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How to implement python3 to implement concurrent access to horizontally split tables

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Release: 2018-09-15 10:25:09
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The content of this article is about how to implement python3 to achieve concurrent access to horizontal split tables. It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.

Scenario Description

Assume that a mysql table is horizontally split and distributed to multiple hosts. Each host has n split tables.
If you need to access these tables concurrently and get query results quickly, what should you do?
Here is a solution that uses python3's asyncio asynchronous io library and aiomysql asynchronous library to achieve this requirement.

Code Demonstration

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()
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