*Redis Cluster Specification

Welcome to the Redis Cluster Specification. Here you'll find information about algorithms and design rationales of Redis Cluster. This document is a work in progress as it is continuously synchronized with the actual implementation of Redis.

*Main properties and rationales of the design

*Redis Cluster goals

Redis Cluster is a distributed implementation of Redis with the following goals, in order of importance in the design:

  • High performance and linear scalability up to 1000 nodes. There are no proxies, asynchronous replication is used, and no merge operations are performed on values.
  • Acceptable degree of write safety: the system tries (in a best-effort way) to retain all the writes originating from clients connected with the majority of the master nodes. Usually there are small windows where acknowledged writes can be lost. Windows to lose acknowledged writes are larger when clients are in a minority partition.
  • Availability: Redis Cluster is able to survive to partitions where the majority of the master nodes are reachable and there is at least a reachable slave for every master node that is no longer reachable. Moreover using replicas migration, masters no longer replicated by any slave, will receive one from a master which is covered by multiple slaves.

What is described in this document is implemented in Redis 3.0 or greater.

*Implemented subset

Redis Cluster implements all the single keys commands available in the non distributed version of Redis. Commands performing complex multi key operations like Set type unions or intersections are implemented as well as long as the keys all belong to the same node.

Redis Cluster implements a concept called hash tags that can be used in order to force certain keys to be stored in the same node. However during manual reshardings, multi-key operations may become unavailable for some time while single keys operations are always available.

Redis Cluster does not support multiple databases like the stand alone version of Redis, there is just database 0, and the SELECT command is not allowed.

*Clients and Servers roles in the Redis cluster protocol

In Redis cluster nodes are responsible for holding the data, and taking the state of the cluster, including mapping keys to the right nodes. Cluster nodes are also able to auto-discover other nodes, detect non working nodes, and performing slave nodes promotion to master when needed, in order to continue to operate when a failure occurs.

To perform their tasks all the cluster nodes are connected using a TCP bus and a binary protocol, called the Redis Cluster Bus. Every node is connected to every other node in the cluster using the cluster bus. Nodes use a gossip protocol to propagate information about the cluster in order to discover new nodes, to send ping packets to make sure all the other nodes are working properly, and to send cluster messages needed to signal specific conditions. The cluster bus is also used in order to propagate Pub/Sub messages across the cluster, and to orchestrate manual failovers when requested by users (manual failovers are failovers which are not initiated by the Redis Cluster failure detector, but by the system administrator directly).

Since cluster nodes are not able to proxy requests, clients may be redirected to other nodes using redirection errors -MOVED and -ASK. The client is in theory free to send requests to all the nodes in the cluster, getting redirected if needed, so the client is not required to hold the state of the cluster. However clients that are able to cache the map between keys and nodes can improve the performance in a sensible way.

*Write safety

Redis Cluster uses asynchronous replication between nodes, and last failover wins implicit merge function. It means that the last elected master dataset eventually replaces all the other replicas. This means that there are always windows when it is possible to lose writes during partitions. However these windows are very different in the case of a client that is connected to the majority of masters, and a client that is connected to the minority of masters.

Redis Cluster tries harder to retain writes that are performed by clients connected to the majority of masters, compared to writes performed into the minority side. The following are examples of scenarios that lead to lose of acknowledged writes received in the majority partitions, during failures:

  1. A write may reach a master, but while the master may be able to reply to the client, the write may not be propagated to slaves via the asynchronous replication used between master and slave nodes. If the master dies without the write reaching the slaves, the write is lost forever in case the master is unreachable for a long enough period that one of its slaves is promoted. This is usually hard to observe in case of total, sudden failure of a master node, since masters try to reply to clients (with the acknowledge of the write) and slaves (propagating the write) about at the same time. However it is a real world failure mode.

  2. Another theoretically possible failure mode where writes are lost is the following:

  • A master is unreachable because of a partition.
  • It gets failed over by one of its slaves.
  • After some time it may be reachable again.
  • A client with a not updated routing table may write to the old master before it is converted into a slave (of the new master) by the cluster.

The second failure mode is unlikely to happen because master nodes not able to communicate with the majority of the other masters for enough time to be failed over, no longer accept writes, and when the partition is fixed writes are still refused for a small amount of time to allow other nodes to inform about configuration changes. This failure mode also requires that the client has a yet not updated table.

Writes targeting the minority side of a partition has fairly larger windows to get lost. For example Redis Cluster loses a non trivial amount of writes on partitions where there is a minority of masters and at least one or more clients, since all the writes sent to the masters may potentially get lost if the masters are failed over in the majority side.

Specifically, for a master to be failed over, it must be not reachable by the majority of masters for at least NODE_TIMEOUT, so if the partition is fixed before that time, no write is lost. When the partition lasts for more than NODE_TIMEOUT, all the writes performed in the minority side up to that point may be lost. However the minority side of a Redis Cluster will start refusing writes as soon as NODE_TIMEOUT time has elapsed without contact with the majority, so there is a maximum window after which the minority becomes no longer available, hence no write is accepted and lost after that time.


Redis Cluster is not available in the minority side of the partition. In the majority side of the partition assuming that there are at least the majority of masters and a slave for every unreachable master, the cluster returns available after NODE_TIMEOUT time, plus a few more seconds required for a slave to get elected and failover its master (failovers usually are executed in a matter of 1 or 2 seconds).

This means that Redis Cluster is designed to survive to failures of a few nodes in the cluster, but is not a suitable solution for applications that require availability in the event of large net splits.

In the example of a cluster composed of N master nodes where every node has a single slave, the majority side of the cluster will remain available as long as a single node is partitioned away, and will remain available with a probability of 1-(1/(N*2-1)) when two nodes are partitioned away (after the first node fails we are left with N*2-1 nodes in total, and the probability of the only master without a replica to fail is 1/(N*2-1)).

For example in a cluster with 5 nodes and a single slave per node, there is a 1/(5*2-1) = 0.1111 probabilities that after two nodes are partitioned away from the majority, the cluster will no longer be available, that is about 11% of probabilities.

Thanks to a Redis Cluster feature called replicas migration the Cluster availability is improved in many real world scenarios by the fact that replicas migrate to orphaned masters (masters no longer having replicas). So at every successful failure event, the cluster may reconfigure the slaves layout in order to resist better to the next failure.


In Redis Cluster nodes don't proxy commands to the right node in charge for a given key, but instead they redirect clients to the right nodes serving a given portion of the key space.

Eventually clients obtain an up to date representation of the cluster and which node serves which subset of keys, so during normal operations clients directly contact the right nodes in order to send a given command.

Because of the use of asynchronous replication, nodes do not wait for other nodes acknowledgment of writes (if not explicitly requested using the WAIT command).

Also, because multiple keys commands are only limited to near keys, data is never moved between nodes if not in case of resharding.

So normal operations are handled exactly as in the case of a single Redis instance. This means that in a Redis Cluster with N master nodes you can expect the same performance as a single Redis instance multiplied by N as the design allows to scale linearly. At the same time the query is usually performed in a single round trip, since clients usually retain persistent connections with the nodes, so latency figures are also the same as the single standalone Redis node case.

Very high performances and scalability while preserving weak but reasonable forms of data safety and availability is the main goal of Redis Cluster.

