Cluster server discovery
Enterprise Edition

In order to join a running cluster, any new member must know the addresses of at least some of the other servers in the cluster. This information is necessary to connect to the servers, run the discovery protocol, and obtain all the information about the cluster.

Neo4j provides several mechanisms for cluster members to discover each other and form a cluster based on the configuration and the environment in which the cluster is running, as well as the version of Neo4j being used.

In Neo4j 5.23, a new discovery service was introduced. You must move to the new version before you start using Neo4j 2025.01.

See Cluster server discovery in Operations Manual version 5 for more information.

Methods for server discovery

Depending on the type of dbms.cluster.discovery.resolver_type currently in use, the discovery service can use a list of server addresses, DNS records, or Kubernetes services to discover other servers in the cluster. The discovery configuration is used for initial discovery and to continuously exchange information about changes to the topology of the cluster.

Regardless of the method used to resolve the list of server addresses, ensure that the endpoint for each server hosting the system database in primary mode is included.

Discovery using a list of server addresses

If the addresses of the other cluster members are known upfront, they can be listed explicitly. However, this method has limitations, such as:

If servers are replaced and the new members have different addresses, the list becomes outdated. An outdated list can be avoided by ensuring that the new members can be reached via the same address as the old members, but this is not always practical.
Under some circumstances the addresses are unknown when configuring the cluster. This can be the case, for example, when using container orchestration to deploy a cluster.

To use this method, set dbms.cluster.discovery.resolver_type=LIST and hard code the addresses in the configuration of each server. For example:

dbms.cluster.discovery.resolver_type=LIST

server.cluster.advertised_address=server01.example.com:6000
dbms.cluster.endpoints=server01.example.com:6000,server02.example.com:6000,server03.example.com:6000

An example of using this method is illustrated by Configure a cluster with three servers.

Discovery using DNS with multiple records

Where it is not practical or possible to explicitly list the addresses of cluster members to discover, you can use DNS-based mechanisms. In such cases, a DNS record lookup is performed when a server starts up based on configuration settings. Once a server has joined a cluster, further topology changes are communicated amongst the servers in the cluster as part of the discovery service.

The following DNS-based mechanisms can be used to get the addresses of other servers in the cluster for discovery:

dbms.cluster.discovery.resolver_type=DNS

With this configuration, the initial discovery members are resolved from DNS A records to find the IP addresses to contact. For example:

dbms.cluster.discovery.resolver_type=DNS

server.cluster.advertised_address=server01.example.com:6000
dbms.cluster.endpoints=cluster01.example.com:6000

When a DNS lookup is performed, the domain name returns an A record for every server in the cluster, where each A record contains the IP address of the server. The configured server uses all the IP addresses from the A records to join or form a cluster.

The discovery port must be the same on all servers when using this configuration. If this is not possible, consider using the discovery type SRV.

dbms.cluster.discovery.resolver_type=SRV

With this configuration, the initial discovery members are resolved from DNS SRV records to find the IP addresses/hostnames and cluster advertised ports to contact.

The value of dbms.cluster.endpoints must be set to a single domain name and the port set to 0. The domain name returns a single SRV record when a DNS lookup is performed. For example:

dbms.cluster.discovery.resolver_type=SRV

server.cluster.advertised_address=server01.example.com:6000
dbms.cluster.endpoints=cluster01.example.com:0

The SRV record returned by DNS should contain the IP address or hostname, and the cluster port for the servers to be discovered. The configured server uses all the addresses from the SRV record to join or form a cluster.

Discovery in Kubernetes

A special case is when a cluster is running in Kubernetes and each server is running as a Kubernetes service. Then, the addresses of the other servers can be obtained using the List Service API, as described in the Kubernetes API documentation.

The following settings are used to configure for this scenario:

Set dbms.cluster.discovery.resolver_type=K8S.
Set dbms.kubernetes.label_selector to the label selector for the cluster services. For more information, see the Kubernetes official documentation.
Set dbms.kubernetes.discovery.service_port_name to the name of the service port used in the Kubernetes service definition for the Core’s discovery port. For more information, see the Kubernetes official documentation.

