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Neo4j Basics

Introduction​

Neo4j is a popular graph database management system that belongs to the NoSQL family. Unlike traditional relational databases that store data in tables, Neo4j uses a graph structure to represent and store data. This makes it exceptionally powerful for managing highly connected data and performing complex relationship-based queries.

In this tutorial, we'll explore the fundamentals of Neo4j, understand its core concepts, and learn how to perform basic operations using the Cypher query language.

What is a Graph Database?​

At its core, a graph database stores data in nodes (entities) and relationships (connections between entities). This structure closely mirrors how we naturally think about many domains - as objects connected by various relationships.

Key Components of Neo4j​

  1. Nodes - Represent entities (like a person, place, or thing)
  2. Relationships - Connect nodes and have a type and direction
  3. Properties - Key-value pairs that can be attached to both nodes and relationships
  4. Labels - Used to group nodes into sets

Let's visualize a simple graph model:

Setting Up Neo4j​

Before diving into code examples, you'll need to set up Neo4j. The easiest way to get started is to use Neo4j Desktop or Neo4j Sandbox.

Using Neo4j Desktop​

  1. Download and install Neo4j Desktop
  2. Create a new project
  3. Add a database to your project
  4. Start the database

Using Neo4j Sandbox​

Neo4j Sandbox provides a free, temporary Neo4j instance in the cloud:

  1. Visit Neo4j Sandbox
  2. Create an account or sign in
  3. Launch a blank sandbox

Basic Cypher Query Language​

Cypher is Neo4j's query language, designed to be visually intuitive and easy to understand. Let's explore some basic operations.

Creating Nodes​

To create a simple node:

cypher
CREATE (n:Person {name: 'John', age: 30})

This creates a node with the label Person and two properties: name and age.

To create multiple nodes at once:

cypher
CREATE (a:Person {name: 'Alice', age: 25}),
       (b:Person {name: 'Bob', age: 27}),
       (c:Company {name: 'Acme', founded: 2010})

Creating Relationships​

To create relationships between nodes:

cypher
MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
CREATE (a)-[:FRIEND_OF]->(b)

This creates a FRIEND_OF relationship from Alice to Bob.

You can also create nodes and relationships in a single query:

cypher
CREATE (a:Person {name: 'Alice', age: 25})-[:WORKS_AT]->(c:Company {name: 'Acme'})

Querying Data​

To retrieve all Person nodes:

cypher
MATCH (p:Person)
RETURN p

To find specific nodes:

cypher
MATCH (p:Person {name: 'Alice'})
RETURN p

To query relationships:

cypher
MATCH (p:Person)-[:WORKS_AT]->(c:Company)
RETURN p.name, c.name

Example output:

╒══════════╤══════════╕
│ p.name   │ c.name   │
╞══════════╪══════════╡
│ "Alice"  │ "Acme"   │
└──────────┴──────────┘

Updating Properties​

To update node properties:

cypher
MATCH (p:Person {name: 'John'})
SET p.age = 31
RETURN p

Deleting Nodes and Relationships​

To delete a relationship:

cypher
MATCH (a:Person {name: 'Alice'})-[r:FRIEND_OF]->(b:Person {name: 'Bob'})
DELETE r

To delete a node (and all its relationships):

cypher
MATCH (p:Person {name: 'John'})
DETACH DELETE p

Practical Example: Building a Movie Recommendation System​

Let's build a simple movie recommendation database to demonstrate Neo4j's power with connected data.

Step 1: Create the Schema​

cypher
// Create Movie nodes
CREATE (matrix:Movie {title: 'The Matrix', released: 1999, tagline: 'Welcome to the Real World'})
CREATE (cloudAtlas:Movie {title: 'Cloud Atlas', released: 2012, tagline: 'Everything is Connected'})
CREATE (forrestGump:Movie {title: 'Forrest Gump', released: 1994, tagline: 'Life is like a box of chocolates'})

// Create Person nodes
CREATE (keanu:Person {name: 'Keanu Reeves', born: 1964})
CREATE (tomHanks:Person {name: 'Tom Hanks', born: 1956})
CREATE (halleBerry:Person {name: 'Halle Berry', born: 1966})

