May 5, 2021

[Golang] protoactor-go 301: How's clustering works to achieve higher availability

NOTE: This article is now officially hosted at with better diagrams and additional notes for other languages’ implementations.

The previous articles described the basics of including its concepts, terms, messaging mechanism, middleware mechanism, and plugin mechanism. This article describes the benefit of clustering architecture, introduces its terms and concepts, and demonstrates how to work with this. For those who are not familiar with, starting from [Golang] protoactor-go 101: Introduction to golang’s actor model implementation is recommended.

The Basic Ideas

To better understand why and when clustering architecture should be adopted, the below subsections show the benefits of actor model in general, its remoting architecture, and its clustering architecture.

Actor Model in General

With the power of actor model, a developer has easier access to concurrent programming. A mutable state is encapsulated in an actor, and its messaging queue – a mailbox – guarantees messages are passed to the actor one at a time. The actor processes a corresponding task against the receiving message, updates its mutable state, and then receives the next message. Therefore only one job is run by an actor at any moment. This solves the difficulty of concurrency and mutable states, and lets developers concentrate on business logic.


While actor model eases the concurrent programming with mutatable states, the system is hard to scale as long as the actor system is hosted by a single machine. Remoting is a good solution to scale out the actor system among multiple machines.

When one sends a message to a specific actor, the sender is not directly referring to the receiving actor itself. Instead, a reference to the actor is exposed to developers for messaging. This reference is called PID, which is short for “process id.” In, the concept of “process” or “process id” is quite similar to that of Erlang. For those who are familiar with Akka’s actor model, PID can be equivalent to ActorRef.

This PID knows where the actual actor instance is located and how to communicate with it. The location may be within the same host machine; Maybe not. The important thing is that a sender does not have to pay extra attention to the data serialization/deserialization and data transport. In short, one can communicate with an actor hosted by another machine just like the way communicating with a locally hosted actor.

With such location transparency, multiple machines can collaborate with each other and work as a single actor system. This is remoting – a key to scaling out the actor system.


While remoting is an important feature to build a scalable actor system, there still is room to improve the actor system’s availability as a whole. A sender enqueues a message to the remotely hosted actor’s mailbox, but the destination host’s availability is not always guaranteed. A hardware outage or power outage on a specific host may occur at any moment. As a matter of fact, even a daily operation such as application deployment may lower the service availability instantaneously. In such a case, messaging a remotely hosted actor results in a dead letter. To work as a distributed actor system, all machines must always be available and ready to interact with each other, or otherwise a messaging fails. Keeping a hundred percent availability for all time is not realistic or pragmatic.

Clustering is built with a service discovery mechanism on top of the remoting feature to give extra robustness to work with the aforementioned availability issue. Multiple server instances work as a single cluster to provide specific types of actors. When one or more server instances go down, such an event is detected by the service discovery mechanism, and messages are always rooted to active actors on active instances.

The following sections introduce some concepts and terms, how a specific actor is located at a specific server instance before and after a topology change, and some executable code to work with clustering.

Concepts and Terms


When an application process joins cluster membership, the application process is explicitly called a “node.” This may be effectively equal to a server instance especially when one server instance hosts one application process. However, multiple application processes can technically run on a single server instance at the same time, so there still is a difference.

Cluster Provider

The core of clustering is cluster provider; this provides a consistent view of active nodes. Once the application process starts, the node constantly interacts with cluster provider to update its own availability and gets other nodes’ membership status. With the up-to-date topology view, automatically distributes actors across cluster nodes based on partitioning by consistent hash. supports several cluster provider implementations:

  • Consul … This implementation uses HashiCorp’s Consul for service discovery. This was the first implementation of cluster provider.
  • etcd … This is an etcd version of cluster provider implementation. If one has experience with Kubernetes, this implementation may be easier to start with.
  • Automanaged … This does not use any centralized service discovery system, but instead each member ping each other to manage membership.

