045.Python线程队列

 

线程队列

1 基本语法和用法

  1. put 往线程队列里防止,超过队列长度,直接阻塞
  2. get 从队列中取值,如果获取不到,直接阻塞
  3. put_nowait: 如果放入的值超过队列长度,直接报错（linux）
  4. get_nowait: 如果获取的值已经没有了,直接报错

(1) queue 先进先出

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from queue import Queue
q = Queue()
q.put(11)
q.put(22)
print(q.get())
print(q.get_nowait())
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执行

[root@node10 python]# python3 test.py
11
22

直接报错

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from queue import Queue
q = Queue()
q.put(11)
q.put(22)
print(q.get())
print(q.get_nowait())
print(q.get_nowait())
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执行

指定队列长度

from queue import Queue
q2 = Queue(2)
q2.put(33)
q2.put(44)
q2.put(55)

直接阻塞

使用put_nowait报错

 LifoQueue 后进先出

数据结构中,栈队列的一种储存顺序

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from queue import LifoQueue
lq = LifoQueue()
lq.put(55)
lq.put(66)
print(lq.get())
print(lq.get())
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执行

[root@node10 python]# python3 test.py
66
55

PriorityQueue 按照优先级顺序排列

  1. 默认按照数字大小排序,然后会按照ascii编码在从小到大排序
  2. 先写先排,后写后排
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from queue import PriorityQueue
pq = PriorityQueue()
pq.put( (12,"John") )
pq.put( (6,"Jim") )
pq.put( (19,"Tom") )
pq.put( (8,"Lucy") )

print(pq.get())
print(pq.get())
print(pq.get())
print(pq.get())
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执行

[root@node10 python]# python3 test.py
(6, 'Jim')
(8, 'Lucy')
(12, 'John')
(19, 'Tom')

当数字一样  按照ascsi值

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from queue import PriorityQueue
pq = PriorityQueue()
pq.put( (12,"John") )
pq.put( (6,"Jim") )
pq.put( (19,"Tom") )
pq.put( (19,"Lucy") )


print(pq.get())
print(pq.get())
print(pq.get())
print(pq.get())
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执行

[root@node10 python]# python3 test.py
(6, 'Jim')
(12, 'John')
(19, 'Lucy')
(19, 'Tom')

单独一个元素,必须放同一种类型

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from queue import PriorityQueue
pq = PriorityQueue()
pg = PriorityQueue()
pg.put(13)
pg.put(18)
pg.put(3)
print(pg.get())
print(pg.get())
print(pg.get())
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执行

[root@node10 python]# python3 test.py
3
13
18

如果不同类型

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from queue import PriorityQueue
pq = PriorityQueue()
pg = PriorityQueue()
pg.put(13)
pg.put(18)
pg.put(3)
pg.put("sdfsdf")
print(pg.get())
print(pg.get())
print(pg.get())
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执行

字符串类型

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from queue import PriorityQueue
pg1 = PriorityQueue()
pg1.put("ab")
pg1.put("cc")
print(pg1.get())
print(pg1.get())
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执行

[root@node10 python]# python3 test.py
ab
cc

2 新版进程池,线程池

进程池 允许cpu并行

执行一个进程，如果使用了进程池,是要控制进程并行数量

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from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
import os,time
def func(i):
        print ("process:",i,os.getpid())
        time.sleep(3)
        print ("process:end")
        return 6666
# 创建进程池对象，8是代表最大8个进程，ProcessPoolExecutor 后面的参数默认是cpu的最大逻辑处理器核心数.
p = ProcessPoolExecutor(8)
#异步触发进程,res 接收的是对象,这个对象可以通过result()来获取返回值
res = p.submit(func,1)
#获取进程任务的返回值
res2 = res.result()
#shutdown,等待所有子进程执行完毕之后,在向下执行,类似于join
p.shutdown()

print("主进程执行完毕")
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执行

[root@node10 python]# python3 test.py
process: 1 42441
process:end
主进程执行完毕

执行多个进程，如果使用了进程池,是要控制进程并行数量

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from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
import os,time
def func(i):
        print ("process:",i,os.getpid())
        time.sleep(3)
        print ("process:end")
        return 6666
# 创建进程池对象
p = ProcessPoolExecutor(8)
#异步触发进程,res 接收的是对象,这个对象可以通过result()来获取返回值
for i in range(12):
        res = p.submit(func,i)
#获取进程任务的返回值
res2 = res.result()
#shutdown,等待所有子进程执行完毕之后,在向下执行,类似于join
p.shutdown()

print("主进程执行完毕")
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执行

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[root@node10 python]# python3 test.py
process: 0 42457
process: 1 42458
process: 2 42459
process: 3 42460
process: 4 42461
process: 5 42462
process: 6 42463
process: 7 42464
process:end
process:end
process:end
process: 8 42463
process: 9 42457
process: 10 42459
process:end
process: 11 42462
process:end
process:end
process:end
process:end
process:end
process:end
process:end
process:end
主进程执行完毕
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3 线程池

