Setting up and using a composite database
Enterprise Edition

Composite databases allow queries that access multiple graphs at once. This is a function that enables:

Data Federation: the ability to access data available in distributed sources in the form of disjoint graphs.
Data Sharding: the ability to access data available in distributed sources in the form of a common graph partitioned on multiple databases.

In this tutorial, you will learn how to:

Model your data for Composite database use
Create databases for the composite
Import data to your databases
Configure a Composite database
Retrieve data with a single Cypher query

Model your data for Composite database use

The example data in this tutorial is based on the Northwind dataset, created by Microsoft. It contains the sales data of a fictitious small company called “Northwind Traders”. The data includes customers, products, customer orders, warehouse stock, shipping, suppliers, employees, and sales territories.

For more information on how Northwind (a relational dataset) is modeled into a graph, run :play northwind in Neo4j Browser to play the built-in guide Northwind Graph.

The Northwind graph model consists of the following data:

Node labels
- :Product
- :Category
- :Supplier
- :Order
- :Customer
Relationship types
- :SUPPLIES
- :PART_OF
- :ORDERS
- :PURCHASED

Figure 1. The Northwind data model

In this scenario, assume that data privacy constraints require customers’ data to be stored in their original region. For simplicity, there are two regions: the Americas (AME) and Europe (EU). The first step is to remodel the Northwind dataset, so that customer data can be separated from the Product catalog, which has no privacy constraints. You create two graphs: one for the Product catalog, which includes :Product, :Category, :Supplier, :PART_OF, :SUPPLIES, and one partitioned graph in two databases for the Customer orders in EU and AME, with :Product, :Order, :Customer, :PURCHASED, and :ORDERS.

Figure 2. The new data model

Data Federation

This way, the Product and Customer data are in two disjoint graphs, with different labels and relationship types. This is called Data Federation.

To query across them, you have to federate the graphs, because relationships cannot span across them. This is done by using a proxy node modeling pattern: nodes with the :Product label must be present in both federated domains.

In the Product catalog graph, nodes with the :Product label contain all the data related to a product, while in the Customer graphs, the same label is associated to a proxy node which only contains productID. The productID property allows you to link data across the graphs in this federation.

Figure 3. Data Federation

Data Sharding

Since the Customer data is for two regions (EU and AME), you have to partition it into two databases. The resulting two graphs have the same model (same labels, same relationship types), but different data. This is called Data Sharding.

Figure 4. Data Sharding

In general, there are a couple of main use cases that require sharding. The most common is scalability, i.e. different shards can be deployed on different servers, splitting the load on different resources. Another reason could be data regulations: different shards can be deployed on servers, residing in different locations, and managed independently.

Create databases for the composite

For this tutorial, you will create the following databases:

db0 for the Product catalog.
db1 for the EU customer data.
db2 for the AME customers.

Start the Neo4j DBMS.
```
bin/neo4j start
```

Check all available databases.

ls -al /data/databases/

total 0
drwxr-xr-x@  5 username  staff   160  9 Jun 12:53 .
drwxr-xr-x@  5 username  staff   160  9 Jun 12:53 ..
drwxr-xr-x  37 username  staff  1184  9 Jun 12:53 neo4j
-rw-r--r--   1 username  staff     0  9 Jun 12:53 store_lock
drwxr-xr-x  38 username  staff  1216  9 Jun 12:53 system

Connect to the Neo4j DBMS using cypher-shell with the default credentials and change the password when prompted:

bin/cypher-shell -u neo4j -p neo4j

Password change required
new password: ********
Connected to Neo4j 2025.01 at neo4j://localhost:7687 as user neo4j.
Type :help for a list of available commands or :exit to exit the shell.
Note that Cypher queries must end with a semicolon.

For more information about the Cypher Shell command-line interface (CLI) and how to use it, see Cypher Shell.

Run the command SHOW DATABASES to list all available databases:

SHOW DATABASES;

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| name     | type       | aliases | access       | address          | role      | writer | requestedStatus | currentStatus | statusMessage | default | home  | constituents |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "neo4j"  | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | TRUE    | TRUE  | []           |
| "system" | "system"   | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
2 rows available after 102 ms, consumed after another 11 ms

Run the command CREATE DATABASE <database-name> to create the databases:

CREATE DATABASE db0;

0 rows available after 137 ms, consumed after another 0 ms

CREATE DATABASE db1;

0 rows available after 141 ms, consumed after another 0 ms

CREATE DATABASE db2;

0 rows available after 135 ms, consumed after another 0 ms

Run the command SHOW DATABASES again to verify that the new databases have been created and are online:

SHOW DATABASES;

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| name     | type       | aliases | access       | address          | role      | writer | requestedStatus | currentStatus | statusMessage | default | home  | constituents |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "db0"    | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
| "db1"    | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
| "db2"    | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
| "neo4j"  | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | TRUE    | TRUE  | []           |
| "system" | "system"   | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows available after 8 ms, consumed after another 7 ms

Import data to your databases

You can use the command LOAD CSV WITH HEADERS FROM to import data to the databases.

