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rmq is short for Redis message queue. It's a message queue system written in Go and backed by Redis.

Let's take a look at how to use rmq.

Of course you need to import rmq wherever you want to use it.

import "github.com/adjust/rmq/v5"

Before we get to queues, we first need to establish a connection. Each rmq connection has a name (used in statistics) and Redis connection details including which Redis database to use. The most basic Redis connection uses a TCP connection to a given host and a port:

connection, err := rmq.OpenConnection("my service", "tcp", "localhost:6379", 1, errChan)

It's also possible to access a Redis listening on a Unix socket:

connection, err := rmq.OpenConnection("my service", "unix", "/tmp/redis.sock", 1, errChan)

For more flexible setup you can pass Redis options or create your own Redis client:

connection, err := OpenConnectionWithRedisOptions("my service", redisOptions, errChan)

connection, err := OpenConnectionWithRedisClient("my service", redisClient, errChan)

If the Redis instance can't be reached you will receive an error indicating this.

Please also note the errChan parameter. There is some rmq logic running in the background which can run into Redis errors. If you pass an error channel to the OpenConnection() functions rmq will send those background errors to this channel so you can handle them asynchronously. For more details about this and handling suggestions see the section about handling background errors below.

In order to connect to a Redis cluster please use OpenClusterConnection():

redisClusterOptions := &redis.ClusterOptions{ /* ... */ }
redisClusterClient := redis.NewClusterClient(redisClusterOptions)
connection, err := OpenClusterConnection("my service", redisClusterClient, errChan)

Note that such an rmq cluster connection uses different Redis than rmq connections opened by OpenConnection() or similar. If you have used a Redis instance with OpenConnection() then it is NOT SAFE to reuse that rmq system by connecting to it via OpenClusterConnection(). The cluster state won't be compatible and this will likely lead to data loss.

If you've previously used OpenConnection() or similar you should only consider using OpenClusterConnection() with a fresh Redis cluster.

Once we have a connection we can use it to finally access queues. Each queue must have a unique name by which we address it. Queues are created once they are accessed. There is no need to declare them in advance. Here we open a queue named "tasks":

taskQueue, err := connection.OpenQueue("tasks")

Again, possibly Redis errors might be returned.

An empty queue is boring, let's add some deliveries! Internally all deliveries are saved to Redis lists as strings. This is how you can publish a string payload to a queue:

delivery := "task payload"
err := taskQueue.Publish(delivery)

In practice, however, it's more common to have instances of some struct that we want to publish to a queue. Assuming task is of some type like Task, this is how to publish the JSON representation of that task:

// create task
taskBytes, err := json.Marshal(task)
if err != nil {
    // handle error
}

err = taskQueue.PublishBytes(taskBytes)

For a full example see example/producer.

Now that our queue starts filling, let's add a consumer. After opening the queue as before, we need it to start consuming before we can add consumers.

err := taskQueue.StartConsuming(10, time.Second)

This sets the prefetch limit to 10 and the poll duration to one second. This means the queue will fetch up to 10 deliveries at a time before giving them to the consumers. To avoid idling consumers while the queues are full, the prefetch limit should always be greater than the number of consumers you are going to add. If the queue gets empty, the poll duration sets how long rmq will wait before checking for new deliveries in Redis.

Once this is set up, we can actually add consumers to the consuming queue.

taskConsumer := &TaskConsumer{}
name, err := taskQueue.AddConsumer("task-consumer", taskConsumer)

To uniquely identify each consumer internally rmq creates a random name with the given prefix. For example in this case name might be task-consumer-WB1zaq. This name is only used in statistics.

In our example above the injected taskConsumer (of type *TaskConsumer) must implement the rmq.Consumer interface. For example:

func (consumer *TaskConsumer) Consume(delivery rmq.Delivery) {
    var task Task
    if err = json.Unmarshal([]byte(delivery.Payload()), &task); err != nil {
        // handle json error
        if err := delivery.Reject(); err != nil {
            // handle reject error
        }
        return
    }

    // perform task
    log.Printf("performing task %s", task)
    if err := delivery.Ack(); err != nil {
        // handle ack error
    }
}

First we unmarshal the JSON package found in the delivery payload. If this fails we reject the delivery. Otherwise we perform the task and ack the delivery.

If you don't actually need a consumer struct you can use AddConsumerFunc instead and pass a consumer function which handles an rmq.Delivery:

name, err := taskQueue.AddConsumerFunc(func(delivery rmq.Delivery) {
    // handle delivery and call Ack() or Reject() on it
})

Please note that delivery.Ack() and similar functions have a built-in retry mechanism which will block your consumers in some cases. This is because failing to acknowledge a delivery is potentially dangerous. For details see the section about background errors below.

For a full example see example/consumer.

