---
project.description: Details about the underlying remoting module for Apache Pekko Cluster.
---
# Artery Remoting

@@@ note

Remoting is the mechanism by which Actors on different nodes talk to each
other internally.

When building a Pekko application, you would usually not use the Remoting concepts
directly, but instead use the more high-level
@ref[Pekko Cluster](index-cluster.md) utilities or technology-agnostic protocols
such as [HTTP]($pekko.doc.dns$/docs/pekko-http/current/),
[gRPC]($pekko.doc.dns$/docs/pekko-grpc/current/) etc.

@@@

If migrating from classic remoting see @ref:[what's new in Artery](#what-is-new-in-artery)

## Dependency

To use Artery Remoting, you must add the following dependency in your project:

@@dependency[sbt,Maven,Gradle] {
  bomGroup=org.apache.pekko bomArtifact=pekko-bom_$scala.binary.version$ bomVersionSymbols=PekkoVersion
  symbol1=PekkoVersion
  value1="$pekko.version$"
  group=org.apache.pekko
  artifact=pekko-remote_$scala.binary.version$
  version=PekkoVersion
}

One option is to use Artery with Aeron, see @ref:[Selecting a transport](#selecting-a-transport).
The Aeron dependency needs to be explicitly added if using the `aeron-udp` transport:

@@dependency[sbt,Maven,Gradle] {
  group=io.aeron
  artifact=aeron-driver
  version="$aeron_version$"
  group2=io.aeron
  artifact2=aeron-client
  version2="$aeron_version$"
}

@@@ note { title="Java 17" }

When using Aeron with Java 17 you have to add JVM flag `--add-opens=java.base/sun.nio.ch=ALL-UNNAMED`.

@@@


## Configuration

To enable remote capabilities in your Pekko project you should, at a minimum, add the following changes
to your `application.conf` file:

```
pekko {
  actor {
    # provider=remote is possible, but prefer cluster
    provider = cluster 
  }
  remote {
    artery {
      transport = tcp # See Selecting a transport below
      canonical.hostname = "127.0.0.1"
      canonical.port = 17355
    }
  }
}
```

As you can see in the example above there are four things you need to add to get started:

 * Change provider from `local`. We recommend using @ref:[Pekko Cluster](cluster-usage.md) over using remoting directly.
 * Enable Artery to use it as the remoting implementation
 * Add host name - the machine you want to run the actor system on; this host
name is exactly what is passed to remote systems in order to identify this
system and consequently used for connecting back to this system if need be,
hence set it to a reachable IP address or resolvable name in case you want to
communicate across the network.
 * Add port number - the port the actor system should listen on, set to 0 to have it chosen automatically

@@@ note

The port number needs to be unique for each actor system on the same machine even if the actor
systems have different names. This is because each actor system has its own networking subsystem
listening for connections and handling messages as not to interfere with other actor systems.

@@@

The example above only illustrates the bare minimum of properties you have to add to enable remoting.
All settings are described in @ref:[Remote Configuration](#remote-configuration-artery).

### Shutdown

Artery first flushes outstanding remote messages and then aborts its streams during `ActorSystem` termination.
The `pekko.remote.artery.advanced.shutdown-streams-timeout` setting limits how long Artery waits for those streams to
complete. If the timeout expires, Artery proceeds with transport shutdown so a stalled stream cannot indefinitely
delay transport-specific shutdown handling. The separate
`pekko.remote.artery.advanced.shutdown-flush-timeout` setting controls the preceding flush.

## Introduction

We recommend @ref:[Pekko Cluster](cluster-usage.md) over using remoting directly. As remoting is the
underlying module that allows for Cluster, it is still useful to understand details about it though.

@@@ note

This page describes the remoting subsystem, codenamed *Artery* that has replaced the
@ref:[classic remoting implementation](remoting.md).

@@@

Remoting enables Actor systems on different hosts or JVMs to communicate with each other. By enabling remoting
the system will start listening on a provided network address and also gains the ability to connect to other
systems through the network. From the application's perspective there is no API difference between local or remote
systems, @apidoc[actor.ActorRef] instances that point to remote systems look exactly the same as local ones: they can be
sent messages to, watched, etc.
Every `ActorRef` contains hostname and port information and can be passed around even on the network. This means
that on a network every `ActorRef` is a unique identifier of an actor on that network.

