Memcached 是一個高性能的分佈式內存對象緩存系統,用於動態Web應用以減輕數據庫負載。它通過在內存中緩存數據和對象來減少讀取數據庫的次數,從而提高動態、數據庫驅動網站的速度。Memcached基於一個存儲鍵/值對的hashmap。其守護進程(daemon )是用C寫的,但是客戶端可以用任何語言來編寫,並通過memcached協議與守護進程通信。
Memcached安裝和基本使用
wget http://memcached.org/latest tar -zxvf memcached-1.x.x.tar.gz cd memcached-1.x.x ./configure && make && make test && sudo make install PS:依賴libevent yum install libevent-devel apt-get install libevent-dev
啓動Memcached
memcached -d -m 10 -u root -l 10.211.55.4 -p 12000 -c 256 -P /tmp/memcached.pid 參數說明: -d 是啓動一個守護進程 -m 是分配給Memcache使用的內存數量,單位是MB -u 是運行Memcache的用戶 -l 是監聽的服務器IP地址 -p 是設置Memcache監聽的端口,最好是1024以上的端口 -c 選項是最大運行的併發連接數,默認是1024,按照你服務器的負載量來設定 -P 是設置保存Memcache的pid文件
Memcached命令
存儲命令: set/add/replace/append/prepend/cas 獲取命令: get/gets 其他命令: delete/stats..
Python操作Memcached
安裝API
python操作Memcached使用Python-memcached模塊 下載安裝:https://pypi.python.org/pypi/python-memcached
1、第一次操作
import memcache mc = memcache.Client(['10.211.55.4:12000'], debug=True) mc.set("foo", "bar") ret = mc.get('foo') print ret
2、天生支持集羣
python-memcached模塊原生支持集羣操作,其原理是在內存維護一個主機列表,且集羣中主機的權重值和主機在列表中重複出現的次數成正比
主機 權重 1.1.1.1 1 1.1.1.2 2 1.1.1.3 1 那麼在內存中主機列表爲: host_list = ["1.1.1.1", "1.1.1.2", "1.1.1.2", "1.1.1.3", ]
如果用戶根據如果要在內存中創建一個鍵值對(如:k1 = "v1"),那麼要執行一下步驟:
根據算法將 k1 轉換成一個數字
將數字和主機列表長度求餘數,得到一個值 N( 0 <= N < 列表長度 )
在主機列表中根據 第2步得到的值爲索引獲取主機,例如:host_list[N]
連接 將第3步中獲取的主機,將 k1 = "v1" 放置在該服務器的內存中
代碼實現如下:
mc = memcache.Client([('1.1.1.1:12000', 1), ('1.1.1.2:12000', 2), ('1.1.1.3:12000', 1)], debug=True) mc.set('k1', 'v1')
redis是一個key-value存儲系統。和Memcached類似,它支持存儲的value類型相對更多,包括string(字符串)、list(鏈表)、set(集合)、zset(sorted set --有序集合)和hash(哈希類型)。這些數據類型都支持push/pop、add/remove及取交集並集和差集及更豐富的操作,而且這些操作都是原子性的。在此基礎上,redis支持各種不同方式的排序。與memcached一樣,爲了保證效率,數據都是緩存在內存中。區別的是redis會週期性的把更新的數據寫入磁盤或者把修改操作寫入追加的記錄文件,並且在此基礎上實現了master-slave(主從)同步。
Redis安裝和基本使用
wget http://download.redis.io/releases/redis-3.0.6.tar.gz tar xzf redis-3.0.6.tar.gz cd redis-3.0.6 make
啓動服務端
src
/
redis
-
server
啓動客戶端
src
/
redis
-
cli
redis>
set
foo bar
OK
redis> get foo
"bar"
Python操作Redis
安裝API
sudo pip install redis
or
sudo easy_install redis
or
源碼安裝
詳見:https:
/
/
github.com
/
WoLpH
/
redis
-
py
常用操作
1、操作模式
redis-py提供兩個類Redis和StrictRedis用於實現Redis的命令,StrictRedis用於實現大部分官方的命令,並使用官方的語法和命令,Redis是StrictRedis的子類,用於向後兼容舊版本的redis-py。
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import
redis
r
=
redis.Redis(host
=
'10.211.55.4'
, port
=
6379
)
r.
set
(
'foo'
,
'Bar'
)
print
r.get(
'foo'
)
2、連接池
redis-py使用connection pool來管理對一個redis server的所有連接,避免每次建立、釋放連接的開銷。默認,每個Redis實例都會維護一個自己的連接池。可以直接建立一個連接池,然後作爲參數Redis,這樣就可以實現多個Redis實例共享一個連接池。
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import
redis
pool
=
redis.ConnectionPool(host
=
'10.211.55.4'
, port
=
6379
)
r
=
redis.Redis(connection_pool
=
pool)
r.
set
(
'foo'
,
'Bar'
)
print
r.get(
'foo'
)
3、管道
redis-py默認在執行每次請求都會創建(連接池申請連接)和斷開(歸還連接池)一次連接操作,如果想要在一次請求中指定多個命令,則可以使用pipline實現一次請求指定多個命令,並且默認情況下一次pipline 是原子性操作。
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import
redis
pool
=
redis.ConnectionPool(host
=
'10.211.55.4'
, port
=
6379
)
r
=
redis.Redis(connection_pool
=
pool)
# pipe = r.pipeline(transaction=False)
pipe
=
r.pipeline(transaction
=
True
)
r.
set
(
'name'
,
'alex'
)
r.