// Create relationships
CREATE (keanu)-[:ACTED_IN {roles: ['Neo']}]->(matrix)
CREATE (keanu)-[:ACTED_IN {roles: ['Robert Frobisher', 'Hae-Joo Chang']}]->(cloudAtlas)
CREATE (tomHanks)-[:ACTED_IN {roles: ['Forrest Gump']}]->(forrestGump)
CREATE (tomHanks)-[:ACTED_IN {roles: ['Dr. Henry Goose', 'Isaac Sachs']}]->(cloudAtlas)
CREATE (halle)-[:ACTED_IN {roles: ['Luisa Rey', 'Jocasta Ayrs']}]->(cloudAtlas)

Step 2: Query for Movie Recommendations​

Now, let's find movie recommendations based on actors:

cypher
// Find movies that Keanu Reeves acted in
MATCH (keanu:Person {name: 'Keanu Reeves'})-[:ACTED_IN]->(movie:Movie)
RETURN movie.title

// Find co-actors (people who acted in the same movies as Keanu)
MATCH (keanu:Person {name: 'Keanu Reeves'})-[:ACTED_IN]->(movie:Movie)<-[:ACTED_IN]-(coActor:Person)
RETURN coActor.name, movie.title

// Find recommendations: Movies that co-actors acted in but Keanu didn't
MATCH (keanu:Person {name: 'Keanu Reeves'})-[:ACTED_IN]->(movie:Movie)<-[:ACTED_IN]-(coActor:Person),
      (coActor)-[:ACTED_IN]->(recommendation:Movie)
WHERE NOT (keanu)-[:ACTED_IN]->(recommendation)
RETURN DISTINCT recommendation.title, coActor.name

Example output of the last query:

╒═════════════════╤═══════════════╕
│ recommendation  │ coActor.name  │
╞═════════════════╪═══════════════╡
│ "Forrest Gump"  │ "Tom Hanks"   │
└─────────────────┴───────────────┘

Advanced Features​

Indexes​

Indexes improve query performance. To create an index:

cypher
CREATE INDEX FOR (p:Person) ON (p.name)

Constraints​

Constraints ensure data integrity. To create a uniqueness constraint:

cypher
CREATE CONSTRAINT FOR (m:Movie) REQUIRE m.title IS UNIQUE

Path Finding​

Neo4j excels at finding paths between nodes:

cypher
// Find the shortest path between two actors
MATCH p=shortestPath((bacon:Person {name: 'Kevin Bacon'})-[*]-(meg:Person {name: 'Meg Ryan'}))
RETURN p

When to Use Neo4j​

Neo4j is particularly well-suited for:

  1. Social networks - Modeling users, friendships, follows, etc.
  2. Recommendation engines - "People who bought X also bought Y"
  3. Fraud detection - Identifying suspicious patterns in connected data
  4. Knowledge graphs - Representing complex domains with many entity types and relationships
  5. Network and IT operations - Modeling infrastructure dependencies

Summary​

In this tutorial, we've explored the fundamentals of Neo4j and graph databases:

  • Neo4j represents data as nodes (entities) and relationships
  • Cypher is a powerful query language for working with graph data
  • Basic operations include creating, reading, updating, and deleting nodes and relationships
  • Neo4j excels at traversing relationships and finding paths between entities

Graph databases like Neo4j offer a flexible and intuitive way to model and query highly connected data. As your applications grow and require more complex relationship-based queries, Neo4j provides a powerful alternative to traditional relational databases.

Additional Resources​

Practice Exercises​

  1. Extend the movie database by adding more movies, actors, and relationship types (like DIRECTED, PRODUCED, etc.)
  2. Create a query to find all actors who have worked with the same director more than once
  3. Build a small social network model with users, posts, comments, and friendships
  4. Implement a query to recommend friends based on mutual connections
  5. Try modeling a real-world domain of your choice (e.g., a transportation network, organization chart, or product catalog)

If you spot any mistakes on this website, please let me know at [email protected]. I’d greatly appreciate your feedback! :)