*Why merge operations are avoided

Redis Cluster design avoids conflicting versions of the same key-value pair in multiple nodes as in the case of the Redis data model this is not always desirable: values in Redis are often very large, it is common to see lists or sorted sets with millions of elements. Also data types are semantically complex. Transferring and merging these kind of values can be a major bottleneck and/or may require a non trivial involvement of application-side logic, additional memory to store meta-data, and so forth.

There are no strict technological limits here, CRDTs or synchronously replicated state machines can model complex data types similar to Redis, however the actual run time behavior of such systems would not be similar to Redis Cluster. Redis Cluster was designed in order to cover the exact use cases of the non clustered Redis version.

*Overview of Redis Cluster main components

*Keys distribution model

The key space is split into 16384 slots, effectively setting an upper limit for the cluster size of 16384 master nodes (however the suggested max size of nodes is in the order of ~ 1000 nodes).

Each master nodes in a cluster handles a subset of the 16384 hash slots. When the cluster is stable, that means there is no cluster reconfiguration in progress (where hash slots are moved from one node to another), a single hash slot will be served exactly by a single node (however the serving node can have one or more slaves that will replace it in the case of net splits or failures, and that can be used in order to scale read operations where reading stale data is acceptable).

The base algorithm used to map keys to hash slots is the following (read the next paragraph for the hash tag exception to this rule):

HASH_SLOT = CRC16(key) mod 16384

The CRC16 is specified as follows:

  • Name: XMODEM (also known as ZMODEM or CRC-16/ACORN)
  • Width: 16 bit
  • Poly: 1021 (That is actually x16 + x12 + x5 + 1)
  • Initialization: 0000
  • Reflect Input byte: False
  • Reflect Output CRC: False
  • Xor constant to output CRC: 0000
  • Output for "123456789": 31C3

14 out of 16 bit of the output of CRC16 are used (this is why there is a modulo 16384 operation in the formula above).

In our tests CRC16 behaved remarkably well in distributing different kind of keys evenly across the 16384 slots.

Note: A reference implementation of the CRC16 algorithm used is available in the Appendix A of this document.

*Keys hash tags

There is an exception for the computation of the hash slot that is used in order to implement hash tags. Hash tags are a way to ensure that multiple keys are allocated in the same hash slot. This is used in order to implement multi-key operations in Redis Cluster.

In order to implement hash tags, the hash slot for a key is computed in a slightly different way in certain conditions. Basically if the key contains a "{...}" pattern only the substring between { and } is hashed in order to obtain the hash slot. However since it is possible that there are multiple occurrences of { or } the algorithm is well specified by the following rules:

  • IF the key contains a { character.
  • AND IF there is a } character on the right of {
  • AND IF there are one or more characters between the first occurrence of { and the first occurrence of }.

Then instead of hashing the key, only what is between the first occurrence of { and the following first occurrence of } is hashed.


  • The two keys {user1000}.following and {user1000}.followers will hash to the same hash slot since only the substring user1000 will be hashed in order to compute the hash slot.
  • For the key foo{}{bar} the whole key will be hashed as usually since the first occurrence of { is followed by } on the right without characters in the middle.
  • For the key foo{{bar}}zap the substring {bar will be hashed, because it is the substring between the first occurrence of { and the first occurrence of } on its right.
  • For the key foo{bar}{zap} the substring bar will be hashed, since the algorithm stops at the first valid or invalid (without bytes inside) match of { and }.
  • What follows from the algorithm is that if the key starts with {}, it is guaranteed to be hashed as a whole. This is useful when using binary data as key names.

Adding the hash tags exception, the following is an implementation of the HASH_SLOT function in Ruby and C language.

Ruby example code:

def HASH_SLOT(key)
    s = key.index "{"
    if s
        e = key.index "}",s+1
        if e && e != s+1
            key = key[s+1..e-1]
    crc16(key) % 16384

C example code:

unsigned int HASH_SLOT(char *key, int keylen) {
    int s, e; /* start-end indexes of { and } */

    /* Search the first occurrence of '{'. */
    for (s = 0; s < keylen; s++)
        if (key[s] == '{') break;

    /* No '{' ? Hash the whole key. This is the base case. */
    if (s == keylen) return crc16(key,keylen) & 16383;

    /* '{' found? Check if we have the corresponding '}'. */
    for (e = s+1; e < keylen; e++)
        if (key[e] == '}') break;

    /* No '}' or nothing between {} ? Hash the whole key. */
    if (e == keylen || e == s+1) return crc16(key,keylen) & 16383;

    /* If we are here there is both a { and a } on its right. Hash
     * what is in the middle between { and }. */
    return crc16(key+s+1,e-s-1) & 16383;

*Cluster nodes attributes

Every node has a unique name in the cluster. The node name is the hex representation of a 160 bit random number, obtained the first time a node is started (usually using /dev/urandom). The node will save its ID in the node configuration file, and will use the same ID forever, or at least as long as the node configuration file is not deleted by the system administrator, or a hard reset is requested via the CLUSTER RESET command.

The node ID is used to identify every node across the whole cluster. It is possible for a given node to change IP and address without any need to also change the node ID. The cluster is also able to detect the change in IP/port and reconfigure using the gossip protocol running over the cluster bus.

The node ID is not the only information associated with each node, but is the only one that is always globally consistent. Every node has also the following set of information associated. Some information is about the cluster configuration detail of this specific node, and is eventually consistent across the cluster. Some other information, like the last time a node was pinged, is instead local to each node.

This is a list of information each node has associated in each other node that knows it: The node ID, IP and port of the node, a set of flags, what is the master of the node if it is flagged as slave, last time the node was pinged and the last time the pong was received, the current configuration epoch of the node (explained later in this specification), the link state and finally the set of hash slots served.

A detailed explanation of all the node fields is described in the CLUSTER NODES documentation.

The CLUSTER NODES command, that can be sent to each of the nodes in the cluster, provides as output the state of the cluster and the information for each node according to the local view the queried node has of the cluster.

The following is an example of output of CLUSTER NODES sent to a master node in a small cluster of three nodes.

$ redis-cli cluster nodes
d1861060fe6a534d42d8a19aeb36600e18785e04 myself - 0 1318428930 1 connected 0-1364
3886e65cc906bfd9b1f7e7bde468726a052d1dae master - 1318428930 1318428931 2 connected 1365-2729
d289c575dcbc4bdd2931585fd4339089e461a27d master - 1318428931 1318428931 3 connected 2730-4095

In the above listing the different fields are in order: node id, address:port, flags, last ping sent, last pong received, configuration epoch, link state, slots. Details about the above fields will be covered as soon as we talk of specific parts of Redis Cluster.

*The Cluster bus

Every Redis Cluster node has an additional TCP port in order to receive incoming connections from other Redis Cluster nodes. This port is at a fixed offset compared to the normal TCP port used to receive incoming connections from clients. To obtain the Redis Cluster port, 10000 should be added to the normal commands port, so for example if a Redis node is listening for client connections to port 6379, the Cluster bus port 16379 will also be opened.

Node to Node communication happens exclusively using the Cluster bus and the Cluster bus protocol, which is a binary protocol composed of frames of different types and sizes. The Cluster bus binary protocol is not publicly documented since it is not indented for external software devices to talk with Redis Cluster nodes using this protocol. However you can obtain more details about the Cluster bus protocol by reading the cluster.h and cluster.c files in the Redis Cluster source code.

*Cluster topology

Redis cluster is a full mesh where every node is connected with every other node using a TCP connection.

In a cluster of N nodes, every node has N-1 outgoing TCP connections, and N-1 incoming connections.

