異步IO-數據庫-隊列-緩存10

Python之路,Day10 - 異步IO\數據庫\隊列\緩存

本節內容

1. Gevent協程
2. Select\Poll\Epoll異步IO與事件驅動
3. Python連接Mysql數據庫操作
4. RabbitMQ隊列
5. Redis\Memcached緩存
6. Paramiko SSH
7. Twsited網絡框架

引子

到目前爲止，我們已經學了網絡併發編程的2個套路， 多進程，多線程，這哥倆的優勢和劣勢都非常的明顯，我們一起來回顧下

協程

協程，又稱微線程，纖程。英文名Coroutine。一句話說明什麼是線程：協程是一種用戶態的輕量級線程。

協程擁有自己的寄存器上下文和棧。協程調度切換時，將寄存器上下文和棧保存到其他地方，在切回來的時候，恢復先前保存的寄存器上下文和棧。因此：

協程能保留上一次調用時的狀態（即所有局部狀態的一個特定組合），每次過程重入時，就相當於進入上一次調用的狀態，換種說法：進入上一次離開時所處邏輯流的位置。

協程的好處：

• 無需線程上下文切換的開銷
• 無需原子操作鎖定及同步的開銷
  • 　　"原子操作(atomic operation)是不需要synchronized"，所謂原子操作是指不會被線程調度機制打斷的操作；這種操作一旦開始，就一直運行到結束，中間不會有任何 context switch （切換到另一個線程）。原子操作可以是一個步驟，也可以是多個操作步驟，但是其順序是不可以被打亂，或者切割掉只執行部分。視作整體是原子性的核心。
• 方便切換控制流，簡化編程模型
• 高併發+高擴展性+低成本：一個CPU支持上萬的協程都不是問題。所以很適合用於高併發處理。

缺點：

• 無法利用多核資源：協程的本質是個單線程,它不能同時將 單個CPU 的多個核用上,協程需要和進程配合才能運行在多CPU上.當然我們日常所編寫的絕大部分應用都沒有這個必要，除非是cpu密集型應用。
• 進行阻塞（Blocking）操作（如IO時）會阻塞掉整個程序

使用yield實現協程操作例子　　　　

import time
import queue
def consumer(name):
    print("--->starting eating baozi...")
    while True:
        new_baozi = yield
        print("[%s] is eating baozi %s" % (name,new_baozi))
        #time.sleep(1)

def producer():

    r = con.__next__()
    r = con2.__next__()
    n = 0
    while n < 5:
        n +=1
        con.send(n)
        con2.send(n)
        print("\033[32;1m[producer]\033[0m is making baozi %s" %n )

if __name__ == '__main__':
    con = consumer("c1")
    con2 = consumer("c2")
    p = producer()

看樓上的例子，我問你這算不算做是協程呢？你說，我他媽哪知道呀，你前面說了一堆廢話，但是並沒告訴我協程的標準形態呀，我腚眼一想，覺得你說也對，那好，我們先給協程一個標準定義，即符合什麼條件就能稱之爲協程：

1. 必須在只有一個單線程裏實現併發
2. 修改共享數據不需加鎖
3. 用戶程序裏自己保存多個控制流的上下文棧
4. 一個協程遇到IO操作自動切換到其它協程

基於上面這4點定義，我們剛纔用yield實現的程並不能算是合格的線程，因爲它有一點功能沒實現，哪一點呢？

Greenlet

greenlet是一個用C實現的協程模塊，相比與python自帶的yield，它可以使你在任意函數之間隨意切換，而不需把這個函數先聲明爲generator

# -*- coding:utf-8 -*-

from greenlet import greenlet

def test1():
    print(12)
    gr2.switch()
    print(34)
    gr2.switch()

def test2():
    print(56)
    gr1.switch()
    print(78)

gr1 = greenlet(test1)
gr2 = greenlet(test2)
gr1.switch()

感覺確實用着比generator還簡單了呢，但好像還沒有解決一個問題，就是遇到IO操作，自動切換，對不對？

　　

　　

Gevent

Gevent 是一個第三方庫，可以輕鬆通過gevent實現併發同步或異步編程，在gevent中用到的主要模式是Greenlet, 它是以C擴展模塊形式接入Python的輕量級協程。 Greenlet全部運行在主程序操作系統進程的內部，但它們被協作式地調度。

import gevent

def func1():
    print('\033[31;1m李闖在跟海濤搞...\033[0m')
    gevent.sleep(2)
    print('\033[31;1m李闖又回去跟繼續跟海濤搞...\033[0m')

def func2():
    print('\033[32;1m李闖切換到了跟海龍搞...\033[0m')
    gevent.sleep(1)
    print('\033[32;1m李闖搞完了海濤，回來繼續跟海龍搞...\033[0m')

gevent.joinall([
    gevent.spawn(func1),
    gevent.spawn(func2),
    #gevent.spawn(func3),
])

　　

輸出：

李闖在跟海濤搞...
李闖切換到了跟海龍搞...
李闖搞完了海濤，回來繼續跟海龍搞...
李闖又回去跟繼續跟海濤搞...

同步與異步的性能區別

import gevent

def task(pid):
    """
    Some non-deterministic task
    """
    gevent.sleep(0.5)
    print('Task %s done' % pid)

def synchronous():
    for i in range(1,10):
        task(i)

def asynchronous():
    threads = [gevent.spawn(task, i) for i in range(10)]
    gevent.joinall(threads)

print('Synchronous:')
synchronous()

print('Asynchronous:')
asynchronous()

上面程序的重要部分是將task函數封裝到Greenlet內部線程的gevent.spawn。 初始化的greenlet列表存放在數組threads中，此數組被傳給gevent.joinall 函數，後者阻塞當前流程，並執行所有給定的greenlet。執行流程只會在 所有greenlet執行完後纔會繼續向下走。　　

遇到IO阻塞時會自動切換任務

from gevent import monkey; monkey.patch_all()
import gevent
from  urllib.request import urlopen

def f(url):
    print('GET: %s' % url)
    resp = urlopen(url)
    data = resp.read()
    print('%d bytes received from %s.' % (len(data), url))

gevent.joinall([
        gevent.spawn(f, 'https://www.python.org/'),
        gevent.spawn(f, 'https://www.yahoo.com/'),
        gevent.spawn(f, 'https://github.com/'),
])

