Python之路,Day9 - 异步IO\数据库\队列\缓存
本节内容
?引子到目前为止,我们已经学了网络并发编程的2个套路, 多进程,多线程,这哥俩的优势和劣势都非常的明显,我们一起来回顾下 协程协程,又称微线程,纤程。英文名Coroutine。一句话说明什么是线程:协程是一种用户态的轻量级线程。 协程拥有自己的寄存器上下文和栈。协程调度切换时,将寄存器上下文和栈保存到其他地方,在切回来的时候,恢复先前保存的寄存器上下文和栈。因此: 协程能保留上一次调用时的状态(即所有局部状态的一个特定组合),每次过程重入时,就相当于进入上一次调用的状态,换种说法:进入上一次离开时所处逻辑流的位置。 协程的好处:
缺点:
使用yield实现协程操作例子 starting eating baozi...")
while True:
new_baozi = yield
print("[%s] is eating baozi %s" % (name,new_baozi))
#time.sleep(1)
def producer():
if name == 'main': 看楼上的例子,我问你这算不算做是协程呢?你说,我他妈哪知道呀,你前面说了一堆废话,但是并没告诉我协程的标准形态呀,我腚眼一想,觉得你说也对,那好,我们先给协程一个标准定义,即符合什么条件就能称之为协程:
基于上面这4点定义,我们刚才用yield实现的程并不能算是合格的线程,因为它有一点功能没实现,哪一点呢? Greenletgreenlet是一个用C实现的协程模块,相比与python自带的yield,它可以使你在任意函数之间随意切换,而不需把这个函数先声明为generator from greenlet import greenlet
def test1(): def test2(): gr1 = greenlet(test1) 感觉确实用着比generator还简单了呢,但好像还没有解决一个问题,就是遇到IO操作,自动切换,对不对?
Gevent?Gevent 是一个第三方库,可以轻松通过gevent实现并发同步或异步编程,在gevent中用到的主要模式是Greenlet,它是以C扩展模块形式接入Python的轻量级协程。 Greenlet全部运行在主程序操作系统进程的内部,但它们被协作式地调度。 def func1():
print(' 33[31;1m李闯在跟海涛搞... 33[0m') gevent.sleep(2) print(' 33[31;1m李闯又回去跟继续跟海涛搞... 33[0m') def func2(): gevent.joinall([
输出:
同步与异步的性能区别? def task(pid):
""" Some non-deterministic task """ gevent.sleep(0.5) print('Task %s done' % pid) def synchronous(): def asynchronous(): print('Synchronous:') print('Asynchronous:') 上面程序的重要部分是将task函数封装到Greenlet内部线程的 遇到IO阻塞时会自动切换任务 def f(url):
print('GET: %s' % url) resp = urlopen(url) data = resp.read() print('%d bytes received from %s.' % (len(data),url)) gevent.joinall([ 通过gevent实现单线程下的多socket并发 server side? from gevent import socket,monkey
monkey.patch_all() def server(port): def handle_request(conn):
if name == 'main':
client side 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)
s.close() <span style="color: #0000ff;">def<span style="color: #000000;"> sock_conn():
<span style="color: #0000ff;">for i <span style="color: #0000ff;">in range(100<span style="color: #000000;">):t = threading.Thread(target=<span style="color: #000000;">sock_conn) t.start()
论事件驱动与异步IO在UI编程中,常常要对鼠标点击进行相应,首先如何获得鼠标点击呢?方式一:创建一个线程,该线程一直循环检测是否有鼠标点击,那么这个方式有以下几个缺点:1. CPU资源浪费,可能鼠标点击的频率非常小,但是扫描线程还是会一直循环检测,这会造成很多的CPU资源浪费;如果扫描鼠标点击的接口是阻塞的呢?2. 如果是堵塞的,又会出现下面这样的问题,如果我们不但要扫描鼠标点击,还要扫描键盘是否按下,由于扫描鼠标时被堵塞了,那么可能永远不会去扫描键盘;3. 如果一个循环需要扫描的设备非常多,这又会引来响应时间的问题;所以,该方式是非常不好的。方式二:就是事件驱动模型目前大部分的UI编程都是事件驱动模型,如很多UI平台都会提供onClick()事件,这个事件就代表鼠标按下事件。事件驱动模型大体思路如下:1. 有一个事件(消息)队列;2. 鼠标按下时,往这个队列中增加一个点击事件(消息);3. 有个循环,不断从队列取出事件,根据不同的事件,调用不同的函数,如onClick()、onKeyDown()等;4. 事件(消息)一般都各自保存各自的处理函数指针,这样,每个消息都有独立的处理函数; <div class="para">? 事件驱动编程是一种编程范式,这里程序的执行流由外部事件来决定。它的特点是包含一个事件循环,当外部事件发生时使用回调机制来触发相应的处理。另外两种常见的编程范式是(单线程)同步以及多线程编程。 让我们用例子来比较和对比一下单线程、多线程以及事件驱动编程模型。下图展示了随着时间的推移,这三种模式下程序所做的工作。这个程序有3个任务需要完成,每个任务都在等待I/O操作时阻塞自身。阻塞在I/O操作上所花费的时间已经用灰色框标示出来了。 ? 在单线程同步模型中,任务按照顺序执行。如果某个任务因为I/O而阻塞,其他所有的任务都必须等待,直到它完成之后它们才能依次执行。这种明确的执行顺序和串行化处理的行为是很容易推断得出的。如果任务之间并没有互相依赖的关系,但仍然需要互相等待的话这就使得程序不必要的降低了运行速度。 在多线程版本中,这3个任务分别在独立的线程中执行。这些线程由操作系统来管理,在多处理器系统上可以并行处理,或者在单处理器系统上交错执行。这使得当某个线程阻塞在某个资源的同时其他线程得以继续执行。与完成类似功能的同步程序相比,这种方式更有效率,但程序员必须写代码来保护共享资源,防止其被多个线程同时访问。多线程程序更加难以推断,因为这类程序不得不通过线程同步机制如锁、可重入函数、线程局部存储或者其他机制来处理线程安全问题,如果实现不当就会导致出现微妙且令人痛不欲生的bug。 在事件驱动版本的程序中,3个任务交错执行,但仍然在一个单独的线程控制中。当处理I/O或者其他昂贵的操作时,注册一个回调到事件循环中,然后当I/O操作完成时继续执行。回调描述了该如何处理某个事件。事件循环轮询所有的事件,当事件到来时将它们分配给等待处理事件的回调函数。这种方式让程序尽可能的得以执行而不需要用到额外的线程。事件驱动型程序比多线程程序更容易推断出行为,因为程序员不需要关心线程安全问题。 当我们面对如下的环境时,事件驱动模型通常是一个好的选择:
