python3多进程和协程处理MySQL数据讲义
下文内容主要给大家带来python3多进程和协程处理MySQL数据讲义,这里所讲到的知识,与书籍略有不同,都是创新互联专业技术人员在与用户接触过程中,总结出来的,具有一定的经验分享价值,希望给广大读者带来帮助。
为茶陵等地区用户提供了全套网页设计制作服务,及茶陵网站建设行业解决方案。主营业务为成都网站设计、网站建设、茶陵网站设计,以传统方式定制建设网站,并提供域名空间备案等一条龙服务,秉承以专业、用心的态度为用户提供真诚的服务。我们深信只要达到每一位用户的要求,就会得到认可,从而选择与我们长期合作。这样,我们也可以走得更远!
python3的多进程 + 协程处理MySQL的数据,主要逻辑是拉取MySQL的数据,然后使用flashtext匹配关键字,在存回MySQL,代码如下(async_mysql.py
):
import time
import asyncio
import random
from concurrent.futures import ProcessPoolExecutor as Pool
import aiomysql
from flashtext import KeywordProcessor
import click
class AttrDict(dict):
"""可以用"."获取属性,没有该属性时返回None的字典"""
def __getattr__(self, name):
try:
return self[name]
except KeyError:
return None
def __setattr__(self, name, value):
self[name] = value
class AttrDictCursor(aiomysql.DictCursor):
"""继承aiomysql的字典cursor"""
dict_type = AttrDict
class MultiProcessMysql(object):
"""用多进程和协程处理MySQL数据"""
def __init__(self, workers=2, pool=10, start=0, end=2000):
"""第一段的参数需要跟随需求变动"""
self.host = "192.168.0.34"
self.port = 3306
self.user = "root"
self.password = "root"
self.db = "mydb"
self.origin_table = "judgment_main_etl" # main
self.dest_table = "laws_finance1"
self.s_sql = f"select uuid, court_idea, judge_result, reason, plt_claim, dft_rep, crs_exm from {self.origin_table} where %s<=id and id<%s;"
self.i_sql = f"insert into {self.dest_table} (uuid, title, reason, keyword) values (%s, %s, %s, %s)"
self.pool = pool # 协程数和MySQL连接数
self.aionum = self.pool
self.step = 2000 # 一次性从MySQL拉取的行数
self.workers = workers # 进程数
self.start = start # MySQL开始的行数
self.end = end # MySQL结束的行数
self.keyword = ['非法经营支付业务', '网络洗钱', '资金池', '支付牌照', '清洁算', '网络支付', '网上支付', '移动支付', '聚合支付', '保本保息', '担保交易', '供应链金融', '网贷', '网络借贷', '网络投资', '虚假标的', '自融', '资金池', '关联交易', '庞氏骗局', '网络金融理财', '线上投资理财', '互联网私募', '互联网股权', '非法集资', '合同欺诈', '众筹投资', '股权转让', '互联网债权转让', '资本自融', '投资骗局', '洗钱', '非法集资', '网络传销', '虚拟币泡沫', '网络互助金融', '金融欺诈', '网上银行', '信用卡盗刷', '网络钓鱼', '信用卡信息窃取', '网上洗钱', '洗钱诈骗', '数字签名更改', '支付命令窃取', '金融诈骗', '引诱投资', '隐瞒项目信息', '风险披露', '夸大收益', '诈骗保险金', '非法经营保险业务', '侵占客户资金', '征信报告窃取', '金融诈骗', '破坏金融管理']
self.kp = KeywordProcessor() # flashtext是一个文本匹配包,在关键词数量大时速度远大于re
self.kp.add_keywords_from_list(self.keyword)
async def createMysqlPool(self, loop):
"""每个进程要有独立的pool,所以不绑定self"""
pool = await aiomysql.create_pool(
loop=loop, host=self.host, port=self.port, user=self.user,
password=self.password, db=self.db, maxsize=self.pool,
charset='utf8', cursorclass=AttrDictCursor
)
return pool
def cutRange(self, start, end, times):
"""将数据区间分段"""
partition = (end - start) // times
ranges = []
tmp_end = start
while tmp_end < end:
tmp_end += partition
# 剩下的不足以再分
if (end - tmp_end) < partition:
tmp_end = end
ranges.append((start, tmp_end))
start = tmp_end
return ranges
async def findKeyword(self, db, start, end):
"""从MySQL数据中匹配出关键字"""
# 随机休息一定时间,防止数据同时到达,同时处理, 应该是一部分等待,一部分处理
await asyncio.sleep(random.random() * self.workers * 2)
print("coroutine start")
async with db.acquire() as conn:
async with conn.cursor() as cur:
while start < end:
tmp_end = start + self.step
if tmp_end > end:
tmp_end = end
print("aio start: %s, end: %s" % (start, tmp_end))
# <=id 和 id<
await cur.execute(self.s_sql, (start, tmp_end))
datas = await cur.fetchall()
uuids = []
for data in datas:
if data:
for key in list(data.keys()):
if not data[key]:
data.pop(key)
keyword = self.kp.extract_keywords(
" ".join(data.values()))
if keyword:
keyword = ' '.join(set(keyword)) # 对关键字去重
# print(keyword)
uuids.append(
(data.uuid, data.title, data.reason, keyword))
await cur.executemany(self.i_sql, uuids)
await conn.commit()
start = tmp_end
def singleProcess(self, start, end):
"""单个进程的任务"""
loop = asyncio.get_event_loop()
# 为每个进程创建一个pool
db = loop.run_until_complete(asyncio.ensure_future(
self.createMysqlPool(loop)))
tasks = []
ranges = self.cutRange(start, end, self.aionum)
print(ranges)
for start, end in ranges:
tasks.append(self.findKeyword(db, start, end))
loop.run_until_complete(asyncio.gather(*tasks))
def run(self):
"""多进程跑"""
tasks = []
ranges = self.cutRange(self.start, self.end, self.workers)
start_time = time.time()
with Pool(max_workers=self.workers) as executor:
for start, end in ranges:
print("processor start: %s, end: %s" % (start, end))
tasks.append(executor.submit(self.singleProcess, start, end))
for task in tasks:
task.result()
print("total time: %s" % (time.time() - start_time))
@click.command(help="运行")
@click.option("-w", "--workers", default=2, help="进程数")
@click.option('-p', "--pool", default=10, help="协程数")
@click.option('-s', '--start', default=0, help='MySQL开始的id')
@click.option('-e', "--end", default=2640000, help="MySQL结束的id")
def main(workers, pool, start, end):
mp = MultiProcessMysql(workers=workers, pool=pool, start=start, end=end)
if workers * pool > 100:
if not click.confirm('MySQL连接数超过100(%s),确认吗?' % (workers * pool)):
return
mp.run()
if __name__ == "__main__":
main()
运行如下:$ python3 async_mysql.py -w 2 # 可以指定其他参数,也可使用默认值
对于以上关于python3多进程和协程处理MySQL数据讲义,如果大家还有更多需要了解的可以持续关注我们创新互联的行业推新,如需获取专业解答,可在官网联系售前售后的,希望该文章可给大家带来一定的知识更新。
当前文章:python3多进程和协程处理MySQL数据讲义
链接URL:http://scyanting.com/article/jgpcgd.html