
for msg_result in result:
bp = msg_result.broker_partition
next_offset = msg_result.next_offset
end_offset = self._end_broker_partitions.get(bp, None)
if end_offset is None or next_offset <= end_offset:
self._bps_to_next_offsets[bp] = next_offset
return result
def fetch(self, max_size=None, retry_limit=3, ignore_failures=False):
"""Return a FetchResult, which can be iterated over as a list of
MessageSets. A MessageSet is returned for every broker partition that
is successfully queried, even if that MessageSet is empty.
FIXME: This is where the adjustment needs to happen. Regardless of
whether a rebalance has occurred or not, we can very easily see if we
are still responsible for the same partitions as we were the last time
we ran, and set self._bps_to_next_offsets --> we just need to check if
it's not None and if we still have the same offsets, and adjust
accordingly.
"""
def needs_offset_values_from_zk(bps_to_offsets):
"""We need to pull offset values from ZK if we have no
BrokerPartitions in our BPs -> Offsets mapping, or if some of those
Offsets are unknown (None)"""
return (not bps_to_offsets) or (None in bps_to_offsets.values())
log.debug("Fetch called on ZKConsumer {0}".format(self.id))
if self._needs_rebalance:
self.rebalance()
# Find where we're starting from. If we've already done a fetch, we use
# our internal value. This is also all we can do in the case where
# autocommit is off, since any value in ZK will be out of date.
bps_to_offsets = dict(self._bps_to_next_offsets)
offsets_pulled_from_zk = False
if needs_offset_values_from_zk(bps_to_offsets):
# We have some offsets, but we've been made responsible for new
# BrokerPartitions that we need to lookup.
if bps_to_offsets:
bps_needing_offsets = [bp for bp, offset in bps_to_offsets.items()
if offset is None]
# Otherwise, it's our first fetch, so we need everything
else:
bps_needing_offsets = self.broker_partitions
bps_to_offsets.update(self._zk_util.offsets_for(self.consumer_group,
self._id,
bps_needing_offsets))
offsets_pulled_from_zk = True
# Do all the fetches we need to (this should get replaced with
# multifetch or performance is going to suck wind later)...
message_sets = []
# We only iterate over those broker partitions for which we have offsets
for bp in bps_to_offsets:
offset = bps_to_offsets[bp]
kafka = self._connections[bp.broker_id]
partition = kafka.partition(bp.topic, bp.partition)
if offset is None:
offset = partition.latest_offset()
try:
offsets_msgs, next_offset = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
# If our fetch fails because it's out of range, and the values came
# from ZK originally (not our internal incrementing), we assume ZK
# is somehow stale, so we just grab the latest and march on.
except OffsetOutOfRange as ex:
if offsets_pulled_from_zk:
log.error("Offset {0} from ZooKeeper is out of range for {1}"
.format(offset, bp))
offset = partition.latest_offset()
log.error("Retrying with offset {0} for {1}"
.format(offset, bp))
offsets_msgs, next_offset = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
else:
raise
except KafkaError as k_err:
if ignore_failures:
log.error("Ignoring failed fetch on {0}".format(bp))
log.exception(k_err)
continue
else:
raise
msg_set = MessageSet(bp, offset, offsets_msgs)
msg_set.next_offset = next_offset
# fetches bytes messages max_fetch
old_stats = self._stats[bp]
self._stats[bp] = ConsumerStats(fetches=old_stats.fetches + 1,
bytes=old_stats.bytes + msg_set.size,
messages=old_stats.messages + len(msg_set),
max_fetch=max(old_stats.max_fetch, msg_set.size))
message_sets.append(msg_set)
result = FetchResult(sorted(message_sets))
# Now persist our new offsets
for msg_set in result:
self._bps_to_next_offsets[msg_set.broker_partition] = msg_set.next_offset
if self._autocommit:
self.commit_offsets()
return result
def fetch(self, max_size=None, retry_limit=3, ignore_failures=False):
"""Return a FetchResult, which can be iterated over as a list of
MessageSets. A MessageSet is returned for every broker partition that
is successfully queried, even if that MessageSet is empty.
FIXME: This is where the adjustment needs to happen. Regardless of
whether a rebalance has occurred or not, we can very easily see if we
are still responsible for the same partitions as we were the last time
we ran, and set self._bps_to_next_offsets --> we just need to check if
it's not None and if we still have the same offsets, and adjust
accordingly.
"""
log.debug("Fetch called on ZKConsumer {0}".format(self.id))
if self._needs_rebalance:
self.rebalance()
# Find where we're starting from...
offsets_pulled_from_zk = False
if self._bps_to_next_offsets:
# We've already done a fetch, we use our internal value. This is
# also all we can do in the case where autocommit is off, since any
# value in ZK will be out of date
bps_to_offsets = self._bps_to_next_offsets
else:
# In this case, it's our first fetch, and we need to ask ZooKeeper
# for our start value. That being said, if the value from ZooKeeper
# is out of range for any given partition, we'll simply start at the
# most recent value for that partition.
bps_to_offsets = self._zk_util.offsets_for(self.consumer_group,
self._id,
self.broker_partitions)
offsets_pulled_from_zk = True
# Do all the fetches we need to (this should get replaced with
# multifetch or performance is going to suck wind later)...
message_sets = []
# We only iterate over those broker partitions for which we have offsets
for bp in bps_to_offsets:
offset = bps_to_offsets[bp]
kafka = self._connections[bp.broker_id]
partition = kafka.partition(bp.topic, bp.partition)
if offset is None:
offset = partition.latest_offset()
try:
offsets_msgs = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
# If our fetch fails because it's out of range, and the values came
# from ZK originally (not our internal incrementing), we assume ZK
# is somehow stale, so we just grab the latest and march on.
except OffsetOutOfRange as ex:
if offsets_pulled_from_zk:
log.error(
"Offset {0} from ZooKeeper is out of range for {1}".
