我得到了一个我能够完全爬行的数据流。数据全部放入Kafka，然后发送到Cassandra。现在卡夫卡消费者非常缓慢，比生产者慢得多。我希望它们完全一样。我可以做些什么来实现这个结果或我的代码有什么问题？
这是我在python中的Kafka消费者代码：
import logging
from cassandra.cluster import Cluster
from kafka.consumer.kafka import KafkaConsumer
from kafka.consumer.multiprocess import MultiProcessConsumer
from kafka.client import KafkaClient
from kafka.producer.simple import SimpleProducer
import json
from datetime import datetime, timedelta
from cassandra import ConsistencyLevel
from dateutil.parser import parse
logging.basicConfig(filename='consumer.log', format='[%(asctime)-15s] %(name)s %(levelname)s %(message)s', level=logging.DEBUG)
class Whitelist(logging.Filter):
def __init__(self, *whitelist):
self.whitelist = [logging.Filter(name) for name in whitelist]
def filter(self, record):
return any(f.filter(record) for f in self.whitelist)
for handler in logging.root.handlers:
handler.addFilter(Whitelist('consumer'))
log = logging.getLogger('consumer')
try:
cluster = Cluster(['localhost']); session = cluster.connect(keyspace)
kafka = KafkaClient('localhost')
consumer = MultiProcessConsumer(kafka, b'default',kafkatopic,num_procs=16, max_buffer_size=None)
article_lookup_stmt = session.prepare("SELECT * FROM articles WHERE id in ?")
article_lookup_stmt.consistency_level = ConsistencyLevel.QUORUM
article_insert_stmt = session.prepare("INSERT INTO articles(id, thumbnail, title, url, created_at, scheduled_for, source, category, channel,genre) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)")
article_by_created_at_insert_stmt = session.prepare("INSERT INTO article_by_created_at(source, created_at, article) VALUES (?, ?, ?)")
article_by_url_insert_stmt = session.prepare("INSERT INTO article_by_url(url, article) VALUES (?, ?)")
schedules_insert_stmt = session.prepare("INSERT INTO schedules(source,type,scheduled_for,id) VALUES (?,?,?,?)")
axes_insert_stmt = session.prepare("INSERT INTO axes(article,at,comments,likes,reads,shares) VALUES (?, ?, ?, ?, ?, ?)")
while True:
messages = consumer.get_messages(count=16)
if len(messages) == 0:
print 'IDLE'
continue
for message in messages:
try:
response = json.loads(message.value)
data = json.loads(response['body'])
print response['body']
articles = data['articles']
idlist = [r['id'] for r in articles]
if len(idlist)>0:
article_rows = session.execute(article_lookup_stmt,[idlist])
rows = [r.id for r in article_rows]
for article in articles:
try:
if not article['id'] in rows:
article['created_at'] = parse(article['created_at'])
scheduled_for=(article['created_at'] + timedelta(minutes=60)).replace(second=0, microsecond=0)
session.execute(article_insert_stmt, (article['id'], article['thumbnail'], article['title'], article['url'], article['created_at'], scheduled_for, article['source'], article['category'], article['channel'],article['genre']))
session.execute(article_by_created_at_insert_stmt, (article['source'], article['created_at'], article['id']))
session.execute(article_by_url_insert_stmt, (article['url'], article['id']))
session.execute(schedules_insert_stmt,(article['source'],'article',scheduled_for,article['id']))
log.debug('%s %s' % (article['id'],article['created_at']))
session.execute(axes_insert_stmt,(article['id'],datetime.utcnow(),article['axes']['comments'],article['axes']['likes'],0,article['axes']['shares']))
except Exception as e:
print 'error==============:',e
continue
except Exception as e:
print 'error is:',e
log.exception(e.message)
except Exception as e:
log.exception(e.message)
编辑：
我还添加了我的个人资料结果，代码缓慢似乎是
article_rows = session.execute(article_lookup_stmt,[idlist])
Sun Feb 14 16:01:01 2016 consumer.out
395793 function calls (394232 primitive calls) in 23.074 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
141 10.695 0.076 10.695 0.076 {select.select}
7564 10.144 0.001 10.144 0.001 {method 'acquire' of 'thread.lock' objects}
1 0.542 0.542 23.097 23.097 consumer.py:5(<module>)
1510 0.281 0.000 0.281 0.000 {method 'recv' of '_socket.socket' objects}
38 0.195 0.005 0.195 0.005 /usr/local/lib/python2.7/json/decoder.py:371(raw_decode)
13 0.078 0.006 0.078 0.006 {time.sleep}
2423 0.073 0.000 0.137 0.000 /usr/local/lib/python2.7/logging/__init__.py:242(__init__)
22112 0.063 0.000 0.095 0.000 /usr/local/lib/python2.7/site-packages/kafka/util.py:73(relative_unpack)
3 0.052 0.017 0.162 0.054 /usr/local/lib/python2.7/site-packages/kafka/protocol.py:386(decode_metadata_response)
2006/2005 0.047 0.000 0.055 0.000 /usr/local/lib/python2.7/site-packages/cassandra/policies.py:350(make_query_plan)
1270 0.032 0.000 0.034 0.000 /usr/local/lib/python2.7/threading.py:259(__init__)
3 0.024 0.008 0.226 0.075 /usr/local/lib/python2.7/site-packages/kafka/client.py:456(load_metadata_for_topics)
33 0.024 0.001 0.031 0.001 /usr/local/lib/python2.7/collections.py:288(namedtuple)
15374 0.024 0.000 0.024 0.000 {built-in method new of type object at 0x788ee0}
141 0.023 0.000 11.394 0.081 /usr/local/lib/python2.7/site-packages/kafka/client.py:153(_send_broker_aware_request)
288 0.020 0.000 0.522 0.002 /usr/local/lib/python2.7/site-packages/kafka/conn.py:84(_read_bytes)
2423 0.018 0.000 0.029 0.000 /usr/local/lib/python2.7/logging/__init__.py:1216(findCaller)
115 0.018 0.000 11.372 0.099 /usr/local/lib/python2.7/site-packages/kafka/consumer/kafka.py:303(fetch_messages)
2423 0.018 0.000 0.059 0.000 /usr/local/lib/python2.7/logging/__init__.py:1303(callHandlers)
24548 0.017 0.000 0.017 0.000 {_struct.unpack}
44228/43959 0.016 0.000 0.016 0.000 {len}
感谢您的回复。
答案 0 :(得分：2)
您可以尝试在不保存到C *的情况下运行消费者，这样您就可以观察到它有多大差异 如果结果是保存到C *是一个阻塞点（我认为是这样），你可以有一个线程池（大于16个线程，你的消费者产生），其唯一的责任是写入C *。
这样，您可以卸载代码的缓慢部分，这只会在消费者代码中留下琐碎的部分。
您可以使用from multiprocessing import Pool
更多here。