线程池

上下文管理

• 线程池中关于上下文管理的相关代码 点我查看更详细的上下文管理介绍

import contextlib

@contextlib.contextmanager

def worker_state(state_list, worker_thread):

    """

    用户记录线程中正在等待的线程数

    :param state_list:

    :param worker_thread:

    :return:

    """

    state_list.append(worker_thread)

    try:

        yield

    finally:

        state_list.remove(worker_thread)

free_list = []

current_thread = "aaa"

with worker_state(free_list, current_thread):

    print(123)

    print(456)

• socket_server 之上下文管理

import contextlib

import socket

@contextlib.contextmanager

def context_socket(host, port):

    sk = socket.socket()

    sk.bind((host, port))

    sk.listen(5)

    try:

        yield sk

    finally:

        sk.close()

with context_socket("127.0.0.1", 8888) as sock:

    sock.sendall(bytes("hehe", encoding="utf-8"))

redis

redis连接池

redis自定义列表

redis事务操作

redis发布订阅

• 创建发布订阅类

# release_subscription.py

import redis

class RedisHelper:

    def __init__(self, **kwargs):

        pool = redis.ConnectionPool(**kwargs)

        self.__conn = redis.Redis(connection_pool=pool)

    def release(self, msg, channel):

        """

        redis发布端

        :param msg: 发送的内容

        :param channel: # 发布的频道

        :return:

        """

        self.__conn.publish(channel, msg)

        return True

    def subscription(self, channel):

        """

        redis订阅端

        :param channel: # 订阅的频道

        :return:

        """

        pub = self.__conn.pubsub()

        pub.subscribe(channel)

        pub.parse_response()

        return pub

• 实例化发布端

# redis_release.py

# 导入发布订阅模块

import release_subscription

obj = release_subscription.RedisHelper(host="127.0.0.1")

obj.release("hehe", "fm103.9")

• 实例化订阅端

# redis_sub.py

# 导入发布订阅模块

import release_subscription

obj = release_subscription.RedisHelper(host="127.0.0.1")

data = obj.subscription("fm103.9")

print(data.parse_response())

RabbitMQ

基于RabbitMQ实现的消息队列

• 生产者

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host="127.0.0.1"))

channel = connection.channel()

channel.queue_declare(queue='hello')   # 创建一个队列,如果存在则不产生任何效果

channel.basic_publish(exchange='',

                      routing_key='hello',

                      body='Hello World!')      # 给队列发送消息 "Hello World!"

print(" [x] Sent 'Hello World!'")

connection.close()

• 消费者

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(

    host='127.0.0.1'))

channel = connection.channel()

channel.queue_declare(queue='hello')

def callback(ch, method, properties, body):

    print(" [x] Received %r" % body)

channel.basic_consume(callback,   # 回调函数, 如果从队列中取到数据之后则执行回调函数

                      queue='hello',    # 队列名

                      no_ack=True)

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

channel.start_consuming()

消息不丢失

• 如果消费者遇到情况(its channel is closed, connection is closed, or TCP connection is lost)挂掉了，那么，通过设置 no-ack＝False，RabbitMQ会重新将该任务添加到队列中

# 消费者代码

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(

        host='10.211.55.4'))

channel = connection.channel()

channel.queue_declare(queue='hello')

def callback(ch, method, properties, body):

    print(" [x] Received %r" % body)

    import time

    time.sleep(10)

    print 'ok'

    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_consume(callback,

                      queue='hello',

                      no_ack=False)  # 设置no_ack=False保证消息不丢失

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

channel.start_consuming()

• durable 消息不丢失

# 生产者代码

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='127.0.0.1'))

channel = connection.channel()

# make message persistent

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

channel.basic_publish(exchange='',

                      routing_key='hello',

                      body='Hello World!',

                      properties=pika.BasicProperties(

                          delivery_mode=2, # make message persistent

                      ))

print(" [x] Sent 'Hello World!'")

connection.close()

# 消费者代码

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='127.0.0.1'))

channel = connection.channel()

# make message persistent

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

def callback(ch, method, properties, body):

    print(" [x] Received %r" % body)

    import time

    time.sleep(10)

    print 'ok'

    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_consume(callback,

                      queue='hello',

                      no_ack=False)   # 设置no_ack=False

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

channel.start_consuming()

