etcd-io__etcd/raft

Raft library

Raft is a protocol with which a cluster of nodes can maintain a replicated state machine. The state machine is kept in sync through the use of a replicated log. For more details on Raft, see "In Search of an Understandable Consensus Algorithm" (https://ramcloud.stanford.edu/raft.pdf) by Diego Ongaro and John Ousterhout.

A simple example application, raftexample, is also available to help illustrate how to use this package in practice: https://github.com/coreos/etcd/tree/master/contrib/raftexample

Notable Users

• cockroachdb A Scalable, Survivable, Strongly-Consistent SQL Database
• etcd A distributed reliable key-value store
• tikv Distributed transactional key value database powered by Rust and Raft
• swarmkit A toolkit for orchestrating distributed systems at any scale.

Usage

The primary object in raft is a Node. You either start a Node from scratch using raft.StartNode or start a Node from some initial state using raft.RestartNode.

To start a node from scratch:

  storage := raft.NewMemoryStorage()
  c := &Config{
    ID:              0x01,
    ElectionTick:    10,
    HeartbeatTick:   1,
    Storage:         storage,
    MaxSizePerMsg:   4096,
    MaxInflightMsgs: 256,
  }
  n := raft.StartNode(c, []raft.Peer{{ID: 0x02}, {ID: 0x03}})

To restart a node from previous state:

  storage := raft.NewMemoryStorage()

  // recover the in-memory storage from persistent
  // snapshot, state and entries.
  storage.ApplySnapshot(snapshot)
  storage.SetHardState(state)
  storage.Append(entries)

  c := &Config{
    ID:              0x01,
    ElectionTick:    10,
    HeartbeatTick:   1,
    Storage:         storage,
    MaxSizePerMsg:   4096,
    MaxInflightMsgs: 256,
  }

  // restart raft without peer information.
  // peer information is already included in the storage.
  n := raft.RestartNode(c)

Now that you are holding onto a Node you have a few responsibilities:

First, you must read from the Node.Ready() channel and process the updates it contains. These steps may be performed in parallel, except as noted in step 2.

1. Write HardState, Entries, and Snapshot to persistent storage if they are not empty. Note that when writing an Entry with Index i, any previously-persisted entries with Index >= i must be discarded.

2. Send all Messages to the nodes named in the To field. It is important that no messages be sent until the latest HardState has been persisted to disk, and all Entries written by any previous Ready batch (Messages may be sent while entries from the same batch are being persisted). To reduce the I/O latency, an optimization can be applied to make leader write to disk in parallel with its followers (as explained at section 10.2.1 in Raft thesis). If any Message has type MsgSnap, call Node.ReportSnapshot() after it has been sent (these messages may be large). Note: Marshalling messages is not thread-safe; it is important that you make sure that no new entries are persisted while marshalling. The easiest way to achieve this is to serialise the messages directly inside your main raft loop.

3. Apply Snapshot (if any) and CommittedEntries to the state machine. If any committed Entry has Type EntryConfChange, call Node.ApplyConfChange() to apply it to the node. The configuration change may be cancelled at this point by setting the NodeID field to zero before calling ApplyConfChange (but ApplyConfChange must be called one way or the other, and the decision to cancel must be based solely on the state machine and not external information such as the observed health of the node).

4. Call Node.Advance() to signal readiness for the next batch of updates. This may be done at any time after step 1, although all updates must be processed in the order they were returned by Ready.

Second, all persisted log entries must be made available via an implementation of the Storage interface. The provided MemoryStorage type can be used for this (if you repopulate its state upon a restart), or you can supply your own disk-backed implementation.

Third, when you receive a message from another node, pass it to Node.Step:

	func recvRaftRPC(ctx context.Context, m raftpb.Message) {
		n.Step(ctx, m)
	}

Finally, you need to call Node.Tick() at regular intervals (probably via a time.Ticker). Raft has two important timeouts: heartbeat and the election timeout. However, internally to the raft package time is represented by an abstract "tick".

The total state machine handling loop will look something like this:

  for {
    select {
    case <-s.Ticker:
      n.Tick()
    case rd := <-s.Node.Ready():
      saveToStorage(rd.State, rd.Entries, rd.Snapshot)
      send(rd.Messages)
      if !raft.IsEmptySnap(rd.Snapshot) {
        processSnapshot(rd.Snapshot)
      }
      for _, entry := range rd.CommittedEntries {
        process(entry)
        if entry.Type == raftpb.EntryConfChange {
          var cc raftpb.ConfChange
          cc.Unmarshal(entry.Data)
          s.Node.ApplyConfChange(cc)
        }
      }
      s.Node.Advance()
    case <-s.done:
      return
    }
  }

To propose changes to the state machine from your node take your application data, serialize it into a byte slice and call:

	n.Propose(ctx, data)

If the proposal is committed, data will appear in committed entries with type raftpb.EntryNormal. There is no guarantee that a proposed command will be committed; you may have to re-propose after a timeout.

To add or remove node in a cluster, build ConfChange struct 'cc' and call:

	n.ProposeConfChange(ctx, cc)

After config change is committed, some committed entry with type raftpb.EntryConfChange will be returned. You must apply it to node through:

	var cc raftpb.ConfChange
	cc.Unmarshal(data)
	n.ApplyConfChange(cc)

Note: An ID represents a unique node in a cluster for all time. A given ID MUST be used only once even if the old node has been removed. This means that for example IP addresses make poor node IDs since they may be reused. Node IDs must be non-zero.

Implementation notes

This implementation is up to date with the final Raft thesis (https://ramcloud.stanford.edu/~ongaro/thesis.pdf), although our implementation of the membership change protocol differs somewhat from that described in chapter 4. The key invariant that membership changes happen one node at a time is preserved, but in our implementation the membership change takes effect when its entry is applied, not when it is added to the log (so the entry is committed under the old membership instead of the new). This is equivalent in terms of safety, since the old and new configurations are guaranteed to overlap.

To ensure that we do not attempt to commit two membership changes at once by matching log positions (which would be unsafe since they should have different quorum requirements), we simply disallow any proposed membership change while any uncommitted change appears in the leader's log.

This approach introduces a problem when you try to remove a member from a two-member cluster: If one of the members dies before the other one receives the commit of the confchange entry, then the member cannot be removed any more since the cluster cannot make progress. For this reason it is highly recommended to use three or more nodes in every cluster.