当应用程序需要在任务间共享可变的数据时,这也是一个不错的选择,因为这里不需要采用同步处理。 网络应用程序通常都有上述这些特点,这使得它们能够很好的契合事件驱动编程模型。 此处要提出一个问题,就是,上面的事件驱动模型中,只要一遇到IO就注册一个事件,然后主程序就可以继续干其它的事情了,只到io处理完毕后,继续恢复之前中断的任务,这本质上是怎么实现的呢?哈哈,下面我们就来一起揭开这神秘的面纱。。。。 SelectPollEpoll异步IOhttp://www.cnblogs.com/alex3714/p/4372426.html 番外篇 http://www.cnblogs.com/alex3714/articles/5876749.html? select 多并发socket 例子
=
<span style="color: #0000ff;">import <span style="color: #000000;"> select<span style="color: #0000ff;">import<span style="color: #000000;"> socket <span style="color: #0000ff;">import<span style="color: #000000;"> sys <span style="color: #0000ff;">import<span style="color: #000000;"> queue server =<span style="color: #000000;"> socket.socket() server_addr = (<span style="color: #800000;">'<span style="color: #800000;">localhost<span style="color: #800000;">',10000<span style="color: #000000;">) <span style="color: #0000ff;">print(<span style="color: #800000;">'<span style="color: #800000;">starting up on %s port %s<span style="color: #800000;">' %<span style="color: #000000;"> server_addr) server.listen(5<span style="color: #000000;">) inputs = [server,] <span style="color: #008000;">#<span style="color: #008000;">自己也要监测呀,因为server本身也是个fd message_queues =<span style="color: #000000;"> {} <span style="color: #0000ff;">while<span style="color: #000000;"> True:
<span style="color: #000000;">
<span style="color: #000000;">
=
<span style="color: #0000ff;">import <span style="color: #000000;"> socket<span style="color: #0000ff;">import<span style="color: #000000;"> sys messages = [ b<span style="color: #800000;">'<span style="color: #800000;">This is the message. <span style="color: #800000;">'<span style="color: #000000;">,b<span style="color: #800000;">'<span style="color: #800000;">It will be sent <span style="color: #800000;">'<span style="color: #000000;">,b<span style="color: #800000;">'<span style="color: #800000;">in parts.<span style="color: #800000;">'<span style="color: #000000;">,]server_address = (<span style="color: #800000;">'<span style="color: #800000;">localhost<span style="color: #800000;">',10000<span style="color: #000000;">) <span style="color: #008000;">#<span style="color: #008000;"> Create a TCP/IP socket <span style="color: #008000;">#<span style="color: #008000;"> Connect the socket to the port where the server is listening <span style="color: #0000ff;">for message <span style="color: #0000ff;">in<span style="color: #000000;"> messages:
selectors模块 This module allows high-level and efficient I/O multiplexing,built upon the??module primitives. Users are encouraged to use this module instead,unless they want precise control over the OS-level primitives used. sel = selectors.DefaultSelector()
def accept(sock,mask): def read(conn,mask): sock = socket.socket() while True:
数据库操作与Paramiko模块?http://www.cnblogs.com/wupeiqi/articles/5095821.html? RabbitMQ队列安装?http://www.rabbitmq.com/install-standalone-mac.html 安装python rabbitMQ module? https://pypi.python.org/pypi/pika
实现最简单的队列通信 send端 connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost')) channel = connection.channel() 声明queuechannel.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!') receive端 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 programwas run before. But we're not yet sure which program to run first. In such cases it's a goodpractice to repeat declaring the queue in both programs.channel.queue_declare(queue='hello') def callback(ch,method,properties,body): channel.basic_consume(callback,queue='hello',no_ack=True) print(' [*] Waiting for messages. To exit press CTRL+C') 远程连接rabbitmq server的话,需要配置权限 噢? 首先在rabbitmq server上创建一个用户 同时还要配置权限,允许从外面访问 ] {} {} {} {}