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from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
from threading import current_thread as cthread
import os,time
def func(i):
        print("thread",i,cthread().ident)
        time.sleep(3)
        print("thread %s end %s"%(i))

#创建线程池。括号里面可以指定并发的线程数
tp = ThreadPoolExecutor(4)
for i in range(20):
        tp.submit(func,i)
tp.shutdown()
print("主线程执行结束。。。")
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执行

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[root@node10 python]# python3 test.py
thread 0 140712745903872
thread 1 140712737511168
thread 2 140712658073344
thread 3 140712649680640
thread 4 140712745903872
thread 5 140712658073344
thread 6 140712737511168
thread 7 140712649680640
thread 8 140712737511168
thread 9 140712658073344
thread 10 140712745903872
thread 11 140712649680640
thread 12 140712737511168
thread 13 140712745903872
thread 14 140712658073344
thread 15 140712649680640
thread 16 140712745903872
thread 17 140712737511168
thread 18 140712649680640
thread 19 140712658073344
主线程执行结束。。。
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4 GIL锁

一个进程中的多条线程同一时间只能被一个cpu执行,不能实现并行操作.
想要解决:更换Jpython 或者 PyPy解释器
为什么加锁:
python是解释性语言,编译一行,就执行一行,不能提前规划系统资源,进行全局分配,根本原因是历史遗留问题.
程序分为两大类:

  • 计算密集型程序，通过c语言改写python部分模块来实现
  • io密集型程序，类似于python_web 运维,数据分析 都可以使用

线程池的返回值

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from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
from threading import current_thread as cthread
import os,time
def func(i):
        #获取当前线程号
        print("thread",i,cthread().ident)
        time.sleep(1)
        #返回线程号，获取返回值，会加阻塞，无需shutdown
        return cthread().ident

#创建线程池。括号里面可以指定并发的线程数
tp = ThreadPoolExecutor(6)
lst = []
setvar = set()
for i in range(12):
        #异步出发
        res = tp.submit(func,i)
        lst.append(res)
for i in lst:
        #获取该进程对象的返回值
        print (i.result())
　　　　#塞到集合里面，可以去重，验证
        setvar.add(i.result())
#打印所有的线程号
print (setvar)
print("主线程执行结束。。。")
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执行

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[root@node10 python]# python3 test.py
thread 0 140423614576384
thread 1 140423606183680
thread 2 140423597790976
thread 3 140423589398272
thread 4 140423581005568
thread 5 140423572612864
thread <Future at 0x7fb6f7ad4b70 state=running> 140423597790976
thread <Future at 0x7fb6f7ad4b70 state=running> 140423572612864
thread <Future at 0x7fb6f7ad4b70 state=running> 140423589398272
thread <Future at 0x7fb6f7ad4b70 state=running> 140423606183680
thread <Future at 0x7fb6f7ad4b70 state=running> 140423581005568
thread <Future at 0x7fb6f7ad4b70 state=finished returned int> 140423614576384
140423614576384
140423606183680
140423597790976
140423589398272
140423581005568
140423572612864
140423597790976
140423572612864
140423589398272
140423606183680
140423581005568
140423614576384
{140423614576384, 140423606183680, 140423581005568, 140423597790976, 140423589398272, 140423572612864}
主线程执行结束。。。
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5 map返回迭代器

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from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor
import os,time
from threading import current_thread as cthread
def func(i):
        time.sleep(0.2)
        print("thread",i,cthread().ident)
        print("thread .. end %s" % (i))
        return "*" * i

tp = ThreadPoolExecutor(5)
it = tp.map(func,range(20))
tp.shutdown()
print("<===>")
from collections import Iterator
res = isinstance(it,Iterator)
print(res)
print(list(it))

# "1234567"
# it = map(int,"1234567")
# print(list(it))
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执行

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[root@node10 python]# python3 test.py
thread 0 140381751781120
thread .. end 0
thread 2 140381734995712
thread .. end 2
thread 1 140381743388416
thread .. end 1
thread 3 140381726603008
thread .. end 3
thread 4 140381718210304
thread .. end 4
thread 5 140381751781120
thread 6 140381734995712
thread .. end 5
thread .. end 6
thread 9 140381718210304
thread 8 140381726603008
thread .. end 8
thread 7 140381743388416
thread .. end 7
thread .. end 9
thread 14 140381718210304
thread .. end 14
thread 10 140381751781120
thread .. end 10
thread 11 140381734995712
thread .. end 11
thread 13 140381743388416
thread .. end 13
thread 12 140381726603008
thread .. end 12
thread 15 140381718210304
thread .. end 15
thread 19 140381726603008
thread .. end 19
thread 16 140381751781120
thread .. end 16
thread 18 140381743388416
thread .. end 18
thread 17 140381734995712
thread .. end 17
<===>
True
['', '*', '**', '***', '****', '*****', '******', '*******', '********', '*********', '**********', '***********', '************', '*************', '**************', '***************', '****************', '*****************', '******************', '*******************']
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posted @ 2021-01-24 22:50  uplee  阅读(170)  评论(0)  编辑  收藏  举报