cb2ec23cee
To let other OpenStack projects move forward with new versions of kafka-python we're forking kafka-python and embedding it in monasca-common. This allows us to migrate to the new async interfaces provided by more recent kafka clients over time and not block other projects. Requiring pykafka to allow us to have ~4x more throughput once we write to their async interfaces. Change-Id: Ifb6ab67ce1335a5ec4ed7dd8b0027dc9d46a6dda Depends-On: I26f9c588f2818059ab6ba24f9fad8e213798a39c
677 lines
27 KiB
Python
677 lines
27 KiB
Python
import collections
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import copy
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import functools
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import logging
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import select
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import time
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import monasca_common.kafka_lib.common as kafka_common
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from monasca_common.kafka_lib.common import (TopicAndPartition, BrokerMetadata,
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ConnectionError, FailedPayloadsError,
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KafkaTimeoutError, KafkaUnavailableError,
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LeaderNotAvailableError, UnknownTopicOrPartitionError,
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NotLeaderForPartitionError, ReplicaNotAvailableError)
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from monasca_common.kafka_lib.conn import collect_hosts, KafkaConnection, DEFAULT_SOCKET_TIMEOUT_SECONDS
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from monasca_common.kafka_lib.protocol import KafkaProtocol
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from monasca_common.kafka_lib.util import kafka_bytestring
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log = logging.getLogger(__name__)
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class KafkaClient(object):
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CLIENT_ID = b'kafka-python'
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# NOTE: The timeout given to the client should always be greater than the
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# one passed to SimpleConsumer.get_message(), otherwise you can get a
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# socket timeout.
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def __init__(self, hosts, client_id=CLIENT_ID,
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timeout=DEFAULT_SOCKET_TIMEOUT_SECONDS,
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correlation_id=0):
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# We need one connection to bootstrap
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self.client_id = kafka_bytestring(client_id)
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self.timeout = timeout
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self.hosts = collect_hosts(hosts)
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self.correlation_id = correlation_id
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# create connections only when we need them
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self.conns = {}
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self.brokers = {} # broker_id -> BrokerMetadata
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self.topics_to_brokers = {} # TopicAndPartition -> BrokerMetadata
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self.topic_partitions = {} # topic -> partition -> PartitionMetadata
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self.load_metadata_for_topics() # bootstrap with all metadata
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##################
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# Private API #
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##################
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def _get_conn(self, host, port):
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"""Get or create a connection to a broker using host and port"""
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host_key = (host, port)
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if host_key not in self.conns:
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self.conns[host_key] = KafkaConnection(
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host,
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port,
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timeout=self.timeout
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)
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return self.conns[host_key]
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def _get_leader_for_partition(self, topic, partition):
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"""
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Returns the leader for a partition or None if the partition exists
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but has no leader.
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UnknownTopicOrPartitionError will be raised if the topic or partition
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is not part of the metadata.
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LeaderNotAvailableError is raised if server has metadata, but there is
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no current leader
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"""
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key = TopicAndPartition(topic, partition)
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# Use cached metadata if it is there
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if self.topics_to_brokers.get(key) is not None:
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return self.topics_to_brokers[key]
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# Otherwise refresh metadata
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# If topic does not already exist, this will raise
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# UnknownTopicOrPartitionError if not auto-creating
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# LeaderNotAvailableError otherwise until partitions are created
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self.load_metadata_for_topics(topic)
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# If the partition doesn't actually exist, raise
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if partition not in self.topic_partitions.get(topic, []):
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raise UnknownTopicOrPartitionError(key)
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# If there's no leader for the partition, raise
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meta = self.topic_partitions[topic][partition]
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if meta.leader == -1:
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raise LeaderNotAvailableError(meta)
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# Otherwise return the BrokerMetadata
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return self.brokers[meta.leader]
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def _get_coordinator_for_group(self, group):
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"""
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Returns the coordinator broker for a consumer group.
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ConsumerCoordinatorNotAvailableCode will be raised if the coordinator
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does not currently exist for the group.
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OffsetsLoadInProgressCode is raised if the coordinator is available
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but is still loading offsets from the internal topic
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"""
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resp = self.send_consumer_metadata_request(group)
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# If there's a problem with finding the coordinator, raise the
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# provided error
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kafka_common.check_error(resp)
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# Otherwise return the BrokerMetadata
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return BrokerMetadata(resp.nodeId, resp.host, resp.port)
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def _next_id(self):
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"""Generate a new correlation id"""
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# modulo to keep w/i int32
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self.correlation_id = (self.correlation_id + 1) % 2**31
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return self.correlation_id
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def _send_broker_unaware_request(self, payloads, encoder_fn, decoder_fn):
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"""
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Attempt to send a broker-agnostic request to one of the available
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brokers. Keep trying until you succeed.