Load the Product catalog in db0

Run the following Cypher query to change the active database to db0, and add the Product data:

:use db0;

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/products.csv" AS row
CREATE (n:Product)
SET n = row,
n.unitPrice = toFloat(row.unitPrice),
n.unitsInStock = toInteger(row.unitsInStock), n.unitsOnOrder = toInteger(row.unitsOnOrder),
n.reorderLevel = toInteger(row.reorderLevel), n.discontinued = (row.discontinued <> "0");

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/categories.csv" AS row
CREATE (n:Category)
SET n = row;

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/suppliers.csv" AS row
CREATE (n:Supplier)
SET n = row;

CREATE INDEX FOR (p:Product) ON (p.productID);
CREATE INDEX FOR (c:Category) ON (c.categoryID);
CREATE INDEX FOR (s:Supplier) ON (s.supplierID);

MATCH (p:Product),(c:Category)
WHERE p.categoryID = c.categoryID
CREATE (p)-[:PART_OF]->(c);

MATCH (p:Product),(s:Supplier)
WHERE p.supplierID = s.supplierID
CREATE (s)-[:SUPPLIES]->(p);

Press Enter.

Verify that the product data is loaded in db0:

MATCH (s:Supplier)-[:SUPPLIES]->(p:Product)-[:PART_OF]->(c:Category)
RETURN s.companyName AS Supplier, p.productName AS Product, c.categoryName AS Category
LIMIT 5;

+--------------------------------------------------------------------------+
| Supplier                     | Product                     | Category    |
+--------------------------------------------------------------------------+
| "Bigfoot Breweries"          | "Sasquatch Ale"             | "Beverages" |
| "Pavlova"                    | "Outback Lager"             | "Beverages" |
| "Bigfoot Breweries"          | "Laughing Lumberjack Lager" | "Beverages" |
| "Bigfoot Breweries"          | "Steeleye Stout"            | "Beverages" |
| "Aux joyeux ecclésiastiques" | "Côte de Blaye"             | "Beverages" |
+--------------------------------------------------------------------------+

5 rows available after 202 ms, consumed after another 5 ms

Load EU customers and related orders in db1

Run the following Cypher query to change the active database to db1, and add the EU customers and orders:

:use db1;

:param europe => ['Germany', 'UK', 'Sweden', 'France', 'Spain', 'Switzerland', 'Austria', 'Italy', 'Portugal', 'Ireland', 'Belgium', 'Norway', 'Denmark', 'Finland'];

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/customers.csv" AS row
WITH row
WHERE row.country IN $europe
CREATE (n:Customer)
SET n = row;

CREATE INDEX FOR (c:Customer) ON (c.customerID);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/orders.csv" AS row
WITH row
MATCH (c:Customer)
WHERE row.customerID = c.customerID
CREATE (o:Order)
SET o = row;

CREATE INDEX FOR (o:Order) ON (o.orderID);

MATCH (c:Customer),(o:Order)
WHERE c.customerID = o.customerID
CREATE (c)-[:PURCHASED]->(o);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/products.csv" AS row
CREATE (n:Product)
SET n.productID = row.productID;

CREATE INDEX FOR (p:Product) ON (p.productID);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/order-details.csv" AS row
MATCH (p:Product), (o:Order)
WHERE p.productID = row.productID AND o.orderID = row.orderID
CREATE (o)-[details:ORDERS]->(p)
SET details = row, details.quantity = toInteger(row.quantity);

Press Enter.

Verify that the EU Customer orders data is loaded in db1:

MATCH (c:Customer)-[:PURCHASED]->(o:Order)-[:ORDERS]->(p:Product)
RETURN c.companyName AS Customer, c.country AS CustomerCountry, o.orderID AS Order, p.productID AS Product
LIMIT 5;

+-------------------------------------------------------------+
| Customer              | CustomerCountry | Order   | Product |
+-------------------------------------------------------------+
| "Alfreds Futterkiste" | "Germany"       | "10692" | "63"    |
| "Alfreds Futterkiste" | "Germany"       | "10835" | "77"    |
| "Alfreds Futterkiste" | "Germany"       | "10835" | "59"    |
| "Alfreds Futterkiste" | "Germany"       | "10702" | "76"    |
| "Alfreds Futterkiste" | "Germany"       | "10702" | "3"     |
+-------------------------------------------------------------+

