326 lines
12 KiB
Python
326 lines
12 KiB
Python
from collections import defaultdict
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from functools import partial
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import logging
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import time
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from kafka.common import count, ErrorMapping, TopicAndPartition
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from kafka.conn import KafkaConnection
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from kafka.protocol import KafkaProtocol
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log = logging.getLogger("kafka")
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class KafkaClient(object):
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CLIENT_ID = "kafka-python"
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ID_GEN = count()
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def __init__(self, host, port, bufsize=4096, client_id=CLIENT_ID):
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# We need one connection to bootstrap
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self.bufsize = bufsize
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self.client_id = client_id
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self.conns = { # (host, port) -> KafkaConnection
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(host, port): KafkaConnection(host, port, bufsize)
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}
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self.brokers = {} # broker_id -> BrokerMetadata
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self.topics_to_brokers = {} # topic_id -> broker_id
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self.topic_partitions = defaultdict(list) # topic_id -> [0, 1, 2, ...]
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self._load_metadata_for_topics()
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##################
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# Private API #
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##################
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def _get_conn_for_broker(self, broker):
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"""
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Get or create a connection to a broker
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"""
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if (broker.host, broker.port) not in self.conns:
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self.conns[(broker.host, broker.port)] = \
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KafkaConnection(broker.host, broker.port, self.bufsize)
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return self.conns[(broker.host, broker.port)]
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def _get_leader_for_partition(self, topic, partition):
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key = TopicAndPartition(topic, partition)
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if key not in self.topics_to_brokers:
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self._load_metadata_for_topics(topic)
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if key not in self.topics_to_brokers:
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raise Exception("Partition does not exist: %s" % str(key))
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return self.topics_to_brokers[key]
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def _load_metadata_for_topics(self, *topics):
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"""
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Discover brokers and metadata for a set of topics. This method will
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recurse in the event of a retry.
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"""
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request_id = self._next_id()
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request = KafkaProtocol.encode_metadata_request(self.client_id,
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request_id, topics)
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response = self._send_broker_unaware_request(request_id, request)
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if response is None:
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raise Exception("All servers failed to process request")
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(brokers, topics) = KafkaProtocol.decode_metadata_response(response)
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log.debug("Broker metadata: %s", brokers)
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log.debug("Topic metadata: %s", topics)
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self.brokers.update(brokers)
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self.topics_to_brokers = {}
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for topic, partitions in topics.items():
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# Clear the list once before we add it. This removes stale entries
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# and avoids duplicates
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self.topic_partitions.pop(topic, None)
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if not partitions:
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log.info("Partition is unassigned, delay for 1s and retry")
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time.sleep(1)
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self._load_metadata_for_topics(topic)
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break
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for partition, meta in partitions.items():
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if meta.leader == -1:
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log.info("Partition is unassigned, delay for 1s and retry")
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time.sleep(1)
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self._load_metadata_for_topics(topic)
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else:
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topic_part = TopicAndPartition(topic, partition)
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self.topics_to_brokers[topic_part] = brokers[meta.leader]
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self.topic_partitions[topic].append(partition)
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def _next_id(self):
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"""
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Generate a new correlation id
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"""
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return KafkaClient.ID_GEN.next()
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def _send_broker_unaware_request(self, requestId, request):
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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 conn in self.conns.values():
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try:
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conn.send(requestId, request)
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response = conn.recv(requestId)
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return response
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except Exception, e:
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log.warning("Could not send request [%r] to server %s, "
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"trying next server: %s" % (request, conn, e))
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continue
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return None
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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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Params
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======
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payloads: list of object-like entities with a topic and
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partition attribute
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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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Return
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======
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List of response objects in the same order as the supplied payloads
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"""
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# Group the requests by topic+partition
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original_keys = []
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payloads_by_broker = defaultdict(list)
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for payload in payloads:
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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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original_keys.append((payload.topic, payload.partition))
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# Accumulate the responses in a dictionary
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acc = {}
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# For each broker, send the list of request payloads
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for broker, payloads in payloads_by_broker.items():
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conn = self._get_conn_for_broker(broker)
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requestId = self._next_id()
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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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conn.send(requestId, request)
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if decoder_fn is None:
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continue
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response = conn.recv(requestId)
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for response in decoder_fn(response):
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acc[(response.topic, response.partition)] = response
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# Order the accumulated responses by the original key order
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return (acc[k] for k in original_keys) if acc else ()
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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 reinit(self):
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for conn in self.conns.values():
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conn.reinit()
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def send_produce_request(self, payloads=[], acks=1, timeout=1000,
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fail_on_error=True, callback=None):
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"""
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Encode and send some ProduceRequests
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ProduceRequests will be grouped by (topic, partition) and then
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sent to a specific broker. Output is a list of responses in the
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same order as the list of payloads specified
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Params
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======
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payloads: list of ProduceRequest
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fail_on_error: boolean, should we raise an Exception if we
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encounter an API error?