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"""
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for (host, port) in self.hosts:
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requestId = self._next_id()
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log.debug('Request %s: %s', requestId, payloads)
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try:
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conn = self._get_conn(host, port)
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request = encoder_fn(client_id=self.client_id,
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correlation_id=requestId,
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payloads=payloads)
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conn.send(requestId, request)
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response = conn.recv(requestId)
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decoded = decoder_fn(response)
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log.debug('Response %s: %s', requestId, decoded)
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return decoded
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except Exception:
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log.exception('Error sending request [%s] to server %s:%s, '
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'trying next server', requestId, host, port)
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raise KafkaUnavailableError('All servers failed to process request')
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def _send_broker_aware_request(self, payloads, encoder_fn, decoder_fn):
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"""
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Group a list of request payloads by topic+partition and send them to
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the leader broker for that partition using the supplied encode/decode
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functions
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Arguments:
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payloads: list of object-like entities with a topic (str) and
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partition (int) attribute; payloads with duplicate topic-partitions
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are not supported.
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encode_fn: a method to encode the list of payloads to a request body,
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must accept client_id, correlation_id, and payloads as
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keyword arguments
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decode_fn: a method to decode a response body into response objects.
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The response objects must be object-like and have topic
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and partition attributes
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Returns:
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List of response objects in the same order as the supplied payloads
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"""
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# encoders / decoders do not maintain ordering currently
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# so we need to keep this so we can rebuild order before returning
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original_ordering = [(p.topic, p.partition) for p in payloads]
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# Group the requests by topic+partition
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brokers_for_payloads = []
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payloads_by_broker = collections.defaultdict(list)
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responses = {}
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for payload in payloads:
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try:
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leader = self._get_leader_for_partition(payload.topic,
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payload.partition)
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payloads_by_broker[leader].append(payload)
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brokers_for_payloads.append(leader)
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except KafkaUnavailableError as e:
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log.warning('KafkaUnavailableError attempting to send request '
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'on topic %s partition %d', payload.topic, payload.partition)
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = FailedPayloadsError(payload)
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# For each broker, send the list of request payloads
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# and collect the responses and errors
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broker_failures = []
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# For each KafkaConnection keep the real socket so that we can use
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# a select to perform unblocking I/O
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connections_by_socket = {}
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for broker, payloads in payloads_by_broker.items():
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requestId = self._next_id()
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log.debug('Request %s to %s: %s', requestId, broker, payloads)
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request = encoder_fn(client_id=self.client_id,
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correlation_id=requestId, payloads=payloads)
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# Send the request, recv the response
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try:
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conn = self._get_conn(broker.host.decode('utf-8'), broker.port)
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conn.send(requestId, request)
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except ConnectionError as e:
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broker_failures.append(broker)
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log.warning('ConnectionError attempting to send request %s '
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'to server %s: %s', requestId, broker, e)
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for payload in payloads:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = FailedPayloadsError(payload)
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# No exception, try to get response
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else:
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# decoder_fn=None signal that the server is expected to not
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# send a response. This probably only applies to
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# ProduceRequest w/ acks = 0
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if decoder_fn is None:
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log.debug('Request %s does not expect a response '
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'(skipping conn.recv)', requestId)
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for payload in payloads:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = None
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continue
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else:
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connections_by_socket[conn.get_connected_socket()] = (conn, broker, requestId)
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conn = None
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while connections_by_socket:
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sockets = connections_by_socket.keys()
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rlist, _, _ = select.select(sockets, [], [], None)
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conn, broker, requestId = connections_by_socket.pop(rlist[0])
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try:
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response = conn.recv(requestId)
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except ConnectionError as e:
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broker_failures.append(broker)
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log.warning('ConnectionError attempting to receive a '
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'response to request %s from server %s: %s',
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requestId, broker, e)
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for payload in payloads_by_broker[broker]:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = FailedPayloadsError(payload)
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else:
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_resps = []
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for payload_response in decoder_fn(response):
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topic_partition = (payload_response.topic,
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payload_response.partition)
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responses[topic_partition] = payload_response
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_resps.append(payload_response)
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log.debug('Response %s: %s', requestId, _resps)
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# Connection errors generally mean stale metadata
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# although sometimes it means incorrect api request
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# Unfortunately there is no good way to tell the difference
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# so we'll just reset metadata on all errors to be safe
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if broker_failures:
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self.reset_all_metadata()
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# Return responses in the same order as provided
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return [responses[tp] for tp in original_ordering]
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def _send_consumer_aware_request(self, group, payloads, encoder_fn, decoder_fn):
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"""
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Send a list of requests to the consumer coordinator for the group
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specified using the supplied encode/decode functions. As the payloads
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that use consumer-aware requests do not contain the group (e.g.
