python使用memcache

easy_install python-memcached # 安装(python2.7+)

import memcache

mc = memcache.Client(['10.152.14.85:12000'],debug=True)

mc.set('name','luo',60)

mc.get('name')

mc.delete('name1')

保存数据

set(key,value,timeout) # 把key映射到value，timeout指的是什么时候这个映射失效

add(key,value,timeout) # 仅当存储空间中不存在键相同的数据时才保存

replace(key,value,timeout) # 仅当存储空间中存在键相同的数据时才保存

获取数据

get(key) # 返回key所指向的value

get_multi(key1,key2,key3) # 可以非同步地同时取得多个键值， 比循环调用get快数十倍

python使用mongodb

原文: http://blog.nosqlfan.com/html/2989.html

easy_install pymongo # 安装(python2.7+)

import pymongo

connection=pymongo.Connection('localhost',27017) # 创建连接

db = connection.test_database # 切换数据库

collection = db.test_collection # 获取collection

# db和collection都是延时创建的，在添加Document时才真正创建

文档添加, _id自动创建

import datetime

post = {"author": "Mike",

"text": "My first blog post!",

"tags": ["mongodb", "python", "pymongo"],

"date": datetime.datetime.utcnow()}

posts = db.posts

posts.insert(post)

ObjectId('...')

批量插入

new_posts = [{"author": "Mike",

"text": "Another post!",

"tags": ["bulk", "insert"],

"date": datetime.datetime(2009, 11, 12, 11, 14)},

{"author": "Eliot",

"title": "MongoDB is fun",

"text": "and pretty easy too!",

"date": datetime.datetime(2009, 11, 10, 10, 45)}]

posts.insert(new_posts)

[ObjectId('...'), ObjectId('...')]

获取所有collection

db.collection_names() # 相当于SQL的show tables

获取单个文档

posts.find_one()

查询多个文档

for post in posts.find():

post

加条件的查询

posts.find_one({"author": "Mike"})

高级查询

posts.find({"date": {"$lt": "d"}}).sort("author")

统计数量

posts.count()

加索引

from pymongo import ASCENDING, DESCENDING

posts.create_index([("date", DESCENDING), ("author", ASCENDING)])

查看查询语句的性能

posts.find({"date": {"$lt": "d"}}).sort("author").explain()["cursor"]

posts.find({"date": {"$lt": "d"}}).sort("author").explain()["nscanned"]

python使用redis

https://pypi.python.org/pypi/redis

pip install redis OR easy_install redis

import redis

r = redis.StrictRedis(host='localhost', port=6379, db=0)

r.set('foo', 'bar')

r.get('foo')

r.save()

分片 # 没搞懂

redis.connection.Connection(host='localhost', port=6379, db=0, parser_class=<class 'redis.connection.PythonParser'>)

redis.ConnectionPool( connection_class=<class 'redis.connection.Connection'>, max_connections=None, **connection_kwargs)

python使用kestrel队列

# pykestrel

import kestrel

q = kestrel.Client(servers=['127.0.0.1:22133'],queue='test_queue')

q.add('some test job')

job = q.get() # 从队列读取工作

job = q.peek() # 读取下一份工作

# 读取一组工作

while True:

job = q.next(timeout=10) # 完成工作并获取下一个工作，如果没有工作，则等待10秒

if job is not None:

try:

# 流程工作

except:

q.abort() # 标记失败工作

q.finish() # 完成最后工作

q.close() # 关闭连接

kestrel状态检查

# kestrel支持memcache协议客户端

#!/usr/local/bin/python

# 10.13.81.125 22133 10000

import memcache

import sys

import traceback

ip="%s:%s" % (sys.argv[1],sys.argv[2])

try:

mc = memcache.Client([ip,])

st=mc.get_stats()

except:

print "kestrel connection exception"

sys.exit(2)

if st:

for s in st[0][1].keys():

if s.startswith('queue_') and s.endswith('_mem_items'):

num = int(st[0][1][s])

if num > int(sys.argv[3]):

print "%s block to %s" %(s[6:-6],num)

sys.exit(2)

print "kestrel ok!"

sys.exit(0)

else:

print "kestrel down"

sys.exit(2)

python使用tarantool

# pip install tarantool-queue

from tarantool_queue import Queue

queue = Queue("localhost", 33013, 0) # 连接读写端口 空间0

tube = queue.tube("name_of_tube") #

tube.put([1, 2, 3])

task = tube.take()

task.data # take task and read data from it

task.ack() # move this task into state DONE