消费者消息获取顺序

# 默认情况下，消息队列里的数据是按照顺序被消费者拿走，例如：消费者1去队列中获取 奇数 序列的任务，消费者2去队列中获取偶数序列的任务

通过设置channel.basic_qos(prefetch_count=1) 表示谁来谁取，不再按照奇偶数排列

# 消费者端代码

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))

channel = connection.channel()

# make message persistent

channel.queue_declare(queue='hello')

def callback(ch, method, properties, body):

    print(" [x] Received %r" % body)

    import time

    time.sleep(10)

    print 'ok'

    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_qos(prefetch_count=1)  # 通过设置该参数，让消费者不按默认顺序取

channel.basic_consume(callback,

                      queue='hello',

                      no_ack=False)

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

channel.start_consuming()

发布订阅

发布订阅和简单的消息队列区别在于，发布订阅会将消息发送给所有的订阅者，而消息队列中的数据被消费一次便消失。所以，RabbitMQ实现发布和订阅时，会为每一个订阅者创建一个队列，而发布者发布消息时，会将消息放置在所有相关队列中。

通过设置exchange 和 type=fanout实现该功能

• 发布端

import pika

import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(

        host='127.0.0.1'))

channel = connection.channel()

channel.exchange_declare(exchange='logs',

                         type='fanout')   # 通过设置exchange来与队列通信

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()

• 订阅端

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(

        host='127.0.0.1'))

channel = connection.channel()

channel.exchange_declare(exchange='logs',

                         type='fanout')

# 生成一个随机的queue

result = channel.queue_declare(exclusive=True)

queue_name = result.method.queue

# 将queue与exchange绑定,发布端给exchange发消息的时候,与该exchange绑定的queue都会收到发布端发布的消息

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()

发布订阅-关键字

上面的例子发送消息时明确指定某个队列并向其中发送消息，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。

通过设置exchange 和 type = direct实现该功能

• 发布端

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(

        host='localhost'))

channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',

                         type='direct')

severity = "info"

message = 'Hello World!'

channel.basic_publish(exchange='direct_logs',

                      routing_key=severity,

                      body=message)

print(" [x] Sent %r:%r" % (severity, message))

connection.close()

• 订阅端

import pika

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 = ["info", "error"]

for severity in severities:

    channel.queue_bind(exchange='direct_logs',

                       queue=queue_name,

                       routing_key=severity)   # 将exchange与关键字routing_key绑定

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()

发布订阅-关键字模糊匹配

通过设置type=topic，可以让队列绑定几个模糊的关键字，之后发送者将数据发送到exchange，exchange将传入”路由值“和 ”关键字“进行匹配，匹配成功，则将数据发送到指定队列

# 表示可以匹配 0 个 或 多个 单词

*  表示只能匹配 一个 单词

发送者路由值              队列中

old.boy.python          old.*  -- 不匹配

old.boy.python          old.#  -- 匹配

• 发布端

import pika

import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(

        host='127.0.0.1'))

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()

• 订阅端

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()

MySQL

PyMySQL

一、安装模块

pip install pymysql

二、使用

• 执行SQL（增删改操作）

#!/usr/bin/env python

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

import pymysql

# 创建连接

conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')

# 创建游标

cursor = conn.cursor()

# 执行SQL，并返回收影响行数

effect_row = cursor.execute("update hosts set host = '1.1.1.2'")

# sql语句中有一个占位符

# 执行SQL，并返回受影响行数

# effect_row = cursor.execute("update hosts set host = '1.1.1.2' where nid > %s", (1,))

# sql语句中有多个占位符

# 执行SQL，并返回受影响行数

# effect_row = cursor.executemany("insert into hosts(host,color_id)values(%s,%s)", [("1.1.1.11",1),("1.1.1.11",2)])

# 提交，不然无法保存新建或者修改的数据

conn.commit()

# 关闭游标

cursor.close()

# 关闭连接

conn.close()

• 插入数据的时候获取新创建数据自增ID

import pymysql

conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')

cursor = conn.cursor()

cursor.executemany("insert into hosts(host,color_id)values(%s,%s)", [("1.1.1.11",1),("1.1.1.11",2)])

conn.commit()

cursor.close()

conn.close()

# 获取最新自增ID

new_id = cursor.lastrowid

• 查询操作

import pymysql

conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')

cursor = conn.cursor()

cursor.execute("select * from hosts")

# 获取第一行数据

row_1 = cursor.fetchone()

# 获取前n行数据

# row_2 = cursor.fetchmany(3)

# 获取所有数据

# row_3 = cursor.fetchall()

conn.commit()

cursor.close()

conn.close()