5 rows available after 47 ms, consumed after another 2 ms

Load AME customers and related orders in db2

Run the following Cypher query to change the active database to db2 and add the AME customers and orders:

:use db2;

:param americas => ['Mexico', 'Canada', 'Argentina', 'Brazil', 'USA', 'Venezuela'];

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/customers.csv" AS row
WITH row
WHERE row.country IN $americas
CREATE (n:Customer)
SET n = row;

CREATE INDEX FOR (c:Customer) ON (c.customerID);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/orders.csv" AS row
WITH row
MATCH (c:Customer)
WHERE row.customerID = c.customerID
CREATE (o:Order)
SET o = row;

CREATE INDEX FOR (o:Order) ON (o.orderID);

MATCH (c:Customer),(o:Order)
WHERE c.customerID = o.customerID
CREATE (c)-[:PURCHASED]->(o);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/products.csv" AS row
CREATE (n:Product)
SET n.productID = row.productID;

CREATE INDEX FOR (p:Product) ON (p.productID);

LOAD CSV WITH HEADERS FROM "https://data.neo4j.com/northwind/order-details.csv" AS row
MATCH (p:Product), (o:Order)
WHERE p.productID = row.productID AND o.orderID = row.orderID
CREATE (o)-[details:ORDERS]->(p)
SET details = row,
details.quantity = toInteger(row.quantity);

Press Enter.

Verify that the AME Customer orders data is loaded in db2:

MATCH (c:Customer)-[:PURCHASED]->(o:Order)-[:ORDERS]->(p:Product)
RETURN c.companyName AS Customer, c.country AS CustomerCountry, o.orderID AS Order, p.productID AS Product
LIMIT 5;

+----------------------------------------------------------------------------+
| Customer                             | CustomerCountry | Order   | Product |
+----------------------------------------------------------------------------+
| "Ana Trujillo Emparedados y helados" | "Mexico"        | "10759" | "32"    |
| "Ana Trujillo Emparedados y helados" | "Mexico"        | "10926" | "72"    |
| "Ana Trujillo Emparedados y helados" | "Mexico"        | "10926" | "13"    |
| "Ana Trujillo Emparedados y helados" | "Mexico"        | "10926" | "19"    |
| "Ana Trujillo Emparedados y helados" | "Mexico"        | "10926" | "11"    |
+----------------------------------------------------------------------------+

5 rows available after 42 ms, consumed after another 1 ms

Configure a Composite database

Set up a Composite database with the CREATE COMPOSITE DATABASE Cypher command and add local database aliases as constituents to the Composite database. In this example, the Composite database is called compositenw.

Run the command CREATE COMPOSITE DATABASE <composite-database-name> to create the Composite database:
```
CREATE COMPOSITE DATABASE compositenw;
```
```
0 rows available after 137 ms, consumed after another 0 ms
```

Run the command CREATE ALIAS <composite-database-name>.<alias-name> FOR DATABASE <database-name> to create the constituent database aliases:

CREATE ALIAS compositenw.product FOR DATABASE db0;

0 rows available after 101 ms, consumed after another 0 ms

CREATE ALIAS compositenw.customerEU FOR DATABASE db1;

0 rows available after 107 ms, consumed after another 0 ms

CREATE ALIAS compositenw.customerAME FOR DATABASE db2;

0 rows available after 98 ms, consumed after another 0 ms

The constituent database aliases in this tutorial are local database aliases (targeting databases in the same Neo4j DBMS), but they can just as well be remote database aliases (targeting databases in another Neo4j DBMS).

Run the command SHOW DATABASES to verify that the Composite database has been configured and is online:

SHOW DATABASES;

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| name          | type        | aliases                     | access       | address          | role      | writer | requestedStatus | currentStatus | statusMessage | default | home  | constituents                                                                 |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "db0"         | "standard"  | ["compositenw.product"]     | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []                                                                           |
| "db1"         | "standard"  | ["compositenw.customerEU"]  | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []                                                                           |
| "db2"         | "standard"  | ["compositenw.customerAME"] | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []                                                                           |
| "compositenw" | "composite" | []                          | "read-only"  | "localhost:7687" | "primary" | FALSE  | "online"        | "online"      | ""            | FALSE   | FALSE | ["compositenw.customerAME", "compositenw.customerEU", "compositenw.product"] |
| "neo4j"       | "standard"  | []                          | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | TRUE    | TRUE  | []                                                                           |
| "system"      | "system"    | []                          | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []                                                                           |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
6 rows available after 242 ms, consumed after another 18 ms

Run the command SHOW ALIASES FOR DATABASES to verify that the database aliases have been configured:

SHOW ALIASES FOR DATABASES;