You need to enable @ref:[serialization](serialization.md) for your actor messages.
@ref:[Serialization with Jackson](serialization-jackson.md) is a good choice in many cases and our
recommendation if you don't have other preference.

Remoting is not a server-client technology. All systems using remoting can contact any other system on the network
if they possess an `ActorRef` pointing to those system. This means that every system that is remoting enabled
acts as a "server" to which arbitrary systems on the same network can connect to.

## Selecting a transport

There are three alternatives of which underlying transport to use. It is configured by property
`pekko.remote.artery.transport` with the possible values:

* `tcp` - Based on @ref:[Pekko Streams TCP](stream/stream-io.md#streaming-tcp) (default if other not configured)
* `tls-tcp` - Same as `tcp` with encryption using @ref:[Pekko Streams TLS](stream/stream-io.md#tls)
* `aeron-udp` - Based on [Aeron (UDP)](https://github.com/real-logic/aeron)

If you are uncertain of what to select a good choice is to use the default, which is `tcp`.

The Aeron (UDP) transport is a high performance transport and should be used for systems
that require high throughput and low latency. It uses more CPU than TCP when the system
is idle or at low message rates. There is no encryption for Aeron.

The TCP and TLS transport is implemented using Pekko Streams TCP/TLS. This is the choice
when encryption is needed, but it can also be used with plain TCP without TLS. It's also
the obvious choice when UDP can't be used.
It has very good performance (high throughput and low latency) but latency at high throughput
might not be as good as the Aeron transport. It has less operational complexity than the
Aeron transport and less risk of trouble in container environments.

Aeron requires 64bit JVM to work reliably and is only officially supported on Linux, Mac and Windows.
It may work on other Unixes e.g. Solaris but insufficient testing has taken place for it to be
officially supported. If you're on a Big Endian processor, such as Sparc, it is recommended to use TCP.

@@@ note

@ref:[Rolling update](additional/rolling-updates.md) is not supported when changing from one transport to another.

@@@

## Canonical address

In order for remoting to work properly, where each system can send messages to any other system on the same network
(for example a system forwards a message to a third system, and the third replies directly to the sender system)
it is essential for every system to have a *unique, globally reachable* address and port. This address is part of the
unique name of the system and will be used by other systems to open a connection to it and send messages. This means
that if a host has multiple names (different DNS records pointing to the same IP address) then only one of these
can be *canonical*. If a message arrives to a system but it contains a different hostname than the expected canonical
name then the message will be dropped. If multiple names for a system would be allowed, then equality checks among
@apidoc[actor.ActorRef] instances would no longer to be trusted and this would violate the fundamental assumption that
an actor has a globally unique reference on a given network. As a consequence, this also means that localhost addresses
(e.g. *127.0.0.1*) cannot be used in general (apart from local development) since they are not unique addresses in a
real network.

In cases, where Network Address Translation (NAT) is used or other network bridging is involved, it is important
to configure the system so that it understands that there is a difference between his externally visible, canonical
address and between the host-port pair that is used to listen for connections. See @ref:[Pekko behind NAT or in a Docker container](#remote-configuration-nat-artery)
for details.

## Acquiring references to remote actors

In order to communicate with an actor, it is necessary to have its @apidoc[actor.ActorRef]. In the local case it is usually
the creator of the actor (the caller of `actorOf()`) is who gets the `ActorRef` for an actor that it can
then send to other actors. In other words:

 * An Actor can get a remote Actor's reference by receiving a message from it (as it's available as @scala[@scaladoc[sender()](pekko.actor.Actor#sender():org.apache.pekko.actor.ActorRef)]@java[@javadoc[getSender()](pekko.actor.AbstractActor#getSender())] then),
or inside of a remote message (e.g. *PleaseReply(message: String, remoteActorRef: ActorRef)*)

Alternatively, an actor can look up another located at a known path using
@apidoc[actor.ActorSelection]. These methods are available even in remoting enabled systems:

 * Remote Lookup    : used to look up an actor on a remote node with @apidoc[actorSelection(path)](actor.ActorRefFactory) {scala="#actorSelection(path:String):org.apache.pekko.actor.ActorSelection" java="#actorSelection(java.lang.String)"}
 * Remote Creation  : used to create an actor on a remote node with @apidoc[actorOf(Props(...), actorName)](actor.ActorRefFactory) {scala="#actorOf(props:org.apache.pekko.actor.Props,name:String):org.apache.pekko.actor.ActorRef" java="#actorOf(org.apache.pekko.actor.Props,java.lang.String)"}

In the next sections the two alternatives are described in detail.

