Concepts
Enterprise EditionNot available on Aura

Scalability is a crucial aspect of database management, allowing a system to handle changing demands by adding and removing resources to meet the demands of a database’s workload. Neo4j supports multiple strategies to achieve scalability, enabling systems to handle larger datasets, more concurrent users, and higher query complexity without compromising performance or availability, i.e. the system’s resiliency. The three main strategies are:

What is scalability?

Scalability is a system’s ability to handle an increasing workload without compromising performance. There are two primary methods to achieve scalability:

Table 1. Scaling methods
Method Description Pros Cons

Vertical Scaling (Scaling Up / Down)

Increase or decrease the capacity of a single server by adding or removing CPUs, memory, or storage.

Simple to manage.

  • Physical limits.

  • Difficult to make online changes.

Horizontal Scaling (Scaling Out / In)

Distribute the workload by adding more servers or reduce the infrastructure by removing existing servers.

  • Greater scalability and fault tolerance.

  • Easier to make online changes.

More complex to manage.

What is database scalability?

Database scalability is the ability of a database management system (DBMS) to handle changing demands. To scale properly, a database must apply strategies that cover all areas: data access, data manipulation in memory, and database computing.

Strategies include:

  • Vertical Scaling

    • Optimize usage (e.g., granular locks, partitioning)

    • Optimize physical resources (multi-threading, tiered storage)

  • Horizontal Scaling (distributed computing architectures):

    • Shared Everything: All servers share data and memory. Flexible, but prone to contention.
      In this model, data is shared between disk and memory across all servers in a cluster. Requests are satisfied by any combination of servers. This approach introduces complexity, as the cluster must implement a way to avoid contention when multiple servers try to update the same data simultaneously.

    • Shared Nothing: Each server manages its own partition (shard). More fault-tolerant, eliminates single points of failure.
      Every update request is handled by a single cluster member, which eliminates single points of failure. Each part of the database on a single cluster member is called a shard.

horizontal scaling strategies
Figure 1. Example of a sharing approach where all the servers share the storage vs a shared-nothing approach (e.g., property sharding).

What is graph database scalability?

Graph database scalability refers to the ability of a database to handle different amounts of data and workloads without compromising performance. It includes:

  • Data volume - involves ensuring a consistent SLA in both query and administration response times, even as the size of the data for storage and retrieval expands.
    Volume depends on data type(s). Vectors occupy a large data space.

  • Query volume

    • Read queries + write queries.

    • Queries and user concurrency — the aim is to ensure a linear response time during the execution of concurrent queries against the same database.

    • Query complexity — provide response time in line with the complexity of a query. The complexity of a query can be set by the combination of:

      • Steps to execute

      • Rows to retrieve

      • Total DB hits

      • Total memory allocation

      • Total execution time

  • Admin volume

    • Data ingestion/extraction — When scaling data ingestion/extraction, the goal is to maintain a linear response time when ingesting or extracting an increasing set of data. This objective remains true regardless of the volume of stored data, provided a similar data structure is used.

    • Multi-tenancy — In SaaS and AaaS environments, the scaling cost for tenants should exhibit linearity. For more general services, such as DBaaS (e.g., Aura), scalability should also be linear, considering all five scalability factors mentioned here.

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).