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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.
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.
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 queue. Let's try it:
q, err := ch.QueueDeclare(
"", // name
false, // durable
false, // delete when unused
true, // exclusive
false, // noWait
nil, // arguments
)
err = ch.Publish(
"", // exchange
"rpc_queue", // routing key
false, // mandatory
false, // immediate
amqp.Publishing{
ContentType: "text/plain",
CorrelationId: corrId,
ReplyTo: q.Name,
Body: []byte(strconv.Itoa(n)),
})
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:
persistent: Marks a message as persistent (with a value oftrue) or transient (false). You may remember this property from the second tutorial.content_type: Used to describe the mime-type of the encoding. For example for the often used JSON encoding it is a good practice to set this property to:application/json.reply_to: Commonly used to name a callback queue.correlation_id: Useful to correlate RPC responses with requests.
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 correlation_id 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 correlation_id 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.
Our RPC will work like this:
reply_to, which is set to the callback queue and correlation_id, which is set to a unique value for every request.rpc_queue queue.reply_to field.correlation_id property. If it matches the value from the request it returns the response to the application.The Fibonacci function:
func fib(n int) int {
if n == 0 {
return 0
} else if n == 1 {
return 1
} else {
return 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 rpc_server.go looks like this:
package main
import (
"log"
"strconv"
amqp "github.com/rabbitmq/amqp091-go"
)
func failOnError(err error, msg string) {
if err != nil {
log.Panicf("%s: %s", msg, err)
}
}
func fib(n int) int {
if n == 0 {
return 0
} else if n == 1 {
return 1
} else {
return fib(n-1) + fib(n-2)
}
}
func main() {
conn, err := amqp.Dial("amqp://guest:guest@localhost:5672/")
failOnError(err, "Failed to connect to RabbitMQ")
defer conn.Close()
ch, err := conn.Channel()
failOnError(err, "Failed to open a channel")
defer ch.Close()
q, err := ch.QueueDeclare(
"rpc_queue", // name
false, // durable
false, // delete when unused
false, // exclusive
false, // no-wait
nil, // arguments
)
failOnError(err, "Failed to declare a queue")
err = ch.Qos(
1, // prefetch count
0, // prefetch size
false, // global
)
failOnError(err, "Failed to set QoS")
msgs, err := ch.Consume(
q.Name, // queue
"", // consumer
false, // auto-ack
false, // exclusive
false, // no-local
false, // no-wait
nil, // args
)
failOnError(err, "Failed to register a consumer")
var forever chan struct{}
go func() {
for d := range msgs {
n, err := strconv.Atoi(string(d.Body))
failOnError(err, "Failed to convert body to integer")
log.Printf(" [.] fib(%d)", n)
response := fib(n)
err = ch.Publish(
"", // exchange
d.ReplyTo, // routing key
false, // mandatory
false, // immediate
amqp.Publishing{
ContentType: "text/plain",
CorrelationId: d.CorrelationId,
Body: []byte(strconv.Itoa(response)),
})
failOnError(err, "Failed to publish a message")
d.Ack(false)
}
}()
log.Printf(" [*] Awaiting RPC requests")
<-forever
}
The server code is rather straightforward:
prefetch setting on channel.Channel.Consume to get the go channel where we receive messages from the queue. Then we enter the goroutine where we do the work and send the response back.The code for our RPC client rpc_client.go:
package main
import (
"log"
"math/rand"
"os"
"strconv"
"strings"
"time"
amqp "github.com/rabbitmq/amqp091-go"
)
func failOnError(err error, msg string) {
if err != nil {
log.Panicf("%s: %s", msg, err)
}
}
func randomString(l int) string {
bytes := make([]byte, l)
for i := 0; i < l; i++ {
bytes[i] = byte(randInt(65, 90))
}
return string(bytes)
}
func randInt(min int, max int) int {
return min + rand.Intn(max-min)
}
func fibonacciRPC(n int) (res int, err error) {
conn, err := amqp.Dial("amqp://guest:guest@localhost:5672/")
failOnError(err, "Failed to connect to RabbitMQ")
defer conn.Close()
ch, err := conn.Channel()
failOnError(err, "Failed to open a channel")
defer ch.Close()
q, err := ch.QueueDeclare(
"", // name
false, // durable
false, // delete when unused
true, // exclusive
false, // noWait
nil, // arguments
)
failOnError(err, "Failed to declare a queue")
msgs, err := ch.Consume(
q.Name, // queue
"", // consumer
true, // auto-ack
false, // exclusive
false, // no-local
false, // no-wait
nil, // args
)
failOnError(err, "Failed to register a consumer")
corrId := randomString(32)
err = ch.Publish(
"", // exchange
"rpc_queue", // routing key
false, // mandatory
false, // immediate
amqp.Publishing{
ContentType: "text/plain",
CorrelationId: corrId,
ReplyTo: q.Name,
Body: []byte(strconv.Itoa(n)),
})
failOnError(err, "Failed to publish a message")
for d := range msgs {
if corrId == d.CorrelationId {
res, err = strconv.Atoi(string(d.Body))
failOnError(err, "Failed to convert body to integer")
break
}
}
return
}
func main() {
rand.Seed(time.Now().UTC().UnixNano())
n := bodyFrom(os.Args)
log.Printf(" [x] Requesting fib(%d)", n)
res, err := fibonacciRPC(n)
failOnError(err, "Failed to handle RPC request")
log.Printf(" [.] Got %d", res)
}
func bodyFrom(args []string) int {
var s string
if (len(args) < 2) || os.Args[1] == "" {
s = "30"
} else {
s = strings.Join(args[1:], " ")
}
n, err := strconv.Atoi(s)
failOnError(err, "Failed to convert arg to integer")
return n
}
Now is a good time to take a look at our full example source code for rpc_client.go and rpc_server.go.
Our RPC service is now ready. We can start the server:
go run rpc_server.go # => [x] Awaiting RPC requests
To request a fibonacci number run the client:
go run rpc_client.go 30 # => [x] Requesting fib(30)
The design presented here is not the only possible implementation of a RPC service, but it has some important advantages:
rpc_server.go in a new console.Our code is still pretty simplistic and doesn't try to solve more complex (but important) problems, like:
If you want to experiment, you may find the management UI useful for viewing the queues.
