实例分析python3实现并发访问水平切分表_Python

实例分析python3实现并发访问水平切分表

2021-04-05 00:21脚本之家 Python

在本文中小编给大家整理了关于python3实现并发访问水平切分表的相关知识点以及实例代码，有兴趣的朋友们参考下。

场景说明

假设有一个mysql表被水平切分，分散到多个host中，每个host拥有n个切分表。

如果需要并发去访问这些表，快速得到查询结果，应该怎么做呢？

这里提供一种方案，利用python3的asyncio异步io库及aiomysql异步库去实现这个需求。

代码演示

									import logging

									import random

									import asynciofrom aiomysql 

									import create_pool

									# 假设mysql表分散在8个host, 每个host有16张子表

									TBLES = {  "192.168.1.01": "table_000-015",

									# 000-015表示该ip下的表明从table_000一直连续到table_015

									  "192.168.1.02": "table_016-031", 

									   "192.168.1.03": "table_032-047",  

									    "192.168.1.04": "table_048-063", 

									     "192.168.1.05": "table_064-079",  

									     "192.168.1.06": "table_080-095", 

									      "192.168.1.07": "table_096-0111", 

									       "192.168.1.08": "table_112-0127",

									}

									USER = "xxx"PASSWD = "xxxx"# wrapper函数，用于捕捉异常def query_wrapper(func):

									  async def wrapper(*args, **kwargs):

									    try:

									      await func(*args, **kwargs)    except Exception as e:

									      print(e)  return wrapper

									      # 实际的sql访问处理函数，通过aiomysql实现异步非阻塞请求@

									      query_wrapperasync def query_do_something(ip, db, table):

									  async with create_pool(host=ip, db=db, user=USER, password=PASSWD) as pool:

									    async with pool.get() as conn:

									      async with conn.cursor() as cur:

									        sql = ("select xxx from {} where xxxx")

									        await cur.execute(sql.format(table))

									        res = await cur.fetchall()    

									 # then do something...# 生成sql访问队列, 队列的每个元素包含要对某个表进行访问的函数及参数def gen_tasks():

									  tasks = []  for ip, tbls in TBLES.items():

									    cols = re.split('_|-', tbls)

									    tblpre = "_".join(cols[:-2])

									    min_num = int(cols[-2])

									    max_num = int(cols[-1])   

									      for num in range(min_num, max_num+1):

									      tasks.append(

									        (query_do_something, ip, 'your_dbname', '{}_{}'.format(tblpre, num))

									      )

									  random.shuffle(tasks)  

									   return tasks# 按批量运行sql访问请求队列def run_tasks(tasks, batch_len):

									  try:  

									    for idx in range(0, len(tasks), batch_len):

									      batch_tasks = tasks[idx:idx+batch_len]

									      logging.info("current batch, start_idx:%s len:%s" % (idx, len(batch_tasks))) 

									            for i in range(0, len(batch_tasks)):

									        l = batch_tasks[i]

									        batch_tasks[i] = asyncio.ensure_future(

									          l[0](*l[1:])

									        )

									      loop.run_until_complete(asyncio.gather(*batch_tasks)) 

									       except Exception as e:

									    logging.warn(e)# main方法, 通过asyncio实现函数异步调用def main():

									  loop = asyncio.get_event_loop()

									  tasks = gen_tasks()

									  batch_len = len(TBLES.keys()) * 5  # all up to you

									  run_tasks(tasks, batch_len)

									  loop.close()