### Looking up Remote Actors

@apidoc[actorSelection(path)](actor.ActorRefFactory) {scala="#actorSelection(path:String):org.apache.pekko.actor.ActorSelection" java="#actorSelection(java.lang.String)"} will obtain an @apidoc[actor.ActorSelection] to an Actor on a remote node, e.g.:

Scala
:   ```
    val selection =
      context.actorSelection("pekko://actorSystemName@10.0.0.1:17355/user/actorName")
    ```
    
Java
:   ```
    ActorSelection selection =
      context.actorSelection("pekko://actorSystemName@10.0.0.1:17355/user/actorName");
    ```
    

As you can see from the example above the following pattern is used to find an actor on a remote node:

```
pekko://<actor system>@<hostname>:<port>/<actor path>
```

@@@ note

Unlike with earlier remoting, the protocol field is always *pekko* as pluggable transports are no longer supported.

@@@

Once you obtained a selection to the actor you can interact with it in the same way you would with a local actor, e.g.:

Scala
:   @@@vars
    ```
    selection ! "Pretty awesome feature"
    ```
    @@@

Java
:   @@@vars
    ```
    selection.tell("Pretty awesome feature", getSelf());
    ```
    @@@


To acquire an @apidoc[actor.ActorRef] for an @apidoc[actor.ActorSelection] you need to
send a message to the selection and use the @scala[@scaladoc[sender()](pekko.actor.Actor#sender():org.apache.pekko.actor.ActorRef)]@java[@javadoc[getSender()](pekko.actor.AbstractActor#getSender())] reference of the reply from
the actor. There is a built-in @apidoc[actor.Identify] message that all Actors will understand
and automatically reply to with a @apidoc[actor.ActorIdentity] message containing the
`ActorRef`. This can also be done with the @apidoc[resolveOne](actor.ActorSelection) {scala="#resolveOne(timeout:scala.concurrent.duration.FiniteDuration):scala.concurrent.Future[org.apache.pekko.actor.ActorRef]" java="#resolveOne(java.time.Duration)"} method of
the `ActorSelection`, which returns a @scala[@scaladoc[Future](scala.concurrent.Future)]@java[@javadoc[CompletionStage](java.util.concurrent.CompletionStage)] of the matching
`ActorRef`.

For more details on how actor addresses and paths are formed and used, please refer to @ref:[Actor References, Paths and Addresses](general/addressing.md).

@@@ note

Message sends to actors that are actually in the sending actor system do not
get delivered via the remote actor ref provider. They're delivered directly,
by the local actor ref provider.

Aside from providing better performance, this also means that if the hostname
you configure remoting to listen as cannot actually be resolved from within
the very same actor system, such messages will (perhaps counterintuitively)
be delivered just fine.

@@@

## Remote Security

See @ref:[Remote Security](remote-security.md) for security considerations for Pekko Remoting.

<a id="remote-tls"></a>
### Configuring SSL/TLS for Pekko Remoting

See @ref:[Configuring SSL/TLS for Pekko Remoting](remote-security.md#remote-tls). For Kubernetes deployments, also see
@ref:[mTLS with rotated certificates in Kubernetes](remote-security.md#mtls-with-rotated-certificates-in-kubernetes).

### Untrusted Mode

See @ref:[Untrusted Mode](remote-security.md#untrusted-mode).

