Cluster Architecture

The cluster architecture of ArangoDB is a CP master/master model with no single point of failure. With "CP" we mean that in the presence of a network partition, the database prefers internal consistency over availability. With "master/master" we mean that clients can send their requests to an arbitrary node, and experience the same view on the database regardless. "No single point of failure" means that the cluster can continue to serve requests, even if one machine fails completely.

In this way, ArangoDB has been designed as a distributed multi-model database. This section gives a short outline on the cluster architecture and how the above features and capabilities are achieved.

Structure of an ArangoDB Cluster

An ArangoDB Cluster consists of a number of ArangoDB instances which talk to each other over the network. They play different roles, which will be explained in detail below. The current configuration of the Cluster is held in the Agency, which is a highly-available resilient key/value store based on an odd number of ArangoDB instances running Raft Consensus Protocol.

For the various instances in an ArangoDB Cluster there are 3 distinct roles:

  • Agents
  • Coordinators
  • DBServers.

In the following sections we will shed light on each of them.

ArangoDB Cluster


One or multiple Agents form the Agency in an ArangoDB Cluster. The Agency is the central place to store the configuration in a Cluster. It performs leader elections and provides other synchronization services for the whole Cluster. Without the Agency none of the other components can operate.

While generally invisible to the outside the Agency is the heart of the Cluster. As such, fault tolerance is of course a must have for the Agency. To achieve that the Agents are using the Raft Consensus Algorithm. The algorithm formally guarantees conflict free configuration management within the ArangoDB Cluster.

At its core the Agency manages a big configuration tree. It supports transactional read and write operations on this tree, and other servers can subscribe to HTTP callbacks for all changes to the tree.


Coordinators should be accessible from the outside. These are the ones the clients talk to. They will coordinate cluster tasks like executing queries and running Foxx services. They know where the data is stored and will optimize where to run user supplied queries or parts thereof. Coordinators are stateless and can thus easily be shut down and restarted as needed.


DBservers are the ones where the data is actually hosted. They host shards of data and using synchronous replication a DBServer may either be leader or follower for a shard.

They should not be accessed from the outside but indirectly through the Coordinators. They may also execute queries in part or as a whole when asked by a Coordinator.

Many sensible configurations

This architecture is very flexible and thus allows many configurations, which are suitable for different usage scenarios:

  1. The default configuration is to run exactly one Coordinator and one DBServer on each machine. This achieves the classical master/master setup, since there is a perfect symmetry between the different nodes, clients can equally well talk to any one of the Coordinators and all expose the same view to the data store. Agents can run on separate, less powerful machines.
  2. One can deploy more Coordinators than DBservers. This is a sensible approach if one needs a lot of CPU power for the Foxx services, because they run on the Coordinators.
  3. One can deploy more DBServers than Coordinators if more data capacity is needed and the query performance is the lesser bottleneck
  4. One can deploy a Coordinator on each machine where an application server (e.g. a node.js server) runs, and the Agents and DBServers on a separate set of machines elsewhere. This avoids a network hop between the application server and the database and thus decreases latency. Essentially, this moves some of the database distribution logic to the machine where the client runs.

As you acn see, the Coordinator layer can be scaled and deployed independently from the DBServer layer.

Cluster ID

Every non-Agency ArangoDB instance in a Cluster is assigned a unique ID during its startup. Using its ID a node is identifiable throughout the Cluster. All cluster operations will communicate via this ID.


Using the roles outlined above an ArangoDB Cluster is able to distribute data in so called shards across multiple DBServers. From the outside this process is fully transparent and as such we achieve the goals of what other systems call "master-master replication".

In an ArangoDB Cluster you talk to any Coordinator and whenever you read or write data it will automatically figure out where the data is stored (read) or to be stored (write). The information about the shards is shared across the Coordinators using the Agency.

ArangoDB organizes its collection data in shards. Sharding allows to use multiple machines to run a cluster of ArangoDB instances that together constitute a single database. This enables you to store much more data, since ArangoDB distributes the data automatically to the different servers. In many situations one can also reap a benefit in data throughput, again because the load can be distributed to multiple machines.

Shards are configured per collection so multiple shards of data form the collection as a whole. To determine in which shard the data is to be stored ArangoDB performs a hash across the values. By default this hash is being created from the document _key.

For further information, please refer to the Cluster Administration section.

Synchronous replication

In an ArangoDB Cluster, the replication among the data stored by the DBServers is synchronous.

Synchronous replication works on a per-shard basis. Using the option replicationFactor, one configures for each collection how many copies of each shard are kept in the Cluster.

At any given time, one of the copies is declared to be the leader and all other replicas are followers. Write operations for this shard are always sent to the DBServer which happens to hold the leader copy, which in turn replicates the changes to all followers before the operation is considered to be done and reported back to the Coordinator. Read operations are all served by the server holding the leader copy, this allows to provide snapshot semantics for complex transactions.

Using synchronous replication alone will guarantee consistency and high availabilty at the cost of reduced performance: write requests will have a higher latency (due to every write-request having to be executed on the followers) and read requests will not scale out as only the leader is being asked.

In a Cluster, synchronous replication will be managed by the Coordinators for the client. The data will always be stored on the DBServers.

The following example will give you an idea of how synchronous operation has been implemented in ArangoDB Cluster:

  1. Connect to a coordinator via arangosh
  2. Create a collection> db._create("test", {"replicationFactor": 2})

  3. the coordinator will figure out a leader and 1 follower and create 1 shard (as this is the default)

  4. Insert data> db.test.insert({"replication": "😎"})

  5. The coordinator will write the data to the leader, which in turn will replicate it to the follower.

  6. Only when both were successful the result is reported to be successful
        "_id" : "test/7987",
        "_key" : "7987",
        "_rev" : "7987"
    When a follower fails, the leader will give up on it after 3 seconds and proceed with the operation. As soon as the follower (or the network connection to the leader) is back up, the two will resynchronize and synchronous replication is resumed. This happens all transparently to the client.