With this configuration, dbms.cluster.endpoints is not used and any value assigned to it is ignored.

The pod running Neo4j must use a service account that has permission to list services. For further information, see the Kubernetes documentation on RBAC authorization or ABAC authorization.
The configured server.cluster.advertised_address must exactly match the Kubernetes-internal DNS name, which is of the form <service-name>.<namespace>.svc.cluster.local.

The discovery configuration is used for initial discovery and to continuously exchange information about changes to the topology of the cluster.

IPv6 support

When using list-based discovery over an IPv6 network, enclose all addresses in square brackets:

dbms.cluster.discovery.resolver_type=LIST
server.cluster.advertised_address=[2001:db8::1]:6000
dbms.cluster.endpoints=[2001:db8::1]:6000,[2001:db8::2]:6000,[2001:db8::3]:6000

Metrics

You can use the following discovery metrics to monitor the discovery service.

Glossary

allocator: A component in the cluster that allocates databases to servers according to the topology constraints specified and an allocation strategy.
asynchronous replication: Asynchronous replication is used by secondary copies to poll for new transactions, which means they cannot be guaranteed to have received the most recent transactions. This enables efficient scale-out of read-performance.
Aura instance: A fully-managed DBMS represented by a single instance ID, that is running in the Neo4j Aura cloud.
auto-commit transaction: An automatically committed transaction that contains a single query.
Bolt protocol: Bolt is a protocol used for interaction between Neo4j instances and drivers.
bookmark: A marker the client can request from the cluster to ensure that it is able to read its own writes so that the application’s state is consistent and only databases that have a copy of the bookmark are permitted to respond.
category (Bloom): A category is based on a node label and is defined in a Perspective as a way of visually distinguishing nodes with the same label(s).
causal consistency: All servers in a cluster agree on the order in which transactions take place. The position of a server on the causal chain can be guaranteed using a bookmark.
cluster: A Neo4j DBMS that spans multiple servers working together to increase fault tolerance and/or read scalability. Databases on a cluster may be configured to replicate across servers in the cluster thus achieving read scalability or high availability.
client application: Software that interacts with a Neo4j server.
commit: A commit is the successful completion of a transaction, which ensures durability of any changes made. For more details, visit Operations Manual → Transaction management.
composite database: Composite databases are the means to access partitioned graph data with a single Cypher query.
constraint: Constraints are sets of data modeling rules that ensure the data is consistent and reliable.
Cypher®: Neo4j’s graph query language.
data model: A data model defines how information is organized in a database. A good data model will make querying and understanding your data easier. In Neo4j, the data models have a graph structure.
database: A database is a container used by the DBMS to manage and store graph data. The physical structure of data is controlled by the database.
database vs graph: Databases are the physical containers of graph data. Graphs are the logical structure of data in Neo4j.
Database Management System: Database Management System, or DBMS, capable of managing multiple databases. A DBMS may run on a single server, or span several servers configured as a cluster.
database schema: The prescribed property existence and datatypes for nodes and relationships.
deallocate: An act of removing a database from a server or a server from a cluster without loss of data or reduced fault tolerance.
degree (of a node): The number of relationships of a specific node; loops are counted twice.
disaster recovery: A manual intervention to restore availability of a cluster, or databases within a cluster.
driver: A software library that provides access to Neo4j from a particular programming language.
election: In the event that the Raft leader becomes unresponsive, followers automatically trigger an election and vote for a new leader.
entity: A node or a relationship.
expression (Cypher): A component of a Cypher query which produces values. It may be used in projections, as a predicate, or when setting properties on graph elements.
fabric: Fabric is the architectural design of a unified system that provides a single access point to local or distributed graph data.
fault tolerance: A guarantee that a cluster can maintain a database’s persistence and availability in the event of one or more servers failing.
follower: A primary copy of a database acting as a follower, receives and acknowledges synchronous writes from the leader.
Generative AI (GenAI): A type of artificial intelligence (AI) system that generates text, images, or other media in response to prompts.
graph: A logical representation of a set of nodes where some pairs are connected by relationships.
index: Data structure that improves read performance of a database.
