raft: support asynchronous storage writes

[nvb]

Fixes etcd-io/etcd#12257.

NOTE: this PR was originally presented in etcd-io/etcd#14627 before etcd/raft was extracted into a separate repository (see etcd-io/etcd#14713). Review comments from the original PR have been addressed.

This change adds opt-in support to raft to perform local storage writes asynchronously from the raft state machine handling loop.

Summary

A new AsyncStorageWrites configuration instructs the raft node to write to its local storage (raft log and state machine) using a request/response message passing interface instead of the default Ready/Advance function call interface. Local storage messages can be pipelined and processed asynchronously (with respect to Ready iteration), facilitating reduced interference between Raft proposals and increased batching of log appends and state machine application. As a result, use of asynchronous storage writes can reduce end-to-end commit latency and increase maximum throughput.

When AsyncStorageWrites is enabled, the Ready.Message slice will include new MsgStorageAppend and MsgStorageApply messages. The messages will target a LocalAppendThread and a LocalApplyThread, respectively. Messages to the same target must be reliably processed in order. In other words, they can't be dropped (like messages over the network) and those targeted at the same thread can't be reordered. Messages to different targets can be processed in any order.

MsgStorageAppend carries Raft log entries to append, election votes to persist, and snapshots to apply. All writes performed in response to a MsgStorageAppend are expected to be durable. The message assumes the role of the Entries, HardState, and Snapshot fields in Ready.

MsgStorageApply carries committed entries to apply. The message assumes the role of the CommittedEntries field in Ready.

Local messages each carry one or more response messages which should be delivered after the corresponding storage write has been completed. These responses may target the same node or may target other nodes. The storage threads are not responsible for understanding the response messages, only for delivering them to the correct target after performing the storage write.

Design Considerations

• There must be no regression for existing users that do not enable AsyncStorageWrites. For instance, CommittedEntries must not wait on unstable entries to be stabilized in cases where a follower is given committed entries in a MsgApp.
• Asynchronous storage work should use a message passing interface, like the rest of this library.
• The Raft leader and followers should behave symmetrically. Both should be able to use asynchronous storage writes for log appends and entry application.
• The LocalAppendThread on a follower should be able to send MsgAppResp messages directly to the leader without passing back through the raft state machine handling loop.
• The unstable log should remain true to its name. It should hold entries until they are stable and should not rely on an intermediate reliable cache.
• Pseudo-targets should be assigned to messages that target the local storage systems to denote required ordering guarantees.
• Code should be maximally unified across AsyncStorageWrites=false and AsyncStorageWrites=true. AsyncStorageWrites=false should be a special case of AsyncStorageWrites=true where the library hides the possibility of asynchrony.
• It should be possible to apply snapshots asynchronously, even though a snapshot touches both the Raft log state and the state machine. The library should make this easy for users to handle by delaying all committed entries until after the snapshot has applied, so snapshot application can be handled by 1) flushing the apply thread, 2) sending the MsgStorageAppend that contains a snapshot to the LocalAppendThread to be applied.

Usage

When asynchronous storage writes is enabled, the responsibility of code using the library is different from what is presented in raft/doc.go (which has been updated to include a section about async storage writes). Users still read from the Node.Ready() channel. However, they process the updates it contains in a different manner. Users no longer consult the HardState, Entries, and Snapshot fields (steps 1 and 3 in doc.go). They also no longer call Node.Advance() to indicate that they have processed all entries in the Ready (step 4 in doc.go). Instead, all local storage operations are also communicated through messages present in the Ready.Message slice.

The local storage messages come in two flavors. The first flavor is log append messages, which target a LocalAppendThread and carry Entries, HardState, and a Snapshot. The second flavor is entry application messages, which target a LocalApplyThread and carry CommittedEntries. Messages to the same target must be reliably processed in order. Messages to different targets can be processed in any order. Each local storage message carries a slice of response messages that must delivered after the corresponding storage write has been completed.

With Asynchronous Storage Writes enabled, the total state machine handling loop will look something like this:

for {
	select {
	case <-s.Ticker:
		n.Tick()
	case rd := <-s.Node.Ready():
		for _, m := range rd.Messages {
			switch m.To {
			case raft.LocalAppendThread:
				toAppend <- m
			case raft.LocalApplyThread:
				toApply <-m
			default:
				sendOverNetwork(m)
			}
		}
	case <-s.done:
		return
	}
}

Usage of Asynchronous Storage Writes will typically also contain a pair of storage handler threads, one for log writes (append) and one for entry application to the local state machine (apply). Those will look something like:

// append thread
go func() {
	for {
		select {
		case m := <-toAppend:
			saveToStorage(m.State, m.Entries, m.Snapshot)
			send(m.Responses)
		case <-s.done:
			return
		}
	}
}

