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Single Instance vs. Cluster

In general, a single server configuration and a cluster configuration of ArangoDB behave very similarly. However, there are differences due to the different nature of these setups. This can lead to a discrepancy in behavior between these two configurations. A summary of potential differences follows.

See Migrating from Single Instance to Cluster for practical information.

Locking and dead-lock prevention

In a single server configuration all data is local and dead-locks can easily be detected. In a cluster configuration data is distributed to many servers and some conflicts cannot be detected easily. Therefore we have to do some things (like locking shards) sequentially and in a strictly predefined order, to avoid dead-locks in this way by design.

Document Keys

In a cluster the autoincrement key generator is not supported. You have to use the traditional or user defined keys.


Unique constraints

There are restrictions on the allowed unique constraints in a cluster. Any unique constraint which cannot be checked locally on a per shard basis is not allowed in a cluster setup. More concretely, unique constraints in a cluster are only allowed in the following situations:

  • there is always a unique constraint on the primary key _key, if the collection is not sharded by _key, then _key must be automatically generated by the database and cannot be prescribed by the client
  • the collection has only one shard, in which case the same unique constraints are allowed as in the single instance case
  • if the collection is sharded by exactly one other attribute than _key, then there can be a unique constraint on that attribute

These restrictions are imposed, because otherwise checking for a unique constraint violation would involve checking with all shards, which would have a considerable performance impact.


It is not possible to rename collections or views in a cluster.


The AQL syntax for single server and cluster is identical. However, there is one additional requirement (regarding with) and possible performance differences.


The WITH keyword in AQL must be used to declare which collections are used in the AQL. For most AQL requires the required collections can be deduced from the query itself. However, with traversals this is not possible, if edge collections are used directly. See AQL WITH operation for details. The WITH statement is not necessary when using named graphs for the traversals.

As deadlocks cannot be detected in a cluster environment easily, the WITH keyword is mandatory for this particular situation in a cluster, but not in a single server.


Performance of AQL queries can vary between single server and cluster. If a query can be distributed to many DBserver and executed in parallel then cluster performance can be better. For example, if you do a distributed COLLECT aggregation or a distributed FILTER operation.

On the other hand, if you do a join or a traversal and the data is not local to one server then the performance can be worse compared to a single server. This is especially true for traversal if the data is not sharded with care. Our SmartGraph feature helps with this for traversals.

Single document operations can have a higher throughput in cluster but will also have a higher latency, due to an additional network hop from coordinator to dbserver.

Any operation that needs to find documents by anything else but the shard key will have to fan out to all shards, so it will be a lot slower than when referring to the documents using the shard key. Optimized lookups by shard key can only be used for equality lookups, e.g. not for range lookups.

Memory usage

Some query results must be built up in memory on a coordinator, for example if a dataset needs to be sorted on the fly. This can relatively easily overwhelm a coordinator if the dataset is sharded across multiple dbservers. Use indexes and streaming cursors (>= 3.4) to circumvent this problem.


Using a single instance of ArangoDB, multi-document / multi-collection queries are guaranteed to be fully ACID. This is more than many other NoSQL database systems support. In cluster mode, single-document operations are also fully ACID. Multi-document / multi-collection queries in a cluster are not ACID, which is equally the case for competing database systems. See Transactions for details.

Batch operations for multiple documents in the same collection are only fully transactional in a single instance.


In SmartGraphs there are restrictions on the values of the _key attributes. Essentially, the _key attribute values for vertices must be prefixed with the string value of the SmartGraph attribute and a colon. A similar restriction applies for the edges.


Foxx apps run on the coordinators of a cluster. Since coordinators are stateless, one must not use regular file accesses in Foxx apps in a cluster.


A cluster deployment needs a central, RAFT-based key/value store called “the agency” to keep the current cluster configuration and manage failover. Being RAFT-based, this is a real-time system. If your servers running the agency instances (typically three or five) receive too much load, the RAFT protocol stops working and the whole stability of the cluster is endangered. If you foresee this problem, run the agency instances on separate nodes. All this is not necessary in a single server deployment.


At the time of this writing, the arangodump utility in a cluster cannot guarantee a consistent snapshot across multiple shards or even multiple collections. This is in line with most other current NoSQL database systems. We are working on a consistent snapshot and incremental backup capability for 3.5. In a single server, arangodump produces a consistent snapshot.