如何实现python3实现并发访问水平切分表

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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()