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 4 distinct roles: Agents, Coordinators, Primary and Secondary DBservers. In the following sections we will shed light on each of them. Note that the tasks for all roles run the same binary from the same Docker image.

Agents

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 it 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

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.

Primary DBservers

Primary DBservers are the ones where the data is actually hosted. They host shards of data and using synchronous replication a primary 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.

Secondaries

Secondary DBservers are asynchronous replicas of primaries. If one is using only synchronous replication, one does not need secondaries at all. For each primary, there can be one or more secondaries. Since the replication works asynchronously (eventual consistency), the replication does not impede the performance of the primaries. On the other hand, their replica of the data can be slightly out of date. The secondaries are perfectly suitable for backups as they don't interfere with the normal cluster operation.

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.

Sharding

Using the roles outlined above an ArangoDB cluster is able to distribute data in so called shards across multiple primaries. 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.

Also see Sharding in the Administration chapter.

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 primary 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.
  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.

These shall suffice for now. The important piece of information here is that the coordinator layer can be scaled and deployed independently from the DBserver layer.

Replication

ArangoDB offers two ways of data replication within a cluster, synchronous and asynchronous. In this section we explain some details and highlight the advantages and disadvantages respectively.

Synchronous replication with automatic fail-over

Synchronous replication works on a per-shard basis. 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.

If a DBserver fails that holds a follower copy of a shard, 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 fails that holds a leader copy of a shard, 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.

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 replication factor 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.

Asynchronous replication with automatic fail-over

Asynchronous replication works differently, in that it is organized using primary and secondary DBservers. Each secondary server replicates all the data held on a primary by polling in an asynchronous way. This process has very little impact on the performance of the primary. The disadvantage is that there is a delay between the confirmation of a write operation that is sent to the client and the actual replication of the data. If the master server fails during this delay, then committed and confirmed data can be lost.

Nevertheless, we also offer automatic fail-over with this setup. Contrary to the synchronous case, here the fail-over management is done from outside the ArangoDB cluster. In a future version we might move this management into the supervision process in the Agency, but as of now, the management is done via the Mesos framework scheduler for ArangoDB (see below).

The granularity of the replication is a whole ArangoDB instance with all data that resides on that instance, which means that you need twice as many instances as without asynchronous replication. Synchronous replication is more flexible in that respect, you can have smaller and larger instances, and if one fails, the data can be rebalanced across the remaining ones.

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.