kiarina-falkordb 1.2.0

FalkorDB Python client (redis-py 6.x compatible fork)

kiarina-falkordb

⚠️ This is a temporary fork of falkordb-py with redis-py 6.x compatibility.

Original work by the FalkorDB team.

This fork will be deprecated once the upstream PR is merged.

FalkorDB Python client with redis-py 6.x compatibility

Installation

pip install kiarina-falkordb

Differences from upstream

• ✅ Compatible with redis-py >= 6.0.0
• ✅ All original functionality preserved
• 🔄 Actively maintained until upstream compatibility is resolved

Usage

Run FalkorDB instance

Docker:

docker run --rm -p 6379:6379 falkordb/falkordb

Or use FalkorDB Cloud

Synchronous Example

from falkordb import FalkorDB

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

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

# Create 100 nodes and return a handful
nodes = g.query('UNWIND range(0, 100) AS i CREATE (n {v:1}) RETURN n LIMIT 10').result_set
for n in nodes:
    print(n)

# Read-only query the graph for the first 10 nodes
nodes = g.ro_query('MATCH (n) RETURN n LIMIT 10').result_set

# Copy the Graph
copy_graph = g.copy('social_copy')

# Delete the Graph
g.delete()

Asynchronous Example

import asyncio
from falkordb.asyncio import FalkorDB
from redis.asyncio import BlockingConnectionPool

async def main():

    # Connect to FalkorDB
    pool = BlockingConnectionPool(max_connections=16, timeout=None, decode_responses=True)
    db = FalkorDB(connection_pool=pool)

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

    # Execute query asynchronously
    result = await g.query('UNWIND range(0, 100) AS i CREATE (n {v:1}) RETURN n LIMIT 10')

    # Process results
    for n in result.result_set:
        print(n)

    # Run multiple queries concurrently
    tasks = [
        g.query('MATCH (n) WHERE n.v = 1 RETURN count(n) AS count'),
        g.query('CREATE (p:Person {name: "Alice"}) RETURN p'),
        g.query('CREATE (p:Person {name: "Bob"}) RETURN p')
    ]

    results = await asyncio.gather(*tasks)

    # Process concurrent results
    print(f"Node count: {results[0].result_set[0][0]}")
    print(f"Created Alice: {results[1].result_set[0][0]}")
    print(f"Created Bob: {results[2].result_set[0][0]}")

    # Close the connection when done
    await pool.aclose()

# Run the async example
if __name__ == "__main__":
    asyncio.run(main())