The name of the virtual host to which to grant the user access,defaulting to? The name of the user to grant access to the specified virtual host. A regular expression matching resource names for which the user is granted configure permissions. A regular expression matching resource names for which the user is granted write permissions. A regular expression matching resource names for which the user is granted read permissions.
客户端连接的时候需要配置认证参数 connection = pika.BlockingConnection(pika.ConnectionParameters(
'10.211.55.5',5672,'/',credentials)) channel = connection.channel()
Work Queues在这种模式下,RabbitMQ会默认把p发的消息依次分发给各个消费者(c),跟负载均衡差不多 消息提供者代码 声明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()
消费者代码 import pika,time
connection = pika.BlockingConnection(pika.ConnectionParameters( def callback(ch,body): channel.basic_consume(callback,queue='task_queue',no_ack=True print(' [*] Waiting for messages. To exit press CTRL+C')
此时,先启动消息生产者,然后再分别启动3个消费者,通过生产者多发送几条消息,你会发现,这几条消息会被依次分配到各个消费者身上 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? 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:
Although this command is correct by itself,it won't work in our setup. That's because we've already defined a queue called?
This? At that point we're sure that the? 消息公平分发如果Rabbit只管按顺序把消息发到各个消费者身上,不考虑消费者负载的话,很可能出现,一个机器配置不高的消费者那里堆积了很多消息处理不完,同时配置高的消费者却一直很轻松。为解决此问题,可以在各个消费者端,配置perfetch=1,意思就是告诉RabbitMQ在我这个消费者当前消息还没处理完的时候就不要再给我发新消息了。 <div class="cnblogs_Highlighter"> 带消息持久化+公平分发的完整代码 生产者端 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!" 消费者端 connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost')) channel = connection.channel() channel.queue_declare(queue='task_queue',durable=True) def callback(ch,body): channel.basic_qos(prefetch_count=1) channel.start_consuming()
PublishSubscribe(消息发布订阅)之前的例子都基本都是1对1的消息发送和接收,即消息只能发送到指定的queue里,但有些时候你想让你的消息被所有的Queue收到,类似广播的效果,这时候就要用到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在定义的时候是有类型的,以决定到底是哪些Queue符合条件,可以接收消息 fanout:?所有bind到此exchange的queue都可以接收消息direct:?通过routingKey和exchange决定的那个唯一的queue可以接收消息topic:所有符合routingKey(此时可以是一个表达式)的routingKey所bind的queue可以接收消息 ?表达式符号说明:#代表一个或多个字符,*代表任何字符? ? ? 例:#.a会匹配a.a,aa.a,aaa.a等? ? ? ? ? *.a会匹配a.a,b.a,c.a等? ? ?注:使用RoutingKey为#,Exchange Type为topic的时候相当于使用fanout headers: 通过headers 来决定把消息发给哪些queue 消息publisher 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!" 消息subscriber 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会随机分配一个名字,exclusive=True会在使用此queue的消费者断开后,自动将queue删除 channel.queue_bind(exchange='logs',queue=queue_name) print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch,body): channel.basic_consume(callback,queue=queue_name,no_ack=True) channel.start_consuming()
有选择的接收消息(exchange type=direct)RabbitMQ还支持根据关键字发送,即:队列绑定关键字,发送者将数据根据关键字发送到消息exchange,exchange根据 关键字 判定应该将数据发送至指定队列。 publisherconnection = 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' subscriberconnection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost')) channel = connection.channel() channel.exchange_declare(exchange='direct_logs',type='direct') result = channel.queue_declare(exclusive=True) severities = sys.argv[1:] for severity in severities: print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch,body): channel.basic_consume(callback,no_ack=True) channel.start_consuming()
