06_python_cache


	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