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OffsetFetchRequest), all payloads must be for a single group.
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Arguments:
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group: the name of the consumer group (str) the payloads are for
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payloads: list of object-like entities with topic (str) and
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partition (int) attributes; payloads with duplicate
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topic+partition are not supported.
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encode_fn: a method to encode the list of payloads to a request body,
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must accept client_id, correlation_id, and payloads as
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keyword arguments
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decode_fn: a method to decode a response body into response objects.
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The response objects must be object-like and have topic
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and partition attributes
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Returns:
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List of response objects in the same order as the supplied payloads
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"""
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# encoders / decoders do not maintain ordering currently
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# so we need to keep this so we can rebuild order before returning
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original_ordering = [(p.topic, p.partition) for p in payloads]
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broker = self._get_coordinator_for_group(group)
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# Send the list of request payloads and collect the responses and
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# errors
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responses = {}
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requestId = self._next_id()
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log.debug('Request %s to %s: %s', requestId, broker, payloads)
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request = encoder_fn(client_id=self.client_id,
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correlation_id=requestId, payloads=payloads)
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# Send the request, recv the response
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try:
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conn = self._get_conn(broker.host.decode('utf-8'), broker.port)
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conn.send(requestId, request)
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except ConnectionError as e:
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log.warning('ConnectionError attempting to send request %s '
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'to server %s: %s', requestId, broker, e)
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for payload in payloads:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = FailedPayloadsError(payload)
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# No exception, try to get response
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else:
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# decoder_fn=None signal that the server is expected to not
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# send a response. This probably only applies to
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# ProduceRequest w/ acks = 0
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if decoder_fn is None:
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log.debug('Request %s does not expect a response '
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'(skipping conn.recv)', requestId)
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for payload in payloads:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = None
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return []
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try:
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response = conn.recv(requestId)
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except ConnectionError as e:
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log.warning('ConnectionError attempting to receive a '
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'response to request %s from server %s: %s',
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requestId, broker, e)
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for payload in payloads:
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topic_partition = (payload.topic, payload.partition)
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responses[topic_partition] = FailedPayloadsError(payload)
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else:
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_resps = []
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for payload_response in decoder_fn(response):
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topic_partition = (payload_response.topic,
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payload_response.partition)
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responses[topic_partition] = payload_response
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_resps.append(payload_response)
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log.debug('Response %s: %s', requestId, _resps)
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# Return responses in the same order as provided
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return [responses[tp] for tp in original_ordering]
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def __repr__(self):
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return '<KafkaClient client_id=%s>' % (self.client_id)
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def _raise_on_response_error(self, resp):
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# Response can be an unraised exception object (FailedPayloadsError)
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if isinstance(resp, Exception):
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raise resp
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# Or a server api error response
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try:
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kafka_common.check_error(resp)
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except (UnknownTopicOrPartitionError, NotLeaderForPartitionError):
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self.reset_topic_metadata(resp.topic)
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raise
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# Return False if no error to enable list comprehensions
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return False
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#################
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# Public API #
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#################
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def close(self):
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for conn in self.conns.values():
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conn.close()
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def copy(self):
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"""
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Create an inactive copy of the client object, suitable for passing
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to a separate thread.
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Note that the copied connections are not initialized, so reinit() must
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be called on the returned copy.
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"""
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c = copy.deepcopy(self)
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for key in c.conns:
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c.conns[key] = self.conns[key].copy()
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return c
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def reinit(self):
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for conn in self.conns.values():
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conn.reinit()
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def reset_topic_metadata(self, *topics):
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for topic in topics:
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for topic_partition in list(self.topics_to_brokers.keys()):
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if topic_partition.topic == topic:
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del self.topics_to_brokers[topic_partition]
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if topic in self.topic_partitions:
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del self.topic_partitions[topic]
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def reset_all_metadata(self):
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self.topics_to_brokers.clear()
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self.topic_partitions.clear()
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def has_metadata_for_topic(self, topic):
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topic = kafka_bytestring(topic)
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return (
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topic in self.topic_partitions
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and len(self.topic_partitions[topic]) > 0
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)
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def get_partition_ids_for_topic(self, topic):
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topic = kafka_bytestring(topic)
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if topic not in self.topic_partitions:
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return []
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return sorted(list(self.topic_partitions[topic]))
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@property
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def topics(self):
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return list(self.topic_partitions.keys())
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def ensure_topic_exists(self, topic, timeout = 30):
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start_time = time.time()
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while not self.has_metadata_for_topic(topic):
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if time.time() > start_time + timeout:
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raise KafkaTimeoutError('Unable to create topic {0}'.format(topic))
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try:
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self.load_metadata_for_topics(topic)
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except LeaderNotAvailableError:
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pass
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except UnknownTopicOrPartitionError:
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# Server is not configured to auto-create
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# retrying in this case will not help
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raise
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time.sleep(.5)
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def load_metadata_for_topics(self, *topics):
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"""
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Fetch broker and topic-partition metadata from the server,
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and update internal data:
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broker list, topic/partition list, and topic/parition -> broker map
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This method should be called after receiving any error
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Arguments:
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*topics (optional): If a list of topics is provided,
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the metadata refresh will be limited to the specified topics only.
