272 lines
13 KiB
Plaintext
272 lines
13 KiB
Plaintext
---
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layout: docs
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page_title: Integrated Storage
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description: Learn about the integrated raft storage in Vault.
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---
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# Integrated Storage
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Vault supports several storage options for the durable storage of Vault's
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information. Each backend offers pros, cons, advantages, and trade-offs. For
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example, some backends support high availability while others provide a more
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robust backup and restoration process.
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As of Vault 1.4, an Integrated Storage option is offered. This storage backend
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does not rely on any third party systems; it implements high availability,
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supports Enterprise Replication features, and provides backup/restore workflows.
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## Consensus Protocol
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Vault's Integrated Storage uses a [consensus
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protocol](<https://en.wikipedia.org/wiki/Consensus_(computer_science)>) to provide
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[Consistency](https://en.wikipedia.org/wiki/CAP_theorem) (as defined by CAP).
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The consensus protocol is based on ["Raft: In search of an Understandable
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Consensus Algorithm"](https://raft.github.io/raft.pdf). For a visual explanation
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of Raft, see [The Secret Lives of Data](http://thesecretlivesofdata.com/raft).
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### Raft Protocol Overview
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Raft is a consensus algorithm that is based on
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[Paxos](https://en.wikipedia.org/wiki/Paxos_%28computer_science%29). Compared
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to Paxos, Raft is designed to have fewer states and a simpler, more
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understandable algorithm.
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The Raft protocol will not be fully covered here. However, a high level description is
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provided to help you build a mental model. Refer to the
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complete specification that's described in [this paper](https://raft.github.io/raft.pdf).
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#### Terminology
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There are a few key terms to know when discussing Raft:
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- **Leader** - At any given time, the peer set elects a single node to be the leader.
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The leader is responsible for ingesting new log entries, replicating to followers,
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and managing when an entry is committed. The leader node is also the active Vault node and followers are standby nodes. Refer to the [High Availability](/vault/docs/internals/high-availability#design-overview) document for more information.
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- **Log** - An ordered sequence of entries (replicated log) to keep track of any cluster changes. The leader is responsible for _log replication_. When new data is written, for example, a new event creates a log entry. The leader then sends the new log entry to its followers. Any inconsistency within the replicated log entries will indicate an issue.
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- **FSM** - [Finite State Machine](https://en.wikipedia.org/wiki/Finite-state_machine).
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A collection of finite states with transitions between them. As new logs
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are applied, the FSM is allowed to transition between states. Application of the
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same sequence of logs must result in the same state, meaning behavior must be deterministic.
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- **Peer set** - The set of all members participating in log replication. All server nodes are in the peer set of the local cluster.
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- **Quorum** - A majority of members from a peer set: for a set of size `n`,
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quorum requires at least `(n+1)/2` members. For example, if there are 5 members
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in the peer set, we would need 3 nodes to form a quorum. If a quorum of nodes is
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unavailable for any reason, the cluster becomes _unavailable_ and no new logs
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can be committed.
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- **Committed Entry** - An entry is considered _committed_ when it is durably stored
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on a quorum of nodes. An entry is applied once its committed.
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#### Node States
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Raft nodes are always in one of three states: follower, candidate, or leader. All
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nodes initially start out as a follower. In this state, nodes can accept log entries
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from a leader and cast votes. If no entries are received for a period of time, nodes
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will self-promote to the candidate state. In the candidate state, nodes request votes from their peers. If a candidate receives a quorum of votes, then it is promoted to a leader. The leader must accept new log entries and replicate to all the other followers.
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#### Writing Logs
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Once a cluster has a leader, it is able to accept new log entries. A client can
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request that a leader append a new log entry (from Raft's perspective, a log entry
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is an opaque binary blob). The leader then writes the entry to durable storage and
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attempts to replicate to a quorum of followers. Once the log entry is considered
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_committed_, it can be _applied_ to a finite state machine. The finite state machine
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is application specific; in Vault's case, we use
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[BoltDB](https://github.com/etcd-io/bbolt) to maintain a cluster state. Vault's writes
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are blocked until they are _committed_ and _applied_.