These TCP connections are kept alive all the time and are not created on demand. When a node expects a pong reply in response to a ping in the cluster bus, before waiting long enough to mark the node as unreachable, it will try to refresh the connection with the node by reconnecting from scratch.

While Redis Cluster nodes form a full mesh, nodes use a gossip protocol and a configuration update mechanism in order to avoid exchanging too many messages between nodes during normal conditions, so the number of message exchanged is not exponential.

*Nodes handshake

Nodes always accept connection in the cluster bus port, and even reply to pings when received, even if the pinging node is not trusted. However all the other packets will be discarded by the receiving node if the sending node is not considered part of the cluster.

A node will accept another node as part of the cluster only in two ways:

  • If a node will present itself with a MEET message. A meet message is exactly like a PING message, but forces the receiver to accept the node as part of the cluster. Nodes will send MEET messages to other nodes only if the system administrator requests this via the following command:

    CLUSTER MEET ip port

  • A node will also register another node as part of the cluster if a node that is already trusted will gossip about this other node. So if A knows B, and B knows C, eventually B will send gossip messages to A about C. When this happens, A will register C as part of the network, and will try to connect with C.

This means that as long as we join nodes in any connected graph, they'll eventually form a fully connected graph automatically. This means that basically the cluster is able to auto-discover other nodes, but only if there is a trusted relationship that was forced by the system administrator.

This mechanism makes the cluster more robust but prevents that different Redis clusters will accidentally mix after change of IP addresses or other network related events.

*Redirection and resharding

*MOVED Redirection

A Redis client is free to send queries to every node in the cluster, including slave nodes. The node will analyze the query, and if it is acceptable (that is, only a single key is mentioned in the query, or the multiple keys mentioned are all to the same hash slot) it will lookup what node is responsible for the hash slot where the key or keys belong.

If the hash slot is served by the node, the query is simply processed, otherwise the node will check its internal hash slot to node map, and will reply to the client with a MOVED error, like in the following example:

-MOVED 3999

The error includes the hash slot of the key (3999) and the ip:port of the instance that can serve the query. The client need to reissue the query to the specified node, specified by IP address and port. Note that even if the client waits a long time before reissuing the query, and in the meantime the cluster configuration changed, the destination node will reply again with a MOVED error if the hash slot 3999 is now served by another node. The same happens if the contacted node had no updated information.

So while from the point of view of the cluster nodes are identified by IDs we try to simply our interface with the client just exposing a map between hash slots and Redis nodes identified by IP:port pairs.

The client is not required to, but should try to memorize that hash slot 3999 is served by This way once a new command needs to be issued it can compute the hash slot of the target key and pick the right node with higher chances.

An alternative is to just refresh the whole client-side cluster layout when a MOVED redirection is received, using the CLUSTER NODES or CLUSTER SLOTS commands, since when a redirection is encountered, likely multiple slots were reconfigured, not just one, so to update the configuration as soon as possible for the client is often the best strategy.

Note that when the Cluster is stable (no ongoing changes in the configuration), eventually all the clients will obtain a map of hash slots -> nodes, making the cluster efficient, with clients directly addressing the right nodes without redirections nor proxies or other single point of failure entities.

A client must be also able to handle -ASK redirections that are described later in this document, otherwise it is not a complete Redis Cluster client.

*Cluster live reconfiguration

Redis cluster supports the ability to add and remove nodes while the cluster is running. Actually adding or removing a node is abstracted into the same operation, that is, moving a hash slot from a node to another. This means that the same basic mechanism can be used in order to rebalance the cluster, add or remove nodes, and so forth.

  • To add a new node to the cluster an empty node is added to the cluster and some hash slot is moved from existing nodes to the new node.
  • To remove a node from the cluster the hash slots assigned to that node are moved to other existing nodes.
  • To rebalance the cluster a given set of hash slots are moved between nodes.

So the core of the implementation is the ability to move hash slots around. Actually from a practical point of view a hash slot is just a set of keys, so what Redis cluster really does during resharding is to move keys from an instance to another instance. Moving an hash slot means moving all the keys that happen to hash into this hash slot.

To understand how this works we need to show the CLUSTER subcommands that are used to manipulate the slots translation table in a Redis cluster node.

The following subcommands are available (among others not useful in this case):

The first two commands, ADDSLOTS and DELSLOTS, are simply used to assign (or remove) slots to a Redis node. Assigning a slot means to tell a given master node that it will be in charge of storing and serving content for the specified hash slot.

After the hash slots are assigned they will propagate across all the cluster using the gossip protocol, as specified later in the configuration propagation section.

The ADDSLOTS command is usually used when a new cluster is created from scratch to assign each master node a subset of all the 16384 hash slots available.

The DELSLOTS is mainly used for manual modification of a cluster configuration or for debugging tasks: in practice it is rarely used.

The SETSLOT subcommand is used to assign a slot to a specific node ID if the SETSLOT <slot> NODE form is used. Otherwise the slot can be set in the two special states MIGRATING and IMPORTING. Those two special states are used in order to migrate an hash slot from one node to another.

  • When a slot is set as MIGRATING, the node will accept all the requests for queries that are about this hash slot, but only if the key in question exists, otherwise the query is forwarded using a -ASK redirection to the node that is target of the migration.
  • When a slot is set as IMPORTING, the node will accept all the requests for queries that are about this hash slot, but only if the request is preceded by an ASKING command. Otherwise if not ASKING command was given by the client, the query is redirected to the real hash slot owner via a -MOVED redirection error, like would happen normally.

Let's make this more clear with an example of hash slot migration. Assume that we have two Redis master nodes, called A and B. We want to move hash slot 8 from A to B, so we issue commands like this:


All the other nodes will continue to point clients to node "A" every time they are queried with a key that belongs to hash slot 8, so what happens is that:

  • All the queries about already existing keys are processed by "A".
  • All the queries about non existing keys in A are processed by "B", because "A" will redirect clients to "B".

This way we no longer create new keys in "A". In the meantime, a special program used during reshardings, that is called redis-trib, and is the default Redis cluster configuration utility, will make sure to migrate existing keys in hash slot 8 from A to B. This is performed using the following command:


The above command will return count keys in the specified hash slot. For every key returned, redis-trib sends node "A" a MIGRATE command, that will migrate the specified key from A to B in an atomic way (both instances are locked for the time (usually very small time) needed to migrate a key so there are no race conditions). This is how MIGRATE works:

MIGRATE target_host target_port key target_database id timeout

MIGRATE will connect to the target instance, send a serialized version of the key, and once an OK code is received will delete the old key from its own dataset. So from the point of view of an external client a key either exists in A or B in a given time.

In Redis cluster there is no need to specify a database other than 0, but MIGRATE can be used for other tasks as well not involving Redis cluster so it is a general enough command. MIGRATE is optimized to be as fast as possible even when moving complex keys such as long lists, but of course in Redis Cluster reconfiguring the cluster where big keys are present is not considered a wise procedure if there are latency constraints in the application using the database.

When finally the migration process is finished, the SETSLOT <slot> NODE <node-id> command is send to the two nodes involved in the migration in order to set the slots in normal state again. Moreover the same command is usually send to all the other instances in order not to wait for the natural propagation of the new configuration across the cluster.

*ASK redirection

In the previous section we briefly talked about ASK redirection, why can't we simply use the MOVED redirection? Because while MOVED means that we think the hash slot is permanently served by a different node and the next queries should be tried against the specified node, ASK means to only ask the next query to the specified node.