通過gevent實現單線程下的多socket併發

server side

import sys
import socket
import time
import gevent

from gevent import socket,monkey
monkey.patch_all()

def server(port):
    s = socket.socket()
    s.bind(('0.0.0.0', port))
    s.listen(500)
    while True:
        cli, addr = s.accept()
        gevent.spawn(handle_request, cli)

def handle_request(conn):
    try:
        while True:
            data = conn.recv(1024)
            print("recv:", data)
            conn.send(data)
            if not data:
                conn.shutdown(socket.SHUT_WR)

    except Exception as  ex:
        print(ex)
    finally:
        conn.close()
if __name__ == '__main__':
    server(8001)

　　

client side 　　

import socket

HOST = 'localhost'    # The remote host
PORT = 8001           # The same port as used by the server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
while True:
    msg = bytes(input(">>:"),encoding="utf8")
    s.sendall(msg)
    data = s.recv(1024)
    #print(data)

    print('Received', repr(data))
s.close()

併發100個sock連接

import socket
import threading

def sock_conn():

    client = socket.socket()

    client.connect(("localhost",8001))
    count = 0
    while True:
        #msg = input(">>:").strip()
        #if len(msg) == 0:continue
        client.send( ("hello %s" %count).encode("utf-8"))

        data = client.recv(1024)

        print("[%s]recv from server:" % threading.get_ident(),data.decode()) #結果
        count +=1
    client.close()

for i in range(100):
    t = threading.Thread(target=sock_conn)
    t.start()

　　

論事件驅動與異步IO

通常，我們寫服務器處理模型的程序時，有以下幾種模型：

1. 每收到一個請求，創建一個新的進程，來處理該請求；
2. 每收到一個請求，創建一個新的線程，來處理該請求；
3. 每收到一個請求，放入一個事件列表，讓主進程通過非阻塞I/O方式來處理請求

上面的幾種方式，各有千秋，

第1中方法，由於創建新的進程的開銷比較大，所以，會導致服務器性能比較差,但實現比較簡單。

第2種方式，由於要涉及到線程的同步，有可能會面臨死鎖等問題。

第3種方式，在寫應用程序代碼時，邏輯比前面兩種都複雜。

綜合考慮各方面因素，一般普遍認爲第3種方式是大多數網絡服務器採用的方式

看圖說話講事件驅動模型

在UI編程中，常常要對鼠標點擊進行相應，首先如何獲得鼠標點擊呢？

方式一：創建一個線程，該線程一直循環檢測是否有鼠標點擊，那麼這個方式有以下幾個缺點：

1. CPU資源浪費，可能鼠標點擊的頻率非常小，但是掃描線程還是會一直循環檢測，這會造成很多的CPU資源浪費；如果掃描鼠標點擊的接口是阻塞的呢？
2. 如果是堵塞的，又會出現下面這樣的問題，如果我們不但要掃描鼠標點擊，還要掃描鍵盤是否按下，由於掃描鼠標時被堵塞了，那麼可能永遠不會去掃描鍵盤；
3. 如果一個循環需要掃描的設備非常多，這又會引來響應時間的問題；
   所以，該方式是非常不好的。

方式二：就是事件驅動模型

目前大部分的UI編程都是事件驅動模型，如很多UI平臺都會提供onClick()事件，這個事件就代表鼠標按下事件。事件驅動模型大體思路如下：

1. 有一個事件（消息）隊列；
2. 鼠標按下時，往這個隊列中增加一個點擊事件（消息）；
3. 有個循環，不斷從隊列取出事件，根據不同的事件，調用不同的函數，如onClick()、onKeyDown()等；
4. 事件（消息）一般都各自保存各自的處理函數指針，這樣，每個消息都有獨立的處理函數；

事件驅動編程是一種編程範式，這裏程序的執行流由外部事件來決定。它的特點是包含一個事件循環，當外部事件發生時使用回調機制來觸發相應的處理。另外兩種常見的編程範式是（單線程）同步以及多線程編程。

讓我們用例子來比較和對比一下單線程、多線程以及事件驅動編程模型。下圖展示了隨着時間的推移，這三種模式下程序所做的工作。這個程序有3個任務需要完成，每個任務都在等待I/O操作時阻塞自身。阻塞在I/O操作上所花費的時間已經用灰色框標示出來了。

在單線程同步模型中，任務按照順序執行。如果某個任務因爲I/O而阻塞，其他所有的任務都必須等待，直到它完成之後它們才能依次執行。這種明確的執行順序和串行化處理的行爲是很容易推斷得出的。如果任務之間並沒有互相依賴的關係，但仍然需要互相等待的話這就使得程序不必要的降低了運行速度。

在多線程版本中，這3個任務分別在獨立的線程中執行。這些線程由操作系統來管理，在多處理器系統上可以並行處理，或者在單處理器系統上交錯執行。這使得當某個線程阻塞在某個資源的同時其他線程得以繼續執行。與完成類似功能的同步程序相比，這種方式更有效率，但程序員必須寫代碼來保護共享資源，防止其被多個線程同時訪問。多線程程序更加難以推斷，因爲這類程序不得不通過線程同步機制如鎖、可重入函數、線程局部存儲或者其他機制來處理線程安全問題，如果實現不當就會導致出現微妙且令人痛不欲生的bug。

在事件驅動版本的程序中，3個任務交錯執行，但仍然在一個單獨的線程控制中。當處理I/O或者其他昂貴的操作時，註冊一個回調到事件循環中，然後當I/O操作完成時繼續執行。回調描述了該如何處理某個事件。事件循環輪詢所有的事件，當事件到來時將它們分配給等待處理事件的回調函數。這種方式讓程序儘可能的得以執行而不需要用到額外的線程。事件驅動型程序比多線程程序更容易推斷出行爲，因爲程序員不需要關心線程安全問題。