knowledge graph: A specific type of graph that has an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle provides an additional layer of structure that adds context to support knowledge discovery.
label: Marks a node as a member of a named and indexed subset. A node may be assigned zero or more labels.
leader: A single primary copy of a database is designated as the leader. It receives all write transactions from clients and replicates writes synchronously to followers and asynchronously to secondary copies of the database.
main database: In terms of Neo4j Enterprise Studio, the database(s) containing the user’s data. Can exist in the same Neo4j deployment as the tool asset database.
motif: A description of a specific pattern within a graph.
node: A node represents an entity or discrete object in your graph data model. Nodes can be connected by relationships, hold data in properties, and are classified by labels.
operator: A symbol representing a mathematical or logical operation.
parameter: Named value provided when running a Cypher statement.
path: A sequence of nodes and the relationships connecting them, that does not contain duplicate relationships. Several paths can match a pattern.
pattern: A specific arrangement of nodes and relationships that can be matched in a graph. A pattern follows a motif.
perspective (Bloom): A Perspective defines a certain business view or domain that can be found in the target Neo4j graph. A single Neo4j graph can be viewed through different Perspectives, each tailored for a different business purpose.
primary: A copy of the database that is able to process write transactions and is eligible to be elected as a leader. It participates in fault tolerant writes as it is part of the majority required to acknowledge and commit write transactions.
primary vs secondary: In a cluster, databases can operate in either primary or secondary mode. Primary databases are able to process write and read transactions, ensuring fault tolerance. Secondary databases are replicated asynchronously from primaries, and their main purpose is to provide read scaling within the cluster.
project (Aura): An isolated environment in the unified Aura console that contains its own database instances, configurations, and resources. Preceded by tenant in the classic Aura console.
property: Properties are key-value pairs that are used for storing data on nodes and relationships.
query (Cypher): A statement that retrieves or writes information to a database.
Raft group: A group of servers that are participating in hosting a particular database in primary mode.
Raft group member: A server that is participating in a Raft group. A server can be a member of one or more groups.
Raft log: A shared log between all Raft group members that is guaranteed to be consistently updated and viewed by those members. The log contains both database data and operational state of the Raft group.
Raft protocol: The networking mechanism that enables a database to replicate its data across multiple servers to give high availability for accessing the data and high durability to the data stored.
read scaling: Distributing query load by creating additional database copies hosted in secondary mode (read-only).
relationship: A relationship represents a connection between nodes in your graph data model. Relationships connect a source node to a target node, hold data in properties, and are classified by type.
secondary: An asynchronously replicated copy of the database that provides read scaling within the cluster.
seed: A seed is a database dump or a full backup used to create a database on a cluster. This is sometimes called seeding.
server: A physical machine, a virtual machine, or a container running an instance of Neo4j. Servers can be standalone or part of a cluster.
session: A causally linked sequence of transactions.
session consistency: An alternative name for Neo4j’s causal consistency.
standalone: A single server running Neo4j and not part of a cluster.
synchronous replication: Synchronous replication requires the leader primary to replicate a transaction and block the commit until a quorum of the follower primaries acknowledges that the transaction is successfully replicated. Once the transaction is replicated, the commit is allowed to proceed. This ensures data durability and consistency within the cluster.
system database: A database used by Neo4j to store system information.
tenant (Aura): An isolated environment in the classic Aura console that contains its own database instances, configurations, and resources. Replaced by project in the unified Aura console.
tool asset database: In terms of Neo4j Enterprise Studio, the database where tools' assets are stored. This can be in the same Neo4j deployment as the main database(s) or in a separate deployment.
topology: A configuration that describes how the copies of a database should be spread across the servers in a cluster, see primary mode and secondary mode.
transaction: A transaction comprises a unit of work performed against a database. It is treated in a coherent and reliable way, independent of other transactions. Transactions comply with the ACID consistency model (atomic, consistent, isolated, and durable).

Cluster server discoveryEnterprise Edition