python的kafka客户端

python连接kafka的标准库比较流行的有
1、kafka-python
2、pykafka
kafka-python使用的人多是比较成熟的库，
pykafka是Samsa的升级版本，使用samsa连接zookeeper然后使用kafka Cluster。

区别：
pykafka的对zookeeper支持而kafka-python并没有zk的支持

kafka-python使用

操作文档

https://kafka-python.readthedocs.io/en/master/apidoc/modules.html

https://kafka-python.readthedocs.io/en/master/index.html

https://pypi.org/project/kafka-python/

生产者

import time
from kafka import KafkaProducer
 
 
producer = KafkaProducer(bootstrap_servers = ['192.168.17.64:9092', '192.168.17.65:9092', '192.168.17.68:9092'])
# Assign a topic
topic = 'test'
 
def test():
    print('begin')
    n = 1
    try:
        while (n<=100):
            producer.send(topic, str(n).encode())
            print("send" + str(n))
            n += 1
            time.sleep(0.5)
    except KafkaError as e:
        print(e)
    finally: 
        producer.close()
        print('done')



def test_json():
	msg_dict = {
		"sleep_time": 10,
		"db_config": {
			"database": "test_1",
			"host": "xxxx",
			"user": "root",
			"password": "root"
		},
		"table": "msg",
		"msg": "Hello World"
	}
	msg = json.dumps(msg_dict)
	producer.send(topic, msg, partition=0)
	producer.close()


if __name__ == '__main__':
    test()

可能遇到的问题–IOError: [Errno 24] Too many open files–多次创建KafkaProducer

在每个controller函数中创建一个SimpleProducer。切换到KafkaProducer后，依然在每个controller中创建新的KafkaProducer。如下所示：

try:
   producer = KafkaProducer(bootstrap_servers=['{kafka_host}:{kafka_port}'.format(
       kafka_host=app.config['KAFKA_HOST'],
       kafka_port=app.config['KAFKA_PORT']
   )])
   message_string = json.dumps(message)
   response = producer.send(kafka_topic, message_string.encode('utf-8'))
   producer.close()

原因是每次创建KafkaProducer都会占用一个文件符号，controller结束时，没有释放，导致后面无法继续创建新的KafkaProducer。

解决方法是创建全局KafkaProducer，供所有controller使用。

注意事项–慎用RecordMetadata.get()

官方例子中有如下的代码

producer = KafkaProducer(bootstrap_servers=['broker1:1234'])
 
# Asynchronous by default
future = producer.send('my-topic', b'raw_bytes')
 
# Block for 'synchronous' sends
try:
    record_metadata = future.get(timeout=10)
except KafkaError:
    # Decide what to do if produce request failed...
    log.exception()
    pass

KafkaProducer.send 返回 RecordMetadata 对象，RecordMetadata.get 可以获取 record 的信息。但在发送大量消息时，get方法可能会造成明显的延时。所以当我们不关心消息是否发送成功时，就不要调用get方法了。

消费者

#!/bin/env python
from kafka import KafkaConsumer
 
#connect to Kafka server and pass the topic we want to consume
consumer = KafkaConsumer('test', group_id = 'test_group', bootstrap_servers = ['192.168.17.64:9092', '192.168.17.65:9092', '192.168.17.68:9092'])
try:
    for msg in consumer:
        print(msg)
        print("%s:%d:%d: key=%s value=%s" % (msg.topic, msg.partition,msg.offset, msg.key, msg.value))
except KeyboardInterrupt, e:
    print(e)

设置不自动提交

自动提交位移设为flase, 默认为取最新的偏移量

consumer = kafka.KafkaConsumer(bootstrap_servers = ['192.168.17.64:9092','192.168.17.65:9092','192.168.17.68:9092'],
                        group_id ='test_group_id',
                        auto_offset_reset ='latest', 
                        enable_auto_commit = False)

