我有一个PySpark应用程序,必须详细说明约5gb的压缩数据(字符串)。我正在使用一台具有12个核心(24个线程)和72Gb RAM的小型服务器。我的PySpark程序仅包含2个 map 操作,并通过3个非常大的正则表达式(每个3gb已编译)进行了帮助,并加载了pickle
。 Spark在独立模式下工作,而工作服务器和主服务器在同一台计算机上。
我的问题是:spark是否为每个执行程序核心复制每个变量?因为它使用了所有可用的内存,然后使用了大量的交换空间。还是将所有分区都加载到RAM中? RDD包含大约1000万个字符串,必须由3个正则表达式进行搜索。 RDD大约有1000个分区。我在完成此任务时遇到了麻烦,因为几分钟后内存已满,并且spark开始使用交换空间变得非常缓慢。我注意到,没有正则表达式的情况是一样的。
这是我的代码,它删除了Twitter推文的所有无用字段,并在推文的文本和描述中查找特定单词:
import json
import re
import twitter_util as twu
import pickle
from pyspark import SparkContext
sc = SparkContext()
prefix = '/home/lucadiliello'
source = prefix + '/data/tweets'
dest = prefix + '/data/complete_tweets'
#Regex's path
companies_names_regex = prefix + '/data/comp_names_regex'
companies_names_dict = prefix + '/data/comp_names_dict'
companies_names_dict_to_legal = prefix + '/data/comp_names_dict_to_legal'
#Loading the regex's
comp_regex = pickle.load(open(companies_names_regex))
comp_dict = pickle.load(open(companies_names_dict))
comp_dict_legal = pickle.load(open(companies_names_dict_to_legal))
#Loading the RDD from textfile
tx = sc.textFile(source).map(lambda a: json.loads(a))
def get_device(input_text):
output_text = re.sub('<[^>]*>', '', input_text)
return output_text
def filter_data(a):
res = {}
try:
res['mentions'] = a['entities']['user_mentions']
res['hashtags'] = a['entities']['hashtags']
res['created_at'] = a['created_at']
res['id'] = a['id']
res['lang'] = a['lang']
if 'place' in a and a['place'] is not None:
res['place'] = {}
res['place']['country_code'] = a['place']['country_code']
res['place']['place_type'] = a['place']['place_type']
res['place']['name'] = a['place']['name']
res['place']['full_name'] = a['place']['full_name']
res['source'] = get_device(a['source'])
res['text'] = a['text']
res['timestamp_ms'] = a['timestamp_ms']
res['user'] = {}
res['user']['created_at'] = a['user']['created_at']
res['user']['description'] = a['user']['description']
res['user']['followers_count'] = a['user']['followers_count']
res['user']['friends_count'] = a['user']['friends_count']
res['user']['screen_name'] = a['user']['screen_name']
res['user']['lang'] = a['user']['lang']
res['user']['name'] = a['user']['name']
res['user']['location'] = a['user']['location']
res['user']['statuses_count'] = a['user']['statuses_count']
res['user']['verified'] = a['user']['verified']
res['user']['url'] = a['user']['url']
except KeyError:
return []
return [res]
results = tx.flatMap(filter_data)
def setting_tweet(tweet):
text = tweet['text'] if tweet['text'] is not None else ''
descr = tweet['user']['description'] if tweet['user']['description'] is not None else ''
del tweet['text']
del tweet['user']['description']
tweet['text'] = {}
tweet['user']['description'] = {}
del tweet['mentions']
#tweet
tweet['text']['original_text'] = text
tweet['text']['mentions'] = twu.find_retweet(text)
tweet['text']['links'] = []
for j in twu.find_links(text):
tmp = {}
try:
tmp['host'] = twu.get_host(j)
tmp['link'] = j
tweet['text']['links'].append(tmp)
except ValueError:
pass
tweet['text']['companies'] = []
for x in comp_regex.findall(text.lower()):
tmp = {}
tmp['id'] = comp_dict[x.lower()]
tmp['name'] = x
tmp['legalName'] = comp_dict_legal[x.lower()]
tweet['text']['companies'].append(tmp)
# descr
tweet['user']['description']['original_text'] = descr
tweet['user']['description']['mentions'] = twu.find_retweet(descr)
tweet['user']['description']['links'] = []
for j in twu.find_links(descr):
tmp = {}
try:
tmp['host'] = twu.get_host(j)
tmp['link'] = j
tweet['user']['description']['links'].append(tmp)
except ValueError:
pass
tweet['user']['description']['companies'] = []
for x in comp_regex.findall(descr.lower()):
tmp = {}
tmp['id'] = comp_dict[x.lower()]
tmp['name'] = x
tmp['legalName'] = comp_dict_legal[x.lower()]
tweet['user']['description']['companies'].append(tmp)
return tweet
res = results.map(setting_tweet)
res.map(lambda a: json.dumps(a)).saveAsTextFile(dest, compressionCodecClass="org.apache.hadoop.io.compress.BZip2Codec")
更新
大约1个小时后,内存(72gb)完全已满,也进行了交换(72gb)。就我而言,使用广播不是解决方案。
更新2
如果不使用pickle加载3个变量,那么使用多达10gb的RAM(而不是144GB)就不会出现问题! (72GB RAM + 72Gb交换)
最佳答案
是的!
每个(局部)变量的副本数等于您分配给Python Worker的线程数。
对于您的问题,请尝试在不使用comp_regex
的情况下加载comp_dict
,comp_dict_legal
和pickle
。
关于python - Spark 会产生多少环境副本?,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/43955326/