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1 Redis Cluster Design Proposal (work in progress)
2
3 28 Nov 2010: Ver 1.0 - initial version
4 22 APr 2010: Ver 1.1 - more details and rationales
5
6 Overview
7 ========
8
9 Redis is a fast key-value store supporting complex aggregate data types as
10 values. For instance keys can be bound to lists with many elements, sets,
11 sub-dictionaries (hashes) and so forth.
12
13 While Redis is very fast, currently it lacks scalability in the form of ability
14 to transparently run across different nodes. This is desirable mainly for the
15 following three rasons:
16
17 A) Fault tolerance. Some node may go off line without affecting the operations.
18 B) Holding bigger datasets without using a single box with a lot of RAM.
19 C) Scaling writes.
20
21 Since a single Redis instance supports 140,000 operations per second in a good
22 Linux box costing less than $1000, the need for Redis Cluster arises more
23 from "A" and "B". Scaling writes can also be useful in very high load
24 environments. Scaling reads is already easily accomplished using Redis built-in
25 replication.
26
27 Design goals
28 ============
29
30 Designing a DHT in 2010 is hard as there is too much bias towards good designs
31 that are already well tested in practice, like the Amazon Dynamo design.
32 Still a Dynamo alike DHT may not be the best fit for Redis.
33
34 Redis is very simple and fast at its core, so Redis cluster should try to
35 follow the same guidelines. The first problem with a Dynamo-alike DHT is that
36 Redis supports complex data types. Merging complex values like lsits, where
37 in the case of a netsplit may diverge in very complex ways, is not going to
38 be easy. The "most recent data" wins is not applicable and all the resolution
39 business should be in the application.
40
41 Even a simple application can end up with complex schema of keys and complex
42 values. Writing code in order to resolve conflicts is not going to be
43 programmer friendly.
44
45 So the author of this document claims that Redis does not need to resist to
46 netsplits, but it is enough to resist to M-1 nodes going offline, where
47 M is the number of nodes storing every key-value pair.
48
49 For instance in a three nodes cluster I may configure the cluster in order to
50 store every key into two instances (M=2). Such a cluster can resist to a single
51 node going offline without interruption of the service.
52
53 When more than M-1 nodes are off line the cluster should detect such a condition
54 and refusing any further query. The system administrator should check why
55 M-1 nodes are offline and bring them back again if possible.
56
57 Once resisting to big net splits is no longer a requirement as there is no
58 conflict resolution stage, since at least an original node responsible of
59 holding every possible key must be online for the cluster to work, there is
60 also no need for a design where every node can act as an independent entity
61 receiving queries and forwarding this queries to other nodes as needed.
62
63 Instead a more decoupled approach can be used, in the form of a Redis Proxy
64 node (or multiple Proxy nodes) that is contacted by clients, and
65 is responsible of forwarding queries and replies back and forth from data nodes.
66
67 Data nodes can be just vanilla redis-server instances.
68
69 Network layout
70 ==============
71
72 - One ore more Data Nodes. Every node is identified by ip:port.
73 - A single Configuration Node.
74 - One more more Proxy Nodes (redis-cluster nodes).
75 - A single Handling Node.
76
77 Data Nodes and the Configuration Node are just vanilla redis-server instances.
78
79 Configuration Node
80 ==================
81
82 - Contains information about all the Data nodes in the cluster.
83 - Contains information about all the Proxy nodes in the cluster.
84 - Contains information about what Data Node holds a given sub-space of keys.
85
86 The keyspace is divided into 1024 different "hashing slots".
87 (1024 is just an example, this value should be configurable)
88
89 Given a key perform SHA1(key) and use the last 10 bits of the result to get a 10 bit number representing the "key slot" (from 0 to 1023).
90
91 The Configuration node maps every slot of the keyspace to M different Data Nodes (every key is stored into M nodes, configurable).
92
93 The Configuration node can be modified by a single client at a time. Locking is performed using SETNX.
94
95 The Configuration node should be replicated as there is a single configuration node for the whole network. It is the only single point of failure of the system.
96 When a Configuration node fails the cluster does not stop operating, but is not
97 able to recover if there is some exceptional condition to handle, like a Data
98 Node going off line or the addition of a new Data Node to the cluster.
99
100 The Configuration node is a standard Redis server, like every other Data node.
101
102 Data Nodes
103 ==========
104
105 Data nodes just hold data, and are normal Redis processes. There is no configuration stored on nodes, nor the nodes are "active" in the cluster, they just receive normal Redis commands.
106
107 Proxy Nodes
108 ===========
109
110 Proxy nodes get requests from clients and route this requests to the right Redis nodes.
111
112 Proxy nodes take persistent connections to all the Data Nodes and the
113 Configuration Node. This connections are keep alive with PING requests from time
114 to time if there is no traffic. This way Proxy Nodes can understand asap if
115 there is a problem in some Data Node or in the Configuration Node.