As described above you can add consumers to a queue. For each consumer rmq takes one of the prefetched unacked deliveries from the delivery channel and passes it to the consumer's Consume() function. The next delivery will only be passed to the same consumer once the prior Consume() call returns. So each consumer will only be consuming a single delivery at any given time.

Furthermore each Consume() call is expected to call either delivery.Ack(), delivery.Reject() or delivery.Push() (see below). If that's not the case these deliveries will remain unacked and the prefetch goroutine won't make progress after a while. So make sure you always call exactly one of those functions in your Consume() implementations.

It's recommended to inject an error channel into the OpenConnection() functions. This section describes it's purpose and how you might use it to monitor rmq background Redis errors.

There are three sources of background errors which rmq detects (and handles internally):

1. The OpenConnection() functions spawn a goroutine which keeps a heartbeat Redis key alive. This is important so that the cleaner (see below) can tell which connections are still alive and must not be cleaned yet. If the heartbeat goroutine fails to update the heartbeat Redis key repeatedly foo too long the cleaner might clean up the connection prematurely. To avoid this the connection will automatically stop all consumers after 45 consecutive heartbeat errors. This magic number is based on the details of the heartbeat key: The heartbeat tries to update the key every second with a TTL of one minute. So only after 60 failed attempts the heartbeat key would be dead.

   Every time this goroutine runs into a Redis error it gets send to the error channel as HeartbeatError.

2. The StartConsuming() function spawns a goroutine which is responsible for prefetching deliveries from the Redis ready list and moving them into a delivery channel. This delivery channels feeds into your consumers Consume() functions. If the prefetch goroutine runs into Redis errors this basically means that there won't be new deliveries being sent to your consumers until it can fetch new ones. So these Redis errors are not dangerous, it just means that your consumers will start idling until the Redis connection recovers.

   Every time this goroutine runs into a Redis error it gets send to the error channel as ConsumeError.

3. The delivery functions Ack(), Reject() and Push() have a built-in retry mechanism. This is because failing to acknowledge a delivery is potentially dangerous. The consumer has already handled the delivery, so if it can't ack it the cleaner might end up moving it back to the ready list so another consumer might end up consuming it again in the future, leading to double delivery.

   So if a delivery failed to be acked because of a Redis error the Ack() call will block and retry once a second until it either succeeds or until consuming gets stopped (see below). In the latter case the Ack() call will return rmq.ErrorConsumingStopped which you should handle in your consume function. For example you might want to log about the delivery so you can manually remove it from the unacked or ready list before you start new consumers. Or at least you can know which deliveries might end up being consumed twice.

   Every time these functions runs into a Redis error it gets send to the error channel as DeliveryError.

Each of those error types has a field Count which tells you how often the operation failed consecutively. This indicates for how long the affected Redis instance has been unavailable. One general way of using this information might be to have metrics about the error types including the error count so you can keep track of how stable your Redis instances and connections are. By monitoring this you might learn about instabilities before they affect your services in significant ways.

Below is some more specific advice on handling the different error cases outlined above. Keep in mind though that all of those errors are likely to happen at the same time, as Redis tends to be up or down completely. But if you're using multi Redis instance setup like nutcracker you might see some of them in isolation from the others.

1. HeartbeatErrors: Once err.Count equals HeartbeatErrorLimit you should know that the consumers of this connection will stop consuming. And they won't restart consuming on their own. This is a condition you should closely monitor because this means you will have to restart your service in order to resume consuming. Before restarting you should check your Redis instance.

2. ConsumeError: These are mostly informational. As long as those errors keep happening the consumers will effectively be paused. But once these operations start succeeding again the consumers will resume consumers on their own.

3. DeliveryError: When you see deliveries failing to ack repeatedly this also means your consumers won't make progress as they will keep retrying to ack pending deliveries before starting to consume new ones. As long as this keeps happening you should avoid stopping the service if you can. That is because the already consumed by not yet unacked deliveries will be returned to ready be the cleaner afterwards, which leads to double delivery. So ideally you try to get Redis connection up again as long as the deliveries are still trying to ack. Once acking works again it's safe to restart again.

   More realistically, if you still need to stop the service when Redis is down, keep in mind that calling StopConsuming() will make the blocking Ack() calls return with ErrorConsumingStopped, so you can handle that case to make an attempt to either avoid the double delivery or at least track it for future investigation.

Sometimes it's useful to have consumers work on batches of deliveries instead of individual ones. For example for bulk database inserts. In those cases you can use AddBatchConsumer():

batchConsumer := &MyBatchConsumer{}
name, err := taskQueue.AddBatchConsumer("my-consumer", 100, time.Second, batchConsumer)

In this example we create a batch consumer which will receive batches of up to 100 deliveries. We set the batchTimeout to one second, so if there are less than 100 deliveries per second we will still consume at least one batch per second (which would contain less than 100 deliveries).