## Quarantine

Pekko remoting is using TCP or Aeron as underlying message transport. Aeron is using UDP and adds
among other things reliable delivery and session semantics, very similar to TCP. This means that
the order of the messages are preserved, which is needed for the @ref:[Actor message ordering guarantees](general/message-delivery-reliability.md#message-ordering).
Under normal circumstances all messages will be delivered but there are cases when messages
may not be delivered to the destination:

 * during a network partition when the TCP connection or the Aeron session is broken, this automatically recovered once the partition is over
 * when sending too many messages without flow control and thereby filling up the outbound send queue (`outbound-message-queue-size` config)
 * if serialization or deserialization of a message fails (only that message will be dropped)
 * if an unexpected exception occurs in the remoting infrastructure

In short, Actor message delivery is “at-most-once” as described in @ref:[Message Delivery Reliability](general/message-delivery-reliability.md)

Some messages in Pekko are called system messages and those cannot be dropped because that would result
in an inconsistent state between the systems. Such messages are used for essentially two features; remote death
watch and remote deployment. These messages are delivered by Pekko remoting with “exactly-once” guarantee by
confirming each message and resending unconfirmed messages. If a system message anyway cannot be delivered the
association with the destination system is irrecoverable failed, and Terminated is signaled for all watched
actors on the remote system. It is placed in a so called quarantined state. Quarantine usually does not
happen if remote watch or remote deployment is not used.

Each @apidoc[actor.ActorSystem] instance has an unique identifier (UID), which is important for differentiating between
incarnations of a system when it is restarted with the same hostname and port. It is the specific
incarnation (UID) that is quarantined. The only way to recover from this state is to restart one of the
actor systems.

Messages that are sent to and received from a quarantined system will be dropped. However, it is possible to
send messages with `actorSelection` to the address of a quarantined system, which is useful to probe if the
system has been restarted.

An association will be quarantined when:

 * Cluster node is removed from the cluster membership.
 * Remote failure detector triggers, i.e. remote watch is used. This is different when @ref:[Pekko Cluster](cluster-usage.md)
is used. The unreachable observation by the cluster failure detector can go back to reachable if the network
partition heals. A cluster member is not quarantined when the failure detector triggers.
 * Overflow of the system message delivery buffer, e.g. because of too many `watch` requests at the same time
(`system-message-buffer-size` config).
 * Unexpected exception occurs in the control subchannel of the remoting infrastructure.

The UID of the @apidoc[actor.ActorSystem] is exchanged in a two-way handshake when the first message is sent to
a destination. The handshake will be retried until the other system replies and no other messages will
pass through until the handshake is completed. If the handshake cannot be established within a timeout
(`handshake-timeout` config) the association is stopped (freeing up resources). Queued messages will be
dropped if the handshake cannot be established. It will not be quarantined, because the UID is unknown.
New handshake attempt will start when next message is sent to the destination.

Handshake requests are actually also sent periodically to be able to establish a working connection
when the destination system has been restarted.

### Watching Remote Actors

Watching a remote actor is API wise not different than watching a local actor, as described in
@ref:[Lifecycle Monitoring aka DeathWatch](actors.md#deathwatch). However, it is important to note, that unlike in the local case, remoting has to handle
when a remote actor does not terminate in a graceful way sending a system message to notify the watcher actor about
the event, but instead being hosted on a system which stopped abruptly (crashed). These situations are handled
by the built-in failure detector.

### Failure Detector

Under the hood remote death watch uses heartbeat messages and a failure detector to generate @apidoc[actor.Terminated]
message from network failures and JVM crashes, in addition to graceful termination of watched
actor.

The heartbeat arrival times is interpreted by an implementation of
[The Phi Accrual Failure Detector](https://dspace.jaist.ac.jp/dspace/bitstream/10119/4784/1/IS-RR-2004-010.pdf).

The suspicion level of failure is given by a value called *phi*.
The basic idea of the phi failure detector is to express the value of *phi* on a scale that
is dynamically adjusted to reflect current network conditions.

The value of *phi* is calculated as:

```
phi = -log10(1 - F(timeSinceLastHeartbeat))
```

where F is the cumulative distribution function of a normal distribution with mean
and standard deviation estimated from historical heartbeat inter-arrival times.

In the @ref:[Remote Configuration](#remote-configuration-artery) you can adjust the `pekko.remote.watch-failure-detector.threshold`
to define when a *phi* value is considered to be a failure.