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callback: function, instead of returning the ProduceResponse,
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first pass it through this function
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Return
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======
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list of ProduceResponse or callback(ProduceResponse), in the
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order of input payloads
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"""
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encoder = partial(
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KafkaProtocol.encode_produce_request,
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acks=acks,
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timeout=timeout)
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if acks == 0:
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decoder = None
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else:
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decoder = KafkaProtocol.decode_produce_response
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resps = self._send_broker_aware_request(payloads, encoder, decoder)
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out = []
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for resp in resps:
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# Check for errors
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if fail_on_error is True and resp.error != ErrorMapping.NO_ERROR:
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raise Exception(
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"ProduceRequest for %s failed with errorcode=%d" %
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(TopicAndPartition(resp.topic, resp.partition),
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resp.error))
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# Run the callback
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if callback is not None:
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out.append(callback(resp))
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else:
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out.append(resp)
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return out
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def send_fetch_request(self, payloads=[], fail_on_error=True,
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callback=None, max_wait_time=100, min_bytes=4096):
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"""
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Encode and send a FetchRequest
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Payloads are grouped by topic and partition so they can be pipelined
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to the same brokers.
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"""
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encoder = partial(KafkaProtocol.encode_fetch_request,
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max_wait_time=max_wait_time,
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min_bytes=min_bytes)
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resps = self._send_broker_aware_request(
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payloads, encoder,
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KafkaProtocol.decode_fetch_response)
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out = []
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for resp in resps:
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# Check for errors
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if fail_on_error is True and resp.error != ErrorMapping.NO_ERROR:
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raise Exception(
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"FetchRequest for %s failed with errorcode=%d" %
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(TopicAndPartition(resp.topic, resp.partition),
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resp.error))
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# Run the callback
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if callback is not None:
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out.append(callback(resp))
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else:
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out.append(resp)
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return out
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def send_offset_request(self, payloads=[], fail_on_error=True,
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callback=None):
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resps = self._send_broker_aware_request(
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payloads,
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KafkaProtocol.encode_offset_request,
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KafkaProtocol.decode_offset_response)
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out = []
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for resp in resps:
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if fail_on_error is True and resp.error != ErrorMapping.NO_ERROR:
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raise Exception("OffsetRequest failed with errorcode=%s",
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resp.error)
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if callback is not None:
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out.append(callback(resp))
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else:
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out.append(resp)
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return out
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def send_offset_commit_request(self, group, payloads=[],
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fail_on_error=True, callback=None):
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encoder = partial(KafkaProtocol.encode_offset_commit_request,
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group=group)
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decoder = KafkaProtocol.decode_offset_commit_response
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resps = self._send_broker_aware_request(payloads, encoder, decoder)
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out = []
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for resp in resps:
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if fail_on_error is True and resp.error != ErrorMapping.NO_ERROR:
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raise Exception("OffsetCommitRequest failed with "
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"errorcode=%s", resp.error)
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if callback is not None:
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out.append(callback(resp))
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else:
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out.append(resp)
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return out
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def send_offset_fetch_request(self, group, payloads=[],
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fail_on_error=True, callback=None):
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encoder = partial(KafkaProtocol.encode_offset_fetch_request,
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group=group)
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decoder = KafkaProtocol.decode_offset_fetch_response
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resps = self._send_broker_aware_request(payloads, encoder, decoder)
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out = []
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for resp in resps:
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if fail_on_error is True and resp.error != ErrorMapping.NO_ERROR:
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raise Exception("OffsetCommitRequest failed with errorcode=%s",
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resp.error)
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if callback is not None:
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out.append(callback(resp))
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else:
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out.append(resp)
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return out
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