实例分析python3实现并发访问水平切分表

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()