Remote Procedure Call (RPC)

In the second tutorial we learned how to use Work Queues to distribute time-consuming tasks among multiple workers.

But what if we need to run a function on a remote computer and wait for the result? Well, that’s a different story. This pattern is commonly known as Remote Procedure Call or RPC.

In this tutorial we’re going to use RabbitMQ to build an RPC system: a client and a scalable RPC server. As we don’t have any time-consuming tasks that are worth distributing, we’re going to create a dummy RPC service that returns Fibonacci numbers.

Client interface

To illustrate how an RPC service could be used we’re going to create a simple client class. It’s going to expose a method named call which sends an RPC request and blocks until the answer is received:

val result = rpcClient(this, 30)
println("fib(30) = $result")

A note on RPC

Although RPC is a pretty common pattern in computing, it’s often criticised. The problems arise when a programmer is not aware whether a function call is local or if it’s a slow RPC. Confusions like that result in an unpredictable system and adds unnecessary complexity to debugging. Instead of simplifying software, misused RPC can result in unmaintainable spaghetti code.

Bearing that in mind, consider the following advice:

• Make sure it’s obvious which function call is local and which is remote.
• Document your system. Make the dependencies between components clear.
• Handle error cases. How should the client react when the RPC server is down for a long time?

When in doubt avoid RPC. If you can, you should use an asynchronous pipeline - instead of RPC-like blocking, results are asynchronously pushed to a next computation stage.

Callback queue

In general doing RPC over RabbitMQ is easy. A client sends a request message and a server replies with a response message. In order to receive a response we need to send a ‘callback’ queue address with the request. We can use the default exchange. Let’s try it:

val callbackQueueDeclared = channel.queueDeclare(
    name = "",
    durable = false,
    exclusive = true,
    autoDelete = true,
    arguments = emptyMap()
)
val callbackQueueName = callbackQueueDeclared.queueName

val requestProps = properties {
    this.replyTo = callbackQueueName
}

channel.basicPublish(
    "30".toByteArray(),
    exchange = "",
    routingKey = "rpc_queue",
    properties = requestProps
)

// ... then code to read a response message from the callback queue ...

Message properties

The AMQP 0-9-1 protocol predefines a set of 14 properties that go with a message. Most of the properties are rarely used, with the exception of the following:

• deliveryMode: Marks a message as persistent (with a value of 2) or transient (any other value)
• contentType: Used to describe the mime-type of the encoding
• replyTo: Commonly used to name a callback queue
• correlationId: Useful to correlate RPC responses with requests

Correlation Id

In the method presented above we suggest creating a callback queue for every RPC request. That’s pretty inefficient, but fortunately there is a better way - let’s create a single callback queue per client.

That raises a new issue, having received a response in that queue it’s not clear to which request the response belongs. That’s when the correlationId property is used. We’re going to set it to a unique value for every request. Later, when we receive a message in the callback queue we’ll look at this property, and based on that we’ll be able to match a response with a request. If we see an unknown correlationId value, we may safely discard the message - it doesn’t belong to our requests.

You may ask, why should we ignore unknown messages in the callback queue, rather than failing with an error? It’s due to a possibility of a race condition on the server side. Although unlikely, it is possible that the RPC server will die just after sending us the answer, but before sending an acknowledgment message for the request. If that happens, the restarted RPC server will process the request again. That’s why on the client we must handle the duplicate responses gracefully, and the RPC should ideally be idempotent.

Summary

sequenceDiagram
    participant Client
    participant RPC_Queue
    participant Server
    participant Callback_Queue

    Client->>Client: Create exclusive callback queue
    Client->>RPC_Queue: Send request (replyTo, correlationId)
    RPC_Queue->>Server: Deliver request
    Server->>Server: Process request (calculate fib)
    Server->>Callback_Queue: Send response (correlationId)
    Callback_Queue->>Client: Deliver response
    Client->>Client: Match correlationId, return result

Our RPC will work like this:

• For an RPC request, the Client sends a message with two properties: replyTo, which is set to an exclusive callback queue created just for the request, and correlationId, which is set to a unique value for every request.
• The request is sent to an rpc_queue queue.
• The RPC worker (aka: server) is waiting for requests on that queue. When a request appears, it does the job and sends a message with the result back to the Client, using the queue from the replyTo field.
• The client waits for data on the callback queue. When a message appears, it checks the correlationId property. If it matches the value from the request it returns the response to the application.

