Getting Started with FalkorDB

This guide will walk you through setting up FalkorDB, modeling a social network as a graph, and accessing it using the FalkorDB Python client with Cypher.

Prerequisites

  1. FalkorDB Instance: Set up FalkorDB (on-prem or cloud).
  2. Python Installed: Ensure you have Python 3.8+ installed.

  3. Install FalkorDB Python Client:

    pip install falkordb

Step 1: Model a Social Network as a Graph

Let’s create a simple graph for a social network where:

  • Nodes represent User and Post.
  • Relationships connect Users with a FRIENDS_WITH relationship, and Users are connected via a CREATED relationship to Posts

Graph Schema

Node Type Properties
User id, name, email
Post id, content, date
Relationship Type Start Node End Node Properties
FRIENDS_WITH User User since
CREATED User Post time

FalkorDB-Model a Social Network as a Graph

Step 2: Load Data into FalkorDB

Here’s how you can model and load the data.

Cypher Query to Create the Data 

CREATE (alice:User {id: 1, name: "Alice", email: "alice@example.com"})
CREATE (bob:User {id: 2, name: "Bob", email: "bob@example.com"})
CREATE (charlie:User {id: 3, name: "Charlie", email: "charlie@example.com"})

CREATE (post1:Post {id: 101, content: "Hello World!", date: 1701388800})
CREATE (post2:Post {id: 102, content: "Graph Databases are awesome!", date: 1701475200})

CREATE (alice)-[:FRIENDS_WITH {since: 1640995200}]->(bob)
CREATE (bob)-[:FRIENDS_WITH {since: 1684108800}]->(charlie)
CREATE (alice)-[:CREATED {time: 1701388800}]->(post1)
CREATE (bob)-[:CREATED {time: 1701475200}]->(post2)

You can execute these commands using the FalkorDB Python client.

Step 3: Access Your Data

Connect to FalkorDB 

from falkordb import FalkorDB

# Connect to FalkorDB
db = FalkorDB(host="localhost", port=6379, password="your-password")
graph = db.select_graph('social')

Execute Cypher Queries

Create the Graph 

create_query = """
CREATE (alice:User {id: 1, name: "Alice", email: "alice@example.com"})
CREATE (bob:User {id: 2, name: "Bob", email: "bob@example.com"})
CREATE (charlie:User {id: 3, name: "Charlie", email: "charlie@example.com"})

CREATE (post1:Post {id: 101, content: "Hello World!", date: 1701388800})
CREATE (post2:Post {id: 102, content: "Graph Databases are awesome!", date: 1701475200})

CREATE (alice)-[:FRIENDS_WITH {since: 1640995200}]->(bob)
CREATE (bob)-[:FRIENDS_WITH {since: 1684108800}]->(charlie)
CREATE (alice)-[:CREATED {time: 1701388800}]->(post1)
CREATE (bob)-[:CREATED {time: 1701475200}]->(post2)
"""

graph.query(create_query)
print("Graph created successfully!")

image

Query the Graph 

# Find all friends of Alice
query = """
MATCH (alice:User {name: 'Alice'})-[:FRIENDS_WITH]->(friend)
RETURN friend.name AS Friend
"""

result = graph.ro_query(query).result_set

print("Alice's friends:")
for record in result:
    print(record[0])

Query Relationships 

# Find posts created by Bob
query = """
MATCH (bob:User {name: 'Bob'})-[:CREATED]->(post:Post)
RETURN post.content AS PostContent
"""

result = graph.ro_query(query).result_set

print("Posts created by Bob:")
for record in result:
    print(record[0])

Step 4: Explore Further

Congratulations! 🎉 You have successfully modeled, loaded, and queried a social network graph with FalkorDB.

Next, dive deeper into FalkorDB’s powerful features:

For questions or support, visit our community forums