format(offset, bp))
offset = partition.latest_offset()
log.error("Retrying with offset {0} for {1}".format(
offset, bp))
offsets_msgs = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
else:
raise
except KafkaError as k_err:
if ignore_failures:
log.error("Ignoring failed fetch on {0}".format(bp))
log.exception(k_err)
continue
else:
raise
message_sets.append(MessageSet(bp, offset, offsets_msgs))
result = FetchResult(sorted(message_sets))
# Now persist our new offsets
for msg_set in result:
self._bps_to_next_offsets[
msg_set.broker_partition] = msg_set.next_offset
if self._autocommit:
self.commit_offsets()
old_stats = self._stats # fetches bytes messages max_fetch
self._stats = ConsumerStats(
fetches=old_stats.fetches + 1,
bytes=old_stats.bytes + result.num_bytes,
messages=old_stats.messages + result.num_messages,
max_fetch=max(old_stats.max_fetch, result.num_bytes))
return result
def fetch(self, max_size=None, retry_limit=3, ignore_failures=False):
"""Return a FetchResult, which can be iterated over as a list of
MessageSets. A MessageSet is returned for every broker partition that
is successfully queried, even if that MessageSet is empty.
FIXME: This is where the adjustment needs to happen. Regardless of
whether a rebalance has occurred or not, we can very easily see if we
are still responsible for the same partitions as we were the last time
we ran, and set self._bps_to_next_offsets --> we just need to check if
it's not None and if we still have the same offsets, and adjust
accordingly.
"""
def needs_offset_values_from_zk(bps_to_offsets):
"""We need to pull offset values from ZK if we have no
BrokerPartitions in our BPs -> Offsets mapping, or if some of those
Offsets are unknown (None)"""
return (not bps_to_offsets) or (None in bps_to_offsets.values())
log.debug("Fetch called on ZKConsumer {0}".format(self.id))
if self._needs_rebalance:
self.rebalance()
# Find where we're starting from. If we've already done a fetch, we use
# our internal value. This is also all we can do in the case where
# autocommit is off, since any value in ZK will be out of date.
bps_to_offsets = dict(self._bps_to_next_offsets)
offsets_pulled_from_zk = False
if needs_offset_values_from_zk(bps_to_offsets):
# We have some offsets, but we've been made responsible for new
# BrokerPartitions that we need to lookup.
if bps_to_offsets:
bps_needing_offsets = [
bp for bp, offset in bps_to_offsets.items()
if offset is None
]
# Otherwise, it's our first fetch, so we need everything
else:
bps_needing_offsets = self.broker_partitions
bps_to_offsets.update(
self._zk_util.offsets_for(self.consumer_group, self._id,
bps_needing_offsets))
offsets_pulled_from_zk = True
# Do all the fetches we need to (this should get replaced with
# multifetch or performance is going to suck wind later)...
message_sets = []
# We only iterate over those broker partitions for which we have offsets
for bp in bps_to_offsets:
offset = bps_to_offsets[bp]
kafka = self._connections[bp.broker_id]
partition = kafka.partition(bp.topic, bp.partition)
if offset is None:
offset = partition.latest_offset()
try:
offsets_msgs = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
# If our fetch fails because it's out of range, and the values came
# from ZK originally (not our internal incrementing), we assume ZK
# is somehow stale, so we just grab the latest and march on.
except OffsetOutOfRange as ex:
if offsets_pulled_from_zk:
log.error(
"Offset {0} from ZooKeeper is out of range for {1}".
format(offset, bp))
offset = partition.latest_offset()
log.error("Retrying with offset {0} for {1}".format(
offset, bp))
offsets_msgs = kafka.fetch(bp.topic,
offset,
partition=bp.partition,
max_size=max_size)
else:
raise
except KafkaError as k_err:
if ignore_failures:
log.error("Ignoring failed fetch on {0}".format(bp))
log.exception(k_err)
continue
else:
raise
msg_set = MessageSet(bp, offset, offsets_msgs)
# fetches bytes messages max_fetch
old_stats = self._stats[bp]
self._stats[bp] = ConsumerStats(
fetches=old_stats.fetches + 1,
bytes=old_stats.bytes + msg_set.size,
messages=old_stats.messages + len(msg_set),
max_fetch=max(old_stats.max_fetch, msg_set.size))
message_sets.append(msg_set)
result = FetchResult(sorted(message_sets))
# Now persist our new offsets
for msg_set in result:
self._bps_to_next_offsets[
msg_set.broker_partition] = msg_set.next_offset
if self._autocommit:
self.commit_offsets()
return result