Putting it all together

The Fibonacci function:

private fun fib(n: Int): Int {
    return when {
        n == 0 -> 0
        n == 1 -> 1
        else -> fib(n - 1) + fib(n - 2)
    }
}

We declare our fibonacci function. It assumes only valid positive integer input. (Don’t expect this one to work for big numbers, and it’s probably the slowest recursive implementation possible).

The code for our RPC server:

suspend fun rpcServer(coroutineScope: CoroutineScope) {
    val config = amqpConfig {
        server {
            host = "localhost"
        }
    }
    val connection = createAMQPConnection(coroutineScope, config)
    val channel = connection.openChannel()

    channel.queueDeclare(
        "rpc_queue",
        durable = false,
        exclusive = false,
        autoDelete = false,
        arguments = emptyMap()
    )

    channel.basicQos(count = 1u, global = false)

    println(" [x] Awaiting RPC requests")

    val consumer = channel.basicConsume("rpc_queue", noAck = false)

    for (delivery in consumer) {
        val props = delivery.message.properties
        val correlationId = props.correlationId
        val replyTo = props.replyTo

        val requestMessage = delivery.message.body.decodeToString()
        val n = requestMessage.toIntOrNull() ?: 0

        println(" [.] fib($n)")
        val response = fib(n)

        val replyProps = properties {
            this.correlationId = correlationId
        }

        if (replyTo != null) {
            channel.basicPublish(
                response.toString().toByteArray(),
                exchange = "",
                routingKey = replyTo,
                properties = replyProps
            )
        }

        channel.basicAck(delivery.message, multiple = false)
    }

    channel.close()
    connection.close()
}

The server code is rather straightforward:

• As usual we start by establishing the connection and declaring the queue.
• We might want to run more than one server process. In order to spread the load equally over multiple servers we need to set the basicQos with the count = 1u setting.
• We use basicConsume to access the queue. Then we enter the loop where we wait for request messages, do the work and send the response back.

The code for our RPC client:

suspend fun rpcClient(coroutineScope: CoroutineScope, n: Int): Int {
    val config = amqpConfig {
        server {
            host = "localhost"
        }
    }
    val connection = createAMQPConnection(coroutineScope, config)
    val channel = connection.openChannel()

    val callbackQueueDeclared = channel.queueDeclare(
        name = "",
        durable = false,
        exclusive = true,
        autoDelete = true,
        arguments = emptyMap()
    )
    val callbackQueueName = callbackQueueDeclared.queueName

    val correlationId = UUID.randomUUID().toString()

    val consumer = channel.basicConsume(callbackQueueName, noAck = true)
    var result = 0

    val requestProps = properties {
        this.correlationId = correlationId
        this.replyTo = callbackQueueName
    }

    channel.basicPublish(
        n.toString().toByteArray(),
        exchange = "",
        routingKey = "rpc_queue",
        properties = requestProps
    )
    println(" [x] Requesting fib($n)")

    for (delivery in consumer) {
        val responseCorrelationId = delivery.message.properties.correlationId

        if (responseCorrelationId == correlationId) {
            result = delivery.message.body.decodeToString().toInt()
            println(" [.] Got $result")
            break
        }
    }

    channel.close()
    connection.close()

    return result
}

We establish a connection and channel. We declare an exclusive callback queue for replies. We subscribe to the callback queue, so that we can receive RPC responses. We generate a unique correlationId number and save it. The loop is waiting for an appropriate response and whenever we get a response we check if the correlationId is the one we’re looking for. If so, we save the response.

Making an RPC request:

fun main() = runBlocking {
    // Start RPC server
    launch {
        rpcServer(this)
    }

    delay(1000)

    // Make RPC call
    val result = rpcClient(this, 30)
    println("fib(30) = $result")
}

The presented design is not the only possible implementation of an RPC service, but it has some important advantages:

• If the RPC server is too slow, you can scale up by just running another one. Try running a second RPC server in a new console.
• On the client side, the RPC requires sending and receiving only one message. No synchronous calls like queueDeclare are required. As a result the RPC client needs only one network round trip for a single RPC request.

Our code is still pretty simplistic and doesn’t try to solve more complex (but important) problems, like:

• How should the client react if there are no servers running?
• Should a client have some kind of timeout for the RPC?
• If the server malfunctions and raises an exception, should it be forwarded to the client?
• Protecting against invalid incoming messages (eg checking bounds, type) before processing.

Now might be a good time to take a look at the management UI and see if you can see the queues and messages.