Python Parallel run function

# -*- coding: utf-8 -*-import multiprocessingimport os, time,randomimport  pymysqlcurdir = os.path.dirname (__file__) def db_conn ():    conn  = pymysql.connect (host= ' localhost ', user= ' root ', password= ' 123456 ', db= ' entries ',  charset= ' UTF8 ')       #conn  = pymysql.connect (host= ' localhost ', user= ' root ', password= ' root ', db= ' db ',  charset= ' UTF8 ',  cursorclass=pymysql.cursors.dictcursor)     return conndef  db_query (Conn, sql):     cursor = conn.cursor ()      cursor.execute (SQL)     result = cursor.fetchall ()      Return resultdef fun_1 ():     fp = open (Os.path.join (curdir,  ' Engines.txt '),  ' W ')     conn = db_conn ()     sql =   ' Select engine,support,comment from engines; '     result = db_query (Conn, sql)     for res  In result:        fp.write ("%s|%s|%s\n"  % (Res[0],res[1],  RES[2])         fp.flush ()     fp.close ()     conn.close () def fun_2 ():     fp = open (Os.path.join ( curdir,  ' collations.txt '),  ' W ')     conn = db_conn ()      sql =  ' Select collation_name,character_set_name,id,is_default, is_compiled from  collations; '     result = db_query (Conn, sql)     for res  In result:        fp.write ("%s|%s|%s|%s|%s\n"  % (res[0],res[1 ], str (res[2]), res[3],res[4])         fp.flush ()     fp.close ()     conn.close () def fun_3 ():     fp = open (Os.path.join (curdir,  ' indexes.txt '),  ' W ')      conn = db_conn ()     sql =  ' Select name,table_id,type,n_ fields,page_no from indexes; '     result = db_query (Conn, sql)     for res  In result:        fp.write ("%s|%s|%s|%s|%s\n"  % (res[0],res[1 ], RES[2],RES[3],RES[4])         fp.flush ()      fp.close ()     conn.close () Def main ():    conn =  Db_conn ()     fun_list = [ fun_1, fun_2, fun_3 ]     print ("parent process %s"  %  os.getpid ())     pool = multiprocessing. Pool (3)     start = time.time ()     for func in  fun_list:        print ("Func name",  func)          pool.apply_async (func)     print (' Waiting for  all subprocess done ... ')     pool.close ()     pool.join ( )     end = time.time ()     print (' All subprocess  Done, run %0.2f seconds '  %  (end - start))     conn.close () if __name__ ==  ' __main__ ':     main ()   # 0.3  -  0.42     "    start = time.time ()      conn = db_conn ()     fun_1 (conn)     fun_2 (cOnn)     fun_3 (conn)     conn.close ()     end =  time.time ()     print (' All done, run %0.2f seconds '  %  ( End - start))    #6 .61 6.94 7.03 7.30 6.91     "

When Fun_1, Fun_2, fun_3 itself is not very time consuming, parallel efficiency is not executed efficiently in sequence.

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Python Parallel run function