那麼，基本上你必須重寫_get _server（）方法來更改服務器分佈算法。
我已經做了一些互聯網搜索，發現這篇文章在谷歌，amix.dk/blog/post/19367，這是一個非常好的材料，由Amir Salihefendic編寫，這有助於瞭解ketama一致哈希算法是如何工作的，也有由他製作的名爲HashRing的Python類上的ketama實現。
因此，我基本上使用他的類，並改變了一點，以適應Memcached客戶端的需求。修改是這是depprecated的MD5模塊的變化，並用於產生從該服務器密鑰的字符串的變化：
key = self.gen_key('%s:%s' % (node, i))
到：
key = self.gen_key(
'%s:%s:%s:%s' % (node.address[0],
node.address[1], i, node.weight)
)
我也固定一個錯誤當算法沒有在第一個循環找到服務器時，這會在get_nodes（）方法上導致無限循環。
舊的get_nodes（）方法（如果沒有服務器產生，將進入無限循環）。
def get_nodes(self, string_key):
"""Given a string key it returns the nodes as a generator that can hold the key.
The generator is never ending and iterates through the ring
starting at the correct position.
"""
if not self.ring:
yield None, None
node, pos = self.get_node_pos(string_key)
for key in self._sorted_keys[pos:]:
yield self.ring[key]
while True:
for key in self._sorted_keys:
yield self.ring[key]
新get_nodes（）方法：
def get_nodes(self, string_key):
if not self.ring:
yield None, None
node, pos = self.get_node_pos(string_key)
for key in self._sorted_keys[pos:]:
if key in self.ring:
yield self.ring[key]
for key in self._sorted_keys[:pos]:
if key in self.ring:
yield self.ring[key]
我對remove_node（）方法添加上add_node（新的for循環的範圍），以及要考慮服務器的添加重量更多副本。
老辦法：
for i in xrange(0, self.replicas):
key = self.gen_key('%s:%s' % (node, i))
self.ring[key] = node
self._sorted_keys.append(key)
新方法：
for i in xrange(0, self.replicas):
for x in range(0, node.weight):
key = self.gen_key(
'%s:%s:%s:%s' % (node.address[0],
node.address[1], i, node.weight)
)
if key not in self.ring:
self.ring[key] = node
self._sorted_keys.append(key)
上面的代碼方面的add_node（）方法，但一些概念適用於remove_node（）。
那麼，也許還有一些其他的改變，我現在只是不記得任何其他的改變。這是適合HashRing類：
from hashlib import md5
class HashRing(object):
def __init__(self, nodes=None, replicas=3):
"""Manages a hash ring.
`nodes` is a list of objects that have a proper __str__ representation.
`replicas` indicates how many virtual points should be used pr. node,
replicas are required to improve the distribution.
"""
self.replicas = replicas
self.ring = dict()
self._sorted_keys = []
if nodes:
for node in nodes:
self.add_node(node)
def add_node(self, node):
"""Adds a `node` to the hash ring (including a number of replicas).
"""
for i in xrange(0, self.replicas):
"""This will ensure that a server with a bigger weight will have
more copies into the ring increasing it's probability to be retrieved.
"""
for x in range(0, node.weight):
key = self.gen_key(
'%s:%s:%s:%s' % (node.address[0],
node.address[1], i, node.weight)
)
if key not in self.ring:
self.ring[key] = node
self._sorted_keys.append(key)
self._sorted_keys.sort()
def remove_node(self, node):
"""Removes `node` from the hash ring and its replicas.
"""
for i in xrange(0, self.replicas):
for x in range(node.weight):
key = self.gen_key(
'%s:%s:%s:%s' % (node.address[0],
node.address[1], i, node.weight)
)
if key in self.ring:
del self.ring[key]
self._sorted_keys.remove(key)
def get_node(self, string_key):
"""
Given a string key a corresponding node in the hash ring is returned.
If the hash ring is empty, `None` is returned.
"""
return self.get_node_pos(string_key)[0]
def get_node_pos(self, string_key):
"""Given a string key a corresponding node in the hash ring is returned
along with it's position in the ring.
If the hash ring is empty, (`None`, `None`) is returned.
"""
if not self.ring:
return None, None
key = self.gen_key(string_key)
nodes = self._sorted_keys
for i in xrange(0, len(nodes)):
node = nodes[i]
if key <= node:
return self.ring[node], i
return self.ring[nodes[0]], 0
def get_nodes(self, string_key):
"""Given a string key it returns the nodes as a generator that can hold
the key.
The generator is never ending and iterates through the ring
starting at the correct position.
"""
if not self.ring:
yield None, None
node, pos = self.get_node_pos(string_key)
for key in self._sorted_keys[pos:]:
if key in self.ring:
yield self.ring[key]
for key in self._sorted_keys[:pos]:
if key in self.ring:
yield self.ring[key]
@staticmethod
def gen_key(key):
"""Given a string key it returns a long value,
this long value represents a place on the hash ring.
md5 is currently used because it mixes well.
