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Peer Discovery Subsystem in RabbitMQ 3.7

In this blog post we're going to take a closer look at a new subsystem introduced in RabbitMQ 3.7.0.

Why Do We Need Peer Discovery?

Users of open source data services such as RabbitMQ have increasing expectations around operations automation. This includes so-called Day 1 operations: initial cluster provisioning.

When a RabbitMQ cluster is first formed, newly booting nodes need to have a way to discover each other. In versions up to and including 3.6.x were two ways of doing this:

  • CLI tools
  • A list of nodes in configuration file

The former option is used by some provisioning tools but is generally not very automation friendly. The latter is more convenient but has its own limitations: the set of nodes is fixed and changing it requires a config file redeployment and node restart.

A Better Way

There is a third option and it has been around in the community for a few years: rabbitmq-autocluster, a plugin originally developed by Gavin Roy. That plugin modifies RabbitMQ boot process and injects a peer discovery step. The list of peers in this case doesn't have to come from the config file: it can be retrieved from an AWS autoscaling group or an external tool such as etcd.

rabbitmq-autocluster authors concluded that there is no one true way of performing peer discovery and that different approaches made sense for different deployment scenarios. As such, they introduced a pluggable interface. A specific implementation of this pluggable interface is called a peer discovery mechanism. Given the explosion of platforms and deployment automation stacks in the last few years, this turned out to be a wise decision.

For RabbitMQ 3.7.0 we took rabbitmq-autocluster and integrated its main ideas into the core with some modifications influenced by our experience supporting production RabbitMQ installations and community feedback.

The result is a new peer discovery subsystem.

How Does it Work?

When a node starts and detects it doesn't have a previously initialised database, it will check if there's a peer discovery mechanism configured. If that's the case, it will then perform the discovery and attempt to contact each discovered peer in order. Finally, it will attempt to join the cluster of the first reachable peer.

Some mechanisms assume all cluster members are known ahead of time (for example, listed in the config file), others are dynamic (nodes can come and go).

RabbitMQ 3.7 ships with a number of mechanisms:

  • AWS (EC2 instance tags or autoscaling groups)
  • Kubernetes
  • etcd
  • Consul
  • Pre-configured DNS records
  • Config file

and it is easy to introduce support for more options in the future.

Since the ability to list cluster nodes in the config file is not new, let's focus on the new features.

Node Registration and Unregistration

Some mechanisms use a data store to keep track of node list. Newly joined cluster members update the data store to indicate their presence. etcd and Consul are two options supported via plugins that ship with RabbitMQ.

With other mechanisms cluster membership is managed out-of-band (by a mechanism that RabbitMQ nodes cannot control). For example, the AWS mechanism uses EC2 instance filtering or autoscaling group membership, both of which are managed and updated by AWS.

Using a Preconfigured Set

But enough theory, let's take a look at what it takes to configure a list of nodes for peer discovery using the new config format that was introduced alongside peer discovery in 3.7:

First we have to tell RabbitMQ to use the classic config mechanism for peer discovery. This is done using the cluster_formation.peer_discovery_backend key. Then list one or more nodes using cluster_formation.classic_config.nodes, which is a collection:

cluster_formation.peer_discovery_backend = rabbit_peer_discovery_classic_config

cluster_formation.classic_config.nodes.1 = [email protected]
cluster_formation.classic_config.nodes.2 = [email protected]

And that's it.

This discovery method is perhaps the easiest to get started with but it has one obvious issue: the list of nodes is static.

Next let's take a look at a mechanism that uses dynamic node lists: the AWS EC2 instance filtering.

Using AWS Instance Filtering

Just like with the earlier example, we have to tell the node to use AWS for peer discovery:

cluster_formation.peer_discovery_backend = rabbit_peer_discovery_aws

There are two ways to use the AWS mechanism but the backend name (module) is the same for both.

To use instance filtering, the plugin requires an AWS region to be configured as well as a pair of credentials. Sensitive configuration file values can be encrypted.

Here's a config file example that does both:

cluster_formation.peer_discovery_backend = rabbit_peer_discovery_aws

cluster_formation.aws.region = us-east-1
cluster_formation.aws.access_key_id = ANIDEXAMPLE
cluster_formation.aws.secret_key = WjalrxuTnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY

The node now has enough information to try consulting the EC2 Instance Metadata service.

Finally the operator needs to provide a set of tags to filter on. The tags are key/value pairs. This means it is possible to filter on more than one tag, for example, rabbitmq and cluster name or environment type (e.g. development or test or production).

