FalkorDB

UNIQUE FEATURES

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our Graph Database.

🆕 FalkorDB is the first queryable Property Graph database to leverage sparse matrices for representing the adjacency matrix in graphs and linear algebra for querying.

Key Features

• Sparse Matrix Representation: Utilizes sparse matrices to represent adjacency matrices, optimizing storage and performance.

• Linear Algebra Querying: Employs linear algebra for query execution, enhancing computational efficiency.

• Property Graph Model Compliance: Supports nodes and relationships with attributes, adhering to the Property Graph Model.

• OpenCypher Support: Compatible with OpenCypher query language, including proprietary extensions for advanced querying capabilities.

Explore FalkorDB in action by visiting the Demos.

GET STARTED

Step 1

To quickly try out FalkorDB, launch an instance using docker:

docker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/var/lib/falkordb/data falkordb/falkordb

If ./data was written by a Redis image of FalkorDB and the image you run is a Valkey one, see Moving a data volume from Redis to Valkey.

Step 2

Then, open your browser and navigate to http://localhost:3000.

You can also interact with FalkorDB using any of the supported Client Libraries

MotoGP League Example

In this example, we'll use the FalkorDB Python client to create a small graph representing a subset of motorcycle riders and teams participating in the MotoGP league. After creating the graph, we'll query the data to explore its structure and relationships.

from falkordb import FalkorDB

# Connect to FalkorDB
db = FalkorDB(host='localhost', port=6379)

# Create the 'MotoGP' graph
g = db.select_graph('MotoGP')
g.query("""CREATE (:Rider {name:'Valentino Rossi'})-[:rides]->(:Team {name:'Yamaha'}),
                  (:Rider {name:'Dani Pedrosa'})-[:rides]->(:Team {name:'Honda'}),
                  (:Rider {name:'Andrea Dovizioso'})-[:rides]->(:Team {name:'Ducati'})""")

# Query which riders represents Yamaha?
res = g.query("""MATCH (r:Rider)-[:rides]->(t:Team)
                 WHERE t.name = 'Yamaha'
                 RETURN r.name""")

for row in res.result_set:
	print(row[0])

# Prints: "Valentino Rossi"

# Query how many riders represent team Ducati ?
res = g.query("""MATCH (r:Rider)-[:rides]->(t:Team {name:'Ducati'})
                 RETURN count(r)""")

print(res.result_set[0][0])
# Prints: 1

USING FALKORDB

You can call FalkorDB's commands from any Redis client. Here are several methods:

With redis-cli

$ redis-cli
127.0.0.1:6379> GRAPH.QUERY social "CREATE (:person {name: 'roi', age: 33, gender: 'male', status: 'married'})"

With any other client

You can interact with FalkorDB using your client's ability to send raw Redis commands.

Note: Depending on your client of choice, the exact method for doing that may vary.

Example: Using FalkorDB with a Python client

This code snippet shows how to use FalkorDB with from Python using falkordb-py:

from falkordb import FalkorDB

# Connect to FalkorDB
db = FalkorDB(host='localhost', port=6379)

# Select the social graph
g = db.select_graph('social')

reply = g.query("CREATE (:person {name:'roi', age:33, gender:'male', status:'married'})")

CLIENT LIBRARIES

Note: Some languages have client libraries that provide support for FalkorDB's commands:

Official Clients

Project	Language	License	Author	Stars	Package	Comment
jfalkordb	Java	BSD	FalkorDB		Maven
falkordb-py	Python	MIT	FalkorDB		pypi
falkordb-ts	Node.JS	MIT	FalkorDB		npm
falkordb-rs	Rust	MIT	FalkorDB		Crate
falkordb-go	Go	BSD	FalkorDB		GitHub
NFalkorDB	C#	Apache-2.0	FalkorDB

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