更细致的消息过滤Although using the? 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??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 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' subscriber connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost')) channel = connection.channel() channel.exchange_declare(exchange='topic_logs',type='topic') result = channel.queue_declare(exclusive=True) binding_keys = sys.argv[1:] for binding_key in binding_keys: print(' [*] Waiting for logs. To exit press CTRL+C') def callback(ch,no_ack=True) channel.start_consuming() To receive all the logs run: python receive_logs_topic.py
To receive all logs from the facility " python receive_logs_topic.py
Or if you want to hear only about " python receive_logs_topic.py
You can create multiple bindings: python receive_logs_topic.py
And to emit a log with a routing key " python emit_log_topic.py
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? RPC server channel = connection.channel()
channel.queue_declare(queue='rpc_queue') def fib(n): def on_request(ch,props,body):
channel.basic_qos(prefetch_count=1) print(" Awaiting RPC requests") RPC client class FibonacciRpcClient(object):
def init(self): self.connection = pika.BlockingConnection(pika.ConnectionParameters( host='localhost'))
fibonacci_rpc = FibonacciRpcClient() print(" Requesting fib(30)")
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异步网络框架Twisted是一个事件驱动的网络框架,其中包含了诸多功能,例如:网络协议、线程、数据库管理、网络操作、电子邮件等。 事件驱动 简而言之,事件驱动分为二个部分:第一,注册事件;第二,触发事件。 自定义事件驱动框架,命名为:“弑君者”: event_drive.py
event_list = [] def run(): class BaseHandler(object): 最牛逼的事件驱动框架 程序员使用“弑君者框架”: from source import event_drive
class MyHandler(event_drive.BaseHandler):
event_drive.event_list.append(MyHandler) ProtocolsProtocols描述了如何以异步的方式处理网络中的事件。HTTP、DNS以及IMAP是应用层协议中的例子。Protocols实现了IProtocol接口,它包含如下的方法: TransportsTransports代表网络中两个通信结点之间的连接。Transports负责描述连接的细节,比如连接是面向流式的还是面向数据报的,流控以及可靠性。TCP、UDP和Unix套接字可作为transports的例子。它们被设计为“满足最小功能单元,同时具有最大程度的可复用性”,而且从协议实现中分离出来,这让许多协议可以采用相同类型的传输。Transports实现了ITransports接口,它包含如下的方法: 将transports从协议中分离出来也使得对这两个层次的测试变得更加简单。可以通过简单地写入一个字符串来模拟传输,用这种方式来检查。
EchoServer class Echo(protocol.Protocol):
def dataReceived(self,data): self.transport.write(data) def main():
if name == 'main':
EchoClient a client protocol
class EchoClient(protocol.Protocol):
class EchoFactory(protocol.ClientFactory):
this connects the protocol to a server running on port 8000def main(): this only runs if the module was not importedif name == 'main': 运行服务器端脚本将启动一个TCP服务器,监听端口1234上的连接。服务器采用的是Echo协议,数据经TCP transport对象写出。运行客户端脚本将对服务器发起一个TCP连接,回显服务器端的回应然后终止连接并停止reactor事件循环。这里的Factory用来对连接的双方生成protocol对象实例。两端的通信是异步的,connectTCP负责注册回调函数到reactor事件循环中,当socket上有数据可读时通知回调处理。 一个传送文件的例子?server side? import optparse,os
from twisted.internet.protocol import ServerFactory,Protocol def parse_args(): This is the Fast Poetry Server,Twisted edition. python fastpoetry.py 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.
class PoetryProtocol(Protocol):
class PoetryFactory(ServerFactory):
def main():
if name == 'main': client side NOTE: This should not be used as the basis for production code.
import optparse from twisted.internet.protocol import Protocol,ClientFactory def parse_args(): This is the Get Poetry Now! client,Twisted version 3.0 python get-poetry-1.py port1 port2 port3 ...
class PoetryProtocol(Protocol):
class PoetryClientFactory(ClientFactory):
def get_poetry(host,port,callback):
def poetry_main():
if name == 'main':
Twisted深入http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/? http://blog.csdn.net/hanhuili/article/details/9389433?
