Skip to content

Latest commit

 

History

History
157 lines (110 loc) · 4.53 KB

getting_started.md

File metadata and controls

157 lines (110 loc) · 4.53 KB
Raw
title nav_order description
Getting Started
2
Getting Started with FalkorDB Graph Database.

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: "[email protected]"})
CREATE (bob:User {id: 2, name: "Bob", email: "[email protected]"})
CREATE (charlie:User {id: 3, name: "Charlie", email: "[email protected]"})

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
client = FalkorDB(host="localhost", port=6379, password="your-password")
graph = client.select_graph('social')

Execute Cypher Queries

Create the Graph

create_query = """
CREATE (alice:User {id: 1, name: "Alice", email: "[email protected]"})
CREATE (bob:User {id: 2, name: "Bob", email: "[email protected]"})
CREATE (charlie:User {id: 3, name: "Charlie", email: "[email protected]"})

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 = """

date and time as they are right now can be confusing, either use Python to create timestamps from actual dates or consider changing the attribute to something else which doesn't require time / date datatype

MATCH (alice:User {name: "Alice"})-[:FRIENDS_WITH]->(friend)
RETURN friend.name AS Friend
"""
result = graph.ro_query(query)

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

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)

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

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