批量发送数据

from kafka import KafkaClient
from kafka.producer import SimpleProducer

def send_data_2_kafka(datas):
  '''
    向kafka解析队列发送数据
  '''
  client = KafkaClient(hosts=KAFKABROKER.split(","), timeout=30)
  producer = SimpleProducer(client, async=False)
  
  curcount = len(datas)/PARTNUM
  for i in range(0, PARTNUM):
    start = i*curcount
    if i != PARTNUM - 1:
      end = (i+1)*curcount
      curdata = datas[start:end]
      producer.send_messages(TOPICNAME, *curdata)
    else:
      curdata = datas[start:]
      producer.send_messages(TOPICNAME, *curdata)
     
  producer.stop()
  client.close()

其中PARTNUM为topic的partition的数目，这样保证批量发送的数据均匀的落在kafka的partition中。

消费者订阅多个主题

# =======订阅多个消费者==========

from kafka import KafkaConsumer
from kafka.structs import TopicPartition

consumer = KafkaConsumer(bootstrap_servers=['127.0.0.1:9092'])
consumer.subscribe(topics=('test','test0'))  #订阅要消费的主题
print(consumer.topics())
print(consumer.position(TopicPartition(topic='test', partition=0))) #获取当前主题的最新偏移量
for message in consumer:
    print ("%s:%d:%d: key=%s value=%s" % (message.topic, message.partition,message.offset, message.key,message.value))

消费者定时拉取

有时候，我们并不需要实时获取数据，因为这样可能会造成性能瓶颈，我们只需要定时去获取队列里的数据然后批量处理就可以，这种情况，我们可以选择主动拉取数据

from kafka import KafkaConsumer
import time

consumer = KafkaConsumer(group_id='123456', bootstrap_servers=['10.43.35.25:4531'])
consumer.subscribe(topics=('test_rhj',))
index = 0
while True:
    msg = consumer.poll(timeout_ms=5)  # 从kafka获取消息
    print msg
    time.sleep(2)
    index += 1
    print '--------poll index is %s----------' % index

每次拉取到的都是前面生产的数据，可能是多条的列表，也可能没有数据，如果没有数据，则拉取到的为空。

消费者读取最早偏移量

from kafka import KafkaConsumer

consumer = KafkaConsumer('test',auto_offset_reset='earliest',bootstrap_servers=['127.0.0.1:9092'])

for message in consumer:
    print("%s:%d:%d: key=%s value=%s" % (message.topic, message.partition,message.offset, message.key,message.value))

auto_offset_reset:重置偏移量，earliest移到最早的可用消息，latest最新的消息，默认为latest
源码定义:{‘smallest’: ‘earliest’, ‘largest’: ‘latest’}

消费者手动设置偏移量

# ==========读取指定位置消息===============
from kafka import KafkaConsumer
from kafka.structs import TopicPartition

consumer = KafkaConsumer('test',bootstrap_servers=['127.0.0.1:9092'])

print(consumer.partitions_for_topic("test"))  #获取test主题的分区信息
print(consumer.topics())  #获取主题列表
print(consumer.subscription())  #获取当前消费者订阅的主题
print(consumer.assignment())  #获取当前消费者topic、分区信息
print(consumer.beginning_offsets(consumer.assignment())) #获取当前消费者可消费的偏移量
consumer.seek(TopicPartition(topic='test', partition=0), 5)  #重置偏移量，从第5个偏移量消费
for message in consumer:
    print ("%s:%d:%d: key=%s value=%s" % (message.topic, message.partition,message.offset, message.key,message.value))

消费者挂起和恢复

# ==============消息恢复和挂起===========

from kafka import KafkaConsumer
from kafka.structs import TopicPartition
import time

consumer = KafkaConsumer(bootstrap_servers=['127.0.0.1:9092'])
consumer.subscribe(topics=('test'))
consumer.topics()
consumer.pause(TopicPartition(topic=u'test', partition=0))  # pause执行后，consumer不能读取，直到调用resume后恢复。
num = 0
while True:
    print(num)
    print(consumer.paused())   #获取当前挂起的消费者
    msg = consumer.poll(timeout_ms=5)
    print(msg)
    time.sleep(2)
    num = num + 1
    if num == 10:
        print("resume...")
        consumer.resume(TopicPartition(topic='test', partition=0))
        print("resume......")