Virtual Actor’s clustering mechanism borrows the idea of “virtual actor” from Microsoft Orleans, where developers are not obligated to handle an actor’s lifecycle. If the destination actor is not yet spawned when the first message is sent, spawns one and lets this newborn actor handle the message; if the actor is already present, the existing actor simply receives the incoming message. From message sender’s point of view, the destination actor is always guaranteed to “exist.” This is highly practical and works well with the clustering mechanism. An actor’s hosting node may crash at any moment, and the messages to that actor may be redirected to a new hosting node. If a developer must be aware of the actor’s lifecycle, a developer is obligated to be aware of such topology change to re-spawn the failing actor. The concept of virtual actor hides such complexity and eases the interaction.


As described in the above “virtual actor” section, an actor always exists. Instead of explicitly spawn a new actor, one may “activate” the destination actor by getting the PID of the destination actor. internally checks the existence of the destination actor and spawns one if one is not present.

An actor may disappear when a hosting node crashes, or an actor may stop itself when an idle interval with no message reception exceeds a certain period of time. Activation works well to re-spawn such actors with no extra care.


Once the grain is initialized by activation, the grain always exists because of the nature of the virtual actor. This, however, is not ideal in terms of limited server resources. lets a developer specify a timeout interval, where the grain terminates itself when this interval passes after the last message reception time.


With virtual actor model, an actor is specifically called a “grain.” However, the implementation of the grain is quite the same as any other actor. A notable difference is that automatically spawns the grain on the initial message reception.


To explicitly state which node is capable of providing what types of grains, a developer needs to register the “kind” on cluster membership initiation. By registering the mapping of a kind and a corresponding grain construction function, the cluster provider knows the node is capable of hosting a set of grains, and the client can compute to which node it must send an activation request.


Other than a grain itself, an “ownership” is an important concept to understand how grains are located in one specific node. The cluster’s topology view changes when a node goes down or a new node is added to the cluster membership. One may assume that grain must be relocated to another node because grains are distributed by using consistent hashing. That, however, is a relatively complicated task. A grain may have its own state and behavior, so serializing them and transferring that information to another node is difficult.

Instead of transferring a grain itself, only transfers the “ownership” of the grain. An owner knows where the grain is currently located. When sending a message to a specific grain, calculates the location of the “owner” instead of the grain with consistent hashing, and then gets the grain’s address from the “owner.” Therefore, an owner and its subordinating grains do not necessarily exist on the same node. The later section covers how the ownership is transferred.

Communication Protocol

Because the topology view may change at any moment and the ownership can be transferred at any moment as well, the fire-and-forget model of messaging may fail from time to time. For example, one may send a message to a specific grain at the same time as the topology change. The ownership could be transferred when the message is received by the previous owner node. To make sure a message is delivered to the destination grain, a gRPC-based communication is available.

Once an IDL file for gRPC is given, a messaging method with a retrial logic is generated by This method computes the location of the ownership and sends an activation request again when the initial messaging fails due to the aforementioned ownership transfer. This gRPC-based communication gives more robustness, but the nature of the request/response communication model may affect performance. In such a case, a developer may simply send a message with the pre-defined messaging methods such as Context.Send(), Context.Request() and Context.RequestFuture().

Locating a Grain

If a developer has experience working on storage sharding, one might be familiar with the idea of consistent hashing. This is a powerful mechanism to decide in a reproducible manner which node on a virtual ring topology has the ownership of a given “key,” and also requires a fewer re-location on topology change. employs this algorithm to decide where the grain – more precisely the owner – must be located.

Initial State

The below image describes how a grain is located. With the latest membership shared by cluster provider, a message sender computes the hash value of the destination grain and elicits where the recipient grain’s owner exists based on the partitioning by consistent hash. Once the owner’s location is known, a sender sends an activation request to the owner. The owner receives the message and sees if the grain instance already exists. If exist, then return the PID of the grain; if not, then spawn one and returns its PID. This is the simplest form of identity lookup.