This is needed because the next query about hash slot 8 can be about the key that is still in A, so we always want that the client will try A and then B if needed. Since this happens only for one hash slot out of 16384 available the performance hit on the cluster is acceptable.

However we need to force that client behavior, so in order to make sure that clients will only try node B after A was tried, node B will only accept queries of a slot that is set as IMPORTING if the client sends the ASKING command before sending the query.

Basically the ASKING command sets a one-time flag on the client that forces a node to serve a query about an IMPORTING slot.

So the full semantics of the ASK redirection is the following, from the point of view of the client.

  • If ASK redirection is received send only the query that was redirected to the specified node, but continue sending the next queries to the old node.
  • Start the redirected query with the ASKING command.
  • Don't update local client tables to map hash slot 8 to B for now.

Once the hash slot 8 migration is completed, A will send a MOVED message and the client may permanently map hash slot 8 to the new IP and port pair. Note that however if a buggy client will perform the map earlier this is not a problem since it will not send the ASKING command before issuing the query, so B will redirect the client to A using a MOVED redirection error.

Slots migration is explained in similar terms but with a different wording, for the sake of redundancy in the documentation, in the CLUSTER SETSLOT command documentation.

*Clients first connection and handling of redirections.

While it is possible to have a Redis Cluster client implementation that does not remember the slots configuration (the map between slot numbers and addresses of nodes serving it) in memory and only works by contacting random nodes waiting to be redirected, such a client would be very inefficient.

Redis Cluster clients should try to be smart enough to memorize the slots configuration. However this configuration is not required to be up to date, since contacting the wrong node will simply result in a redirection, that will trigger an update of the client view.

Clients usually need to fetch a complete list of slots and mapped node addresses at two different moments:

  • At startup in order to populate the initial slots configuration.
  • When a MOVED redirection is received.

Note that a client may handle the MOVED redirection updating just the moved slot in its table, however this is usually not efficient since often the configuration of multiple slots is modified at once (for example if a slave is promoted to master, all the slots served by the old master will be remapped). It is much simpler to react to a MOVED redirection fetching the full map of slots to nodes from scratch.

In order to retrieve the slots configuration Redis Cluster offers an alternative to the CLUSTER NODES command that does not require parsing, and only provides the information strictly needed to clients.

The new command is called CLUSTER SLOTS and provides an array of slots ranges, and the associated master and slave nodes serving the specified range.

The following is an example of output of CLUSTER SLOTS:> cluster slots
1) 1) (integer) 5461
   2) (integer) 10922
   3) 1) ""
      2) (integer) 7001
   4) 1) ""
      2) (integer) 7004
2) 1) (integer) 0
   2) (integer) 5460
   3) 1) ""
      2) (integer) 7000
   4) 1) ""
      2) (integer) 7003
3) 1) (integer) 10923
   2) (integer) 16383
   3) 1) ""
      2) (integer) 7002
   4) 1) ""
      2) (integer) 7005

The first two sub-elements of every element of the returned array are the start-end slots of the range. The additional elements represent address-port pairs. The first address-port pair is the master serving the slot, and the additional address-port pairs are all the slaves serving the same slot that are not in an error condition (the FAIL flag is not set).

For example the first element of the output says that slots from 5461 to 10922 (start and end included) are served by, and it is possible to scale read-only load contacting the slave at

CLUSTER SLOTS does not guarantee to return ranges that will cover all the 16384 slots if the cluster is misconfigured, so clients should initialize the slots configuration map filling the target nodes with NULL objects, and report an error if the user will try to execute commands about keys that belong to unassigned slots.

However before returning an error to the caller, when a slot is found to be unassigned, the client should try to fetch the slots configuration again to check if the cluster is now configured properly.

*Multiple keys operations

Using hash tags clients are free to use multiple-keys operations. For example the following operation is valid:

MSET {user:1000}.name Angela {user:1000}.surname White

However multi-key operations may become unavailable when a resharding of the hash slot the keys belong to is in progress.

More specifically, even during a resharding, the multi-key operations targeting keys that all exist and are still all in the same node (either the source or destination node) are still available.

Operations about keys that don't exist or are, during the resharding, split between the source and destination nodes, will generate a -TRYAGAIN error. The client can try the operation after some time, or report back the error.

As soon as the migration of the specified hash slot has terminated, all the multi key operations are available again for this hash slot.

*Scaling reads using slave nodes

Normally slave nodes will redirect clients to the authoritative master for the hash slot involved in a given command, however clients can use slaves in order to scale reads using the READONLY command.

READONLY tells a Redis cluster slave node that the client is ok reading possibly stale data and is not interested in running write queries.

When the connection is in readonly mode, the cluster will send a redirection to the client only in the context of an operation involving keys not served by the slave's master node. This may happen because:

  1. The client sent a command about hash slots never served by the master of this slave.
  2. The cluster was reconfigured (for example resharded) and the slave is no longer able to serve commands for a given hash slot.

When this happens the client should update its hashslot map as explained in the previous sections.

The readonly state of the connection can be cleared using the READWRITE command.

*Fault Tolerance

*Nodes heartbeat and gossip messages

Redis Cluster nodes continuously exchange ping and pong packets. Those two kind of packets have the same structure, and both carry important configuration information. The only actual difference is the message type field. We'll refer to the sum of ping and pong packets as heartbeat packets.

Usually nodes send ping packets that will trigger the receivers to reply with pong packets. However this is not necessarily true. It is possible for nodes to just send pong packets to send information to other nodes about their configuration, without triggering a reply. This is useful, for example, in order to broadcast a new configuration as soon as possible.

Usually a node will ping a few random nodes every second so that the total number of ping packets sent (and pong packets received) by each node is a constant amount regardless of the number of nodes in the cluster.

However every node makes sure to ping every other node that we don't either sent a ping or received a pong for longer than half the NODE_TIMEOUT time. Before NODE_TIMEOUT has elapsed, nodes also try to reconnect the TCP link with another node to make sure nodes are not believed to be unreachable only because there is a problem in the current TCP connection.

The amount of messages globally exchanged can be sizable if NODE_TIMEOUT is set to a small figure and the number of nodes (N) is very large, since every node will try to ping every other node for which we don't have fresh information for half the NODE_TIMEOUT time.

For example in a 100 nodes cluster with a node timeout set to 60 seconds, every node will try to send 99 pings every 30 seconds, with a total amount of pings of 3.3 per second, that multiplied for 100 nodes is 330 pings per second in the total cluster.

There are ways to lower the number of messages, however no issue currently was even reported with the bandwidth used by Redis Cluster failure detection, so for now the obvious and direct design is used. Note that even in the above example, the 330 packets per seconds exchanged are evenly divided among 100 different nodes, so the traffic each node receives is acceptable.

*Heartbeat packets content

Ping and pong packets contain a header that is common to all the kind of packets (for instance packets to request a failover vote), and a special Gossip Section that is specific of Ping and Pong packets.

The common header has the following information:

  • Node ID, that is a 160 bit pseudorandom string that is assigned the first time a node is created and remains the same for all the life of a Redis Cluster node.
  • The currentEpoch and configEpoch fields of the sending node, that are used in order to mount the distributed algorithms used by Redis Cluster (this is explained in detail in the next sections). If the node is a slave the configEpoch is the last known configEpoch of its master.
  • The node flags, indicating if the node is a slave, a master, and other single-bit node information.
  • A bitmap of the hash slots served by the sending node, or if the node is a slave, a bitmap of the slots served by its master.
  • The sender TCP base port (that is, the port used by Redis to accept client commands, add 10000 to this to obtain the cluster port).
  • The state of the cluster from the point of view of the sender (down or ok).
  • The master node ID of the sending node, if it is a slave.

