Scaling with Neo4j

Neo4j offers various options for scaling, tailored to specific use cases and requirements. You can mix and match these strategies to create a scalable solution that meets your needs.

Here are some of the supported scaling strategies:

  • Data replication via Neo4j analytics clustering (read scalability) — A Neo4j cluster is a high-availability cluster with multi-DB support. It is a collection of servers running Neo4j that are configured to communicate with each other. This means that servers and databases are decoupled: servers provide computation and storage power for databases to use. Each database relies on its own cluster architecture, organized into primaries (with a minimum of 3 for high availability) and secondaries (for read scaling). Scalability, allocation/reallocation, service elasticity, load balancing, and automatic routing are automatically provided (or they can be finely controlled).

    • Horizontal, read scalability

    • Always on, highly available with disaster recovery and rolling upgrades (Neo4j 5.0+).

    • Flexible infrastructure from 1 to many copies of the same database.

    • Servers may be service-specific (analytical/transactional workloads, data science, reporting, etc.). Multi-region, multi-tenant, SaaS-style scalability.

  • Data federation and sharding via composite database — using federated queries, Neo4j allows you to query multiple Neo4j databases with a single query. The data is partitioned into smaller, more manageable pieces, called shards. Each shard can be stored on a separate server, splitting the load on resources and storage. Alternatively, you can deploy shards in different locations, allowing you to manage them independently or split the load on network traffic. Composite databases are good for:

    • Accessing remote databases, queries executed on federated data.

    • Parallel execution of sub-queries on large data volumes.

    • Horizontal, READ & WRITE scalability.

      Sharding logic adopts sharding functions, optimal time-based sharding, and other sharding keys. The main advantage is obtained by combining Neo4j clustering and composite databases.

  • Data distribution via Infinigraph — using a distributed graph architecture to extend a single system without fragmenting the graph.

    Property sharding (part of Infinigraph) allows you to decouple the properties attached to nodes and relationships and store them in separate graphs. This architecture enables the independent scaling of property data, allowing for the handling of high volumes, heavy queries, and high read concurrency.

The following table summarizes the similarities and differences between analytics clustering, composite databases, and sharded property databases:

Table 1. Similarities and differences between analytics cluster, composite databases, and sharded property databases
Analytics cluster Composite database Sharded property database

Typical use cases

High Availability
GDS dedicated server

Federated data
Time-based sharding
Application-based access

Graphs with a large volume of properties
Ideal for vector and full-text search

Scalability

Data volume: limited to single server size
Read concurrency: horizontal scale on multiple instances

Data volume: unlimited
Read concurrency: horizontal scale on multiple instances
Write concurrency: horizontal scale depending on the graph model

Data volume: up to 100TB
Read concurrency: horizontal scale on multiple instances
Write concurrency: single instance

Transactions

Causal consistency
Standard transaction management

Parallel read transactions
Single-shard write transactions
CALL {} IN TRANSACTION for multiple, isolated read/write transactions with manual error handling

Parallel read & write transactions on all shards
Standard transaction management

Data load

Initial and incremental data import via neo4j-admin and Aura importer

Manually orchestrated import
Ad-hoc, project-based, sharded import

Initial and incremental data import via neo4j-admin and Aura importer

Cypher queries

Single database queries.

Parallel execution on shards.
Single database queries must be modified according to the sharding rules.
Automated shard pruning using sharding functions.

Parallel execution on shards.
Single database queries run as is.
Automated shard pruning based on node selection.

User tools

All tools supported.

Work with Browser and Cypher Shell.
Tools used on individual shards and Bloom are not supported on composite databases.

All tools supported.

Admin tools

All tools supported.

Tools used on individual shards are not supported on composite databases.

All tools supported.

Libraries

All libraries supported.

Supported on individual shards.

All libraries supported.

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).