Here's a complete config example that uses 3 tags, region, service and environment:

cluster_formation.peer_discovery_backend = rabbit_peer_discovery_aws

cluster_formation.aws.region = us-east-1
cluster_formation.aws.access_key_id = ANIDEXAMPLE
cluster_formation.aws.secret_key = WjalrxuTnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY

cluster_formation.aws.instance_tags.region = us-east-1
cluster_formation.aws.instance_tags.service = rabbitmq
cluster_formation.aws.instance_tags.environment = staging

We are all set with this example. The only thing left to discuss is how to handle a natural race condition that occurs when a cluster is first formed and node listing therefore can only return an empty set. This will be covered in a separate section below.

IAM Roles and Permissions

If an IAM role is assigned to EC2 instances running RabbitMQ nodes, a policy has to be used to allow said instances use EC2 Instance Metadata Service. Here's an example of such policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "autoscaling:DescribeAutoScalingInstances",
                "ec2:DescribeInstances"
            ],
            "Resource": [
                "*"
            ]
        }
    ]
}

Without this policy in place the AWS peer discovery plugin won't be able to list instances and discovery will fail. When discovery fails, the node will consider it to be a fatal condition and terminate.

Node Names

By default node names with AWS peer discovery will be computed using private hostnames. It is possible to switch to private IP addresses as well:

cluster_formation.aws.use_private_ip = true

The Chicken and Egg Problem of Peer Discovery

Consider a deployment where the entire cluster is provisioned at once and all nodes start in parallel. For example, they may have been just created by BOSH or an AWS Cluster Formation template. In this case there's a natural race condition between node registration and more than one node can become "first to register" (discovers no existing peers and thus starts as standalone).

Different peer discovery backends use different approaches to minimize the probability of such scenario. Some acquire a lock with their data service (etcd, Consul) and release it after registering, retrying if lock acquisition fails.

Others use a technique known as randomized startup delay. With randomized startup delay nodes will delay their startup for a randomly picked value (between 5 and 60 seconds by default). While this strategy may seem naive at first, it works quite well in practice with sufficiently high max delay intervals. It is also used for leader election in some distributed system algorithms, for example, Raft.

Some backends (config file, DNS) rely on a pre-configured set of peers and do not suffer from this issue since when a node attempts to join its peer, it will continue retrying for a period of time.

What Peer Discovery Does not Do

Peer discovery was introduced to solve a narrow set of problems. It does not change how RabbitMQ clusters operate once formed. Even though some mechanisms introduce additional features, some problems (shared secret distribution and monitoring, for example) should be solved by different tools.

Peer discovery is also performed by blank (uninitialised) nodes. If a node previously was a cluster member, it will try to contact its "last seen" peer on boot for a period of time. In this case, no peer discovery will be performed. This is no different from how earlier RabbitMQ versions worked in this scenario.

Peer Discovery Troubleshooting

Reasoning about an automated cluster formation system that also uses a peer discovery mechanism that has external dependencies (e.g. AWS APIs or etcd) can be tricky. For this reason all peer discovery implementations log key decisions and most log all external requests at debug log level. When in doubt, enable debug logging and take a look at node logs!

And keep in mind what's covered in the above section on when peer discovery is not meant to kick in.

Differences from rabbitmq-autocluster

While the new peer discovery subsystem is similar to rabbitmq-autocluster in many ways, there is a couple of important differences that matter to operators.

With rabbitmq-autocluster, nodes will reset themselves before joining its peers. This makes sense in some environments and doesn't in other. Peer discovery in RabbitMQ core does not do this.

rabbitmq-autocluster allows environment variables to be used for mechanism-specific configuration in addition to RabbitMQ config file. While this feature was retained to simplify migration, it should be considered deprecated by the peer discovery subsystem in 3.7.0.

Peer discovery in the core uses the new configuration file format heavily. rabbitmq-autocluster does not support that format since it now is effectively a 3.6.x-only plugin.

Future Work

Most major aspects of the peer discovery subsystem described in this post have a few years of battle testing via rabbitmq-autocluster. However, as more and more users adopt this feature in more and more environments, new feedback from a broader set of users and use cases accumulates.

Currently one open ended question is whether inability to contact an external service used by a peer discovery mechanism (e.g. an AWS API endpoint or etcd or DNS) should immediately be considered a fatal failure that makes the node stop, or should peer discovery be retried for a period of time. You feedback is welcome on the RabbitMQ mailing list.

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