116
117 When a Proxy Node is started it needs to know the Configuration node address in order to load the infomration about the Data nodes and the mapping between the key space and the nodes.
118
119 On startup a Proxy Node will also register itself in the Configuration node, and will make sure to refresh it's configuration every N seconds (via an EXPIREing key) so that it's possible to detect when a Proxy node fails.
120
121 Clients can submit queries to any Proxy Node, so well designed clients may ask
122 at startup the list of Proxy Nodes querying the Configuration Node. Then if
123 a query fails against a given Proxy Node it can be retried against the next.
124
125 The Proxy Node is also in charge of signaling failing Data nodes to the Configuration node, so that the Handling Node can take appropriate actions.
126
127 When a new Data node joins or leaves the cluster, and in general when the cluster configuration changes, all the Proxy nodes will receive a notification and will reload the configuration from the Configuration node.
128
129 Proxy Nodes - how queries are submited
130 ======================================
131
132 This is how a query is processed:
133
134 1) A client sends a query to a Proxy Node, using the Redis protocol like if it was a plain Redis Node.
135 2) The Proxy Node inspects the command arguments to detect the key. The key is hashed. The Proxy Node has the table mapping a given key to M nodes, and persistent connections to all the nodes.
136
137 At this point the process is different in case of read or write queries:
138
139 WRITE QUERY:
140
141 3a) The Proxy Node forwards the query to M Data Nodes at the same time, waiting for replies.
142 3b) Once all the replies are received the Proxy Node checks that the replies are consistent. For instance all the M nodes need to reply with OK and so forth. If the query fails in a subset of nodes but succeeds in other nodes, the failing nodes are considered unreliable and are put off line notifying the configuration node.
143 3c) The reply is transfered back to the client.
144
145 READ QUERY:
146
147 3d) The Proxy Node forwards the query to a single random client, passing the reply back to the client.
148
149 Handling Node
150 =============
151
152 The handling node is a special Redis client with the following role:
153
154 - Handles the cluster configuration stored in the Config node.
155 - Is in charge for adding and removing nodes dynamically from the net.
156 - Relocates keys on nodes additions / removal.
157 - Signal a configuration change to Proxy nodes.
158
159 More details on hashing slots
160 ============================
161
162 The Configuration node holds 1024 keys in the following form:
163
164 hashingslot:0
165 hashingslot:1
166 ...
167 hashingslot:1023
168
169 Every hashing slot is actually a Redis list, containing a single or more ip:port pairs. For instance:
170
171 hashingslot:10 => 192.168.1.19:6379, 192.168.1.200:6379
172
173 This mean that keys hashing to slot 10 will be saved in the two Data nodes 192.168.1.19:6379 and 192.168.1.200:6379.
174
175 When a client performs a read operation (via a proxy node), the proxy will contact a random Data node among the data nodes in charge for the given slot.
176
177 For instance a client can ask for the following operation to a given Proxy node:
178
179 GET mykey
180
181 "mykey" hashes to (for instance) slot 10, so the Proxy will forward the request to either Data node 192.168.1.19:6379 or 192.168.1.200:6379, and then forward back the reply to the client.
182
183 When a write operation is performed, it is forwarded to both the Data nodes in the example (and in general to all the data nodes).
184
185 Adding or removing a node
186 =========================
187
188 When a Data node is added to the cluster, it is added via an LPUSH operation into a Redis list representing a queue of Data nodes that are ready to enter the cluster. This list is hold by the Configuration node of course, and can be added manually or via a configuration utility.
189
190 LPUSH newnodes 192.168.1.55:6379
191
192 The Handling node will check from time to time for this new elements in the "newode" list. If there are new nodes pending to enter the cluster, they are processed one after the other in this way:
193
194 For instance let's assume there are already two Data nodes in the cluster:
195
196 192.168.1.1:6379
197 192.168.1.2:6379
198
199 We add a new node 192.168.1.3:6379 via the LPUSH operation.
200
201 We can imagine that the 1024 hash slots are assigned equally among the two inital nodes. In order to add the new (third) node what we have to do is to move incrementally 341 slots form the two old servers to the new one.
202
203 For now we can think that every hash slot is only stored in a single server, to generalize the idea later.
204
205 In order to simplify the implementation every slot can be moved from one Data node to another one in a blocking way, that is, read operations will continue to all the 1024 slots, but a single slot at a time will delay write operations until the moving from one Data node to another is completed.
206
207 In order to do so the Handler node, before to move a given node, marks it as "write-locked" in the Configuration server, than asks all the Proxy nodes to refresh the configuration.
208
209 Then the slot is moved (1/1024 of all the keys). The Configuration server is modified to reflect the new hashing slots configuration, the slot is unlocked, the Proxy nodes notified.
210
211 Implementation details
212 ======================
213
214 To run the Handling node and the Configuration node in the same physical computer is probably a good idea.