"""
m = md5()
m.update(key)
return long(m.hexdigest(), 16)
我改變了你的類中的一點，以使其成爲決定何時使用ketama算法，或默認更加靈活 - 模。
我注意到，在編寫add_server（）方法時，您忘記了在將服務器附加到存儲區列表時考慮服務器的權重。
因此，這是新MemcacheClient會是什麼樣子：
from consistent_hash import HashRing
class MemcacheClient(memcache.Client):
""" A memcache subclass. It currently allows you to add a new host at run
time.
"""
available_algorithms = ['ketama', 'modulo']
hash_algorithm_index = 0
def __init__(self, hash_algorithm='ketama', *args, **kwargs):
super(MemcacheClient, self).__init__(*args, **kwargs)
if hash_algorithm in self.available_algorithms:
self.hash_algorithm_index = self.available_algorithms.index(
hash_algorithm)
if hash_algorithm == 'ketama':
self.consistent_hash_manager = HashRing(nodes=self.servers)
else:
self.consistent_hash_manager = None
else:
raise Exception(
"The algorithm \"%s\" is not implemented for this client. The "
"options are \"%s\""
"" % (hash_algorithm, " or ".join(self.available_algorithms))
)
def _get_server(self, key):
""" Returns the most likely server to hold the key
"""
if self.hash_algorithm == 'ketama':
""" Basic concept of the Implementation of ketama algorithm
e.g. ring = {100:server1, 110:server2, 120:server3, 140:server4}
If the hash of the current key is 105, it server will be the next
bigger integer in the ring which is 110 (server2)
If a server is added on position 108 the key will be now allocated
to it and not to server 110. Otherwise if the server on position
110 is removed the key will now belong to de server 120.
If there's no bigger integer position in the ring then the hash of
the key, it will take the first server from the ring.
"""
# The variable "servers" is the list of the servers in the ring
# starting from the next bigger integer to the hash of the key,
# till it finds the one that holds the key
servers_generator = self.consistent_hash_manager.get_nodes(key)
for server in servers_generator:
if server.connect():
#print server.address[1]
return server, key
return None, None
else:
return super(MemcacheClient, self)._get_server(key)
def add_server(self, server):
""" Adds a host at runtime to client
"""
# Uncomment this to protect the Client from adding a server in case
# there's no reliable consistent hash algorithm such as MODULO
"""
if not self.consistent_hash_manager:
raise Exception("The current consistent hash algorithm (\"%s\") is"
" not reliable for adding a new server"
"" % self.hash_algorithm)
"""
# Create a new host entry
server = memcache._Host(
server, self.debug, dead_retry=self.dead_retry,
socket_timeout=self.socket_timeout,
flush_on_reconnect=self.flush_on_reconnect
)
# Add this to our server choices
self.servers.append(server)
"""This for statement will ensure that a server with a bigger weight
will have more copies into the buckets increasing it's probability to
be retrieved.
"""
for i in range(server.weight):
self.buckets.append(server)
# Adds this node to the circle
if self.consistent_hash_manager:
self.consistent_hash_manager.add_node(server)
def random_key(size):
""" Generates a random key
"""
return ''.join(random.choice(string.letters) for _ in range(size))
def run_consistent_hash_test(client_obj):
# We have 500 keys to split across our servers
keys = [random_key(100) for i in range(500)]
print(
"\n/////////// CONSISTENT HASH ALGORITHM \"%s\" //////////////"
"" % client_obj.hash_algorithm.upper()
)
print("\n->These are the %s servers:" % len(client_obj.servers))
str_servers = ""
for server in client_obj.servers:
str_servers += "%s:%s, " % (server.address[0], server.address[1])
print("******************************************************************")
print(str_servers)
print("******************************************************************")
# Clear all previous keys from memcache
client_obj.flush_all()
# Distribute the keys over the servers
for key in keys:
client_obj.set(key, 1)
print(
"\n%d keys distributed for %d server(s)\n"
"" % (len(keys), len(client_obj.servers))
)
# Check how many keys come back
valid_keys = client_obj.get_multi(keys)
print(
"%s percent of keys matched, before adding extra servers.\n" \
"" %((len(valid_keys)/float(len(keys))) * 100)
)
# Add 5 new extra servers
interval_extra_servers = range(19, 24)
extra_servers = ['127.0.0.1:112%d' % i for i in interval_extra_servers]
for server in extra_servers:
client_obj.add_server(server)
# Check how many keys come back after adding the extra servers
valid_keys = client_obj.get_multi(keys)
print (
"Added %d new server(s).\n%s percent of keys still matched" \
"" % (len(interval_extra_servers),
(len(valid_keys)/float(len(keys))) * 100)
)
print("\n***************************************************************"
"****\n")
if __name__ == '__main__':
# We have 8 running memcached servers
interval_servers = range(11, 19)
servers = ['127.0.0.1:112%d' % i for i in interval_servers]
"""
Init our subclass. The hash_algorithm paramether can be "modulo"<-
(default) or "ketama" (the new one).
"""
client = MemcacheClient(servers=servers, hash_algorithm='ketama')
run_consistent_hash_test(client)
如果你直接在終端上運行這個類，它會顯示一個合適的輸出
在這裏在我的github我創建了一個簡單的測試，以超越上面的實現：[鏈接到我的github]（https://github.com/mau21mau/python-ketama） – Mauricio