當我們面對如下的環境時，事件驅動模型通常是一個好的選擇：

1. 程序中有許多任務，而且…
2. 任務之間高度獨立（因此它們不需要互相通信，或者等待彼此）而且…
3. 在等待事件到來時，某些任務會阻塞。

當應用程序需要在任務間共享可變的數據時，這也是一個不錯的選擇，因爲這裏不需要採用同步處理。

網絡應用程序通常都有上述這些特點，這使得它們能夠很好的契合事件驅動編程模型。

此處要提出一個問題，就是，上面的事件驅動模型中，只要一遇到IO就註冊一個事件，然後主程序就可以繼續幹其它的事情了，只到io處理完畢後，繼續恢復之前中斷的任務，這本質上是怎麼實現的呢？哈哈，下面我們就來一起揭開這神祕的面紗。。。

Select\Poll\Epoll異步IO

http://www.cnblogs.com/alex3714/p/4372426.html　

番外篇 http://www.cnblogs.com/alex3714/articles/5876749.html

select 多併發socket 例子

select socket server

#_*_coding:utf-8_*_
__author__ = 'Alex Li'

import select
import socket
import sys
import queue

server = socket.socket()
server.setblocking(0)

server_addr = ('localhost',10000)

print('starting up on %s port %s' % server_addr)
server.bind(server_addr)

server.listen(5)

inputs = [server, ] #自己也要監測呀,因爲server本身也是個fd
outputs = []

message_queues = {}

while True:
    print("waiting for next event...")

    readable, writeable, exeptional = select.select(inputs,outputs,inputs) #如果沒有任何fd就緒,那程序就會一直阻塞在這裏

    for s in readable: #每個s就是一個socket

        if s is server: #別忘記,上面我們server自己也當做一個fd放在了inputs列表裏,傳給了select,如果這個s是server,代表server這個fd就緒了,
            #就是有活動了, 什麼情況下它纔有活動? 當然 是有新連接進來的時候 呀
            #新連接進來了,接受這個連接
            conn, client_addr = s.accept()
            print("new connection from",client_addr)
            conn.setblocking(0)
            inputs.append(conn) #爲了不阻塞整個程序,我們不會立刻在這裏開始接收客戶端發來的數據, 把它放到inputs裏, 下一次loop時,這個新連接
            #就會被交給select去監聽,如果這個連接的客戶端發來了數據 ,那這個連接的fd在server端就會變成就續的,select就會把這個連接返回,返回到
            #readable 列表裏,然後你就可以loop readable列表,取出這個連接,開始接收數據了, 下面就是這麼幹 的

            message_queues[conn] = queue.Queue() #接收到客戶端的數據後,不立刻返回 ,暫存在隊列裏,以後發送

        else: #s不是server的話,那就只能是一個 與客戶端建立的連接的fd了
            #客戶端的數據過來了,在這接收
            data = s.recv(1024)
            if data:
                print("收到來自[%s]的數據:" % s.getpeername()[0], data)
                message_queues[s].put(data) #收到的數據先放到queue裏,一會返回給客戶端
                if s not  in outputs:
                    outputs.append(s) #爲了不影響處理與其它客戶端的連接 , 這裏不立刻返回數據給客戶端

            else:#如果收不到data代表什麼呢? 代表客戶端斷開了呀
                print("客戶端斷開了",s)

                if s in outputs:
                    outputs.remove(s) #清理已斷開的連接

                inputs.remove(s) #清理已斷開的連接

                del message_queues[s] ##清理已斷開的連接

    for s in writeable:
        try :
            next_msg = message_queues[s].get_nowait()

        except queue.Empty:
            print("client [%s]" %s.getpeername()[0], "queue is empty..")
            outputs.remove(s)

        else:
            print("sending msg to [%s]"%s.getpeername()[0], next_msg)
            s.send(next_msg.upper())

    for s in exeptional:
        print("handling exception for ",s.getpeername())
        inputs.remove(s)
        if s in outputs:
            outputs.remove(s)
        s.close()

        del message_queues[s]

select socket client

#_*_coding:utf-8_*_
__author__ = 'Alex Li'

import socket
import sys

messages = [ b'This is the message. ',
             b'It will be sent ',
             b'in parts.',
             ]
server_address = ('localhost', 10000)

# Create a TCP/IP socket
socks = [ socket.socket(socket.AF_INET, socket.SOCK_STREAM),
          socket.socket(socket.AF_INET, socket.SOCK_STREAM),
          ]

# Connect the socket to the port where the server is listening
print('connecting to %s port %s' % server_address)
for s in socks:
    s.connect(server_address)

for message in messages:

    # Send messages on both sockets
    for s in socks:
        print('%s: sending "%s"' % (s.getsockname(), message) )
        s.send(message)

    # Read responses on both sockets
    for s in socks:
        data = s.recv(1024)
        print( '%s: received "%s"' % (s.getsockname(), data) )
        if not data:
            print(sys.stderr, 'closing socket', s.getsockname() )

selectors模塊

This module allows high-level and efficient I/O multiplexing, built upon the select module primitives. Users are encouraged to use this module instead, unless they want precise control over the OS-level primitives used.

import selectors
import socket

sel = selectors.DefaultSelector()

def accept(sock, mask):
    conn, addr = sock.accept()  # Should be ready
    print('accepted', conn, 'from', addr)
    conn.setblocking(False)
    sel.register(conn, selectors.EVENT_READ, read)

def read(conn, mask):
    data = conn.recv(1000)  # Should be ready
    if data:
        print('echoing', repr(data), 'to', conn)
        conn.send(data)  # Hope it won't block
    else:
        print('closing', conn)
        sel.unregister(conn)
        conn.close()

sock = socket.socket()
sock.bind(('localhost', 10000))
sock.listen(100)
sock.setblocking(False)
sel.register(sock, selectors.EVENT_READ, accept)

while True:
    events = sel.select()
    for key, mask in events:
        callback = key.data
        callback(key.fileobj, mask)

　　

數據庫操作與Paramiko模塊
http://www.cnblogs.com/wupeiqi/articles/5095821.html

RabbitMQ隊列　　
安裝 http://www.rabbitmq.com/install-standalone-mac.html

安裝python rabbitMQ module

pip install pika
or
easy_install pika
or
源碼

https://pypi.python.org/pypi/pika

實現最簡單的隊列通信

send端

#!/usr/bin/env python
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
               'localhost'))
channel = connection.channel()

#聲明queue
channel.queue_declare(queue='hello')