Ping and pong packets also contain a gossip section. This section offers to the receiver a view of what the sender node thinks about other nodes in the cluster. The gossip section only contains information about a few random nodes among the set of known nodes of the sender. The amount of nodes mentioned in a gossip section is proportional to the cluster size.

For every node added in the gossip section the following fields are reported:

  • Node ID.
  • IP and port of the node.
  • Node flags.

Gossip sections allow receiving nodes to get information about the state of other nodes from the point of view of the sender. This is useful both for failure detection and to discover other nodes in the cluster.

*Failure detection

Redis Cluster failure detection is used to recognize when a master or slave node is no longer reachable by the majority of nodes, and as a result of this event, either promote a slave to the role of master, or when this is not possible, put the cluster in an error state to stop receiving queries from clients.

As already mentioned, every node takes a list of flags associated with other known nodes. There are two flags that are used for failure detection that are called PFAIL and FAIL. PFAIL means Possible failure, and is a non acknowledged failure type. FAIL means that a node is failing and that this condition was confirmed by a majority of masters within a fixed amount of time.

PFAIL flag:

A node flags another node with the PFAIL flag when the node is not reachable for more than NODE_TIMEOUT time. Both master and slave nodes can flag another node as PFAIL, regardless of its type.

The concept of non reachability for a Redis Cluster node is that we have an active ping (a ping that we sent for which we still have to get a reply) pending for more than NODE_TIMEOUT, so for this mechanism to work the NODE_TIMEOUT must be large compared to the network round trip time. In order to add reliability during normal operations, nodes will try to reconnect with other nodes in the cluster as soon as half of the NODE_TIMEOUT has elapsed without a reply to a ping. This mechanism ensures that connections are kept alive so broken connections should usually not result into false failure reports between nodes.

FAIL flag:

The PFAIL flag alone is just a local information every node has about other nodes, but it is not used in order to act and is not sufficient to trigger a slave promotion. For a node to be really considered down the PFAIL condition needs to be escalated to a FAIL condition.

As outlined in the node heartbeats section of this document, every node sends gossip messages to every other node including the state of a few random known nodes. So every node eventually receives the set of node flags for every other node. This way every node has a mechanism to signal other nodes about failure conditions they detected.

This mechanism is used in order to escalate a PFAIL condition to a FAIL condition, when the following set of conditions are met:

  • Some node, that we'll call A, has another node B flagged as PFAIL.
  • Node A collected, via gossip sections, information about the state of B from the point of view of the majority of masters in the cluster.
  • The majority of masters signaled the PFAIL or PFAIL condition within NODE_TIMEOUT * FAIL_REPORT_VALIDITY_MULT time. (The validity factor is set to 2 in the current implementation, so this is just two times the NODE_TIMEOUT time).

If all the above conditions are true, Node A will:

  • Mark the node as FAIL.
  • Send a FAIL message to all the reachable nodes.

The FAIL message will force every receiving node to mark the node in FAIL state, whether or not it already flagged the node in PFAIL state.

Note that the FAIL flag is mostly one way, that is, a node can go from PFAIL to FAIL, but for the FAIL flag to be cleared there are only two possibilities:

  • The node is already reachable and it is a slave. In this case the FAIL flag can be cleared as slaves are not failed over.
  • The node is already reachable and it is a master not serving any slot. In this case the FAIL flag can be cleared as masters without slots do not really participate to the cluster, and are waiting to be configured in order to join the cluster.
  • The node is already reachable, is a master, but a long time (N times the NODE_TIMEOUT) has elapsed without any detectable slave promotion. Better for it to rejoin the cluster and continue in this case.

It is useful to note that while the PFAIL -> FAIL transition uses a form of agreement, the agreement used is weak:

  1. Nodes collect views of other nodes during some time, so even if the majority of master nodes need to "agree", actually this is just state that we collected from different nodes at different times and we are not sure, nor we require, that at a given moment the majority of masters agreed. However we discard failure reports which are old, so the failure was signaled by the majority of masters within a window of time.
  2. While every node detecting the FAIL condition will force that condition on other nodes in the cluster using the FAIL message, there is no way to ensure the message will reach all the nodes. For instance a node may detect the FAIL condition and because of a partition will not be able to reach any other node.

However the Redis Cluster failure detection has a liveness requirement: eventually all the nodes should agree about the state of a given node. There are two cases that can originate from split brain conditions, either some minority of nodes believe the node is in FAIL state, or a minority of nodes believe the node is not in FAIL state. In both the cases eventually the cluster will have a single view of the state of a given node:

Case 1: If an actual majority of masters flagged a node as FAIL, because of the failure detector and the chain effect it generates, every other node will flag the master as FAIL eventually, since in the specified window of time enough failures will be reported.

Case 2: When only a minority of masters flagged a node as FAIL, the slave promotion will not happen (as it uses a more formal algorithm that makes sure everybody will know about the promotion eventually) and every node will clear the FAIL state for the FAIL state clearing rules above (no promotion after some time > of N times the NODE_TIMEOUT).

Basically the FAIL flag is only used as a trigger to run the safe part of the algorithm for the slave promotion. In theory a slave may act independently and start a slave promotion when its master is not reachable, and wait for the masters to refuse to provide the acknowledgment, if the master is actually reachable by the majority. However the added complexity of the PFAIL -> FAIL state, the weak agreement, and the FAIL message to force the propagation of the state in the shortest amount of time in the reachable part of the cluster, have practical advantages. Because of this mechanisms usually all the nodes will stop accepting writes about at the same time if the cluster is in an error condition, that is a desirable feature from the point of view of applications using Redis Cluster. Also not needed election attempts, initiated by slaves that can't reach its master for local problems (that is otherwise reachable by the majority of the other master nodes), are avoided.

*Configuration handling, propagation, and failovers

*Cluster current epoch

Redis Cluster uses a concept similar to the Raft algorithm "term". In Redis Cluster the term is called epoch instead, and it is used in order to give an incremental version to events, so that when multiple nodes provide conflicting information, it is possible for another node to understand which state is the most up to date.

The currentEpoch is a 64 bit unsigned number.

At node creation every Redis Cluster node, both slaves and master nodes, set the currentEpoch to 0.

Every time a packet is received from another node, if the epoch of the sender (part of the cluster bus messages header) is greater than the local node epoch, the currentEpoch is updated to the sender epoch.

Because of this semantics eventually all the nodes will agree to the greatest configEpoch in the cluster.

This information is used when the state of the cluster is changed and a node seeks agreement in order to perform some action.

Currently this happens only during slave promotion, as described in the next section. Basically the epoch is a logical clock for the cluster and dictates whatever a given information wins over one with a smaller epoch.

*Configuration epoch

Every master always advertises its configEpoch in ping and pong packets along with a bitmap advertising the set of slots it serves.

The configEpoch is set to zero in masters when a new node is created.

A new configEpoch is created during slave election. Slaves trying to replace failing masters increment their epoch and try to get the authorization from a majority of masters. When a slave is authorized, a new unique configEpoch is created, the slave turns into a master using the new configEpoch.