+----------------------------------------------------------------------------------------------+
| name                      | composite     | database  | location | url  | credentials | user |
+----------------------------------------------------------------------------------------------+
| "compositenw.product"     | "compositenw" | "db0"     | "local"  | null | null        | null |
| "compositenw.customerEU"  | "compositenw" | "db1"     | "local"  | null | null        | null |
| "compositenw.customerAME" | "compositenw" | "db2"     | "local"  | null | null        | null |
+----------------------------------------------------------------------------------------------+
3 rows available after 203 ms, consumed after another 16 ms

Retrieve data with a single Cypher query

Query a single database

When connected to a Composite database you can retrieve data from a single database by using the Cypher clause USE and the name of an alias:

:use compositenw

USE compositenw.product
MATCH (p:Product)
RETURN p.productName AS product
LIMIT 5;

+--------------------------------+
| product                        |
+--------------------------------+
| "Chai"                         |
| "Chang"                        |
| "Aniseed Syrup"                |
| "Chef Anton's Cajun Seasoning" |
| "Chef Anton's Gumbo Mix"       |
+--------------------------------+

5 rows available after 6 ms, consumed after another 21 ms

Query across multiple shards

Use the Composite database to query both shards and get customers whose name starts with A:

:use compositenw

USE compositenw.customerAME
MATCH (c:Customer)
WHERE c.customerID STARTS WITH 'A'
RETURN c.customerID AS name, c.country AS country
  UNION
USE compositenw.customerEU
MATCH (c:Customer)
WHERE c.customerID STARTS WITH 'A'
RETURN c.customerID AS name, c.country AS country
LIMIT 5;

+---------------------+
| name    | country   |
+---------------------+
| "ANATR" | "Mexico"  |
| "ANTON" | "Mexico"  |
| "ALFKI" | "Germany" |
| "AROUT" | "UK"      |
+---------------------+

4 rows available after 25 ms, consumed after another 56 ms

Or, using a more common Composite database idiom:

:use compositenw

UNWIND ['compositenw.customerAME', 'compositenw.customerEU'] AS g
CALL(g) {
  USE graph.byName(g)
  MATCH (c:Customer)
  WHERE c.customerID STARTS WITH 'A'
  RETURN c.customerID AS name, c.country AS country
}
RETURN name, country
LIMIT 5;

+---------------------+
| name    | country   |
+---------------------+
| "ANATR" | "Mexico"  |
| "ANTON" | "Mexico"  |
| "ALFKI" | "Germany" |
| "AROUT" | "UK"      |
+---------------------+

4 rows available after 61 ms, consumed after another 8 ms

Query across federation and shards

Here is a more complex query that uses all 3 databases to find all customers who have bought discontinued products in the Meat/Poultry category:

:use compositenw

CALL() {
  USE compositenw.product
  MATCH (p:Product)-[:PART_OF]->(c:Category)
  WHERE p.discontinued = true
    AND c.categoryName = 'Meat/Poultry'
  RETURN COLLECT(p.productID) AS pids
}
WITH *
UNWIND [g IN graph.names() WHERE g STARTS WITH 'compositenw.customer'] AS g
CALL(g, pids) {
  USE graph.byName(g)
  UNWIND pids as pid
  MATCH (p:Product{productID:pid})<-[:ORDERS]-(:Order)<-[:PURCHASED]-(c:Customer)
  RETURN DISTINCT c.customerID AS customer, c.country AS country
}
RETURN customer, country
LIMIT 20;

+--------------------------+
| customer | country       |
+--------------------------+
| "RICSU"  | "Switzerland" |
| "PERIC"  | "Mexico"      |
| "WARTH"  | "Finland"     |
| "WELLI"  | "Brazil"      |
| "DRACD"  | "Germany"     |
| "RATTC"  | "USA"         |
| "HUNGO"  | "Ireland"     |
| "QUEDE"  | "Brazil"      |
| "SEVES"  | "UK"          |
| "ANTON"  | "Mexico"      |
| "BERGS"  | "Sweden"      |
| "SAVEA"  | "USA"         |
| "AROUT"  | "UK"          |
| "FAMIA"  | "Brazil"      |
| "WANDK"  | "Germany"     |
| "WHITC"  | "USA"         |
| "ISLAT"  | "UK"          |
| "LONEP"  | "USA"         |
| "QUICK"  | "Germany"     |
| "HILAA"  | "Venezuela"   |
+--------------------------+

20 rows available after 51 ms, consumed after another 2 ms

The way this query works is by compositenw calling database db0 to retrieve all discontinued products in the Meat/Poultry category. Then, using the returned product IDs, it queries both db1 and db2 in parallel and gets the customers who have purchased these products and their country.

You have just learned how to store and retrieve data from multiple databases using a single Cypher query.

For more details on Composite databases, see Concepts.

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).

Setting up and using a composite databaseEnterprise Edition