#n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.
channel.basic_publish(exchange='',
                      routing_key='hello',
                      body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()

receive端

#_*_coding:utf-8_*_
__author__ = 'Alex Li'
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
               'localhost'))
channel = connection.channel()

#You may ask why we declare the queue again ‒ we have already declared it in our previous code.
# We could avoid that if we were sure that the queue already exists. For example if send.py program
#was run before. But we're not yet sure which program to run first. In such cases it's a good
# practice to repeat declaring the queue in both programs.
channel.queue_declare(queue='hello')

def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)

channel.basic_consume(callback,
                      queue='hello',
                      no_ack=True)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

遠程連接rabbitmq server的話，需要配置權限 噢

首先在rabbitmq server上創建一個用戶

sudo rabbitmqctl  add_user alex alex3714

　
同時還要配置權限，允許從外面訪問

sudo rabbitmqctl set_permissions -p / alex ".*" ".*" ".*"

set_permissions [-p vhost] {user} {conf} {write} {read}

vhost

The name of the virtual host to which to grant the user access, defaulting to /.

user

The name of the user to grant access to the specified virtual host.

conf

A regular expression matching resource names for which the user is granted configure permissions.

write

A regular expression matching resource names for which the user is granted write permissions.

read

A regular expression matching resource names for which the user is granted read permissions.

　　

客戶端連接的時候需要配置認證參數

credentials = pika.PlainCredentials('alex', 'alex3714')

connection = pika.BlockingConnection(pika.ConnectionParameters(
    '10.211.55.5',5672,'/',credentials))
channel = connection.channel()

　　

　　

Work Queues

在這種模式下，RabbitMQ會默認把p發的消息依次分發給各個消費者(c),跟負載均衡差不多

消息提供者代碼

import pika
import time
connection = pika.BlockingConnection(pika.ConnectionParameters(
    'localhost'))
channel = connection.channel()

# 聲明queue
channel.queue_declare(queue='task_queue')

# n RabbitMQ a message can never be sent directly to the queue, it always needs to go through an exchange.
import sys

message = ' '.join(sys.argv[1:]) or "Hello World! %s" % time.time()
channel.basic_publish(exchange='',
                      routing_key='task_queue',
                      body=message,
                      properties=pika.BasicProperties(
                          delivery_mode=2,  # make message persistent
                      )
                      )
print(" [x] Sent %r" % message)
connection.close()

　　

消費者代碼

#_*_coding:utf-8_*_

import pika, time

connection = pika.BlockingConnection(pika.ConnectionParameters(
    'localhost'))
channel = connection.channel()

def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)
    time.sleep(20)
    print(" [x] Done")
    print("method.delivery_tag",method.delivery_tag)
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(callback,
                      queue='task_queue',
                      no_ack=True
                      )

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

　　

此時，先啓動消息生產者，然後再分別啓動3個消費者，通過生產者多發送幾條消息，你會發現，這幾條消息會被依次分配到各個消費者身上　　

Doing a task can take a few seconds. You may wonder what happens if one of the consumers starts a long task and dies with it only partly done. With our current code once RabbitMQ delivers message to the customer it immediately removes it from memory. In this case, if you kill a worker we will lose the message it was just processing. We'll also lose all the messages that were dispatched to this particular worker but were not yet handled.

But we don't want to lose any tasks. If a worker dies, we'd like the task to be delivered to another worker.

In order to make sure a message is never lost, RabbitMQ supports message acknowledgments. An ack(nowledgement) is sent back from the consumer to tell RabbitMQ that a particular message had been received, processed and that RabbitMQ is free to delete it.

If a consumer dies (its channel is closed, connection is closed, or TCP connection is lost) without sending an ack, RabbitMQ will understand that a message wasn't processed fully and will re-queue it. If there are other consumers online at the same time, it will then quickly redeliver it to another consumer. That way you can be sure that no message is lost, even if the workers occasionally die.

There aren't any message timeouts; RabbitMQ will redeliver the message when the consumer dies. It's fine even if processing a message takes a very, very long time.

Message acknowledgments are turned on by default. In previous examples we explicitly turned them off via the no_ack=True flag. It's time to remove this flag and send a proper acknowledgment from the worker, once we're done with a task.

def callback(ch, method, properties, body):
    print " [x] Received %r" % (body,)
    time.sleep( body.count('.') )
    print " [x] Done"
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_consume(callback,
                      queue='hello')

Using this code we can be sure that even if you kill a worker using CTRL+C while it was processing a message, nothing will be lost. Soon after the worker dies all unacknowledged messages will be redelivered

　　　　

消息持久化

We have learned how to make sure that even if the consumer dies, the task isn't lost(by default, if wanna disable use no_ack=True). But our tasks will still be lost if RabbitMQ server stops.

When RabbitMQ quits or crashes it will forget the queues and messages unless you tell it not to. Two things are required to make sure that messages aren't lost: we need to mark both the queue and messages as durable.

First, we need to make sure that RabbitMQ will never lose our queue. In order to do so, we need to declare it as durable:

channel.queue_declare(queue='hello', durable=True)

　　

Although this command is correct by itself, it won't work in our setup. That's because we've already defined a queue called hello which is not durable. RabbitMQ doesn't allow you to redefine an existing queue with different parameters and will return an error to any program that tries to do that. But there is a quick workaround - let's declare a queue with different name, for exampletask_queue:

channel.queue_declare(queue='task_queue', durable=True)

　　

This queue_declare change needs to be applied to both the producer and consumer code.

