This chapter describes ArangoDB’s deployment modes and provides useful information about different data models and scalability, data sharding, the storage engine that lies at the very bottom of an ArangoDB database system, and also about the replication methods that ArangoDB offers.
Available deployment modes
ArangoDB can be deployed in a variety of configurations, depending on your needs.
You can deploy it on-premises as a single server, optionally as a resilient pair with asynchronous replication and automatic failover, or as a cluster comprised of multiple nodes with synchronous replication and automatic failover for high availability and resilience. For the highest level of data safety, you can additionally set up off-site replication for your entire cluster.
Read more about deployment modes to find out all important details about each mode and the included features.
The full ArangoDB package ships with a set of programs and tools that may simplify your workflow, such as:
- ArangoDB Server or
arangod, the core component of ArangoDB
- ArangoDB Shell or
arangosh, a client tool that you can use for administration of ArangoDB servers
- ArangoDB Starter or
arangodb, a tool that helps you deploy ArangoDB in an easy way
and many more.
For more information, including the full list of available tools plus examples, read the Programs & Tools chapter.
Deploying by technology
There are different ways that can be used to deploy an environment. You can manually start all the needed processes localy or in Docker containers. Or use the ArangoDB Starter, the arangodb binary program, for local setups using processes or Docker containers.
If you want to deploy in your Kubernetes cluster, you can use the
ArangoDB Kubernetes Operator (
The fastest way to get ArangoDB up and running is to run it in the cloud - the ArangoGraph Platform offers a fully managed cloud service, available on AWS, Microsoft Azure, and Google Cloud Platform.
ArangoDB is a distributed database supporting multiple data models, and can thus be scaled horizontally, that is, by using many servers, typically based on commodity hardware. This approach not only delivers performance as well as capacity improvements, but also achieves resilience by means of replication and automatic fail-over. Furthermore, one can build systems that scale their capacity dynamically up and down automatically according to demand.
One can also scale ArangoDB vertically, that is, by using ever larger servers. There is no built in limitation in ArangoDB, for example, the server will automatically use more threads if more CPUs are present.
However, scaling vertically has the disadvantage that the costs grow faster than linear with the size of the server, and none of the resilience and dynamical capabilities can be achieved in this way.