glace 0.1.6

Python bindings for the Glace embedded graph database

glace

Embedded Graph Database for Python

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Glace Python Bindings

Python bindings for Glace, an embedded graph database with Cypher support.

Installation

pip install glace

From Source

# Clone the repository
git clone https://github.com/lovelace-labs/glace-py.git
cd glace-py

# Install maturin
pip install maturin

# Build and install in development mode
maturin develop

Quick Start

import glace

# Create an in-memory database
db = glace.Database.in_memory()
conn = db.connect()

# Create schema
conn.execute("CREATE NODE TABLE Person(name STRING, age INT64, PRIMARY KEY(name))")
conn.execute("CREATE REL TABLE Knows(FROM Person TO Person)")

# Insert data
conn.execute("CREATE (p:Person {name: 'Alice', age: 30})")
conn.execute("CREATE (p:Person {name: 'Bob', age: 25})")
conn.execute("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:Knows]->(b)
""")

# Query data
result = conn.execute("MATCH (p:Person)-[:Knows]->(f:Person) RETURN p.name, f.name")
for row in result:
    print(f"{row['p.name']} knows {row['f.name']}")

# Convert to DataFrame (requires pandas)
df = result.to_df()
print(df)

Features

• Cypher Support: Full Cypher query language support
• Embedded: Runs in-process, no external dependencies
• High Performance: Written in Rust with columnar storage
• ACID Transactions: Full transactional support

API Reference

Database

# Open a persistent database
db = glace.Database.open("./my_graph.db")

# Create an in-memory database
db = glace.Database.in_memory()

# Get database info
print(db.path)         # Path or None for in-memory
print(db.is_in_memory) # True for in-memory databases

# Create a connection
conn = db.connect()

# Checkpoint (flush to disk)
db.checkpoint()

Connection

# Execute queries
result = conn.execute("MATCH (p:Person) RETURN p.name")

# Execute multiple statements
results = conn.execute_multi("""
    CREATE (p:Person {name: 'Alice'});
    CREATE (p:Person {name: 'Bob'});
""")

# Transactions
conn.begin()
try:
    conn.execute("CREATE (p:Person {name: 'Charlie'})")
    conn.commit()
except:
    conn.rollback()
    raise

# Context manager for transactions
with conn:  # Automatically begins transaction
    conn.execute("CREATE (p:Person {name: 'Dave'})")
# Automatically commits on success, rollbacks on exception

QueryResult

result = conn.execute("MATCH (p:Person) RETURN p.name, p.age")

# Iterate over rows
for row in result:
    print(row["name"], row["age"])

# Access by index
first_row = result[0]
last_row = result[-1]

# Get metadata
print(result.columns)  # ['name', 'age']
print(len(result))     # Number of rows
print(result.message)  # Success message for DDL/DML

# Convert to list
rows = result.to_list()

# Convert to DataFrame (requires pandas)
df = result.to_df()

Development

Building

# Install development dependencies
pip install maturin pytest

# Build in development mode
maturin develop

# Run tests
pytest

Running Tests

pytest tests/

License

MIT License - see LICENSE for details.