python消费kafka数据批量插入到es的方法

1、es的批量插入这是为了方便后期配置的更改，把配置信息放在logging.conf中用elasticsearch来

1、es的批量插入

这是为了方便后期配置的更改，把配置信息放在logging.conf中

用elasticsearch来实现批量操作，先安装依赖包，sudo pip install Elasticsearch2

from elasticsearch import Elasticsearch 
class ImportEsData:

 logging.config.fileConfig("logging.conf")
 logger = logging.getLogger("msg")

 def __init__(self,hosts,index,type):
 self.es = Elasticsearch(hosts=hosts.strip(',').split(','), timeout=5000)
 self.index = index
 self.type = type


 def set_date(self,data): 
 # 批量处理 
 # es.index(index="test-index",doc_type="test-type",id=42,body={"any":"data","timestamp":datetime.now()})
 self.es.index(index=self.index,doc_type=self.index,body=data)

2、使用pykafka消费kafka

1.因为kafka是0.8，pykafka不支持zk，只能用get_simple_consumer来实现

2.为了实现多个应用同时消费而且不重消费，所以一个应用消费一个partition

3. 为是确保消费数据量在不满足10000这个批量值，能在一个时间范围内插入到es中，这里设置consumer_timeout_ms一个超时等待时间，退出等待消费阻塞。

4.退出等待消费阻塞后导致无法再消费数据，因此在获取self.consumer 的外层加入了while True 一个死循环

#!/usr/bin/python
# -*- coding: UTF-8 -*-
from pykafka import KafkaClient
import logging
import logging.config
from ConfigUtil import ConfigUtil
import datetime


class KafkaPython:
 logging.config.fileConfig("logging.conf")
 logger = logging.getLogger("msg")
 logger_data = logging.getLogger("data")

 def __init__(self):
 self.server = ConfigUtil().get("kafka","kafka_server")
 self.topic = ConfigUtil().get("kafka","topic")
 self.group = ConfigUtil().get("kafka","group")
 self.partition_id = int(ConfigUtil().get("kafka","partition"))
 self.consumer_timeout_ms = int(ConfigUtil().get("kafka","consumer_timeout_ms"))
 self.consumer = None
 self.hosts = ConfigUtil().get("es","hosts")
 self.index_name = ConfigUtil().get("es","index_name")
 self.type_name = ConfigUtil().get("es","type_name")


 def getConnect(self):
 client = KafkaClient(self.server)
 topic = client.topics[self.topic]
 p = topic.partitions
 ps={p.get(self.partition_id)}

 self.consumer = topic.get_simple_consumer(
 consumer_group=self.group,
 auto_commit_enable=True,
 consumer_timeout_ms=self.consumer_timeout_ms,
 # num_consumer_fetchers=1,
 # consumer_id='test1',
 partitions=ps
 )
 self.starttime = datetime.datetime.now()


 def beginConsumer(self):
 print("beginConsumer kafka-python")
 imprtEsData = ImportEsData(self.hosts,self.index_name,self.type_name)
 #创建ACTIONS 
 count = 0
 ACTIONS = [] 

 while True:
 endtime = datetime.datetime.now()
 print (endtime - self.starttime).seconds
 for message in self.consumer:
 if message is not None:
 try:
 count = count + 1
 # print(str(message.partition.id)+","+str(message.offset)+","+str(count))
 # self.logger.info(str(message.partition.id)+","+str(message.offset)+","+str(count))
 action = { 
 "_index": self.index_name, 
 "_type": self.type_name, 
 "_source": message.value
 }
 ACTIONS.append(action)
 if len(ACTIONS) >= 10000:
 imprtEsData.set_date(ACTIONS)
 ACTIONS = []
 self.consumer.commit_offsets()
 endtime = datetime.datetime.now()
 print (endtime - self.starttime).seconds
 #break
 except (Exception) as e:
 # self.consumer.commit_offsets()
 print(e)
 self.logger.error(e)
 self.logger.error(str(message.partition.id)+","+str(message.offset)+","+message.value+"\n")
 # self.logger_data.error(message.value+"\n")
 # self.consumer.commit_offsets()


 if len(ACTIONS) > 0:
 self.logger.info("等待时间超过，consumer_timeout_ms，把集合数据插入es")
 imprtEsData.set_date(ACTIONS)
 ACTIONS = []
 self.consumer.commit_offsets()




 def disConnect(self):
 self.consumer.close()


from elasticsearch import Elasticsearch 
from elasticsearch.helpers import bulk
class ImportEsData:

 logging.config.fileConfig("logging.conf")
 logger = logging.getLogger("msg")

 def __init__(self,hosts,index,type):
 self.es = Elasticsearch(hosts=hosts.strip(',').split(','), timeout=5000)
 self.index = index
 self.type = type


 def set_date(self,data): 
 # 批量处理 
 success = bulk(self.es, data, index=self.index, raise_on_error=True) 
 self.logger.info(success) 

3、运行

if __name__ == '__main__':
 kp = KafkaPython()
 kp.getConnect()
 kp.beginConsumer()
 # kp.disConnect()

注：简单的写了一个从kafka中读取数据到一个list里，当数据达到一个阈值时，在批量插入到 es的插件

现在还在批量的压测中。。。

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