A low `threshold` is prone to generate many false positives but ensures
a quick detection in the event of a real crash. Conversely, a high `threshold`
generates fewer mistakes but needs more time to detect actual crashes. The
default `threshold` is 10 and is appropriate for most situations. However in
cloud environments, such as Amazon EC2, the value could be increased to 12 in
order to account for network issues that sometimes occur on such platforms.

The following chart illustrates how *phi* increase with increasing time since the
previous heartbeat.

![phi1.png](./images/phi1.png)

Phi is calculated from the mean and standard deviation of historical
inter arrival times. The previous chart is an example for standard deviation
of 200 ms. If the heartbeats arrive with less deviation the curve becomes steeper,
i.e. it is possible to determine failure more quickly. The curve looks like this for
a standard deviation of 100 ms.

![phi2.png](./images/phi2.png)

To be able to survive sudden abnormalities, such as garbage collection pauses and
transient network failures the failure detector is configured with a margin,
`pekko.remote.watch-failure-detector.acceptable-heartbeat-pause`. You may want to
adjust the @ref:[Remote Configuration](#remote-configuration-artery) of this depending on you environment.
This is how the curve looks like for `acceptable-heartbeat-pause` configured to
3 seconds.

![phi3.png](./images/phi3.png)

## Serialization

You need to enable @ref:[serialization](serialization.md) for your actor messages.
@ref:[Serialization with Jackson](serialization-jackson.md) is a good choice in many cases and our
recommendation if you don't have other preference.

<a id="remote-bytebuffer-serialization"></a>
### ByteBuffer based serialization

Artery introduces a new serialization mechanism which allows the @apidoc[ByteBufferSerializer] to directly write into a
shared @javadoc[java.nio.ByteBuffer](java.nio.ByteBuffer) instead of being forced to allocate and return an `Array[Byte]` for each serialized
message. For high-throughput messaging this API change can yield significant performance benefits, so we recommend
changing your serializers to use this new mechanism.

This new API also plays well with new versions of Google Protocol Buffers and other serialization libraries, which gained
the ability to serialize directly into and from ByteBuffers.

As the new feature only changes how bytes are read and written, and the rest of the serialization infrastructure
remained the same, we recommend reading the @ref:[Serialization](serialization.md) documentation first.

Implementing an `org.apache.pekko.serialization.ByteBufferSerializer` works the same way as any other serializer,

Scala
:  @@snip [Serializer.scala](/actor/src/main/scala/org/apache/pekko/serialization/Serializer.scala) { #ByteBufferSerializer }

Java
:  @@snip [ByteBufferSerializerDocTest.java](/docs/src/test/java/jdocs/actor/ByteBufferSerializerDocTest.java) { #ByteBufferSerializer-interface }

Implementing a serializer for Artery is therefore as simple as implementing this interface, and binding the serializer
as usual (which is explained in @ref:[Serialization](serialization.md)).

Implementations should typically extend @apidoc[SerializerWithStringManifest] and in addition to the `ByteBuffer` based
@apidoc[toBinary](ByteBufferSerializer) {scala="#toBinary(o:AnyRef,buf:java.nio.ByteBuffer):Unit" java="#toBinary(java.lang.Object,java.nio.ByteBuffer)"} and @apidoc[fromBinary](ByteBufferSerializer) {scala="#fromBinary(buf:java.nio.ByteBuffer,manifest:String):AnyRef" java="#fromBinary(java.nio.ByteBuffer,java.lang.String)"} methods also implement the array based @apidoc[toBinary](SerializerWithStringManifest) {scala="#toBinary(o:AnyRef):Array[Byte]" java="#toBinary(java.lang.Object)"} and @apidoc[fromBinary](SerializerWithStringManifest) {scala="#fromBinary(bytes:Array[Byte],manifest:Option[Class[_]]):AnyRef" java="#fromBinary(byte%5B%5D,scala.Option)"} methods.
The array based methods will be used when `ByteBuffer` is not used, e.g. in Pekko Persistence.

Note that the array based methods can be implemented by delegation like this:

Scala
:  @@snip [ByteBufferSerializerDocSpec.scala](/docs/src/test/scala/docs/actor/ByteBufferSerializerDocSpec.scala) { #bytebufserializer-with-manifest }

Java
:  @@snip [ByteBufferSerializerDocTest.java](/docs/src/test/java/jdocs/actor/ByteBufferSerializerDocTest.java) { #bytebufserializer-with-manifest }

## Routers with Remote Destinations

It is absolutely feasible to combine remoting with @ref:[Routing](routing.md).