pykafka使用

操作文档

http://pykafka.readthedocs.io/en/latest/
https://github.com/Parsely/pykafka

需要注意的点

kafaka和zookeeper的群集，使用samsa的时候生产者和消费者都连接了zookeeper，但pykafka文档中生产者直接连接kafaka服务器列表，消费者才用zookeeper。

生产者

#coding=utf-8
 
import time
from pykafka import KafkaClient
 
 
class KafkaTest(object):
    """
    测试kafka常用api
    """
    def __init__(self, host="192.168.0.10:9092"):
        self.host = host
        self.client = KafkaClient(hosts=self.host)
 
    def producer_partition(self, topic):
        """
        生产者分区查看，主要查看生产消息时offset的变化
        :return:
        """
        topic = self.client.topics[topic.encode()]
        partitions = topic.partitions
        print (u"查看所有分区 {}".format(partitions))
 
        earliest_offset = topic.earliest_available_offsets()
        print(u"获取最早可用的offset {}".format(earliest_offset))
 
        # 生产消息之前看看offset
        last_offset = topic.latest_available_offsets()
        print(u"最近可用offset {}".format(last_offset))
 
        # 同步生产消息
        p = topic.get_producer(sync=True)
        p.produce(str(time.time()).encode())
 
        # 查看offset的变化
        last_offset = topic.latest_available_offsets()
        print(u"最近可用offset {}".format(last_offset))
 
    def producer_designated_partition(self, topic):
        """
        往指定分区写消息，如果要控制打印到某个分区，
        需要在获取生产者的时候指定选区函数，
        并且在生产消息的时候额外指定一个key
        :return:
        """
 
        def assign_patition(pid, key):
            """
            指定特定分区, 这里测试写入第一个分区(id=0)
            :param pid: 为分区列表
            :param key:
            :return:
            """
            print("为消息分配partition {} {}".format(pid, key))
            return pid[0]
 
        topic = self.client.topics[topic.encode()]
        p = topic.get_producer(sync=True, partitioner=assign_patition)
        p.produce(str(time.time()).encode(), partition_key=b"partition_key_0")
 
    def async_produce_message(self, topic):
        """
        异步生产消息，消息会被推到一个队列里面，
        另外一个线程会在队列中消息大小满足一个阈值（min_queued_messages）
        或到达一段时间（linger_ms）后统一发送,默认5s
        :return:
        """
        topic = self.client.topics[topic.encode()]
        last_offset = topic.latest_available_offsets()
        print("最近的偏移量 offset {}".format(last_offset))
 
        # 记录最初的偏移量
        old_offset = last_offset[0].offset[0]
        p = topic.get_producer(sync=False, partitioner=lambda pid, key: pid[0])
        p.produce(str(time.time()).encode())
        s_time = time.time()
        while True:
            last_offset = topic.latest_available_offsets()
            print("最近可用offset {}".format(last_offset))
            if last_offset[0].offset[0] != old_offset:
                e_time = time.time()
                print('cost time {}'.format(e_time-s_time))
                break
            time.sleep(1)
 
    def get_produce_message_report(self, topic):
        """
        查看异步发送消报告,默认会等待5s后才能获得报告
        """
        topic = self.client.topics[topic.encode()]
        last_offset = topic.latest_available_offsets()
        print("最近的偏移量 offset {}".format(last_offset))
        p = topic.get_producer(sync=False, delivery_reports=True, partitioner=lambda pid, key: pid[0])
        p.produce(str(time.time()).encode())
        s_time = time.time()
        delivery_report = p.get_delivery_report()
        e_time = time.time()
        print ('等待{}s, 递交报告{}'.format(e_time-s_time, delivery_report))
        last_offset = topic.latest_available_offsets()
        print("最近的偏移量 offset {}".format(last_offset))
 
 
if __name__ == '__main__':
    host = '192.168.0.10:9092,192.168.0.12:9092,192.168.0.13:9092'
    kafka_ins = KafkaTest(host)
    topic = 'test'
    # kafka_ins.producer_partition(topic)
    # kafka_ins.producer_designated_partition(topic)
    # kafka_ins.async_produce_message(topic)
    kafka_ins.get_produce_message_report(topic)

