怎样完成python3完成并发接见程度切分表【MySQL教程】,mysql,python
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场景申明
假定有一个mysql表被程度切分,疏散到多个host中,每一个host具有n个切分表。
假如须要并发去接见这些表,疾速获得查询效果, 应当怎么做呢?
这里供应一种计划,应用python3的asyncio异步io库及aiomysql异步库去完成这个需求。
代码演示
import logging import random import asynciofrom aiomysql import create_pool # 假定mysql表疏散在8个host, 每一个host有16张子表 TBLES = { "192.168.1.01": "table_000-015", # 000-015示意该ip下的表明从table_000一向一连到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,用于捕获非常def query_wrapper(func): async def wrapper(*args, **kwargs): try: await func(*args, **kwargs) except Exception as e: print(e) return wrapper # 现实的sql接见处置惩罚函数,经由过程aiomysql完成异步非壅塞要求@ 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接见行列, 行列的每一个元素包括要对某个表举行接见的函数及参数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# 按批量运转sql接见要求行列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要领, 经由过程asyncio完成函数异步挪用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()
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