At that point we're sure that the task_queue queue won't be lost even if RabbitMQ restarts. Now we need to mark our messages as persistent - by supplying a delivery_mode property with a value 2.

channel.basic_publish(exchange='',
                      routing_key="task_queue",
                      body=message,
                      properties=pika.BasicProperties(
                         delivery_mode = 2, # make message persistent
                      ))

消息公平分發

如果Rabbit只管按順序把消息發到各個消費者身上，不考慮消費者負載的話，很可能出現，一個機器配置不高的消費者那裏堆積了很多消息處理不完，同時配置高的消費者卻一直很輕鬆。爲解決此問題，可以在各個消費者端，配置perfetch=1,意思就是告訴RabbitMQ在我這個消費者當前消息還沒處理完的時候就不要再給我發新消息了。

channel.basic_qos(prefetch_count=1)

帶消息持久化+公平分發的完整代碼

生產者端

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.queue_declare(queue='task_queue', durable=True)

message = ' '.join(sys.argv[1:]) or "Hello World!"
channel.basic_publish(exchange='',
                      routing_key='task_queue',
                      body=message,
                      properties=pika.BasicProperties(
                         delivery_mode = 2, # make message persistent
                      ))
print(" [x] Sent %r" % message)
connection.close()

消費者端

#!/usr/bin/env python
import pika
import time

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.queue_declare(queue='task_queue', durable=True)
print(' [*] Waiting for messages. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)
    time.sleep(body.count(b'.'))
    print(" [x] Done")
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback,
                      queue='task_queue')

channel.start_consuming()

　　

Publish\Subscribe(消息發佈\訂閱)　

之前的例子都基本都是1對1的消息發送和接收，即消息只能發送到指定的queue裏，但有些時候你想讓你的消息被所有的Queue收到，類似廣播的效果，這時候就要用到exchange了，

An exchange is a very simple thing. On one side it receives messages from producers and the other side it pushes them to queues. The exchange must know exactly what to do with a message it receives. Should it be appended to a particular queue? Should it be appended to many queues? Or should it get discarded. The rules for that are defined by the exchange type.

Exchange在定義的時候是有類型的，以決定到底是哪些Queue符合條件，可以接收消息

fanout: 所有bind到此exchange的queue都可以接收消息
direct: 通過routingKey和exchange決定的那個唯一的queue可以接收消息
topic: 所有符合routingKey(此時可以是一個表達式)的routingKey所bind的queue可以接收消息

　　 表達式符號說明：#代表一個或多個字符，*代表任何字符

  例：#.a會匹配a.a，aa.a，aaa.a等

      *.a會匹配a.a，b.a，c.a等

 注：使用RoutingKey爲#，Exchange Type爲topic的時候相當於使用fanout　

headers: 通過headers 來決定把消息發給哪些queue

消息publisher

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='logs',
                         type='fanout')

message = ' '.join(sys.argv[1:]) or "info: Hello World!"
channel.basic_publish(exchange='logs',
                      routing_key='',
                      body=message)
print(" [x] Sent %r" % message)
connection.close()

消息subscriber

#_*_coding:utf-8_*_
__author__ = 'Alex Li'
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='logs',
                         type='fanout')

result = channel.queue_declare(exclusive=True) #不指定queue名字,rabbit會隨機分配一個名字,exclusive=True會在使用此queue的消費者斷開後,自動將queue刪除
queue_name = result.method.queue

channel.queue_bind(exchange='logs',
                   queue=queue_name)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r" % body)

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

　　

有選擇的接收消息(exchange type=direct)

RabbitMQ還支持根據關鍵字發送，即：隊列綁定關鍵字，發送者將數據根據關鍵字發送到消息exchange，exchange根據 關鍵字 判定應該將數據發送至指定隊列。

publisher

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

severity = sys.argv[1] if len(sys.argv) > 1 else 'info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)
print(" [x] Sent %r:%r" % (severity, message))
connection.close()

subscriber

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

severities = sys.argv[1:]
if not severities:
    sys.stderr.write("Usage: %s [info] [warning] [error]\n" % sys.argv[0])
    sys.exit(1)

for severity in severities:
    channel.queue_bind(exchange='direct_logs',
                       queue=queue_name,
                       routing_key=severity)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r:%r" % (method.routing_key, body))

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

　　

更細緻的消息過濾

Although using the direct exchange improved our system, it still has limitations - it can't do routing based on multiple criteria.

In our logging system we might want to subscribe to not only logs based on severity, but also based on the source which emitted the log. You might know this concept from the syslog unix tool, which routes logs based on both severity (info/warn/crit...) and facility (auth/cron/kern...).

That would give us a lot of flexibility - we may want to listen to just critical errors coming from 'cron' but also all logs from 'kern'.

publisher

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='topic_logs',
                         type='topic')

routing_key = sys.argv[1] if len(sys.argv) > 1 else 'anonymous.info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='topic_logs',
                      routing_key=routing_key,
                      body=message)
print(" [x] Sent %r:%r" % (routing_key, message))
connection.close()

subscriber

import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='topic_logs',
                         type='topic')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

binding_keys = sys.argv[1:]
if not binding_keys:
    sys.stderr.write("Usage: %s [binding_key]...\n" % sys.argv[0])
    sys.exit(1)

for binding_key in binding_keys:
    channel.queue_bind(exchange='topic_logs',
                       queue=queue_name,
                       routing_key=binding_key)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r:%r" % (method.routing_key, body))

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

To receive all the logs run:

python receive_logs_topic.py "#"
To receive all logs from the facility "kern":

python receive_logs_topic.py "kern.*"
Or if you want to hear only about "critical" logs:

python receive_logs_topic.py "*.critical"
You can create multiple bindings:

python receive_logs_topic.py "kern." ".critical"
And to emit a log with a routing key "kern.critical" type:

python emit_log_topic.py "kern.critical" "A critical kernel error"
　　

Remote procedure call (RPC)

To illustrate how an RPC service could be used we're going to create a simple client class. It's going to expose a method named call which sends an RPC request and blocks until the answer is received:

fibonacci_rpc = FibonacciRpcClient()
result = fibonacci_rpc.call(4)
print("fib(4) is %r" % result)

RPC server

#_*_coding:utf-8_*_
__author__ = 'Alex Li'
import pika
import time
connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))

channel = connection.channel()

channel.queue_declare(queue='rpc_queue')

def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n-1) + fib(n-2)

def on_request(ch, method, props, body):
    n = int(body)

    print(" [.] fib(%s)" % n)
    response = fib(n)

    ch.basic_publish(exchange='',
                     routing_key=props.reply_to,
                     properties=pika.BasicProperties(correlation_id = \
                                                         props.correlation_id),
                     body=str(response))
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(on_request, queue='rpc_queue')

print(" [x] Awaiting RPC requests")
channel.start_consuming()

