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由于KafkaTimeoutError,无法使用kafka python从django应用程序向kafka发送消息_Python_Django_Apache Kafka_Kafka Python - Fatal编程技术网

由于KafkaTimeoutError,无法使用kafka python从django应用程序向kafka发送消息

由于KafkaTimeoutError,无法使用kafka python从django应用程序向kafka发送消息,python,django,apache-kafka,kafka-python,Python,Django,Apache Kafka,Kafka Python,我有一个基于Django的web应用程序,我试图在这个名为的库的帮助下集成Kafka。但是,当我尝试向特定主题发送消息时,我收到一个超时错误,说明: Traceback (most recent call last): File "/home/paras/vertex/vertex-1.6/vertex-portal-backend/vertex_app/kafka_service.py", line 67, in send_message x = producer.send(top

我有一个基于Django的web应用程序,我试图在这个名为的库的帮助下集成Kafka。但是,当我尝试向特定主题发送消息时,我收到一个超时错误,说明:

Traceback (most recent call last):
  File "/home/paras/vertex/vertex-1.6/vertex-portal-backend/vertex_app/kafka_service.py", line 67, in send_message
    x = producer.send(topic, json_data)
  File "/home/paras/.local/lib/python3.6/site-packages/kafka/producer/kafka.py", line 555, in send
    self._wait_on_metadata(topic, self.config['max_block_ms'] / 1000.0)
  File "/home/paras/.local/lib/python3.6/site-packages/kafka/producer/kafka.py", line 682, in _wait_on_metadata
    "Failed to update metadata after %.1f secs." % (max_wait,))
kafka.errors.KafkaTimeoutError: KafkaTimeoutError: Failed to update metadata after 60.0 secs.
生成消息:

def put_order_into_kafka(order,obj) :
    try :
        if order is None or offering is None :
            raise Exception("Unable to put order into queue order or offering is null")
        topic_name = create_kafka_topic_name(obj)
        send_message(topic_name,order)
    except Exception as e :
        print(e)
卡夫卡服务

#kafka_service.py
from kafka import KafkaProducer
from kafka.admin import KafkaAdminClient, NewTopic
from .constants import KAFKA_BROKER_URL

import json

KAFKA_PRODUCER = None
def get_kafka_producer():
    KAFKA_PRODUCER = init_kafka_producer_instance()
    return KAFKA_PRODUCER

def init_kafka_producer_instance():
    try:

        if KAFKA_PRODUCER is not None :
            return KAFKA_PRODUCER

        producer = None
        producer = KafkaProducer(bootstrap_servers=[
                                 KAFKA_BROKER_URL], value_serializer=lambda x: json.dumps(x).encode('utf-8'))
        return producer
    except Exception as e:
        import traceback
        print(traceback.format_exc())
    return None

def create_kafka_topic_instance(topic_name,num_partitions=1,replication_factor=1) :
    try :
        if topic_name is None :
            raise Exception("Invalid argument topic name")
        topic_list = []
        topic_list.append(NewTopic(name=topic_name, num_partitions=num_partitions, replication_factor=replication_factor))
        create_topic(topic_list)
    except Exception as e :
        import traceback
        print(traceback.format_exc())

def create_topic(topics,validate_only=False):
    try:
        if topics is None:
            raise Exception("Topic is None")
        admin_client = get_kafka_admin_instance()
        if admin_client is None:
            return False
        result = admin_client.create_topics(topics,validate_only)
        print(result)
    except Exception as e:
        import traceback
        print(traceback.format_exc())


def get_kafka_admin_instance():
    try:
        admin_client = KafkaAdminClient(bootstrap_servers=KAFKA_BROKER_URL)
        return admin_client
    except Exception as e:
        import traceback
        print(traceback.format_exc())


def send_message(topic, json_data):
    try:
        if topic is None or json_data is None:
            raise Exception("Invalid argument topic or data")
        producer = get_kafka_producer()
        if producer is not None:
            x = producer.send(topic, json_data)
            print(x)
    except Exception as e:
        import traceback
        print(traceback.format_exc())


def delete_topic(topic):
    try:
        if topic is None:
            raise Exception("Topic is None")
    except Exception as e:
        import traceback
        print(traceback.format_exc())

##Utility Functions

def create_kafka_topic_name(obj) :
    try :
        if offering is None :
            raise Exception("Invalid argument offering, unable to create topic name")
        return str(obj.order_id)
    except Exception as e :
        print(e)
    return None
卡夫卡服务器。属性

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# see kafka.server.KafkaConfig for additional details and defaults

############################# Server Basics #############################

# The id of the broker. This must be set to a unique integer for each broker.
broker.id=0

############################# Socket Server Settings #############################

# The address the socket server listens on. It will get the value returned from 
# java.net.InetAddress.getCanonicalHostName() if not configured.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
#listeners=PLAINTEXT://:9092

# Hostname and port the broker will advertise to producers and consumers. If not set, 
# it uses the value for "listeners" if configured.  Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#advertised.listeners=PLAINTEXT://your.host.name:9092
advertised.listeners=PLAINTEXT://localhost:9092
# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL

# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3

# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8

# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400

# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400

# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600


############################# Log Basics #############################

# A comma separated list of directories under which to store log files
log.dirs=/tmp/kafka-logs

# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1

# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1

############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended for to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1

############################# Log Flush Policy #############################

# Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.

# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000

# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000

############################# Log Retention Policy #############################

# The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.

# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168

# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824

# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824

# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

############################# Zookeeper #############################

# Zookeeper connection string (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# You can also append an optional chroot string to the urls to specify the
# root directory for all kafka znodes.
zookeeper.connect=localhost:2181

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# Group Coordinator Settings #############################

# The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0

然而,我试图从这个应用程序中编写一个虚拟函数,我能够将消息放入队列中。我对python和卡夫卡还相当陌生,我不确定我会错在哪里。有人能帮我吗?

您还必须定义
侦听器

listeners=PLAINTEXT://:9092

它适用于硬编码的dictionary对象,但是当我尝试发送实际的Django模型对象时,我又返回到相同的超时错误。@您确定要将所有代理传递给
引导\u服务器吗?
?我只有一个代理。KAFKA_BROKER_URL=“localhost:9092”@您是否将KAFKA作为Docker容器运行?我怀疑您的制作人在刷新所有挂起的消息之前被杀。另外,如果您想保存数据,请不要将日志目录存储在/tmp上