A pool of remote deployed routees can be configured as:

@@snip [RouterDocSpec.scala](/docs/src/test/scala/docs/routing/RouterDocSpec.scala) { #config-remote-round-robin-pool-artery }

This configuration setting will clone the actor defined in the `Props` of the `remotePool` 10
times and deploy it evenly distributed across the two given target nodes.

When using a pool of remote deployed routees you must ensure that all parameters of the `Props` can
be @ref:[serialized](serialization.md).

A group of remote actors can be configured as:

@@snip [RouterDocSpec.scala](/docs/src/test/scala/docs/routing/RouterDocSpec.scala) { #config-remote-round-robin-group-artery }

This configuration setting will send messages to the defined remote actor paths.
It requires that you create the destination actors on the remote nodes with matching paths.
That is not done by the router.

## What is new in Artery

Artery is a reimplementation of the old remoting module aimed at improving performance and stability. It is mostly
source compatible with the old implementation and it is a drop-in replacement in many cases. Main features
of Artery compared to the previous implementation:

 * Based on Pekko Streams TCP/TLS or [Aeron](https://github.com/real-logic/Aeron) (UDP) instead of Netty TCP
 * Focused on high-throughput, low-latency communication
 * Isolation of internal control messages from user messages improving stability and reducing false failure detection
in case of heavy traffic by using a dedicated subchannel.
 * Mostly allocation-free operation
 * Support for a separate subchannel for large messages to avoid interference with smaller messages
 * Compression of actor paths on the wire to reduce overhead for smaller messages
 * Support for faster serialization/deserialization using ByteBuffers directly
 * Built-in Java Flight Recorder (JFR) to help debugging implementation issues without polluting users logs with implementation
specific events
 * Providing protocol stability across major Pekko versions to support rolling updates of large-scale systems

The main incompatible change from the previous implementation is that the protocol field of the string representation of an
@apidoc[actor.ActorRef] is always *pekko* instead of the previously used *pekko.tcp* or *pekko.ssl.tcp*. Configuration properties
are also different.


## Performance tuning

### Lanes

Message serialization and deserialization can be a bottleneck for remote communication. Therefore there is support for parallel inbound and outbound lanes to perform serialization and other tasks for different destination actors in parallel. Using multiple lanes is of most value for the inbound messages, since all inbound messages from all remote systems share the same inbound stream. For outbound messages there is already one stream per remote destination system, so multiple outbound lanes only add value when sending to different actors in same destination system.

The selection of lane is based on consistent hashing of the recipient ActorRef to preserve message ordering per receiver.

Note that lowest latency can be achieved with `inbound-lanes=1` and `outbound-lanes=1` because multiple lanes introduce an asynchronous boundary. 

Also note that the total amount of parallel tasks are bound by the `remote-dispatcher` and the thread pool size should not exceed the number of CPU cores minus headroom for actually processing the messages in the application, i.e. in practice the pool size should be less than half of the number of cores.

See `inbound-lanes` and `outbound-lanes` in the @ref:[reference configuration](general/configuration-reference.md#config-pekko-remote-artery) for default values.

### Dedicated subchannel for large messages

All the communication between user defined remote actors are isolated from the channel of Pekko internal messages so
a large user message cannot block an urgent system message. While this provides good isolation for Pekko services, all
user communications by default happen through a shared network connection. When some actors
send large messages this can cause other messages to suffer higher latency as they need to wait until the full
message has been transported on the shared channel (and hence, shared bottleneck). In these cases it is usually
helpful to separate actors that have different QoS requirements: large messages vs. low latency.