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Exceptions:
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----------
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If the broker is configured to not auto-create topics,
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expect UnknownTopicOrPartitionError for topics that don't exist
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If the broker is configured to auto-create topics,
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expect LeaderNotAvailableError for new topics
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until partitions have been initialized.
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Exceptions *will not* be raised in a full refresh (i.e. no topic list)
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In this case, error codes will be logged as errors
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Partition-level errors will also not be raised here
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(a single partition w/o a leader, for example)
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"""
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topics = [kafka_bytestring(t) for t in topics]
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if topics:
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for topic in topics:
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self.reset_topic_metadata(topic)
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else:
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self.reset_all_metadata()
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resp = self.send_metadata_request(topics)
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log.debug('Updating broker metadata: %s', resp.brokers)
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log.debug('Updating topic metadata: %s', resp.topics)
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self.brokers = dict([(broker.nodeId, broker)
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for broker in resp.brokers])
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for topic_metadata in resp.topics:
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topic = topic_metadata.topic
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partitions = topic_metadata.partitions
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# Errors expected for new topics
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try:
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kafka_common.check_error(topic_metadata)
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except (UnknownTopicOrPartitionError, LeaderNotAvailableError) as e:
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# Raise if the topic was passed in explicitly
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if topic in topics:
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raise
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# Otherwise, just log a warning
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log.error('Error loading topic metadata for %s: %s', topic, type(e))
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continue
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self.topic_partitions[topic] = {}
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for partition_metadata in partitions:
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partition = partition_metadata.partition
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leader = partition_metadata.leader
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self.topic_partitions[topic][partition] = partition_metadata
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|
|
# Populate topics_to_brokers dict
|
|
topic_part = TopicAndPartition(topic, partition)
|
|
|
|
# Check for partition errors
|
|
try:
|
|
kafka_common.check_error(partition_metadata)
|
|
|
|
# If No Leader, topics_to_brokers topic_partition -> None
|
|
except LeaderNotAvailableError:
|
|
log.error('No leader for topic %s partition %d', topic, partition)
|
|
self.topics_to_brokers[topic_part] = None
|
|
continue
|
|
# If one of the replicas is unavailable -- ignore
|
|
# this error code is provided for admin purposes only
|
|
# we never talk to replicas, only the leader
|
|
except ReplicaNotAvailableError:
|
|
log.debug('Some (non-leader) replicas not available for topic %s partition %d', topic, partition)
|
|
|
|
# If Known Broker, topic_partition -> BrokerMetadata
|
|
if leader in self.brokers:
|
|
self.topics_to_brokers[topic_part] = self.brokers[leader]
|
|
|
|
# If Unknown Broker, fake BrokerMetadata so we dont lose the id
|
|
# (not sure how this could happen. server could be in bad state)
|
|
else:
|
|
self.topics_to_brokers[topic_part] = BrokerMetadata(
|
|
leader, None, None
|
|
)
|
|
|
|
def send_metadata_request(self, payloads=[], fail_on_error=True,
|
|
callback=None):
|
|
encoder = KafkaProtocol.encode_metadata_request
|
|
decoder = KafkaProtocol.decode_metadata_response
|
|
|
|
return self._send_broker_unaware_request(payloads, encoder, decoder)
|
|
|
|
def send_consumer_metadata_request(self, payloads=[], fail_on_error=True,
|
|
callback=None):
|
|
encoder = KafkaProtocol.encode_consumer_metadata_request
|
|
decoder = KafkaProtocol.decode_consumer_metadata_response
|
|
|
|
return self._send_broker_unaware_request(payloads, encoder, decoder)
|
|
|
|
def send_produce_request(self, payloads=[], acks=1, timeout=1000,
|
|
fail_on_error=True, callback=None):
|
|
"""
|
|
Encode and send some ProduceRequests
|
|
|
|
ProduceRequests will be grouped by (topic, partition) and then
|
|
sent to a specific broker. Output is a list of responses in the
|
|
same order as the list of payloads specified
|
|
|
|
Arguments:
|
|
payloads (list of ProduceRequest): produce requests to send to kafka
|
|
ProduceRequest payloads must not contain duplicates for any
|
|
topic-partition.