The rmq.BatchConsumer interface is very similar to rmq.Consumer.

func (consumer *MyBatchConsumer) Consume(batch rmq.Deliveries) {
    payloads := batch.Payloads()
    // handle payloads
    if errors := batch.Ack(); len(errors) > 0 {
        // handle ack errors
    }
}

Note that batch.Ack() acknowledges all deliveries in the batch. It's also possible to ack some of the deliveries and reject the rest. It uses the same retry mechanism per delivery as discussed above. If some of the deliveries continue to fail to ack when consuming gets stopped (see below), then batch.Ack() will return an error map map[int]error. For each entry in this map the key will be the index of the delivery which failed to ack and the value will be the error it ran into. That way you can map the errors back to the deliveries to know which deliveries are at risk of being consumed again in the future as discussed above.

For a full example see example/batch_consumer.

Another thing which can be useful is a mechanism for retries. Let's say you have tasks which can fail for external reasons but you'd like to retry them a few times after a while before you give up. In that case you can set up a chain of push queues like this:

incomingQ -> pushQ1 -> pushQ2

In the queue setup code it would look like this (error handling omitted for brevity):

incomingQ, err := connection.OpenQueue("incomingQ")
pushQ1, err := connection.OpenQueue("pushQ1")
pushQ2, err := connection.OpenQueue("pushQ2")
incomingQ.SetPushQueue(pushQ1)
pushQ1.SetPushQueue(pushQ2)
_, err := incomingQ.AddConsumer("incomingQ", NewConsumer())
_, err := pushQ1.AddConsumer("pushQ1", NewConsumer())
_, err := pushQ2.AddConsumer("pushQ2", NewConsumer())

If you have set up your queues like this, you can now call delivery.Push() in your Consume() function to push the delivery from the consuming queue to the associated push queue. So if consumption fails on incomingQ, then the delivery would be moved to pushQ1 and so on. If you have the consumers wait until the deliveries have a certain age you can use this pattern to retry after certain durations.

Note that delivery.Push() has the same affect as delivery.Reject() if the queue has no push queue set up. So in our example above, if the delivery fails in the consumer on pushQ2, then the Push() call will reject the delivery.

If you want to stop consuming from the queue, you can call StopConsuming():

finishedChan := taskQueue.StopConsuming()

When StopConsuming() is called, it will immediately stop fetching more deliveries from Redis and won't send any more of the already prefetched deliveries to consumers.

In the background it will make pending Ack() calls return rmq.ErrorConsumingStopped if they still run into Redis errors (see above) and wait for all consumers to finish consuming their current delivery before closing the returned finishedChan. So while StopConsuming() returns immediately, you can wait on the returned channel until all consumers are done:

<-finishedChan

You can also stop consuming on all queues in your connection:

finishedChan := connection.StopAllConsuming()

Wait on the finishedChan to wait for all consumers on all queues to finish.

This is useful to implement a graceful shutdown of a consumer service. Please note that after calling StopConsuming() the queue might not be in a state where you can add consumers and call StartConsuming() again. If you have a use case where you actually need that sort of flexibility, please let us know. Currently for each queue you are only supposed to call StartConsuming() and StopConsuming() at most once.

Also note that StopAllConsuming() will stop the heartbeat for this connection. It's advised to also not publish to any queue opened by this connection anymore.

Even if you don't have a push queue setup there are cases where you need to consume previously failed deliveries again. For example an external dependency might have an issue or you might have deployed a broken consumer service which rejects all deliveries for some reason.

In those cases you would wait for the external party to recover or fix your mistake to get ready to reprocess the deliveries again. Now you can return the deliveries by opening affected queue and call ReturnRejected():

returned, err := queue.ReturnRejected(10000)

In this case we ask rmq to return up to 10k deliveries from the rejected list to the ready list. To return all of them you can pass math.MaxInt64.

If there was no error it returns the number of deliveries that were moved.

If you find yourself doing this regularly on some queues consider setting up a push queue to automatically retry failed deliveries regularly.

See example/returner

You might run into the case where you have rejected deliveries which you don't intend to retry again for one reason or another. In those cases you can clear the full rejected list by calling PurgeRejected():

count, err := queue.PurgeRejected()

It returns the number of purged deliveries.

Similarly, there's a function to clear the ready list of deliveries:

count, err := queue.PurgeReady()

See example/purger.

You should regularly run a queue cleaner to make sure no unacked deliveries are stuck in the queue system. The background is that a consumer service prefetches deliveries by moving them from the ready list to an unacked list associated with the queue connection. If the consumer dies by crashing or even by being gracefully shut down by calling StopConsuming(), the unacked deliveries will remain in that Redis list.

If you run a queue cleaner regularly it will detect queue connections whose heartbeat expired and will clean up all their consumer queues by moving their unacked deliveries back to the ready list.