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#### Compacting Logs
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It would be undesirable to allow a replicated log to grow in an unbounded
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fashion. Raft provides a mechanism by which the current state is saved to
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snapshots and its related logs are compacted. Because of the FSM abstraction,
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restoring the state of the FSM must result in the same state as a replay of old
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logs. This allows Raft to capture the FSM state at a point in time and then remove
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all the logs that were used to reach that state. This is performed automatically
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without user intervention and prevents unbounded disk usage while also minimizing
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the time spent replaying logs. One of the advantages of using BoltDB is that it
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allows Vault's snapshots to be very light weight. Since Vault's data is already
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persisted to disk in BoltDB, the snapshot process just needs to truncate the raft logs.
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#### Quorum
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Consensus is fault-tolerant while a cluster has quorum.
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If a quorum of nodes is unavailable, it is impossible to process log entries or reason
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about peer membership. For example, suppose there are only 2 peers: A and B. The quorum
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size is also 2, meaning both nodes must agree to commit a log entry. If either A or B
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fails, it is now impossible to reach quorum. This means the cluster is unable to add
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or remove a node or to commit any additional log entries. This results in
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_unavailability_. At this point, manual intervention is required to remove
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either A or B and restart the remaining node in bootstrap mode.
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A Raft cluster of 3 nodes can tolerate a single node failure while a cluster of
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5 can tolerate 2 node failures. The recommended Vault production deployment is
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to run 5 Vault servers per cluster. See the [Minimum &
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Scaling](#minimums-scaling) and [Deployment Table](#deployment-table) to learn
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more about the failure tolerance using Integrated Storage.
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#### Performance
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In terms of performance, Raft is comparable to Paxos. Assuming stable leadership,
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committing a log entry requires a single round trip to half of the cluster.
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Thus, performance is bound by disk I/O and network latency.
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### Raft in Vault
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When getting started, a single Vault server is
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[initialized](/vault/docs/commands/operator/init/#operator-init). At this point, the
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cluster is of size 1, which allows the node to self-elect as a leader. Once a
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leader is elected, other servers can be added to the peer set in a way that
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preserves consistency and safety.
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The join process is how new nodes are added to the Vault cluster; it uses an
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encrypted challenge/answer workflow. To accomplish this, all nodes in a single
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Raft cluster must share the same seal configuration. If using an Auto Unseal, the
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join process can use the configured seal to automatically decrypt the challenge
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and respond with the answer. If using a Shamir seal, the unseal keys must be
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provided to the node attempting to join the cluster before it can decrypt the
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challenge and respond with the decrypted answer.
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Since all servers participate as part of the peer set, they all know the current
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leader. When an API request arrives at a non-leader server, the request is
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forwarded to the leader.
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Similar to other storage backends, data that is written to the Raft log and FSM
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will be encrypted by Vault's barrier.
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### Quorum Management
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#### Autopilot
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An [Autopilot feature](/vault/docs/concepts/integrated-storage/autopilot)
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is available since 1.7.x & later versions that include configurable parameters
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for when a node is treated as healthy before it's considered an eligible voter in the
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quorum list. Other features which may be enabled include the ability to remove nodes
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considered as dead from the quorum list after a certain period.
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Autopilot is enabled by default in Vault 1.7+. The default configuration values
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should work well for most Vault deployments, but they can be changed if needed.
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Autopilot includes stabilization logic for nodes joining the cluster.
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Recently joined nodes are
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accepted as non-voter initially until they are in sync with matching Raft index
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and only after a stability thresholds are they then full voting members.
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Setting the stability threshold too low can result in cluster instability as nodes will be
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counted as voters before they are capable of voting.
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As of Vault 1.7, a dead server cleanup capability is available. With this feature
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enabled, unhealthy nodes are automatically removed from the Raft cluster without
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manual operator intervention. This is enabled via the [Autopilot API](/vault/api-docs/system/storage/raftautopilot).