|
|
acks (int, optional): how many acks the servers should receive from replica
|
|
brokers before responding to the request. If it is 0, the server
|
|
will not send any response. If it is 1, the server will wait
|
|
until the data is written to the local log before sending a
|
|
response. If it is -1, the server will wait until the message
|
|
is committed by all in-sync replicas before sending a response.
|
|
For any value > 1, the server will wait for this number of acks to
|
|
occur (but the server will never wait for more acknowledgements than
|
|
there are in-sync replicas). defaults to 1.
|
|
timeout (int, optional): maximum time in milliseconds the server can
|
|
await the receipt of the number of acks, defaults to 1000.
|
|
fail_on_error (bool, optional): raise exceptions on connection and
|
|
server response errors, defaults to True.
|
|
callback (function, optional): instead of returning the ProduceResponse,
|
|
first pass it through this function, defaults to None.
|
|
|
|
Returns:
|
|
list of ProduceResponses, or callback results if supplied, in the
|
|
order of input payloads
|
|
"""
|
|
|
|
encoder = functools.partial(
|
|
KafkaProtocol.encode_produce_request,
|
|
acks=acks,
|
|
timeout=timeout)
|
|
|
|
if acks == 0:
|
|
decoder = None
|
|
else:
|
|
decoder = KafkaProtocol.decode_produce_response
|
|
|
|
resps = self._send_broker_aware_request(payloads, encoder, decoder)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if resp is not None and
|
|
(not fail_on_error or not self._raise_on_response_error(resp))]
|
|
|
|
def send_fetch_request(self, payloads=[], fail_on_error=True,
|
|
callback=None, max_wait_time=100, min_bytes=4096):
|
|
"""
|
|
Encode and send a FetchRequest
|
|
|
|
Payloads are grouped by topic and partition so they can be pipelined
|
|
to the same brokers.
|
|
"""
|
|
|
|
encoder = functools.partial(KafkaProtocol.encode_fetch_request,
|
|
max_wait_time=max_wait_time,
|
|
min_bytes=min_bytes)
|
|
|
|
resps = self._send_broker_aware_request(
|
|
payloads, encoder,
|
|
KafkaProtocol.decode_fetch_response)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if not fail_on_error or not self._raise_on_response_error(resp)]
|
|
|
|
def send_offset_request(self, payloads=[], fail_on_error=True,
|
|
callback=None):
|
|
resps = self._send_broker_aware_request(
|
|
payloads,
|
|
KafkaProtocol.encode_offset_request,
|
|
KafkaProtocol.decode_offset_response)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if not fail_on_error or not self._raise_on_response_error(resp)]
|
|
|
|
def send_offset_commit_request(self, group, payloads=[],
|
|
fail_on_error=True, callback=None):
|
|
encoder = functools.partial(KafkaProtocol.encode_offset_commit_request,
|
|
group=group)
|
|
decoder = KafkaProtocol.decode_offset_commit_response
|
|
resps = self._send_broker_aware_request(payloads, encoder, decoder)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if not fail_on_error or not self._raise_on_response_error(resp)]
|
|
|
|
def send_offset_fetch_request(self, group, payloads=[],
|
|
fail_on_error=True, callback=None):
|
|
|
|
encoder = functools.partial(KafkaProtocol.encode_offset_fetch_request,
|
|
group=group)
|
|
decoder = KafkaProtocol.decode_offset_fetch_response
|
|
resps = self._send_broker_aware_request(payloads, encoder, decoder)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if not fail_on_error or not self._raise_on_response_error(resp)]
|
|
|
|
def send_offset_fetch_request_kafka(self, group, payloads=[],
|
|
fail_on_error=True, callback=None):
|
|
|
|
encoder = functools.partial(KafkaProtocol.encode_offset_fetch_request,
|
|
group=group, from_kafka=True)
|
|
decoder = KafkaProtocol.decode_offset_fetch_response
|
|
resps = self._send_consumer_aware_request(group, payloads, encoder, decoder)
|
|
|
|
return [resp if not callback else callback(resp) for resp in resps
|
|
if not fail_on_error or not self._raise_on_response_error(resp)]
|