可能出现的问题–dliver_report（包括同步）子进程阻塞

多进程使用pykafka共享一个client，会造成只有进程能够正常的写入数据，如果使用了dliver_report（包括同步），会导致子进程彻底阻塞掉不可用

可能出现的问题–Producer.produce accepts a bytes object as message

使用producer.produce发送数据出现故障，如下

#!/bin/env python
from pykafka import KafkaClient
host = '192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092'
client = KafkaClient(hosts = host)
topic = client.topics["test"]
with topic.get_sync_producer() as producer:
   for i in range(100):
       producer.produce('test message ' + str(i ** 2))

报错：

Traceback (most recent call last):
  File "TaxiKafkaProduce.py", line 15, in 
    producer.produce(('test message ' + str(i ** 2)))
  File "/root/anaconda3/lib/python3.6/site-packages/pykafka/producer.py", line 325, in produce
    "got '%s'", type(message))
TypeError: ("Producer.produce accepts a bytes object as message, but it got '%s'", )

因为kafka传递的是字节，不是字符串，因此在传递字符串处encode()即可，分别是client.topics和producer.produce()，如下：

#!/bin/env python
from pykafka import KafkaClient
host = '192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092'
client = KafkaClient(hosts = host)
topic = client.topics["test".encode()]
# 将产生kafka同步消息，这个调用仅仅在我们已经确认消息已经发送到集群之后
with topic.get_sync_producer() as producer:
    for i in range(100):
        producer.produce(('test message ' + str(i ** 2)).encode())

同步与异步

from pykafka import KafkaClient
#可接受多个client
client = KafkaClient(hosts ="192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092") 
#查看所有的topic
client.topics
print client.topics
 
topic = client.topics['test_kafka_topic']#选择一个topic
 
message = "test message test message"
# 当有了topic之后呢，可以创建一个producer,来发消息，生产kafka数据,通过字符串形式，
with topic.get_sync_producer() as producer:
    producer.produce(message)
# 以上的例子将产生kafka同步消息，这个调用仅仅在我们已经确认消息已经发送到集群之后
 
#但生产环境，为了达到高吞吐量，要采用异步的方式，通过delivery_reports =True来启用队列接口；
producer = topic.get_producer(sync=False, delivery_reports=True)
producer.produce(message)
try:
    msg, exc = producer.get_delivery_report(block=False)
    if exc is not None:
        print 'Failed to deliver msg {}: {}'.format(msg.partition_key, repr(exc))
    else:
        print 'Successfully delivered msg {}'.format(msg.partition_key)
except Queue.Empty:
    pass

消费者

pykafka消费者分为simple和balanced两种

simple适用于需要消费指定分区且不需要自动的重分配(自定义)
balanced自动分配则选择

#coding=utf-8
 
from pykafka import KafkaClient
 
 
class KafkaTest(object):
    def __init__(self, host="192.168.237.129:9092"):
        self.host = host
        self.client = KafkaClient(hosts=self.host)
 
    def simple_consumer(self, topic, offset=0):
        """
        消费者指定消费
        :param offset:
        :return:
        """
 
