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Redis Cluster Design Proposal (work in progress)
28 Nov 2010: Ver 1.0 - initial version
22 APr 2010: Ver 1.1 - more details and rationales
Redis is a fast key-value store supporting complex aggregate data types as
values. For instance keys can be bound to lists with many elements, sets,
sub-dictionaries (hashes) and so forth.
While Redis is very fast, currently it lacks scalability in the form of ability
to transparently run across different nodes. This is desirable mainly for the
following three rasons:
A) Fault tolerance. Some node may go off line without affecting the operations.
B) Holding bigger datasets without using a single box with a lot of RAM.
C) Scaling writes.
Since a single Redis instance supports 140,000 operations per second in a good
Linux box costing less than $1000, the need for Redis Cluster arises more
from "A" and "B". Scaling writes can also be useful in very high load
environments. Scaling reads is already easily accomplished using Redis built-in
Design goals
Designing a DHT in 2010 is hard as there is too much bias towards good designs
that are already well tested in practice, like the Amazon Dynamo design.
Still a Dynamo alike DHT may not be the best fit for Redis.
Redis is very simple and fast at its core, so Redis cluster should try to
follow the same guidelines. The first problem with a Dynamo-alike DHT is that
Redis supports complex data types. Merging complex values like lsits, where
in the case of a netsplit may diverge in very complex ways, is not going to
be easy. The "most recent data" wins is not applicable and all the resolution
business should be in the application.
Even a simple application can end up with complex schema of keys and complex
values. Writing code in order to resolve conflicts is not going to be
programmer friendly.
So the author of this document claims that Redis does not need to resist to
netsplits, but it is enough to resist to M-1 nodes going offline, where
M is the number of nodes storing every key-value pair.
For instance in a three nodes cluster I may configure the cluster in order to
store every key into two instances (M=2). Such a cluster can resist to a single
node going offline without interruption of the service.
When more than M-1 nodes are off line the cluster should detect such a condition
and refusing any further query. The system administrator should check why
M-1 nodes are offline and bring them back again if possible.
Once resisting to big net splits is no longer a requirement as there is no
conflict resolution stage, since at least an original node responsible of
holding every possible key must be online for the cluster to work, there is
also no need for a design where every node can act as an independent entity
receiving queries and forwarding this queries to other nodes as needed.
Instead a more decoupled approach can be used, in the form of a Redis Proxy
node (or multiple Proxy nodes) that is contacted by clients, and
is responsible of forwarding queries and replies back and forth from data nodes.
Data nodes can be just vanilla redis-server instances.
Network layout
- One ore more Data Nodes. Every node is identified by ip:port.
- A single Configuration Node.
- One more more Proxy Nodes (redis-cluster nodes).
- A single Handling Node.
Data Nodes and the Configuration Node are just vanilla redis-server instances.
Configuration Node
- Contains information about all the Data nodes in the cluster.
- Contains information about all the Proxy nodes in the cluster.
- Contains information about what Data Node holds a given sub-space of keys.
The keyspace is divided into 1024 different "hashing slots".
(1024 is just an example, this value should be configurable)
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).
The Configuration node maps every slot of the keyspace to M different Data Nodes (every key is stored into M nodes, configurable).
The Configuration node can be modified by a single client at a time. Locking is performed using SETNX.
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.
When a Configuration node fails the cluster does not stop operating, but is not
able to recover if there is some exceptional condition to handle, like a Data
Node going off line or the addition of a new Data Node to the cluster.
The Configuration node is a standard Redis server, like every other Data node.
Data Nodes
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.
Proxy Nodes
Proxy nodes get requests from clients and route this requests to the right Redis nodes.
Proxy nodes take persistent connections to all the Data Nodes and the
Configuration Node. This connections are keep alive with PING requests from time
to time if there is no traffic. This way Proxy Nodes can understand asap if
there is a problem in some Data Node or in the Configuration Node.
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.
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.
Clients can submit queries to any Proxy Node, so well designed clients may ask
at startup the list of Proxy Nodes querying the Configuration Node. Then if
a query fails against a given Proxy Node it can be retried against the next.
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.
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.
Proxy Nodes - how queries are submited
This is how a query is processed:
1) A client sends a query to a Proxy Node, using the Redis protocol like if it was a plain Redis Node.
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.
At this point the process is different in case of read or write queries:
3a) The Proxy Node forwards the query to M Data Nodes at the same time, waiting for replies.
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.
3c) The reply is transfered back to the client.
3d) The Proxy Node forwards the query to a single random client, passing the reply back to the client.
Handling Node
The handling node is a special Redis client with the following role:
- Handles the cluster configuration stored in the Config node.
- Is in charge for adding and removing nodes dynamically from the net.
- Relocates keys on nodes additions / removal.
- Signal a configuration change to Proxy nodes.
More details on hashing slots
The Configuration node holds 1024 keys in the following form:
Every hashing slot is actually a Redis list, containing a single or more ip:port pairs. For instance:
hashingslot:10 =>,
This mean that keys hashing to slot 10 will be saved in the two Data nodes and
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.
For instance a client can ask for the following operation to a given Proxy node:
GET mykey
"mykey" hashes to (for instance) slot 10, so the Proxy will forward the request to either Data node or, and then forward back the reply to the client.
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).
Adding or removing a node
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.
LPUSH newnodes
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:
For instance let's assume there are already two Data nodes in the cluster:
We add a new node via the LPUSH operation.
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.
For now we can think that every hash slot is only stored in a single server, to generalize the idea later.
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.
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.
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.
Implementation details
To run the Handling node and the Configuration node in the same physical computer is probably a good idea.
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