As explained in the next sections the configEpoch helps to resolve conflicts due to different nodes claiming diverging configurations (a condition that may happen because of network partitions and node failures).

Slave nodes also advertise the configEpoch field in ping and pong packets, but in case of slaves the field represents the configEpoch of its master the last time they exchanged packets. This allows other instances to detect when a slave has an old configuration that needs to be updated (Master nodes will not grant votes to slaves with an old configuration).

Every time the configEpoch changes for some known node, it is permanently stored in the nodes.conf file by all the nodes that received this information. The same also happens for the currentEpoch value. This two variables are guaranteed to be saved and fsync-ed to disk when updated before a node continues its operations.

New, incremental, and guaranteed to be unique configEpoch values are generated using a simple algorithm during failovers.

*Slave election and promotion

Slave election and promotion is handled by slave nodes, with the help of master nodes that vote for the slave to promote. A slave election happens when a master is in FAIL state from the point of view of at least one of its slaves that has the prerequisites in order to become a master.

In order for a slave to promote itself to master, it requires to start an election and win it. All the slaves for a given master can start an election if the master is in FAIL state, however only one slave will win the election and promote itself to master.

A slave starts an election when the following conditions are met:

  • The slave's master is in FAIL state.
  • The master was serving a non-zero number of slots.
  • The slave replication link was disconnected from the master for no longer than a given amount of time, in order to ensure to promote a slave with a reasonable data freshness. This time is user configurable.

In order to be elected, the first step for a slave is to increment its currentEpoch counter, and request votes from master instances.

Votes are requested by the slave by broadcasting a FAILOVER_AUTH_REQUEST packet to every master node of the cluster. Then it waits for replies to arrive for a maximum time of two times the NODE_TIMEOUT, but always for at least 2 seconds.

Once a master voted for a given slave, replying positively with a FAILOVER_AUTH_ACK, it can no longer vote for another slave of the same master for a period of NODE_TIMEOUT * 2. In this period it will not be able to reply to other authorization requests for the same master. This is not needed to guarantee safety, but useful to avoid multiple slaves to get elected (even if with a different configEpoch) about at the same time, which is usually not wanted.

A slave discards all the AUTH_ACK replies that are received having an epoch that is less than the currentEpoch at the time the vote request was sent, in order to never count as valid votes that are about a previous election.

Once the slave receives ACKs from the majority of masters, it wins the election. Otherwise if the majority is not reached within the period of two times NODE_TIMEOUT (but always at least 2 seconds), the election is aborted and a new one will be tried again after NODE_TIMEOUT * 4 (and always at least 4 seconds).

*Slave rank

A slave does not try to get elected as soon as the master is in FAIL state, but there is a little delay, that is computed as:

DELAY = 500 milliseconds + random delay between 0 and 500 milliseconds +
        SLAVE_RANK * 1000 milliseconds.

The fixed delay ensures that we wait for the FAIL state to propagate across the cluster, otherwise the slave may try to get elected when the masters are still not aware of the FAIL state, refusing to grant their vote.

The random delay is used to desynchronize slaves so they'll likely start an election in different moments.

The SLAVE_RANK is the rank of this slave regarding the amount of replication stream it processed from the master. Slaves exchange messages when the master is failing in order to establish a (best effort) rank: the slave with the most updated replication offset is at rank 0, the second most updated at rank 1, and so forth. In this way the most updated slaves try to get elected before others.

However if a slave of higher rank fails to be elected, the others will try shortly, so the order is not enforced in a strict way.

Once a slave wins the election, it obtains a new unique and incremental configEpoch which is higher than any other existing master. It starts advertising itself as master in ping and pong packets, providing the set of served slots with a configEpoch that will win over the past ones.

In order to speedup the reconfiguration of other nodes, a pong packet is broadcasted to all the nodes of the cluster (however nodes not currently reachable will eventually receive a ping or pong packet and will be reconfigured, or will receive an UPDATE packet from another node, if the information it publishes via heartbeat packets are detected to be out of date).

The other nodes will detect that there is a new master serving the same slots served by the old master but with a greater configEpoch, and will upgrade the configuration. Slaves of the old master, or the failed over master that rejoins the cluster, will not just upgrade the configuration but will also configure to replicate from the new master. How nodes rejoining the cluster are configured is explained in one of the next sections.

*Masters reply to slave vote request

In the previous section it was discussed how slaves try to get elected, this section explains what happens from the point of view of a master that is requested to vote for a given slave.

Masters receive requests for votes in form of FAILOVER_AUTH_REQUEST requests from slaves.

For a vote to be granted the following conditions need to be met:

  1. A master only votes a single time for a given epoch, and refuses to vote for older epochs: every master has a lastVoteEpoch field and will refuse to vote again as long as the currentEpoch in the auth request packet is not greater than the lastVoteEpoch. When a master replies positively to a vote request, the lastVoteEpoch is updated accordingly, and safely stored on disk.
  2. A master votes for a slave only if the slave's master is flagged as FAIL.
  3. Auth requests with a currentEpoch that is less than the master currentEpoch are ignored. Because of this the Master reply will always have the same currentEpoch as the auth request. If the same slave asks again to be voted, incrementing the currentEpoch, it is guaranteed that an old delayed reply from the master can not be accepted for the new vote.

Example of the issue caused by not using rule number 3:

Master currentEpoch is 5, lastVoteEpoch is 1 (this may happen after a few failed elections)

  • Slave currentEpoch is 3.
  • Slave tries to be elected with epoch 4 (3+1), master replies with an ok with currentEpoch 5, however the reply is delayed.
  • Slave will try to be elected again, at a later time, with epoch 5 (4+1), the delayed reply reaches the slave with currentEpoch 5, and is accepted as valid.
  1. Masters don't vote a slave of the same master before NODE_TIMEOUT * 2 has elapsed since a slave of that master was already voted. This is not strictly required as it is not possible that two slaves win the election in the same epoch, but in practical terms it ensures that normally when a slave is elected it has plenty of time to inform the other slaves avoiding that another slave will win a new election, doing a new unwanted failover.
  2. Masters don't try to select the best slave in any way, simply if the slave's master is in FAIL state and the master did not voted in the current term, the positive vote is granted. However the best slave is the most likely to start the election and win it before the other slaves, since it usually will be able to start the voting process earlier, because of its higher rank as explained in the previous section.
  3. When a master refuses to vote for a given slave there is no negative response, the request is simply ignored.
  4. Masters don't grant the vote to slaves sending a configEpoch that is less than any configEpoch in the master table for the slots claimed by the slave. Remember that the slave sends the configEpoch of its master, and the bitmap of the slots served by its master. What this means is basically that the slave requesting the vote must have a configuration, for the slots it wants to failover, that is newer or equal the one of the master granting the vote.

*Practical example of configuration epoch usefulness during partitions

This section illustrates how the concept of epoch is used to make the slave promotion process more resistant to partitions.

  • A master is no longer reachable indefinitely. The master has three slaves A, B, C.
  • Slave A wins the election and is promoted to master.
  • A network partition makes A not available for the majority of the cluster.
  • Slave B wins the election and is promoted as master.
  • A partition makes B not available for the majority of the cluster.
  • The previous partition is fixed, and A is available again.