# 注：在fetch数据时按照顺序进行，可以使用cursor.scroll(num,mode)来移动游标位置，如：

cursor.scroll(1,mode='relative')  # 相对当前位置移动，将游标移动到下一个位置

cursor.scroll(-3,mode='relative')  # 相对当前位置移动，将游标移动到上三个位置

cursor.scroll(2,mode='absolute') # 绝对位置移动，将游标移动到第2个位置

cursor.scroll(10,mode='absolute') # 绝对位置移动，将游标移动到第10个位置

• fetch数据类型

import pymysql

conn = pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123', db='t1')

# 游标设置为字典类型

cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)

r = cursor.execute("select * from hosts")

result = cursor.fetchone()		# 返回的是一个字典，以字段名为key,value为值

conn.commit()

cursor.close()

conn.close()

Python ORM --> SQLAchemy

SQLAlchemy是Python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。



SQLAlchemy本身无法操作数据库，其必须以来pymsql等第三方插件，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：

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...]

更多详见：http://docs.sqlalchemy.org/en/latest/dialects/index.html

一、底层处理

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+pymysql://tuocigaoshou:Eb^BEF38E9FBC36CA775@111.204.117.99:3306/test", max_overflow=5)

# 执行SQL 插入一条数据 (增删改)

cur = engine.execute("insert into users (name, extra) values ('aaa', 'aaa')")

print(cur.lastrowid)    # 可以获取到新插入数据行的自增id

# 执行SQL  插入多条数据

cur = engine.execute("insert into users (name, extra) values (%s, %s)", [["bbb", "bbb"], ["ccc", "ccc"],])

print(cur.lastrowid)  # 插入多条数据的时候只能获取到第一行的自增id

# 执行SQL 另一种插入方式,只能插入一条

cur = engine.execute("INSERT INTO users (name, extra) VALUES (%(name)s, %(extra)s)", name="ddd", extra="ddd")

# 执行SQL 查询

cur = engine.execute('select * from users')

# 获取第一行数据

print(cur.fetchone())

# 获取第n行数据

print(cur.fetchmany(3))

# 获取所有数据

print(cur.fetchall())

二、ORM功能使用

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。

• 创建表

from sqlalchemy.ext.declarative import declarative_base

from sqlalchemy import Column, Integer, String, ForeignKey, UniqueConstraint, Index

from sqlalchemy.orm import sessionmaker, relationship

from sqlalchemy import create_engine

engine = create_engine("mysql+pymysql://tuocigaoshou:Eb^BEF38E9FBC36CA775@111.204.117.99:3306/test", max_overflow=5)

Base = declarative_base()

# 创建单表

class Users(Base):  # 必须继承Base类

    __tablename__ = 'users'     # 表名

    # 创建三列数据

    id = Column(Integer, primary_key=True)      # primary_key 主键,自增ID

    name = Column(String(32))

    extra = Column(String(16))

    # 联合索引

    __table_args__ = (

    UniqueConstraint('id', 'name', name='uix_id_name'),

        Index('ix_id_name', 'name', 'extra'),

    )

# 一对多

class Favor(Base):

    __tablename__ = 'favor'

    nid = Column(Integer, primary_key=True)     # primary_key 主键,自增ID

    caption = Column(String(50), default='red', unique=True)    # unique唯一约束

class Person(Base):

    __tablename__ = 'person'

    nid = Column(Integer, primary_key=True)     # primary_key 主键,自增ID

    name = Column(String(32), index=True, nullable=True)

    favor_id = Column(Integer, ForeignKey("favor.nid"))     # 外键

# 多对多

class Group(Base):

    __tablename__ = 'group'

    id = Column(Integer, primary_key=True)

    name = Column(String(64), unique=True, nullable=False)

class Server(Base):

    __tablename__ = 'server'

    id = Column(Integer, primary_key=True, autoincrement=True)

    hostname = Column(String(64), unique=True, nullable=False)

    port = Column(Integer, default=22)

class ServerToGroup(Base):      # 通过第三张表创建上两张表多对多的关系

    __tablename__ = 'servertogroup'

    nid = Column(Integer, primary_key=True, autoincrement=True)

    server_id = Column(Integer, ForeignKey('server.id'))

    group_id = Column(Integer, ForeignKey('group.id'))

# Base.metadata.create_all(engine)    # 创建表,会执行Base类的所有子类创建所有表

# Base.metadata.drop_all(engine)      # 删除表,会执行Base类的所有子类删除所有表