Topology Update

When the cluster membership is updated and the topology changes due to the Node B’s outage, all cluster members acquire such an event from the cluster provider. Each server instance then re-computes the hash value of its owning grains and checks if it still owns them. If a grain needs to be owned by another server instance, the ownership is transferred to the new owner. This guarantees that owners are always placed on each ideal node that is determined by consistent hashing while grain instances stay where they are currentlylocated.

Grain Re-activation

After the topology refresh, a sender re-computes where the owner of Actor 2 exists. This sends an activation request to the new owner – node A –, and node A returns the PID of actor 2 on node D. The sender now can send a message to actor 2 on node D. In this way, the existing grain and its internal state is not re-located on topology change; only the ownership does.

For better performance, internally caches the location of known grains and refresh this when topology view changes.

Messaging with a Grain

With the basics introduced in the previous sections, this section works on a project where a pinger actor sends a “ping” message and a ponger grain sends back a “pong” message. In addition to simply sending an empty signal, a ping message contains a cumulative count of the ping messages being sent; pong message contains the count a ping message contained.

Below is the detailed spec.

  • Use automanaged cluster provider to minimize the implementation
  • One application process hosts a pinger actor and sends a ping message every second
  • Another application process hosts a ponger grain
    • This grain is capable of handling gRPC-based Ping() request and a plain message
    • This grain is initialized with a passivation interval setting of ten seconds

The complete code is located at

Message Definition

Because messages are sent from one node to another over wire, they must be serializable. employs pre-existing, well-known Protocol Buffers for data serialization instead of inventing a new serialization protocol. Before getting started, be sure to install protoc and gogoprotobuf’s protoc-gen-gogoslick to generate Golang code. In addition to those tools, one tool is required. Run the below command to install the binary. A developer needs to specify dev branch by adding @dev at the end since this is not yet merged to master branch as of 2021-05-03.

$ go get

Below is an example of two messages: PingMessage and PongMessage. These two message definitions are sufficient to send ping and pong messages to each other. However, a service definition is required to utilize the gRPC-based messaging. That is Ponger. Ponger lets the caller send a Ping message with SendPing() method and the receiver sends back Pong message.

syntax = "proto3";
package messages;

message PingMessage {
    uint64 cnt = 1;

message PongMessage {
    uint64 cnt = 1;

service Ponger {
    rpc Ping(PingMessage) returns (PongMessage) {}

Name this file “protos.proto” and locate under cluster/messages. When the IDL file is ready, run the below command to generate required Go code. Two files other than the IDL – protos.pb.go and protos_protoactor.go – are generated.

$ protoc --gogoslick_out=. ./cluster/messages/protos.proto
$ protoc --gograinv2_out=. ./cluster/messages/protos.proto 
$ tree ./cluster 
└── messages
    ├── protos.pb.go
    ├── protos.proto
    └── protos_protoactor.go

1 directory, 3 files

protos_protoactor.go defines interface, struct, and function. They are covered in the below sections:

Grain Implementation


protos_protoactor.go contains a PongerActor struct in it, which receives the incoming message and makes a gRPC-based method call or simply proxies the message to a defaut message reception method. A developer only has to provide such methods by providing Ponger implementation.

Ponger Interface

Ponger interface is defined in protos_protoactor.go, of which a developer must provide an implementation to set up a ponger grain.

