以多进程读取oss符合条件的数据为例，综合使用多进程间的通信、获取多进程的数据

import datetime
import sys
import oss2
from itertools import islice
import pandas as pd
import re
import json
from pandas.tseries.offsets import Day
from multiprocessing import Process, JoinableQueue, cpu_count, Manager
import time

def mkbuck(bk):
	auth = oss2.Auth(username, password)
	bucket = oss2.Bucket(auth, address, bk)
	return bucket

#获取前天最后一小时的paths
def getbflastpt(bucket, bfyespattern):
	bfpamax = []
	for bf in islice(oss2.ObjectIterator(bucket, prefix=bfyespattern), sys.maxsize):
		c = bf.key
		if c[-1:] != '/':
			bfpamax.append(int(c.split('/')[4]))
	last = pd.Series(bfpamax).unique().max()
	if last < 10:
		bflastpt = bfyespattern + '/0' + str(last)
	else:
		bflastpt = bfyespattern + '/' + str(last)
	return bflastpt

#获取当天第一个小时的paths
def getnowfirstpt(bucket, nowpattern):
	bfpamin = []
	for bf in islice(oss2.ObjectIterator(bucket, prefix=nowpattern), sys.maxsize):
		c = bf.key
		if c[-1:] != '/':
			bfpamin.append(int(c.split('/')[4]))
	first = pd.Series(bfpamin).unique().min()
	if first < 10:
		nowfirstpt = nowpattern + '/0' + str(first)
	else:
		nowfirstpt = nowpattern + '/' + str(first)
	return nowfirstpt

#获取所有的昨日paths，并合并得到完全的paths和数量
def getfullnum(bk, bfyespattern, nowpattern, yespattern):
	lists = []
	bucket = mkbuck(bk)
	bfyespattern = getbflastpt(bucket, bfyespattern)
	nowpattern = getnowfirstpt(bucket, nowpattern)
	timelist = (s for s in (bfyespattern, yespattern, nowpattern))
	for pter in timelist:
		for bf in islice(oss2.ObjectIterator(bucket, prefix=pter), sys.maxsize):
			c = bf.key
			lists.append(c)
	return lists, len(lists)

#以下为进程间通信，即生产者、消费者模型
def getfull(bk, bfyespattern, nowpattern, yespattern, q):
	lists, num = getfullnum(bk, bfyespattern, nowpattern, yespattern)
	for c in lists:
		q.put(c)
	q.join()

def consumer(bk, q, d):
	bucket = mkbuck(bk)
	repattern2 = re.compile('{.*"adadji",.*}')
	while True:
		js = []
		ress = q.get()
		if ress[-1:] != '/':
			remote_data = bucket.get_object(ress).read().decode('utf-8')
			aa = (d for d in repattern2.findall(remote_data))
			for a in aa:
				temdic = json.loads(a)
				if (starttime <= temdic['created_at']) and (temdic['created_at'] <= endtime):
					js.append(temdic)
		df = pd.DataFrame(js, columns=['dd','cc'])
		d[ress] = df##d为通过主进程Manager共享变量将数据取出
		# print(ress)
		q.task_done()# 向q.join()发送一次信号,证明一个数据已经被取走了

if __name__ == '__main__':
	s1 = time.time()
	now_time = datetime.datetime.now()  # 获取当前时间
	bfyes_time = (now_time - 2 * Day()).strftime('%Y/%m/%d')
	yes_time = (now_time - 1 * Day()).strftime('%Y/%m/%d')
	yesdate = (now_time - 1 * Day()).strftime('%Y-%m-%d')
	yesdate1 = (now_time - 1 * Day()).strftime('%Y%m%d')
	endtime = (now_time - 1 * Day()).strftime('%Y-%m-%d 23:59:59')
	starttime = (now_time - 1 * Day()).strftime('%Y-%m-%d 00:00:00')
	nowdate = now_time.strftime('%Y/%m/%d')

	bk = 'xxx'
	bfyespattern = '%s/%s' % (bk, bfyes_time)
	yespattern = '%s/%s' % (bk, yes_time)
	nowpattern = '%s/%s' % (bk, nowdate)

	q = JoinableQueue(cpu_count())
	m = Manager()
	d = m.dict()  ##创建进程间的共享内存字典，方便各个进程处理好的数据
	p1 = Process(target=getfull, args=('xx', bfyespattern, nowpattern, yespattern, q))
	#####生成consumer多进程
	cc = []
	for c in range(cpu_count() - 1):
		c1 = Process(target=consumer, args=('xx', q, d))
		cc.append(c1)

	p_l = [p1]
	for c in cc:
		c.daemon = True
		p_l.append(c)

	for p in p_l:
		p.start()
	p1.join()
	d = d.values()
	df1 = pd.concat(d, ignore_index=True)
	df1.sort_values('created_at', inplace=True)
	print(time.time() - s1)
	print('=' * 20)
	print(df1)

　　说明：需求为获取昨日的数据即可，因oss实时数据存储可能存在提前或延迟情况，因此读取前天的最后一小时，昨日全部，当天最开始一小时数据，读者可根据自身情况进行修改