RPC client

import pika
import uuid

class FibonacciRpcClient(object):
    def __init__(self):
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(
                host='localhost'))

        self.channel = self.connection.channel()

        result = self.channel.queue_declare(exclusive=True)
        self.callback_queue = result.method.queue

        self.channel.basic_consume(self.on_response, no_ack=True,
                                   queue=self.callback_queue)

    def on_response(self, ch, method, props, body):
        if self.corr_id == props.correlation_id:
            self.response = body

    def call(self, n):
        self.response = None
        self.corr_id = str(uuid.uuid4())
        self.channel.basic_publish(exchange='',
                                   routing_key='rpc_queue',
                                   properties=pika.BasicProperties(
                                         reply_to = self.callback_queue,
                                         correlation_id = self.corr_id,
                                         ),
                                   body=str(n))
        while self.response is None:
            self.connection.process_data_events()
        return int(self.response)

fibonacci_rpc = FibonacciRpcClient()

print(" [x] Requesting fib(30)")
response = fibonacci_rpc.call(30)
print(" [.] Got %r" % response)

　　

　　

Memcached & Redis使用

memcached

http://www.cnblogs.com/wupeiqi/articles/5132791.html 　

redis 使用

http://www.cnblogs.com/alex3714/articles/6217453.html　　

　

Twsited異步網絡框架

Twisted是一個事件驅動的網絡框架，其中包含了諸多功能，例如：網絡協議、線程、數據庫管理、網絡操作、電子郵件等。　

事件驅動

簡而言之，事件驅動分爲二個部分：第一，註冊事件；第二，觸發事件。

自定義事件驅動框架，命名爲：“弒君者”：

#!/usr/bin/env python
# -*- coding:utf-8 -*-

# event_drive.py

event_list = []

def run():
    for event in event_list:
        obj = event()
        obj.execute()

class BaseHandler(object):
    """
    用戶必須繼承該類，從而規範所有類的方法（類似於接口的功能）
    """
    def execute(self):
        raise Exception('you must overwrite execute')

最牛逼的事件驅動框架

程序員使用“弒君者框架”：　　

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from source import event_drive

class MyHandler(event_drive.BaseHandler):

    def execute(self):
        print 'event-drive execute MyHandler'

event_drive.event_list.append(MyHandler)
event_drive.run()

Protocols

Protocols描述瞭如何以異步的方式處理網絡中的事件。HTTP、DNS以及IMAP是應用層協議中的例子。Protocols實現了IProtocol接口，它包含如下的方法：

makeConnection ------> 在transport對象和服務器之間建立一條連接

connectionMade ------> 連接建立起來後調用

dataReceived ------> 接收數據時調用

connectionLost ------> 關閉連接時調用

Transports
Transports代表網絡中兩個通信結點之間的連接。Transports負責描述連接的細節，比如連接是面向流式的還是面向數據報的，流控以及可靠性。TCP、UDP和Unix套接字可作爲transports的例子。它們被設計爲“滿足最小功能單元，同時具有最大程度的可複用性”，而且從協議實現中分離出來，這讓許多協議可以採用相同類型的傳輸。Transports實現了ITransports接口，它包含如下的方法：

write ---------> 以非阻塞的方式按順序依次將數據寫到物理連接上

writeSequence ---------> 將一個字符串列表寫到物理連接上

loseConnection ---------> 將所有掛起的數據寫入，然後關閉連接

getPeer ---------> 取得連接中對端的地址信息

getHost ---------> 取得連接中本端的地址信息

將transports從協議中分離出來也使得對這兩個層次的測試變得更加簡單。可以通過簡單地寫入一個字符串來模擬傳輸，用這種方式來檢查。

　　

EchoServer

from twisted.internet import protocol
from twisted.internet import reactor

class Echo(protocol.Protocol):
    def dataReceived(self, data):
        self.transport.write(data)

def main():
    factory = protocol.ServerFactory()
    factory.protocol = Echo

    reactor.listenTCP(1234,factory)
    reactor.run()

if __name__ == '__main__':
    main()

　　

EchoClient

from twisted.internet import reactor, protocol

# a client protocol

class EchoClient(protocol.Protocol):
    """Once connected, send a message, then print the result."""

    def connectionMade(self):
        self.transport.write("hello alex!")

    def dataReceived(self, data):
        "As soon as any data is received, write it back."
        print "Server said:", data
        self.transport.loseConnection()

    def connectionLost(self, reason):
        print "connection lost"

class EchoFactory(protocol.ClientFactory):
    protocol = EchoClient

    def clientConnectionFailed(self, connector, reason):
        print "Connection failed - goodbye!"
        reactor.stop()

    def clientConnectionLost(self, connector, reason):
        print "Connection lost - goodbye!"
        reactor.stop()

# this connects the protocol to a server running on port 8000
def main():
    f = EchoFactory()
    reactor.connectTCP("localhost", 1234, f)
    reactor.run()

# this only runs if the module was *not* imported
if __name__ == '__main__':
    main()

運行服務器端腳本將啓動一個TCP服務器，監聽端口1234上的連接。服務器採用的是Echo協議，數據經TCP transport對象寫出。運行客戶端腳本將對服務器發起一個TCP連接，回顯服務器端的迴應然後終止連接並停止reactor事件循環。這裏的Factory用來對連接的雙方生成protocol對象實例。兩端的通信是異步的，connectTCP負責註冊回調函數到reactor事件循環中，當socket上有數據可讀時通知回調處理。

一個傳送文件的例子

server side

#_*_coding:utf-8_*_
# This is the Twisted Fast Poetry Server, version 1.0

import optparse, os

from twisted.internet.protocol import ServerFactory, Protocol

def parse_args():
    usage = """usage: %prog [options] poetry-file

This is the Fast Poetry Server, Twisted edition.
Run it like this:

  python fastpoetry.py <path-to-poetry-file>

If you are in the base directory of the twisted-intro package,
you could run it like this:

  python twisted-server-1/fastpoetry.py poetry/ecstasy.txt

to serve up John Donne's Ecstasy, which I know you want to do.
"""

    parser = optparse.OptionParser(usage)

    help = "The port to listen on. Default to a random available port."
    parser.add_option('--port', type='int', help=help)

    help = "The interface to listen on. Default is localhost."
    parser.add_option('--iface', help=help, default='localhost')

    options, args = parser.parse_args()
    print("--arg:",options,args)

    if len(args) != 1:
        parser.error('Provide exactly one poetry file.')

