本篇文章给大家带来的内容是关于如何实现python3实现并发访问水平切分表，有一定的参考价值，有需要的朋友可以参考一下，希望对你有所帮助。
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() 