Pekko remoting provides a dedicated channel for large messages if configured. Since actor message ordering must
not be violated the channel is actually dedicated for *actors* instead of messages, to ensure all of the messages
arrive in send order. It is possible to assign actors on given paths to use this dedicated channel by using
path patterns that have to be specified in the actor system's configuration on both the sending and the receiving side:

```
pekko.remote.artery.large-message-destinations = [
   "/user/largeMessageActor",
   "/user/largeMessagesGroup/*",
   "/user/anotherGroup/*/largeMesssages",
   "/user/thirdGroup/**",
   "/temp/session-ask-actor*"
]
```

This means that all messages sent to the following actors will pass through the dedicated, large messages channel:

 * `/user/largeMessageActor`
 * `/user/largeMessageActorGroup/actor1`
 * `/user/largeMessageActorGroup/actor2`
 * `/user/anotherGroup/actor1/largeMessages`
 * `/user/anotherGroup/actor2/largeMessages`
 * `/user/thirdGroup/actor3/`
 * `/user/thirdGroup/actor4/actor5`
 * `/temp/session-ask-actor$abc`

Messages destined for actors not matching any of these patterns are sent using the default channel as before.

To notice large messages you can enable logging of message types with payload size in bytes larger than the
configured `log-frame-size-exceeding`.

```
pekko.remote.artery {
  log-frame-size-exceeding = 10000b
}
```

Example log messages:

```
[INFO] Payload size for [java.lang.String] is [39068] bytes. Sent to Actor[pekko://Sys@localhost:53039/user/destination#-1908386800]
[INFO] New maximum payload size for [java.lang.String] is [44068] bytes. Sent to Actor[pekko://Sys@localhost:53039/user/destination#-1908386800].
```

The large messages channel can still not be used for extremely large messages, a few MB per message at most.
An alternative is to use the @ref:[Reliable delivery](typed/reliable-delivery.md) that has support for 
automatically @ref[splitting up large messages](typed/reliable-delivery.md#chunk-large-messages) and assemble
them again on the receiving side.

### External, shared Aeron media driver

The Aeron transport is running in a so called [media driver](https://github.com/real-logic/Aeron/wiki/Media-Driver-Operation).
By default, Pekko starts the media driver embedded in the same JVM process as application. This is
convenient and simplifies operational concerns by only having one process to start and monitor.

The media driver may use rather much CPU resources. If you run more than one Pekko application JVM on the
same machine it can therefore be wise to share the media driver by running it as a separate process.

The media driver has also different resource usage characteristics than a normal application and it can
therefore be more efficient and stable to run the media driver as a separate process.

Given that Aeron jar files are in the classpath the standalone media driver can be started with:

```
java io.aeron.driver.MediaDriver
```

The classpath would be approximately (but you will need to fix up the version numbers):

```
agrona-2.4.1.jar:aeron-driver-1.50.4.jar:aeron-client-1.50.4.jar
```

You find those jar files on [Maven Central](https://search.maven.org/), or you can create a
package with your preferred build tool.

You can pass [Aeron properties](https://github.com/real-logic/Aeron/wiki/Configuration-Options) as
command line *-D* system properties:

```
-Daeron.dir=/dev/shm/aeron
```

You can also define Aeron properties in a file:

```
java io.aeron.driver.MediaDriver config/aeron.properties
```

An example of such a properties file:

```
aeron.mtu.length=16384
aeron.socket.so_sndbuf=2097152
aeron.socket.so_rcvbuf=2097152
aeron.rcv.buffer.length=16384
aeron.rcv.initial.window.length=2097152
agrona.disable.bounds.checks=true

aeron.threading.mode=SHARED_NETWORK

# low latency settings
#aeron.threading.mode=DEDICATED
#aeron.sender.idle.strategy=org.agrona.concurrent.BusySpinIdleStrategy
#aeron.receiver.idle.strategy=org.agrona.concurrent.BusySpinIdleStrategy

# use same director in pekko.remote.artery.advanced.aeron-dir config
# of the Pekko application
aeron.dir=/dev/shm/aeron
```

Read more about the media driver in the [Aeron documentation](https://github.com/real-logic/Aeron/wiki/Media-Driver-Operation).

To use the external media driver from the Pekko application you need to define the following two
configuration properties:

```
pekko.remote.artery.advanced.aeron {
  embedded-media-driver = off
  aeron-dir = /dev/shm/aeron
}
```

The `aeron-dir` must match the directory you started the media driver with, i.e. the `aeron.dir` property.

Several Pekko applications can then be configured to use the same media driver by pointing to the
same directory.