set
(
'role'
,
'sb'
)
pipe.execute()
4、發佈訂閱
#!/usr/bin/env python # -*- coding:utf-8 -*- import redis class RedisHelper: def __init__(self): self.__conn = redis.Redis(host='10.211.55.4') self.chan_sub = 'fm104.5' self.chan_pub = 'fm104.5' def public(self, msg): self.__conn.publish(self.chan_pub, msg) return True def subscribe(self): pub = self.__conn.pubsub() pub.subscribe(self.chan_sub) pub.parse_response() return pub RedisHelper
訂閱者: #!/usr/bin/env python # -*- coding:utf-8 -*- from monitor.RedisHelper import RedisHelper obj = RedisHelper() redis_sub = obj.subscribe() while True: msg= redis_sub.parse_response() print msg 發佈者: #!/usr/bin/env python # -*- coding:utf-8 -*- from monitor.RedisHelper import RedisHelper obj = RedisHelper() obj.public('hello')
更多參見:https://github.com/andymccurdy/redis-py/
RabbitMQ是一個在AMQP基礎上完整的,可複用的企業消息系統。他遵循Mozilla Public License開源協議。
MQ全稱爲Message Queue, 消息隊列(MQ)是一種應用程序對應用程序的通信方法。應用程序通過讀寫出入隊列的消息(針對應用程序的數據)來通信,而無需專用連接來鏈接它們。消 息傳遞指的是程序之間通過在消息中發送數據進行通信,而不是通過直接調用彼此來通信,直接調用通常是用於諸如遠程過程調用的技術。排隊指的是應用程序通過 隊列來通信。隊列的使用除去了接收和發送應用程序同時執行的要求。
RabbitMQ安裝
安裝配置epel源 $ rpm -ivh http://dl.fedoraproject.org/pub/epel/6/i386/epel-release-6-8.noarch.rpm 安裝erlang $ yum -y install erlang 安裝RabbitMQ $ yum -y install rabbitmq-server
注意:service rabbitmq-server start/stop
安裝API
pip install pika or easy_install pika or 源碼 https://pypi.python.org/pypi/pika
使用API操作RabbitMQ
基於Queue實現生產者消費者模型
#!/usr/bin/env python # -*- coding:utf-8 -*- import Queue import threading message = Queue.Queue(10) def producer(i): while True: message.put(i) def consumer(i): while True: msg = message.get() for i in range(12): t = threading.Thread(target=producer, args=(i,)) t.start() for i in range(10): t = threading.Thread(target=consumer, args=(i,)) t.start()
對於RabbitMQ來說,生產和消費不再針對內存裏的一個Queue對象,而是某臺服務器上的RabbitMQ Server實現的消息隊列
#!/usr/bin/env python
import
pika
# ######################### 生產者 #########################
connection
=
pika.BlockingConnection(pika.ConnectionParameters(
host
=
'localhost'
))
channel
=
connection.channel()
channel.queue_declare(queue
=
'hello'
)
channel.basic_publish(exchange
=
'',
routing_key
=
'hello'
,
body
=
'Hello World!'
)
print
(
" [x] Sent 'Hello World!'"
)
connection.close()
#!/usr/bin/env python
import
pika
# ########################## 消費者 ##########################
connection
=
pika.BlockingConnection(pika.ConnectionParameters(
host
=
'localhost'
))
channel
=
connection.channel()
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()
1、acknowledgment 消息不丟失
no-ack = False,如果生產者遇到情況(its channel is closed, connection is closed, or TCP connection is lost)掛掉了,那麼,RabbitMQ會重新將該任務添加到隊列中。
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) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming() 消費者
2、durable 消息不丟失
#!/usr/bin/env python import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4')) channel = connection.channel() # make message persistent channel.queue_declare(queue='hello', 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() 生產者 #!/usr/bin/env python # -*- coding:utf-8 -*- import pika connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4')) channel = connection.channel() # make message persistent channel.queue_declare(queue='hello', 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) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming() 消費者
3、消息獲取順序
默認消息隊列裏的數據是按照順序被消費者拿走,例如:消費者1 去隊列中獲取 奇數 序列的任務,消費者1去隊列中獲取 偶數 序列的任務。
channel.basic_qos(prefetch_count=1) 表示誰來誰取,不再按照奇偶數排列
#!/usr/bin/env python # -*- coding:utf-8 -*- 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) channel.basic_consume(callback, queue='hello', no_ack=False) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming() 消費者
4、發佈訂閱
發佈訂閱和簡單的消息隊列區別在於,發佈訂閱會將消息發送給所有的訂閱者,而消息隊列中的數據被消費一次便消失。所以,RabbitMQ實現發佈和訂閱時,會爲每一個訂閱者創建一個隊列,而發佈者發佈消息時,會將消息放置在所有相關隊列中。
exchange type = fanout
#!/usr/bin/env python 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() 發佈者 #!/usr/bin/env python 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) 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() 訂閱者
5、關鍵字發送
exchange type = direct
之前事例,發送消息時明確指定某個隊列並向其中發送消息,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。
#!/usr/bin/env python 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() 消費者 #!/usr/bin/env python 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() 生產者
6、模糊匹配
exchange type = topic
在topic類型下,可以讓隊列綁定幾個模糊的關鍵字,之後發送者將數據發送到exchange,exchange將傳入”路由值“和 ”關鍵字“進行匹配,匹配成功,則將數據發送到指定隊列。
# 表示可以匹配 0 個 或 多個 單詞
* 表示只能匹配 一個 單詞
發送者路由值 隊列中
old.boy.python old.
*
-
-
不匹配
old.boy.python old.
# -- 匹配
#!/usr/bin/env python 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() 消費者 #!/usr/bin/env python 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() 生產者
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