// Ponger interfaces the services available to the Ponger
type Ponger interface {
	Init(id string)
	ReceiveDefault(ctx actor.Context)
	Ping(*PingMessage, cluster.GrainContext) (*PongMessage, error)

A common method for initialization – Init() – is already implemented by cluster.Grain so a Ponger implementation can re-use this by embedding cluster.Grain as below:

type ponger struct {

However, Terminate(), ReceiveDefault() and Ping() still need to be implemented by a developer. Terminate() is called on passivation right before PongerActor stops and hence the subordinating ponger instance also must stop. ReceiveDefault() is a method to receive any message that are not expected to be handled in gRPC manner; Ping() is a method to recieve PingMessage and return PongMessage in gRPC manner.

type ponger struct {

var _ messages.Ponger = (*ponger)(nil) // This guarantees ponger implements messages.Ponger.

func (*ponger) Terminate() { 
	// A virtual actor always exists, so this usually does not terminate once the actor is initialized.
	// However, a timeout can be set so a virtual actor terminates itself when no message comes for a certain period of time.
	// Do the finalization if required.

func (*ponger) ReceiveDefault(ctx actor.Context) {
	// Do something with a received message. Not necessarily in request-response manner.

func (*ponger) Ping(ping *messages.PingMessage, ctx cluster.GrainContext) (*messages.PongMessage, error) {
	// Receive ping and return pong in gRPC-based protocol
	return nil, nil

Method implementations could be somewhat like below. Because the actor struct is already generated and exported to protos_protoactor.go by protoc-ge-gograinv2, the implementations are pretty simple.

// Terminate takes care of the finalization.
func (p *ponger) Terminate() {
	// Do finalization if required. e.g. Store the current state to storage and switch its behavior to reject further messages.
	// This method is called when a pre-configured idle interval passes from the last message reception.
	// The actor will be re-initialized when a message comes for the next time.
	// Terminating the idle actor is effective to free unused server resource.
	// A poison pill message is enqueued right after this method execution and the actor eventually stops.
	log.Printf("Terminating ponger: %s", p.ID())

// ReceiveDefault is a default method to receive and handle incoming messages.
func (*ponger) ReceiveDefault(ctx actor.Context) {
	switch msg := ctx.Message().(type) {
	case *messages.PingMessage:
		pong := &messages.PongMessage{Cnt: msg.Cnt}
		log.Print("Received ping message")

		// Do nothing


// Ping is a method to support gRPC Ponger service.
func (*ponger) Ping(ping *messages.PingMessage, ctx cluster.GrainContext) (*messages.PongMessage, error) {
	// The sender process is not a sending actor, but a future process
	log.Printf("Sender: %+v", ctx.Sender())

	pong := &messages.PongMessage{
		Cnt: ping.Cnt,
	return pong, nil

Overall ponger process

To activate the ponger grain, a process must be defined as below code. Comments are added to each steps.

Sender Implementation


For a message sender, protos_protoactor.go provides GetPongerGrainClient() function. By calling this function, one can acquire PongerGrainClient instance to initiate gRPC request with PongerGrainClient.Ping(). Making a request in gRPC manner is preferable while the fire-and-forget messaging method such as Context.Send() also works to send message to the destination grain. The gRPC request method calls Cluster.Call to get a hold of ponger grain’s PID, where it retries up to pre-defined threshold count to get the destination PID. As introduced in “Communication Protocol” section, the ownership of the grain may transfer at the same time as one sends a message to it. Retrial logic is vital to make sure the message is actually received by the destination grain. One can pass the retry setting
The implementation can be somewhat like below:

// Setup cluster
c := cluster.Configure(...)
// Get PID of ponger grain
grain := messages.GetPongerGrainClient(clustr, "ponger-1")

// Build a PingMessage payload and make a gRPC request.
ping := &messages.PingMessage{
	Cnt: 1,

// Explicitly define the retrial count
option := cluster.NewGrainCallOptions(c).WithRetry(3)

// Make a request and receive a response
pong, err := grain.Ping(ping, option)

Overall pinger process

Below is the example code to run pinger actor.


As illustrated in this article, clustering is a good way to scale the actor system and have higher availability. A developer can interact with actors in the same way as interacting with a local one because takes care of locating the destination grain, grain activation, and data transport. Thanks to such architecture, a developer may concentrate on the business logic instead of designing an architecture from scratch.

Further Readings