At this point B is down, and A is available again, having a role of master (actually UPDATE messages would reconfigure it promptly, but here we assume all get lost). At the same time, slave C will try to get elected in order to fail over B. This is what happens:

  1. B will try to get elected and will succeed, since for the majority of masters its master is actually down. It will obtain a new incremental configEpoch.
  2. A will not be able to claim to be the master for its hash slots, because the other nodes already have the same hash slots associated with an higher configuration epoch (the one of B) compared to the one published by A.
  3. So, all the nodes will upgrade their table to assign the hash slots to C, and the cluster will continue its operations.

As you'll see in the next sections, actually a stale node rejoining a cluster will usually get notified as soon as possible about the configuration change, since as soon as it pings any other node, the receiver will detect it has stale information and will send an UPDATE message.

*Hash slots configuration propagation

An important part of Redis Cluster is the mechanism used to propagate the information about which cluster node is serving a given set of hash slots. This is vital to both the startup of a fresh cluster and the ability to upgrade the configuration after a slave was promoted to serve the slots of its failing master.

The same mechanism allows nodes partitioned away for an indefinite amount of time to rejoin the cluster in a sensible way.

There are two ways hash slot configurations are propagated:

  1. Heartbeat messages. The sender of a ping or pong packet always adds information about the set of hash slots it (or its master, if it is a slave) servers.
  2. UPDATE messages. Since in every heartbeat packet there is information about the sender configEpoch and set of hash slots served, if a receiver of an heartbeat packet will find the sender information not updated, it will send a packet with the new information, forcing the stale node to update its info.

The receiver of an heartbeat or UPDATE message uses certain simple rules in order to update its table mapping hash slots to nodes. When a new Redis Cluster node is created, its local hash slot table is simple initialized to NULL entries, so that each hash slot is not bound, not linked to any node. Something like the following:

0 -> NULL
1 -> NULL
2 -> NULL
16383 -> NULL

The first rule followed by a node in order to update its hash slot table is the following:

Rule 1: If a hash slot is unassigned (set to NULL), and a known node claims it, I'll modify my hash slot table associating the claimed hash slots to it.

So if we receive an heartbeat from node A, claiming to serve hash slots 1 and 2 with a configuration epoch value of 3, the table will be modified into:

0 -> NULL
1 -> A [3]
2 -> A [3]
16383 -> NULL

Because of this rule, when a new cluster is created, it is only needed to manually assign (using the CLUSTER ADDSLOTS command, via the redis-trib command line tool, or by any other mean) the slots served by each master node to the node itself, and the information will rapidly propagate across the cluster.

However this rule is not enough. We know that hash slot mapping can change because of two events:

  1. A slave replaces its master during a failover.
  2. A slot is resharded from a node to a different one.

For now let's focus on failovers. When a slave failover its master, it obtains a configuration epoch which is guaranteed to be greater than the one of its master (and more generally greater than any other configuration epoch generated before). For example node B, which is a slave of A, may failover B with configuration epoch of 4. It will start to send heartbeat packets (the first time mass-broadcasting cluster-wide), because of the following second rule, receivers will update their tables:

Rule 2: If a hash slot is already assigned, and a known node is advertising it using a configEpoch that is greater than the configEpoch of the master currently associated with the slot, I'll rebind the hash slot to the new node.

So after receiving messages from B that claims to serve hash slots 1 and 2 with configuration epoch of 4, the receivers will update their table in the following way:

0 -> NULL
1 -> B [4]
2 -> B [4]
16383 -> NULL

Liveness property: because of the second rule eventually all the nodes in the cluster will agree that the owner of a slot is the one with the greatest configEpoch among the nodes advertising it.

This mechanism in Redis Cluster is called last failover wins.

The same happens during reshardings. When a node importing an hash slot ends the import operation, its configuration epoch is incremented to make sure the information will be updated in the cluster.

*UPDATE messages, a closer look

With the previous section in mind, it is easy now to check how update messages work. Node A may rejoin the cluster after some time. It will send heartbeat packets where it claims it serves hash slots 1 and 2 with configuration epoch of 3. All the receivers with an updated information will instead see that the same hash slots are associated with node B having an higher configuration epoch. Because of this they'll send to A an UPDATE message with the new configuration for the slots. A will update its configuration because of the rule 2 above.

*How nodes rejoin the cluster

The same basic mechanism is also used in order for a node to rejoin a cluster in a proper way. Continuing with the example above, node A will be notified that hash slots 1 and 2 are now served by B. Assuming that these two were the only hash slots served by A, the count of hash slots served by A will drop to 0! So A will reconfigure to be a slave of the new master.

The actual rule followed is a bit more complex than this. In general it may happen that A rejoins after a lot of time, in the meantime it may happen that hash slots originally served by A are served by multiple nodes, for example hash slot 1 may be served by B, and hash slot 2 by C.

So the actual Redis Cluster node role switch rule is: A master node will change its configuration to replicate (be a slave of) the node that stolen its last hash slot.

So during the reconfiguration eventually the number of served hash slots will drop to zero, and the node will reconfigure accordingly. Note that in the base case this just means that the old master will be a slave of the slave that replaced it after a failover. However in the general form the rule covers all the possible cases.

Slaves do exactly the same: they reconfigure to replicate to the node that stole the last hash slot of its former master.

*Replica migration

Redis Cluster implements a concept called replica migration in order to improve the availability of the system. The idea is that in a cluster with a master-slave setup, if the map between slaves and masters is fixed there is limited availability over time if multiple independent failures of single nodes happen.

For example in a cluster where every master has a single slave, the cluster can continue the operations as long the master or the slave fail, but not if both fail the same time. However there is a class of failures, that are the independent failures of single nodes caused by hardware or software issues that can accumulate over time. For example:

  • Master A has a single slave A1.
  • Master A fails. A1 is promoted as new master.
  • Three hours later A1 fails in an independent manner (not related to the failure of A). No other slave is available for promotion since also node A is still down. The cluster cannot continue normal operations.

If the map between masters and slaves is fixed, the only way to make the cluster more resistant to the above scenario is to add slaves to every master, however this is costly as it requires more instances of Redis to be executed, more memory, and so forth.

An alternative is to create an asymmetry in the cluster, and let the cluster layout automatically change over time. For example the cluster may have three masters A, B, C. A and B have a single slave each, A1 and B1. However the master C is different and has two slaves: C1 and C2.

Replica migration is the process of automatic reconfiguration of a slave in order to migrate to a master that has no longer coverage (no working slaves). With replica migration the scenario mentioned above turns into the following:

  • Master A fails. A1 is promoted.
  • C2 migrates as slave of A1, that is otherwise not backed by any slave.
  • Three hours later A1 fails as well.
  • C2 is promoted as new master to replace A1.
  • The cluster can continue the operations.

*Replica migration algorithm

The migration algorithm does not use any form of agreement, since the slaves layout in a Redis Cluster is not part of the cluster configuration that requires to be consistent and/or versioned with config epochs. Instead it uses an algorithm to avoid mass-migration of slaves when a master is not backed. The algorithm guarantees that eventually, once the cluster configuration is stable, every master will be backed by at least one slave.

This is how the algorithm works. To start we need to define what is a good slave in this context: a good slave is a slave not in FAIL state from the point of view of a given node.

The execution of the algorithm is triggered in every slave that detects that there is at least a single master without good slaves. However among all the slaves detecting this condition, only a subset should act. This subset is actually often a single slave unless different slaves have in a given moment a slightly different vision of the failure state of other nodes.

The acting slave is the slave among the masters having the maximum number of attached slaves, that is not in FAIL state and has the smallest node ID.