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If you wish to decommission a node manually, this can be done with the
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`remove peer` [command](/vault/docs/commands/operator/raft#remove-peer).
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#### Without Autopilot
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Older versions of Vault, 1.6.x & lower, as well as cases where Autopilot may be
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disabled or misconfigured, behave differently.
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In scenarios involving those when a node joins a Raft cluster, it attempts to
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catch up with the rest of the nodes through the data that it's replicating from
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the leader. While in this initial synchronisation state, the node cannot
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vote but is counted for the purposes of quorum. If a number of new nodes join
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the cluster simultaneously or at similar times, and thereby exceeding the failure
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tolerance of the cluster, quorum may be lost and the cluster can fail.
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For example, consider a scenario where there is a 3-node cluster with a large
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amount of data and a failure tolerance of 1. An additional 3 new nodes then
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join the cluster. The cluster now consists of 6 nodes with a failure tolerance
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of 2, but since all 3 nodes are still catching up, this will result in a loss of
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quorum.
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* A 3 node cluster with a large amount of data that's at a failure tolerance of 1.
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* Another 3 new nodes then join the cluster together.
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* Now the cluster consists of 6 nodes with a failure tolerance of 2, but all 3 new nodes are still catching up, resulting in a loss of quorum.
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For this reason, we recommend ensuring new nodes have Raft indexes that are
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close to the leader before adding additional nodes. Raft indexes are visible via
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`vault status`.
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### Deployment Table
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Below is a table that shows quorum size and failure tolerance for various
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cluster sizes. The recommended production deployment consists of 5 servers. A
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single server deployment is _**highly**_ discouraged as data loss is inevitable
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in a failure scenario.
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<table class="table table-bordered table-striped">
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<thead>
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<tr>
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<th>Servers</th>
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<th>Quorum Size</th>
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<th>Failure Tolerance</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>1</td>
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<td>1</td>
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<td>0</td>
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</tr>
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<tr>
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<td>2</td>
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<td>2</td>
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<td>0</td>
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</tr>
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<tr class="warning">
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<td>3</td>
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<td>2</td>
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<td>1</td>
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</tr>
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<tr>
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<td>4</td>
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<td>3</td>
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<td>1</td>
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</tr>
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<tr class="warning">
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<td>5</td>
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<td>3</td>
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<td>2</td>
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</tr>
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<tr>
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<td>6</td>
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<td>4</td>
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<td>2</td>
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</tr>
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<tr>
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<td>7</td>
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<td>4</td>
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<td>3</td>
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</tr>
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</tbody>
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</table>
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### Minimums & Scaling
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The [Vault Reference Architecture](/vault/tutorials/day-one-raft/raft-reference-architecture#recommended-architecture)
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recommends a 5 node cluster to ensure a minimum failure tolerance of at least 2.
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It is good practise, wherever possible, to retain a failure tolerance of 2 or
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more.
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A scaling approach can be pursued in the event of maintenance and other changes
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where an additional pair of nodes (ie two) are added in an existing 5 node cluster
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making for a 7 node cluster. Once new joiners are confirmed to be in sync then
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the 2 older nodes can be stopped and or destroyed with the same processes being
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repeated until all other nodes have been replaced. This use of additional nodes
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on a temporary basis of a 7 node cluster arrangement, concluding back to 5 nodes,
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may be one way to ensure sufficient failure tolerance is maintained and that
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changes are made progressively in proportion to the cluster failure tolerance and
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never exceeding the available failure tolerance in any given time.
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The intent with any change or scaling ought to be with the lose of quorum and
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reduction of the quorum failure tolerances at the forefront and discouraging
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any practises that compromise that.
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Scaling clusters up or down in pairs with 2 nodes each time also has the added
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advantage of avoiding even numbers and it is always recommended to
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allow for an odd number of total voters in any cluster.
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