// apply thread
go func() {
	for {
		select {
		case m := <-toApply:
			for _, entry := range m.CommittedEntries {
				process(entry)
				if entry.Type == raftpb.EntryConfChange {
					var cc raftpb.ConfChange
					cc.Unmarshal(entry.Data)
					s.Node.ApplyConfChange(cc)
				}
			}
			send(m.Responses)
		case <-s.done:
			return
		}
	}
}

Compatibility

The library remains backwards compatible with existing users and the change does not introduce any breaking changes. Users that do not set AsyncStorageWrites to true in the Config struct will not notice a difference with this change. This is despite the fact that the existing "synchronous storage writes" interface was adapted to share a majority of the same code. For instance, Node.Advance has been adapted to transparently acknowledge an asynchronous log append attempt and an asynchronous state machine application attempt, internally using the same message passing mechanism introduced in this change.

The change has no cross-version compatibility concerns. All changes are local to a process and nodes using asynchronous storage writes appear to behave no differently from the outside. Clusters are free to mix nodes running with and without asynchronous storage writes.

Performance

The bulk of the performance evaluation of this functionality thus far has been done with rafttoy, a benchmarking harness developed to experiment with Raft proposal pipeline optimization. The harness can be used to run single-node benchmarks or multi-node benchmarks. It supports plugable raft logs, storage engines, network transports, and pipeline implementations.

To evaluate this change, we fixed the raft log (etcd/wal), storage engine (pebble), and network transport (grpc). We then built (nvb/rafttoy#3) a pipeline implementation on top of the new asynchronous storage writes functionality and compared it against two other pipeline implementations.

The three pipeline implementations we compared were:

• basic (P1): baseline stock raft usage, similar to the code in doc.go
• parallel append + early ack (P2): CockroachDB's current pipeline, which includes two significant variations to the basic pipeline. The first is that it sends MsgApp messages to followers before writing to local Raft log (see commit for explanation), allowing log appends to occur in parallel across replicas. The second is that it acknowledges committed log entries before applying them (see commit for explanation).
• async append + async apply + early ack (P3): A pipelining using asynchronous storage writes with a separate append thread and a separate apply thread. Also uses the same early acknowledgement optimization from above to ack committed entries before handing them to the apply thread.

All testing was performed on a 3 node AWS cluster of m5.4xlarge instances with gp3 EBS volumes (16000 IOPS, 1GB/s throughput). The testing used an open-loop workload to increase the rate of new raft proposals until a saturation point was reached.

The comparison demonstrates two different benefits of asynchronous storage writes.

The first is that it reduces end-to-end latency of proposals by 20-25%. For instance, when serving 16MB/s of write traffic, P1's average latency was 13.2ms, P2's average latency was 7.3ms, and P3's average latency was 5.2ms. This is a reduction in average latency of 29% from the optimized pipeline that does not use asynchronous storage writes. This matches the expectations outlined in cockroachdb/cockroach#17500.

The second is that it increases the maximum throughput at saturation. In this test, P1 and P2 topped out at 30MB/s, while P3 could push up to 52MB/s, an increase in maximum throughput of 73%. This is because asynchronous storage writes can improve batching for both log appends and log application. In this experiment, we saw the average append batch size under saturation increase from 928 to 1542, which is a similar ratio to the increase in peak throughput. We see a similar difference for apply batch sizes.

An additional performance comparison was presented in etcd-io/etcd#14627 (comment).

cc. @tbg @bdarnell

[tbg]

This isn't the right place for this comment but I didn't want to post it in the main thread (no "Resolve" feature there):

I wonder if there's a simple UX improvement we could make to ready handling when using async writes. Right now, there are a bunch of fields that are unused in that case:

• MustSync
• CommittedEntries
• Entries
• Snapshot
• any others?

What about a helper method

func AsyncStorageWritesReady(rd Ready) AsyncReady {
  // ...
}

that basically returns only the things you need to care about as a user of the async interface? This would also provide a convenient place to anchor documentation without having to interleave the two different usage patterns.

[nvb]

I like this idea. Let me try it out and see how it looks.

[tbg]

Getting close! No substantial comments.
I'd classify my comment about an AsyncReady struct is semi-substantial. I wouldn't want it to necessarily block the merge here, but I think there is a significant improvement to be made here in how accessible this new functionality it is to other adopters. So I consider it important that we discuss it and follow it to its conclusion in a timely manner.

[tbg]

Can't review the diffs (no fixups?) but I looked at the one last new commit, still LGTM.

You may want to merge this and pursue #8 (comment) separately.

[tbg]

Merging with @nvanbenschoten's consent.

[ahrtr]

When async write is enabled, you get rd.Entries, rd.HardState and rd.Snapshot encapsulated in MsgStorageAppend message. But you do not clear the fields, so you expect application ignore the fields, and only process the MsgStorageAppend message?

The same for the MsgStorageApply message.