    poetry_file = args[0]

    if not os.path.exists(args[0]):
        parser.error('No such file: %s' % poetry_file)

    return options, poetry_file

class PoetryProtocol(Protocol):

    def connectionMade(self):
        self.transport.write(self.factory.poem)
        self.transport.loseConnection()

class PoetryFactory(ServerFactory):

    protocol = PoetryProtocol

    def __init__(self, poem):
        self.poem = poem

def main():
    options, poetry_file = parse_args()

    poem = open(poetry_file).read()

    factory = PoetryFactory(poem)

    from twisted.internet import reactor

    port = reactor.listenTCP(options.port or 9000, factory,
                             interface=options.iface)

    print 'Serving %s on %s.' % (poetry_file, port.getHost())

    reactor.run()

if __name__ == '__main__':
    main()

client side 　　

# This is the Twisted Get Poetry Now! client, version 3.0.

# NOTE: This should not be used as the basis for production code.

import optparse

from twisted.internet.protocol import Protocol, ClientFactory

def parse_args():
    usage = """usage: %prog [options] [hostname]:port ...

This is the Get Poetry Now! client, Twisted version 3.0
Run it like this:

  python get-poetry-1.py port1 port2 port3 ...
"""

    parser = optparse.OptionParser(usage)

    _, addresses = parser.parse_args()

    if not addresses:
        print parser.format_help()
        parser.exit()

    def parse_address(addr):
        if ':' not in addr:
            host = '127.0.0.1'
            port = addr
        else:
            host, port = addr.split(':', 1)

        if not port.isdigit():
            parser.error('Ports must be integers.')

        return host, int(port)

    return map(parse_address, addresses)

class PoetryProtocol(Protocol):

    poem = ''

    def dataReceived(self, data):
        self.poem += data

    def connectionLost(self, reason):
        self.poemReceived(self.poem)

    def poemReceived(self, poem):
        self.factory.poem_finished(poem)

class PoetryClientFactory(ClientFactory):

    protocol = PoetryProtocol

    def __init__(self, callback):
        self.callback = callback

    def poem_finished(self, poem):
        self.callback(poem)

def get_poetry(host, port, callback):
    """
    Download a poem from the given host and port and invoke

      callback(poem)

    when the poem is complete.
    """
    from twisted.internet import reactor
    factory = PoetryClientFactory(callback)
    reactor.connectTCP(host, port, factory)

def poetry_main():
    addresses = parse_args()

    from twisted.internet import reactor

    poems = []

    def got_poem(poem):
        poems.append(poem)
        if len(poems) == len(addresses):
            reactor.stop()

    for address in addresses:
        host, port = address
        get_poetry(host, port, got_poem)

    reactor.run()

    for poem in poems:
        print poem

if __name__ == '__main__':
    poetry_main()

　　

　　

Twisted深入

http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/

http://blog.csdn.net/hanhuili/article/details/9389433

　　

　　

SqlAlchemy ORM　　
SQLAlchemy是Python編程語言下的一款ORM框架，該框架建立在數據庫API之上，使用關係對象映射進行數據庫操作，簡言之便是：將對象轉換成SQL，然後使用數據API執行SQL並獲取執行結果

Dialect用於和數據API進行交流，根據配置文件的不同調用不同的數據庫API，從而實現對數據庫的操作，如：

MySQL-Python
    mysql+mysqldb://<user>:<password>@<host>[:<port>]/<dbname>

pymysql
    mysql+pymysql://<username>:<password>@<host>/<dbname>[?<options>]

MySQL-Connector
    mysql+mysqlconnector://<user>:<password>@<host>[:<port>]/<dbname>

cx_Oracle
    oracle+cx_oracle://user:pass@host:port/dbname[?key=value&key=value...]

更多詳見：http://docs.sqlalchemy.org/en/latest/dialects/index.html
　　

步驟一：

使用 Engine/ConnectionPooling/Dialect 進行數據庫操作，Engine使用ConnectionPooling連接數據庫，然後再通過Dialect執行SQL語句。

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from sqlalchemy import create_engine

engine = create_engine("mysql+mysqldb://root:[email protected]:3306/s11", max_overflow=5)

engine.execute(
    "INSERT INTO ts_test (a, b) VALUES ('2', 'v1')"
)

engine.execute(
     "INSERT INTO ts_test (a, b) VALUES (%s, %s)",
    ((555, "v1"),(666, "v1"),)
)
engine.execute(
    "INSERT INTO ts_test (a, b) VALUES (%(id)s, %(name)s)",
    id=999, name="v1"
)

result = engine.execute('select * from ts_test')
result.fetchall()

　　

步驟二：

使用 Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 進行數據庫操作。Engine使用Schema Type創建一個特定的結構對象，之後通過SQL Expression Language將該對象轉換成SQL語句，然後通過 ConnectionPooling 連接數據庫，再然後通過 Dialect 執行SQL，並獲取結果。
#!/usr/bin/env python

-- coding:utf-8 --

from sqlalchemy import create_engine, Table, Column, Integer, String, MetaData, ForeignKey

metadata = MetaData()

user = Table('user', metadata,
    Column('id', Integer, primary_key=True),
    Column('name', String(20)),
)

color = Table('color', metadata,
    Column('id', Integer, primary_key=True),
    Column('name', String(20)),
)
engine = create_engine("mysql+mysqldb://root@localhost:3306/test", max_overflow=5)

metadata.create_all(engine)

增刪改查

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from sqlalchemy import create_engine, Table, Column, Integer, String, MetaData, ForeignKey

metadata = MetaData()

user = Table('user', metadata,
    Column('id', Integer, primary_key=True),
    Column('name', String(20)),
)

color = Table('color', metadata,
    Column('id', Integer, primary_key=True),
    Column('name', String(20)),
)
engine = create_engine("mysql+mysqldb://root:[email protected]:3306/s11", max_overflow=5)

conn = engine.connect()

# 創建SQL語句，INSERT INTO "user" (id, name) VALUES (:id, :name)
conn.execute(user.insert(),{'id':7,'name':'seven'})
conn.close()

