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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()