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import base64
from collections import defaultdict
from functools import partial
from itertools import count, cycle
import logging
from operator import attrgetter
import struct
import time
import zlib
from kafka.common import *
from kafka.conn import KafkaConnection
from kafka.protocol import KafkaProtocol
log = logging.getLogger("kafka")
class KafkaClient(object):
CLIENT_ID = "kafka-python"
ID_GEN = count()
def __init__(self, host, port, bufsize=4096):
# We need one connection to bootstrap
self.bufsize = bufsize
self.conns = { # (host, port) -> KafkaConnection
(host, port): KafkaConnection(host, port, bufsize)
}
self.brokers = {} # broker_id -> BrokerMetadata
self.topics_to_brokers = {} # topic_id -> broker_id
self.topic_partitions = defaultdict(list) # topic_id -> [0, 1, 2, ...]
self._load_metadata_for_topics()
##################
# Private API #
##################
def _get_conn_for_broker(self, broker):
"Get or create a connection to a broker"
if (broker.host, broker.port) not in self.conns:
self.conns[(broker.host, broker.port)] = KafkaConnection(broker.host, broker.port, self.bufsize)
return self.conns[(broker.host, broker.port)]
def _get_leader_for_partition(self, topic, partition):
key = TopicAndPartition(topic, partition)
if key not in self.topics_to_brokers:
self._load_metadata_for_topics(topic)
if key not in self.topics_to_brokers:
raise Exception("Partition does not exist: %s" % str(key))
return self.topics_to_brokers[key]
def _load_metadata_for_topics(self, *topics):
"""
Discover brokers and metadata for a set of topics. This method will
recurse in the event of a retry.
"""
requestId = self._next_id()
request = KafkaProtocol.encode_metadata_request(KafkaClient.CLIENT_ID, requestId, topics)
response = self._send_broker_unaware_request(requestId, request)
if response is None:
raise Exception("All servers failed to process request")
(brokers, topics) = KafkaProtocol.decode_metadata_response(response)
log.debug("Broker metadata: %s", brokers)
log.debug("Topic metadata: %s", topics)
self.brokers.update(brokers)
self.topics_to_brokers = {}
for topic, partitions in topics.items():
for partition, meta in partitions.items():
if meta.leader == -1:
log.info("Partition is unassigned, delay for 1s and retry")
time.sleep(1)
self._load_metadata_for_topics(topic)
else:
self.topics_to_brokers[TopicAndPartition(topic, partition)] = brokers[meta.leader]
self.topic_partitions[topic].append(partition)
def _next_id(self):
"Generate a new correlation id"
return KafkaClient.ID_GEN.next()
def _send_broker_unaware_request(self, requestId, request):
"""
Attempt to send a broker-agnostic request to one of the available brokers.
Keep trying until you succeed.
"""
for conn in self.conns.values():
try:
conn.send(requestId, request)
response = conn.recv(requestId)
return response
except Exception, e:
log.warning("Could not send request [%r] to server %s, trying next server: %s" % (request, conn, e))
continue
return None
def _send_broker_aware_request(self, payloads, encoder_fn, decoder_fn):
"""
Group a list of request payloads by topic+partition and send them to the
leader broker for that partition using the supplied encode/decode functions
Params
======
payloads: list of object-like entities with a topic and partition attribute
encode_fn: a method to encode the list of payloads to a request body, must accept
client_id, correlation_id, and payloads as keyword arguments
decode_fn: a method to decode a response body into response objects. The response
objects must be object-like and have topic and partition attributes
Return
======
List of response objects in the same order as the supplied payloads
"""
# Group the requests by topic+partition
original_keys = []
payloads_by_broker = defaultdict(list)
for payload in payloads:
payloads_by_broker[self._get_leader_for_partition(payload.topic, payload.partition)].append(payload)
original_keys.append((payload.topic, payload.partition))
# Accumulate the responses in a dictionary
acc = {}
# For each broker, send the list of request payloads
for broker, payloads in payloads_by_broker.items():
conn = self._get_conn_for_broker(broker)
requestId = self._next_id()
request = encoder_fn(client_id=KafkaClient.CLIENT_ID, correlation_id=requestId, payloads=payloads)
# Send the request, recv the response
conn.send(requestId, request)
response = conn.recv(requestId)
for response in decoder_fn(response):
acc[(response.topic, response.partition)] = response
# Order the accumulated responses by the original key order
return (acc[k] for k in original_keys)
#################
# Public API #
#################
def close(self):
for conn in self.conns.values():
conn.close()
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
Params
======
payloads: list of ProduceRequest
fail_on_error: boolean, should we raise an Exception if we encounter an API error?
callback: function, instead of returning the ProduceResponse, first pass it through this function
Return
======
list of ProduceResponse or callback(ProduceResponse), in the order of input payloads
"""
resps = self._send_broker_aware_request(payloads,
partial(KafkaProtocol.encode_produce_request, acks=acks, timeout=timeout),
KafkaProtocol.decode_produce_response)
out = []
for resp in resps:
# Check for errors
if fail_on_error == True and resp.error != ErrorMapping.NO_ERROR:
raise Exception("ProduceRequest for %s failed with errorcode=%d" %
(TopicAndPartition(resp.topic, resp.partition), resp.error))
# Run the callback
if callback is not None:
out.append(callback(resp))
else:
out.append(resp)
return out
def send_fetch_request(self, payloads=[], fail_on_error=True, callback=None):
"""
Encode and send a FetchRequest
Payloads are grouped by topic and partition so they can be pipelined to the same
brokers.
"""
resps = self._send_broker_aware_request(payloads,
KafkaProtocol.encode_fetch_request,
KafkaProtocol.decode_fetch_response)
out = []
for resp in resps:
# Check for errors
if fail_on_error == True and resp.error != ErrorMapping.NO_ERROR:
raise Exception("FetchRequest for %s failed with errorcode=%d" %
(TopicAndPartition(resp.topic, resp.partition), resp.error))
# Run the callback
if callback is not None:
out.append(callback(resp))
else:
out.append(resp)
return out
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)
out = []
for resp in resps:
if fail_on_error == True and resp.error != ErrorMapping.NO_ERROR:
raise Exception("OffsetRequest failed with errorcode=%s", resp.error)
if callback is not None:
out.append(callback(resp))
else:
out.append(resp)
return out
def send_offset_commit_request(self, group, payloads=[], fail_on_error=True, callback=None):
raise NotImplementedError("Broker-managed offsets not supported in 0.8")
resps = self._send_broker_aware_request(payloads,
partial(KafkaProtocol.encode_offset_commit_request, group=group),
KafkaProtocol.decode_offset_commit_response)
out = []
for resp in resps:
if fail_on_error == True and resp.error != ErrorMapping.NO_ERROR:
raise Exception("OffsetCommitRequest failed with errorcode=%s", resp.error)
if callback is not None:
out.append(callback(resp))
else:
out.append(resp)
return out
def send_offset_fetch_request(self, group, payloads=[], fail_on_error=True, callback=None):
raise NotImplementedError("Broker-managed offsets not supported in 0.8")
resps = self._send_broker_aware_request(payloads,
partial(KafkaProtocol.encode_offset_fetch_request, group=group),
KafkaProtocol.decode_offset_fetch_response)
out = []
for resp in resps:
if fail_on_error == True and resp.error != ErrorMapping.NO_ERROR:
raise Exception("OffsetCommitRequest failed with errorcode=%s", resp.error)
if callback is not None:
out.append(callback(resp))
else:
out.append(resp)
return out
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