Automatic failover

If a DBServer that holds a follower copy of a shard fails, then the leader can no longer synchronize its changes to that follower. After a short timeout (3 seconds), the leader gives up on the follower, declares it to be out of sync, and continues service without the follower. When the server with the follower copy comes back, it automatically resynchronizes its data with the leader and synchronous replication is restored.

If a DBserver that holds a leader copy of a shard fails, then the leader can no longer serve any requests. It will no longer send a heartbeat to the Agency. Therefore, a supervision process running in the Raft leader of the Agency, can take the necessary action (after 15 seconds of missing heartbeats), namely to promote one of the servers that hold in-sync replicas of the shard to leader for that shard. This involves a reconfiguration in the Agency and leads to the fact that coordinators now contact a different DBserver for requests to this shard. Service resumes. The other surviving replicas automatically resynchronize their data with the new leader. When the DBserver with the original leader copy comes back, it notices that it now holds a follower replica, resynchronizes its data with the new leader and order is restored.

The following example will give you an idea of how failover has been implemented in ArangoDB Cluster:

  1. The leader of a shard (lets name it DBServer001) is going down.
  2. A Coordinator is asked to return a document:> db.test.document("100069")

  3. The Coordinator determines which server is responsible for this document and finds DBServer001

  4. The Coordinator tries to contact DBServer001 and timeouts because it is not reachable.
  5. After a short while the supervision (running in parallel on the Agency) will see that heartbeats from DBServer001 are not coming in
  6. The supervision promotes one of the followers (say DBServer002), that is in sync, to be leader and makes DBServer001 a follower.
  7. As the Coordinator continues trying to fetch the document it will see that the leader changed to DBServer002
  8. The Coordinator tries to contact the new leader (DBServer002) and returns the result:

         "_key" : "100069",
         "_id" : "test/100069",
         "_rev" : "513",
         "replication" : "😎"
  9. After a while the supervision declares DBServer001 to be completely dead.
  10. A new follower is determined from the pool of DBservers.
  11. The new follower syncs its data from the _leade_r and order is restored.

Please note that there may still be timeouts. Depending on when exactly the request has been done (in regard to the supervision) and depending on the time needed to reconfigure the Cluster the Coordinator might fail with a timeout error!

Shard movement and resynchronization

All shard data synchronizations are done in an incremental way, such that resynchronizations are quick. This technology allows to move shards (follower and leader ones) between DBServers without service interruptions. Therefore, an ArangoDB Cluster can move all the data on a specific DBServer to other DBServers and then shut down that server in a controlled way. This allows to scale down an ArangoDB Cluster without service interruption, loss of fault tolerance or data loss. Furthermore, one can re-balance the distribution of the shards, either manually or automatically.

All these operations can be triggered via a REST/JSON API or via the graphical web UI. All fail-over operations are completely handled within the ArangoDB Cluster.

Obviously, synchronous replication involves a certain increased latency for write operations, simply because there is one more network hop within the Cluster for every request. Therefore the user can set the replicationFactor to 1, which means that only one copy of each shard is kept, thereby switching off synchronous replication. This is a suitable setting for less important or easily recoverable data for which low latency write operations matter.

Microservices and zero administation

The design and capabilities of ArangoDB are geared towards usage in modern microservice architectures of applications. With the Foxx services it is very easy to deploy a data centric microservice within an ArangoDB Cluster.

In addition, one can deploy multiple instances of ArangoDB within the same project. One part of the project might need a scalable document store, another might need a graph database, and yet another might need the full power of a multi-model database actually mixing the various data models. There are enormous efficiency benefits to be reaped by being able to use a single technology for various roles in a project.

To simplify life of the devops in such a scenario we try as much as possible to use a zero administration approach for ArangoDB. A running ArangoDB Cluster is resilient against failures and essentially repairs itself in case of temporary failures. See the next section for further capabilities in this direction.

Apache Mesos integration

For the distributed setup, we use the Apache Mesos infrastructure by default.

ArangoDB is a fully certified package for DC/OS and can thus be deployed essentially with a few mouse clicks or a single command, once you have an existing DC/OS cluster. But even on a plain Apache Mesos cluster one can deploy ArangoDB via Marathon with a single API call and some JSON configuration.

The advantage of this approach is that we can not only implement the initial deployment, but also the later management of automatic replacement of failed instances and the scaling of the ArangoDB cluster (triggered manually or even automatically). Since all manipulations are either via the graphical web UI or via JSON/REST calls, one can even implement auto-scaling very easily.

A DC/OS cluster is a very natural environment to deploy microservice architectures, since it is so convenient to deploy various services, including potentially multiple ArangoDB cluster instances within the same DC/OS cluster. The built-in service discovery makes it extremely simple to connect the various microservices and Mesos automatically takes care of the distribution and deployment of the various tasks.

See the Deployment chapter and its subsections for instructions.

It is possible to deploy an ArangoDB cluster by simply launching a bunch of Docker containers with the right command line options to link them up, or even on a single machine starting multiple ArangoDB processes. In that case, synchronous replication will work within the deployed ArangoDB cluster, and automatic fail-over in the sense that the duties of a failed server will automatically be assigned to another, surviving one. However, since the ArangoDB cluster cannot within itself launch additional instances, replacement of failed nodes is not automatic and scaling up and down has to be managed manually. This is why we do not recommend this setup for production deployment.