多维数据快速查找的数据模型与数据存储技术

Question

我有一个parent hashmap数据结构，其字符串作为键，hashmap数据结构作为子结构(猜测child1、child2、.、childN)。每个子元素都是一个简单的键值映射，有一个数字作为键，一个字符串作为值。在伪码中：

parent['key1'] = child1;    // child1 is a hash map data structure
child1[0] = 'foo';
child[1] = 'bar';
...

我需要将这个数据结构实现为数据库系统中的快速查找表。让我们以Python作为参考语言。

解决方案的要求：

1. 尽快找回孩子们的地图！
2. parent散列的估计总重量最多为500 MB。

用例如下：

1. 客户端Python程序在数据存储中查询特定的子哈希。
2. 数据存储返回子哈希。
3. Python程序将整个散列传递给特定的函数，从散列中提取一个特定的值(它已经知道要使用哪个键)，并将它传递给第二个函数。

您会推荐内存中的键值数据存储(如Redis)还是更经典的“关系”数据库解决方案？你建议我使用哪种数据模型？

Answer 1

使用红皮的示例代码，假设您已经安装了Redis (以及理想的雇工)，将每个父节点保存为一个散列字段，将子元素作为序列化字符串保存，并在客户端处理序列化和反序列化：

JSON版本：

## JSON version
import json 
# you could use pickle instead, 
# just replace json.dumps/json.loads with pickle/unpickle

import redis

# set up the redis client
r = redis.StrictRedis(host = '', port = 6379, db = 0)

# sample parent dicts
parent0 = {'child0': {0:'a', 1:'b', 2:'c',}, 'child1':{5:'e', 6:'f', 7:'g'}}
parent1 = {'child0': {0:'h', 1:'i', 2:'j',}, 'child1':{5:'k', 6:'l', 7:'m'}}

# save the parents as hashfields, with the children as serialized strings
# bear in mind that JSON will convert the int keys to strings in the dumps() process
r.hmset('parent0', {key: json.dumps(parent0[key]) for key in parent0})
r.hmset('parent1', {key: json.dumps(parent0[key]) for key in parent1})


# Get a child dict from a parent
# say child1 of parent0
childstring = r.hget('parent0', 'child1') 
childdict = json.loads(childstring) 
# this could have been done in a single line... 

# if you want to convert the keys back to ints:
for key in childdict.keys():
    childdict[int(key)] = childdict[key]
    del childdict[key]

print childdict

泡菜版本：

## pickle version
# For pickle, you need a file-like object. 
# StringIO is the native python one, whie cStringIO 
# is the c implementation of the same.
# cStringIO is faster
# see http://docs.python.org/library/stringio.html and
# http://www.doughellmann.com/PyMOTW/StringIO/ for more information
import pickle
# Find the best implementation available on this platform
try:
    from cStringIO import StringIO
except:
    from StringIO import StringIO

import redis

# set up the redis client
r = redis.StrictRedis(host = '', port = 6379, db = 0)

# sample parent dicts
parent0 = {'child0': {0:'a', 1:'b', 2:'c',}, 'child1':{5:'e', 6:'f', 7:'g'}}
parent1 = {'child0': {0:'h', 1:'i', 2:'j',}, 'child1':{5:'k', 6:'l', 7:'m'}}

# define a class with a reusable StringIO object
class Pickler(object):
    """Simple helper class to use pickle with a reusable string buffer object"""
    def __init__(self):
        self.tmpstr = StringIO()

    def __del__(self):
        # close the StringIO buffer and delete it
        self.tmpstr.close()
        del self.tmpstr

    def dump(self, obj):
        """Pickle an object and return the pickled string"""
        # empty current buffer
        self.tmpstr.seek(0,0)
        self.tmpstr.truncate(0)
        # pickle obj into the buffer
        pickle.dump(obj, self.tmpstr)
        # move the buffer pointer to the start
        self.tmpstr.seek(0,0)
        # return the pickled buffer as a string
        return self.tmpstr.read()

    def load(self, obj):
        """load a pickled object string and return the object"""
        # empty the current buffer
        self.tmpstr.seek(0,0)
        self.tmpstr.truncate(0)
        # load the pickled obj string into the buffer
        self.tmpstr.write(obj)
        # move the buffer pointer to start
        self.tmpstr.seek(0,0)
        # load the pickled buffer into an object
        return pickle.load(self.tmpstr)


pickler = Pickler()

# save the parents as hashfields, with the children as pickled strings, 
# pickled using our helper class
r.hmset('parent0', {key: pickler.dump(parent0[key]) for key in parent0})
r.hmset('parent1', {key: pickler.dump(parent1[key]) for key in parent1})


# Get a child dict from a parent
# say child1 of parent0
childstring = r.hget('parent0', 'child1') 
# this could be done in a single line... 
childdict = pickler.load(childstring) 

# we don't need to do any str to int conversion on the keys.

print childdict

Answer 2

绝对要和Redis一起去。它不仅速度非常快，而且精确地处理了所需的结构：http://redis.io/commands#hash

在您的示例中，您可以避免读取整个‘子哈希’，因为客户端“从散列中提取特定值(它已经知道要使用哪个键)”。

redis> HMSET myhash field1 "Hello" field2 "World"
OK
redis> HGET myhash field1
"Hello"
redis> HGET myhash field2
"World"

或者，如果你真的想要整个哈希：

redis> HGETALL myhash
1) "field1"
2) "Hello"
3) "field2"
4) "World"
redis>

当然，使用客户库可以在一个可行的对象中给出正确的结果，在您的例子中，这是一个Python字典。

Answer 3

在基于哈维尔提示的快速搜索之后，我想出了一个解决方案:我可以在Redis中实现一个parent散列，其中的值字段将是子散列的字符串表示。通过这种方式，我可以快速地阅读它们，并从Python程序中对它们进行评估。

仅举一个例子，我的Redis数据结构将类似于：

//write a hash with N key-value pairs: each value is an M key-value pairs hash
redis> HMSET parent_key1 child_hash "c1k1:c1v1, c1k2:c1v2, [...], c1kM:c1vM"
  OK
redis> HMSET parent_key2 child_hash "c2k1:c2v1, c2k2:c2v2, [...], c2kM:c2vM"
  OK
[...]
redis> HMSET parent_keyN child_hash "cNk1:cNv1, cNk2:cNv2, [...], cNkM:cNvM"
  OK

//read data
redis> HGET parent_key1 child_hash
  "c1k1:c1v1, c1k2:c1v2, [...], c1kM:c1vM"

然后，我的Python代码只需要使用Redis绑定来查询所需的子哈希，并返回它们的实际字符串表示；剩下要做的是将字符串表示转换为相应的字典，因此可以在方便的情况下查找。

示例代码(如这个答案中所建议的)：

>>> import ast
>>> # Redis query:
>>> #   1. Setup Redis bindings
>>> #   2. Ask for value at key: parent_key1
>>> #   3. Store the value to 's' string
>>> dictionary = ast.literal_eval('{' + s + '}')
>>> d
{c1k1:c1v1, c1k2:c1v2, [...], c1kM:c1vM}

希望我没有错过任何东西！