Note that if the media driver process is stopped the Pekko applications that are using it will also be stopped.

### Aeron Tuning

See Aeron documentation about [Performance Testing](https://github.com/real-logic/Aeron/wiki/Performance-Testing).

### Fine-tuning CPU usage latency tradeoff

Artery has been designed for low latency and as a result it can be CPU hungry when the system is mostly idle.
This is not always desirable. When using the Aeron transport it is possible to tune the tradeoff between CPU
usage and latency with the following configuration:

```
# Values can be from 1 to 10, where 10 strongly prefers low latency
# and 1 strongly prefers less CPU usage
pekko.remote.artery.advanced.aeron.idle-cpu-level = 1
```

By setting this value to a lower number, it tells Pekko to do longer "sleeping" periods on its thread dedicated
for [spin-waiting](https://en.wikipedia.org/wiki/Busy_waiting) and hence reducing CPU load when there is no
immediate task to execute at the cost of a longer reaction time to an event when it actually happens. It is worth
to be noted though that during a continuously high-throughput period this setting makes not much difference
as the thread mostly has tasks to execute. This also means that under high throughput (but below maximum capacity)
the system might have less latency than at low message rates.

<a id="remote-configuration-artery"></a>
## Remote Configuration

There are lots of configuration properties that are related to remoting in Pekko. We refer to the
@ref:[reference configuration](general/configuration-reference.md#config-pekko-remote-artery) for more information.

@@@ note

Setting properties like the listening IP and port number programmatically is
best done by using something like the following:

@@snip [RemoteDeploymentDocTest.java](/docs/src/test/java/jdocs/remoting/RemoteDeploymentDocTest.java) { #programmatic-artery }

@@@

<a id="remote-configuration-nat-artery"></a>
### Pekko behind NAT or in a Docker container

In setups involving Network Address Translation (NAT), Load Balancers or Docker
containers the hostname and port pair that Pekko binds to will be different than the "logical"
host name and port pair that is used to connect to the system from the outside. This requires
special configuration that sets both the logical and the bind pairs for remoting.

```
pekko {
  remote {
    artery {
      canonical.hostname = my.domain.com      # external (logical) hostname
      canonical.port = 8000                   # external (logical) port

      bind.hostname = local.address # internal (bind) hostname
      bind.port = 17355              # internal (bind) port
    }
 }
}
```

You can look at the
@java[@extref[Cluster with docker-compose example project](samples:pekko-sample-cluster-docker-compose-java/)]
@scala[@extref[Cluster with docker-compose example project](samples:pekko-sample-cluster-docker-compose-scala/)]
to see what this looks like in practice.

### Running in Docker/Kubernetes

When using `aeron-udp` in a containerized environment special care must be taken that the media driver runs on a ram disk.
This by default is located in `/dev/shm` which on most physical Linux machines will be mounted as half the size of the system memory.

Docker and Kubernetes mount a 64Mb ram disk. This is unlikely to be large enough. For docker this can be overridden with `--shm-size="512mb"`.

In Kubernetes there is no direct support (yet) for setting `shm` size. Instead mount an `EmptyDir` with type `Memory` to `/dev/shm` for example in a
deployment.yml:

```
spec:
  containers:
  - name: artery-udp-cluster
    // rest of container spec...
    volumeMounts:
    - mountPath: /dev/shm
      name: media-driver
  volumes:
  - name: media-driver
    emptyDir:
      medium: Memory
      name: media-driver
```

There is currently no way to limit the size of a memory empty dir but there is a [pull request](https://github.com/kubernetes/kubernetes/pull/63641) for adding it.

Any space used in the mount will count towards your container's memory usage.


### Flight Recorder

When running on JDK 11 Artery specific flight recording is available through the [Java Flight Recorder (JFR)](https://openjdk.java.net/jeps/328).
The flight recorder is automatically enabled by detecting JDK 11 but can be disabled if needed by setting `pekko.java-flight-recorder.enabled = false`.

Low overhead Artery specific events are emitted by default when JFR is enabled, higher overhead events needs a custom settings template and are not enabled automatically with the `profiling` JFR template.
To enable those create a copy of the `profiling` template and enable all `Pekko` sub category events, for example through the JMC GUI. 
