Scenario description
Suppose there is a mysql table that is sliced horizontally, spread across multiple hosts, each with n sharded tables.
What should I do if I need to access these tables concurrently and get the query results quickly?
Here is a solution to implement this requirement using the asyncio asyncio library and the aiomysql asynchronous library of python3.
Code demo
import logging
import random
import asyncio
from aiomysql import create_pool
# Suppose the mysql table is spread across 8 hosts, each with 16 sub-tables
TBLES = {
"192.168.1.01": "table_000-015", #000-015 indicates that the indication under this IP is continuous from table_000 to 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 function to catch exceptions
def query_wrapper(func):
async def wrapper(*args, **kwargs):
try:
await func(*args, **kwargs)
except Exception as e:
print(e)
return wrapper
# The actual SQL access handling function implements asynchronous non-blocking requests through AIOMYSQL
@query_wrapper
async 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...
# Generate a SQL access queue, each element of the queue contains functions and parameters to access a table
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
# Run the SQL access request queue in batches
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
batch_tasks= asyncio.ensure_future(
l[0](*l[1:])
)
loop.run_until_complete(asyncio.gather(*batch_tasks))
except Exception as e:
logging.warn(e)
# main method, which implements asynchronous call of functions through asyncio
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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Posted on 11/29/2017 9:15:55 PM
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Posted on 11/30/2017 9:03:52 AM
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