Although it should be safe to run multiple cleaners, it's recommended to run exactly one instance per queue system and have it trigger the cleaning process regularly, like once a minute.

See example/cleaner.

Redis protocol does not define a specific way to pass additional data like header. However, there is often need to pass them (for example for traces propagation).

This implementation injects optional header values marked with a signature into payload body during publishing. When message is consumed, if signature is present, header and original payload are extracted from augmented payload.

Header is defined as http.Header for better interoperability with existing libraries, for example with propagation.HeaderCarrier.

 // ....
 
 h := make(http.Header)
 h.Set("X-Baz", "quux")

 // You can add header to your payload during publish.
 _ = pub.Publish(rmq.PayloadWithHeader(`{"foo":"bar"}`, h))

 // ....

 _, _ = con.AddConsumerFunc("tag", func(delivery rmq.Delivery) {
     // And receive header back in consumer.
     delivery.(rmq.WithHeader).Header().Get("X-Baz") // "quux"
     
     // ....
 })

Adding a header is an explicit opt-in operation and so it does not affect library's backwards compatibility by default (when not used).

Please note that adding header may lead to compatibility issues if:

• consumer is built with older version of rmq when publisher has already started using header, this can be avoided by upgrading consumers before publishers;
• consumer is not using rmq (other libs, low level tools like redis-cli) and is not aware of payload format extension.

To simplify testing of queue producers and consumers we include test mocks.

As before, we first need a queue connection, but this time we use a rmq.TestConnection that doesn't need any connection settings.

testConn := rmq.NewTestConnection()

If you are using a testing framework that uses test suites, you can reuse that test connection by setting it up once for the suite and resetting it with testConn.Reset() before each test.

Now let's say we want to test the function publishTask() that creates a task and publishes it to a queue from that connection.

// call the function that should publish a task
publishTask(testConn)

// check that the task is published
assert.Equal(t, "task payload", suite.testConn.GetDelivery("tasks", 0))

The assert.Equal part is from testify, but it will look similar for other testing frameworks. Given a rmq.TestConnection, we can check the deliveries that were published to its queues (since the last Reset() call) with GetDelivery(queueName, index). In this case we want to extract the first (and possibly only) delivery that was published to queue tasks and just check the payload string.

If the payload is JSON again, the unmarshalling and check might look like this:

var task Task
err := json.Unmarshal([]byte(suite.testConn.GetDelivery("tasks", 0)), &task)
assert.NoError(t, err)
assert.NotNil(t, task)
assert.Equal(t, "value", task.Property)

If you expect a producer to create multiple deliveries you can use different indexes to access them all.

assert.Equal(t, "task1", suite.testConn.GetDelivery("tasks", 0))
assert.Equal(t, "task2", suite.testConn.GetDelivery("tasks", 1))

For convenience there's also a function GetDeliveries that returns all published deliveries to a queue as string array.

assert.Equal(t, []string{"task1", "task2"}, suite.testConn.GetDeliveries("tasks"))

These examples assume that you inject the rmq.Connection into your testable functions. If you inject instances of rmq.Queue instead, you can use rmq.TestQueue instances in tests and access their LastDeliveries (since Reset()) directly.

Testing consumers is a bit easier because consumers must implement the rmq.Consumer interface. In the tests just create an rmq.TestDelivery and pass it to your Consume() function. This example creates a test delivery from a string and then checks that the delivery was acked.

consumer := &TaskConsumer{}
delivery := rmq.NewTestDeliveryString("task payload")

consumer.Consume(delivery)

assert.Equal(t, rmq.Acked, delivery.State)

The State field will always be one of these values:

• rmq.Acked: The delivery was acked
• rmq.Rejected: The delivery was rejected
• rmq.Pushed: The delivery was pushed (see below)
• rmq.Unacked: Nothing of the above

If your packages are JSON marshalled objects, then you can create test deliveries out of those like this:

task := Task{Property: "bad value"}
delivery := rmq.NewTestDelivery(task)

If you want to write integration tests which exercise both producers and consumers at the same time, you can use the rmq.OpenConnectionWithTestRedisClient constructor. It returns a real rmq.Connection instance which is backed by an in-memory Redis client implementation. That way it behaves exactly as in production, just without the durability of a real Redis client. Don't use this in production!

Given a connection, you can call connection.CollectStats() to receive rmq.Stats about all open queues, connections and consumers. If you run example/handler you can see what's available:

In this example you see 5 connections consuming task_kind1, each wich 5 consumers each. They have a total of 1007 packages unacked. Below the marker you see connections which are not consuming. One of the handler connections died because I stopped the handler. Running the cleaner would clean that up (see below).

If you are using Prometheus, rmqprom collects statistics about all open queues and exposes them as Prometheus metrics.