design-documents/REDIS-CLUSTER

   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.