        topic = self.client.topics[topic.encode()]
        partitions = topic.partitions
        last_offset = topic.latest_available_offsets()
        print("最近可用offset {}".format(last_offset))  # 查看所有分区
        consumer = topic.get_simple_consumer(b"simple_consumer_group", partitions=[partitions[0]])  # 选择一个分区进行消费
        offset_list = consumer.held_offsets
        print("当前消费者分区offset情况{}".format(offset_list))  # 消费者拥有的分区offset的情况
        consumer.reset_offsets([(partitions[0], offset)])  # 设置offset
        msg = consumer.consume()
        print("消费 :{}".format(msg.value.decode()))
        msg = consumer.consume()
        print("消费 :{}".format(msg.value.decode()))
        msg = consumer.consume()
        print("消费 :{}".format(msg.value.decode()))
        offset = consumer.held_offsets
        print("当前消费者分区offset情况{}".format(offset)) # 3
 
    def balance_consumer(self, topic, offset=0):
        """
        使用balance consumer去消费kafka
        :return:
        """
        topic = self.client.topics["kafka_test".encode()]
        # managed=True 设置后，使用新式reblance分区方法，不需要使用zk，而False是通过zk来实现reblance的需要使用zk
        consumer = topic.get_balanced_consumer(b"consumer_group_balanced2", managed=True)
        partitions = topic.partitions
        print("分区 {}".format(partitions))
        earliest_offsets = topic.earliest_available_offsets()
        print("最早可用offset {}".format(earliest_offsets))
        last_offsets = topic.latest_available_offsets()
        print("最近可用offset {}".format(last_offsets))
        offset = consumer.held_offsets
        print("当前消费者分区offset情况{}".format(offset))
        while True:
            msg = consumer.consume()
            offset = consumer.held_offsets
            print("{}, 当前消费者分区offset情况{}".format(msg.value.decode(), offset))
 
if __name__ == '__main__':
    host = '192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092'
    kafka_ins = KafkaTest(host)
    topic = 'test'
    # kafka_ins.simple_consumer(topic)
    kafka_ins.balance_consumer(topic)

连接zookeeper

>>>> balanced_consumer = topic.get_balanced_consumer(
 consumer_group='testgroup',
 auto_commit_enable=True,  # 设置为Flase的时候不需要添加 consumer_group
 zookeeper_connect='myZkClusterNode1.com:2181,myZkClusterNode2.com:2181/myZkChroot' # 这里就是连接多个zk
)

使用consumber_group和consumer_id

# -* coding:utf8 *-  
from pykafka import KafkaClient
 
host = '192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092'
client = KafkaClient(hosts = host)
 
print(client.topics)
 
# 消费者  
topic = client.topics['test'.encode()]
consumer = topic.get_simple_consumer(consumer_group='test_group', 
                            # 设置为False的时候不需要添加consumer_group，直接连接topic即可取到消息
                            auto_commit_enable=True, 
                            auto_commit_interval_ms=1,  
                            #这里就是连接多个zk
                            zookeeper_connect='192.168.17.64:2181,192.168.17.65:2181,192.168.17.68:2181' 
                            consumer_id='test_id')
 
for message in consumer:
    if message is not None:
        #打印接收到的消息体的偏移个数和值
        print(message.offset, message.value)

可能遇到的问题–AttributeError: ‘SimpleConsumer’ object has no attribute ‘_consumer_group’

因为kafka在传输的时候需要bytes，而不是str，所以在str上加上b标识就可以，如下：

# -* coding:utf8 *-  
from pykafka import KafkaClient
 
host = '192.168.17.64:9092,192.168.17.65:9092,192.168.17.68:9092'
client = KafkaClient(hosts = host)
 
print(client.topics)
 
# 消费者  
topic = client.topics['test'.encode()]
consumer = topic.get_simple_consumer(consumer_group=b'test_group', auto_commit_enable=True, auto_commit_interval_ms=1,  consumer_id=b'test_id')
 
for message in consumer:
    if message is not None:
        print(message.offset, message.value.decode('utf-8'))

不要重复消费，对已经消费过的信息进行舍弃

不希望消费历史数据的时候，需要使用auto_commit_enable这个参数

 consumer = topic.get_simple_consumer(consumer_group=b'test_group', 
                             auto_commit_enable=True, 
                             auto_commit_interval_ms=1, 
                             consumer_id=b'test_id')