SqlAlchemy ORMSQLAlchemy是编程语言下的一款ORM框架,该框架建立在数据库API之上,使用关系对象映射进行数据库操作,简言之便是:将对象转换成SQL,然后使用数据API执行SQL并获取执行结果 Dialect用于和数据API进行交流,根据配置文件的不同调用不同的数据库API,从而实现对数据库的操作,如: :
pymysql MySQL-Connector cx_Oracle 更多详见:http://docs.sqlalchemy.org/en/latest/dialects/index.html
步骤一: 使用 Engine/ConnectionPooling/Dialect 进行数据库操作,Engine使用ConnectionPooling连接数据库,然后再通过Dialect执行SQL语句。 from sqlalchemy import create_engine
engine = create_engine("mysql+mysqldb://root:123@127.0.0.1:3306/s11",max_overflow=5) engine.execute( engine.execute( result = engine.execute('select * from ts_test')
步骤二: 使用 Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 进行数据库操作。Engine使用Schema Type创建一个特定的结构对象,之后通过SQL Expression Language将该对象转换成SQL语句,然后通过?ConnectionPooling 连接数据库,再然后通过?Dialect 执行SQL,并获取结果。 from sqlalchemy import create_engine,Table,Column,Integer,String,MetaData,ForeignKey
metadata = MetaData() user = Table('user',metadata,Column('id',primary_key=True),Column('name',String(20)),) color = Table('color',) metadata.create_all(engine) 增删改查 from sqlalchemy import create_engine,)
engine = create_engine("mysql+mysqldb://root:123@127.0.0.1:3306/s11",max_overflow=5) conn = engine.connect() 创建SQL语句,INSERT INTO "user" (id,name) VALUES (:id,:name)conn.execute(user.insert(),{'id':7,'name':'seven'}) 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()一个简单的完整例子 Base = declarative_base() #生成一个SqlORM 基类
engine = create_engine("mysql+mysqldb://root@localhost:3306/test",echo=False) class Host(Base): Base.metadata.create_all(engine) #创建所有表结构 if name == 'main': h1 = Host(hostname='localhost',ip_addr='127.0.0.1')
更多内容详见: ? ??http://www.jianshu.com/p/e6bba189fcbd ? ? http://docs.sqlalchemy.org/en/latest/core/expression_api.html 注:SQLAlchemy无法修改表结构,如果需要可以使用SQLAlchemy开发者开源的另外一个软件Alembic来完成。 步骤三: 使用 ORM/Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 所有组件对数据进行操作。根据类创建对象,对象转换成SQL,执行SQL。 from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column,String from sqlalchemy.orm import sessionmaker from sqlalchemy import create_engine engine = create_engine("mysql+mysqldb://root:123@127.0.0.1:3306/s11",max_overflow=5) Base = declarative_base() class User(Base): 寻找Base的所有子类,按照子类的结构在数据库中生成对应的数据表信息Base.metadata.create_all(engine)Session = sessionmaker(bind=engine) ########## 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()########## 删除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 retret = session.query(User).filter(User.name.in_(['sb','bb'])).all()print retret = session.query(User.name.label('name_label')).all()print ret,type(ret)ret = session.query(User).order_by(User.id).all()print retret = session.query(User).order_by(User.id)[1:3]print retsession.commit()外键关联A one to many relationship places a foreign key on the child table referencing the parent.?is then specified on the parent,as referencing a collection of items represented by the child <span class="n">Base <span class="o">= <span class="n">declarative_base<span class="p">()
<pre class="brush:python;gutter:true;">class Parent(Base): tablename = 'parent' id = Column(Integer,primary_key=True) children = relationship("Child") class Child(Base): To establish a bidirectional relationship in one-to-many,where the “reverse” side is a many to one,specify an additional??and connect the two using the?parameter: class Child(Base):
tablename = 'child' id = Column(Integer,ForeignKey('parent.id')) parent = relationship("Parent",back_populates="children")
Alternatively,the??option may be used on a single??instead of using:
附,原生sql join查询 几个Join的区别?http://stackoverflow.com/questions/38549/difference-between-inner-and-outer-joins?
in SQLAchemy
group by 查询 in SQLAchemy 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
?
?本节作业一题目:IO多路复用版FTP 需求:
本节作业二题目:rpc命令端 需求:
>>:run "df -h" --hosts 192.168.3.55 10.4.3.4 task id: 45334>>: check_task 45334 >>: ?
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