So for example if there are 10 masters with 1 slave each, and 2 masters with 5 slaves each, the slave that will try to migrate is, among the 2 masters having 5 slaves, the one with the lowest node ID. Given that no agreement is used, it is possible that when the cluster configuration is not stable, a race condition occurs where multiple slaves think to be the non-failing slave with the lower node ID (but it is a hard to trigger condition in practice). If this happens, the result is multiple slaves migrating to the same master, which is harmless. If the race happens in a way that will left the ceding master without slaves, as soon as the cluster is stable again the algorithm will be re-executed again and will migrate the slave back to the original master.

Eventually every master will be backed by at least one slave, however normally the behavior is that a single slave migrates from a master with multiple slaves to an orphaned master.

The algorithm is controlled by a user-configurable parameter called cluster-migration-barrier, that is the number of good slaves a master will be left with for a slave to migrate. So for example if this parameter is set to 2, a slave will try to migrate only if its master remains with two working slaves.

*configEpoch conflicts resolution algorithm

When new configEpoch values are created via slave promotions during failovers, they are guaranteed to be unique.

However there are two distinct events where new configEpoch values are created in an unsafe way, just incrementing the local currentEpoch of the local node, hoping there are no conflicts at the same time. Both the events are system-administrator triggered:

  1. CLUSTER FAILOVER command with TAKEOVER option is able to manually promote a slave node into a master without the majority of masters being available. This is useful, for example, in multi data center setups.
  2. Migration of slots for cluster rebalancing also generates new configuration epochs inside the local node without agreement for performance reasons.

Specifically, during manual reshardings, when a hash slot is migrated from a node A to a node B, the resharding program will force B to upgrade its configuration to an epoch which is the greatest found in the cluster, plus 1 (unless the node is already the one with the greatest configuration epoch), without to require for an agreement from other nodes. Usually a real world resharding involves moving several hundred hash slots, especially in small clusters, so to require an agreement to generate new configuration epochs during reshardings, for each hash slot moved, is inefficient. Moreover it requires an fsync every time in all the cluster nodes in order to store the new configuration. Because of the way it is performed instead, we need a new config epoch only when the first hash slot is moved usually, making it much more efficient in production environments.

However because of the two cases above, it is possible, while unlikely, to end with multiple nodes having the same configuration epoch (think for example a resharding operation performed by the system administrator, and a failover happening at the same time, plus a lot of bad luck so that the currentEpoch is not propagated fast enough to avoid a collision).

Moreover software bugs and filesystem corruptions are other causes that may lead to multiple nodes to have the same configuration epoch.

When masters serving different hash slots have the same configEpoch, there are no issues, and we are more interested in making sure slaves failing over a master have a different and unique configuration epoch.

However manual interventions or more reshardings may change the cluster configuration in different ways. The Redis Cluster main liveness property is that the slot configurations always converges, so we really want under every condition that all the master nodes have a different configEpoch.

In order to enforce this, a conflicts resolution algorithm is used in the event that two nodes end with the same configEpoch.

  • IF a master node detects another master node is advertising itself with the same configEpoch.
  • AND IF the node has a lexicographically smaller Node ID compared to the other node claiming the same configEpoch.
  • THEN it increments its currentEpoch by 1, and uses it as the new configEpoch.

If there are any set of nodes with the same configEpoch, all the nodes but the one with the greatest Node ID will move forward, guaranteeing that, eventually, every node will pick a unique configEpoch regardless of what happened.

This mechanism also guarantees that after a fresh cluster is created, all nodes start with a different configEpoch, even if this is not actually used since redis-trib makes sure to use CONFIG SET-CONFIG-EPOCH at startup. However if for some reason a node is left misconfigured, it will update its configuration to a different configuration epoch automatically.

*Nodes reset

Nodes can be software reset (without restarting them) in order to be reused in a different role or in a different cluster. This is useful in normal operations, in testing, and in cloud environments where a given node can be reprovisioned to join a different set of nodes to enlarge or create a new cluster.

In Redis Cluster nodes are reset using the CLUSTER RESET command. The command is provided in two variants:


The command must be sent directly to the node to reset, and the default reset type if no explicit type is provided is to perform a soft reset.

The following is a list of operations performed by reset:

  1. Soft and hard reset: If the node is a slave, it is turned into a master, and its dataset is discarded. If the node is a master and contains keys the reset operation is aborted.
  2. Soft and hard reset: All the slots are released, and the manual failover state is reset.
  3. Soft and hard reset: All the other nodes in the nodes table are removed, so the node no longer knows any other node.
  4. Hard reset only: currentEpoch, configEpoch, and lastVoteEpoch are set to 0.
  5. Hard reset only: the Node ID is changed to a new random ID.

Master nodes with non-empty data sets can't be reset (since normally you want to reshard data to the other nodes), however in special conditions when this is appropriate, like when a cluster is totally destroyed in order to create a new one, FLUSHALL must be executed before to proceed with the reset.

*Removing nodes from a cluster

It is possible to practically remove a node from an existing cluster by resharding all its data to other nodes (if it is a master node) and finally by shutting it down, however the other nodes will still remember its node ID and address, and will attempt to connect with it.

For this reason when a node is removed, we want to also remove its entry from all the other nodes tables. This is accomplished by using the CLUSTER FORGET <node-id> command.

The command does two things:

  1. It removes the node with the specified node ID from the nodes table.
  2. It sets a 60 seconds ban to prevent a node with the same node ID to be re-added.

The second operation is needed because Redis Cluster uses gossip in order to auto-discover nodes, so removing the node X from node A, could result into node B to gossip node X to A again. Because of the 60 seconds ban, the Redis Cluster administration tools have 60 seconds in order to remove the node from all the nodes, preventing the re-addition of the node because of auto discovery.

Further information is available in the CLUSTER FORGET documentation.


In a Redis Cluster clients can subscribe to every node, and can also publish to every other node. The cluster will make sure that publish messages are forwarded as needed.

The current implementation will simply broadcast all the publish messages to all the other nodes, but at some point this will be optimized either using bloom filters or other algorithms.


*Appendix A: CRC16 reference implementation in ANSI C

 * Copyright 2001-2010 Georges Menie (www.menie.org)
 * Copyright 2010 Salvatore Sanfilippo (adapted to Redis coding style)
 * All rights reserved.
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *       notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *       notice, this list of conditions and the following disclaimer in the
 *       documentation and/or other materials provided with the distribution.
 *     * Neither the name of the University of California, Berkeley nor the
 *       names of its contributors may be used to endorse or promote products
 *       derived from this software without specific prior written permission.

/* CRC16 implementation according to CCITT standards.
 * Note by @antirez: this is actually the XMODEM CRC 16 algorithm, using the
 * following parameters:
 * Name                       : "XMODEM", also known as "ZMODEM", "CRC-16/ACORN"
 * Width                      : 16 bit
 * Poly                       : 1021 (That is actually x^16 + x^12 + x^5 + 1)
 * Initialization             : 0000
 * Reflect Input byte         : False
 * Reflect Output CRC         : False
 * Xor constant to output CRC : 0000
 * Output for "123456789"     : 31C3

static const uint16_t crc16tab[256]= {

uint16_t crc16(const char *buf, int len) {
    int counter;
    uint16_t crc = 0;
    for (counter = 0; counter < len; counter++)
            crc = (crc<<8) ^ crc16tab[((crc>>8) ^ *buf++)&0x00FF];
    return crc;