# sql = user.insert().values(id=123, name='wu')
# conn.execute(sql)
# conn.close()

# sql = user.delete().where(user.c.id > 1)

# sql = user.update().values(fullname=user.c.name)
# sql = user.update().where(user.c.name == 'jack').values(name='ed')

# sql = select([user, ])
# sql = select([user.c.id, ])
# sql = select([user.c.name, color.c.name]).where(user.c.id==color.c.id)
# sql = select([user.c.name]).order_by(user.c.name)
# sql = select([user]).group_by(user.c.name)

# result = conn.execute(sql)
# print result.fetchall()
# conn.close()

一個簡單的完整例子

from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
from  sqlalchemy.orm import sessionmaker

Base = declarative_base() #生成一個SqlORM 基類

engine = create_engine("mysql+mysqldb://root@localhost:3306/test",echo=False)

class Host(Base):
    __tablename__ = 'hosts'
    id = Column(Integer,primary_key=True,autoincrement=True)
    hostname = Column(String(64),unique=True,nullable=False)
    ip_addr = Column(String(128),unique=True,nullable=False)
    port = Column(Integer,default=22)

Base.metadata.create_all(engine) #創建所有表結構

if __name__ == '__main__':
    SessionCls = sessionmaker(bind=engine) #創建與數據庫的會話session class ,注意,這裏返回給session的是個class,不是實例
    session = SessionCls()
    #h1 = Host(hostname='localhost',ip_addr='127.0.0.1')
    #h2 = Host(hostname='ubuntu',ip_addr='192.168.2.243',port=20000)
    #h3 = Host(hostname='ubuntu2',ip_addr='192.168.2.244',port=20000)
    #session.add(h3)
    #session.add_all( [h1,h2])
    #h2.hostname = 'ubuntu_test' #只要沒提交,此時修改也沒問題
    #session.rollback()
    #session.commit() #提交
    res = session.query(Host).filter(Host.hostname.in_(['ubuntu2','localhost'])).all()
    print(res)

　　

更多內容詳見：

[http://www.jianshu.com/p/e6bba189fcbd](http://www.jianshu.com/p/e6bba189fcbd)

[http://docs.sqlalchemy.org/en/latest/core/expression_api.html](http://docs.sqlalchemy.org/en/latest/core/expression_api.html)

注：SQLAlchemy無法修改表結構，如果需要可以使用SQLAlchemy開發者開源的另外一個軟件Alembic來完成。

步驟三：

使用 ORM/Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 所有組件對數據進行操作。根據類創建對象，對象轉換成SQL，執行SQL。

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine

engine = create_engine("mysql+mysqldb://root:[email protected]:3306/s11", max_overflow=5)

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String(50))

# 尋找Base的所有子類，按照子類的結構在數據庫中生成對應的數據表信息
# Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

# ########## 增 ##########
# u = User(id=2, name='sb')
# session.add(u)
# session.add_all([
#     User(id=3, name='sb'),
#     User(id=4, name='sb')
# ])
# session.commit()

# ########## 刪除 ##########
# session.query(User).filter(User.id > 2).delete()
# session.commit()

# ########## 修改 ##########
# session.query(User).filter(User.id > 2).update({'cluster_id' : 0})
# session.commit()
# ########## 查 ##########
# ret = session.query(User).filter_by(name='sb').first()

# ret = session.query(User).filter_by(name='sb').all()
# print ret

# ret = session.query(User).filter(User.name.in_(['sb','bb'])).all()
# print ret

# ret = session.query(User.name.label('name_label')).all()
# print ret,type(ret)

# ret = session.query(User).order_by(User.id).all()
# print ret

# ret = session.query(User).order_by(User.id)[1:3]
# print ret
# session.commit()

外鍵關聯

A one to many relationship places a foreign key on the child table referencing the parent.relationship() is then specified on the parent, as referencing a collection of items represented by the child

from sqlalchemy import Table, Column, Integer, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

<br>class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key=True)
    children = relationship("Child")

class Child(Base):
    __tablename__ = 'child'
    id = Column(Integer, primary_key=True)
    parent_id = Column(Integer, ForeignKey('parent.id'))

To establish a bidirectional relationship in one-to-many, where the “reverse” side is a many to one, specify an additional relationship() and connect the two using therelationship.back_populates parameter:

class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key=True)
    children = relationship("Child", back_populates="parent")

class Child(Base):
    __tablename__ = 'child'
    id = Column(Integer, primary_key=True)
    parent_id = Column(Integer, ForeignKey('parent.id'))
    parent = relationship("Parent", back_populates="children")

Child will get a parent attribute with many-to-one semantics.

Alternatively, the backref option may be used on a single relationship() instead of usingback_populates:

class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key=True)
    children = relationship("Child", backref="parent")

　　

　　

附，原生sql join查詢

幾個Join的區別 http://stackoverflow.com/questions/38549/difference-between-inner-and-outer-joins

INNER JOIN: Returns all rows when there is at least one match in BOTH tables

LEFT JOIN: Return all rows from the left table, and the matched rows from the right table

RIGHT JOIN: Return all rows from the right table, and the matched rows from the left table

select host.id,hostname,ip_addr,port,host_group.name from host right join host_group on host.id = host_group.host_id

in SQLAchemy

session.query(Host).join(Host.host_groups).filter(HostGroup.name=='t1').group_by("Host").all()

　　

group by 查詢

select name,count(host.id) as NumberOfHosts from host right join host_group on host.id= host_group.host_id group by name;

in SQLAchemy

from sqlalchemy import func
session.query(HostGroup, func.count(HostGroup.name )).group_by(HostGroup.name).all()

#another example
session.query(func.count(User.name), User.name).group_by(User.name).all() SELECT count(users.name) AS count_1, users.name AS users_name
FROM users GROUP BY users.name

　　

---

更多ORM內容猛點這裏

本節作業一
題目:IO多路複用版FTP

需求:

1. 實現文件上傳及下載功能
2. 支持多連接併發傳文件
3. 使用select or selectors

本節作業二
題目：rpc命令端

需求：

1. 可以異步的執行多個命令
2. 對多臺機器

>:run "df -h" --hosts 192.168.3.55 10.4.3.4

task id: 45334

>: check_task 45334

>:

該文章由alex的blog搬運而來