ocpg 0.3.0

OpenCypher PetGraph - 100% openCypher-compliant graph database engine

ocpg - OpenCypher PetGraph

ocpg is the Python package for nxcypher, a 100% openCypher-compliant graph database engine written in Rust.

OpenCypher + PetGraph = Fast, memory-safe graph queries in Python.

Features

• ✅ 100% openCypher TCK compliant (3,893 scenarios passing)
• 🚀 High performance - Pure Rust implementation with Python bindings
• 🔒 Memory safe - No unsafe code, guaranteed by Rust
• 🧪 Well tested - Comprehensive test coverage
• 📦 Easy to install - Single pip install command
• 🐍 Pythonic API - Natural Python interface

Installation

pip install ocpg

Quick Start

import ocpg

# Create a graph
g = ocpg.Graph()

# Create nodes and relationships
g.execute("""
    CREATE (alice:Person {name: 'Alice', age: 30})
    CREATE (bob:Person {name: 'Bob', age: 25})
    CREATE (alice)-[:KNOWS]->(bob)
""")

# Query the graph
result = g.execute("MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age")

# Iterate over results
for row in result:
    print(f"Name: {row['p.name']}, Age: {row['p.age']}")

# Or convert to list
data = result.to_list()
print(data)

API Reference

Graph

The main graph database class.

Methods

• execute(query: str, params: dict = None) -> QueryResult

  Execute a Cypher query with optional parameters.

  result = g.execute(
      "MATCH (n:Person) WHERE n.age > $min_age RETURN n",
      {"min_age": 18}
  )
  

• node_count() -> int

  Get the number of nodes in the graph.

• edge_count() -> int

  Get the number of relationships in the graph.

• clear()

  Remove all nodes and relationships from the graph.

• create_node(labels: list[str], properties: dict = None) -> int 🚀 NEW

  Create a node directly without parsing Cypher (bulk loading). ~99x faster than CREATE queries.

  # Create a node and get its ID
  alice_id = g.create_node(["Person"], {"name": "Alice", "age": 30})
  bob_id = g.create_node(["Person"], {"name": "Bob", "age": 25})
  
  # No properties
  empty_id = g.create_node(["Label"])
  

• create_relationship(from_id: int, to_id: int, rel_type: str, properties: dict = None) -> int 🚀 NEW

  Create a relationship directly without parsing Cypher (bulk loading). ~99x faster than CREATE queries.

  # Create nodes first
  alice_id = g.create_node(["Person"], {"name": "Alice"})
  bob_id = g.create_node(["Person"], {"name": "Bob"})
  
  # Create relationship between them
  edge_id = g.create_relationship(
      alice_id,
      bob_id,
      "KNOWS",
      {"since": 2020}
  )
  

QueryResult

Query result object containing rows and columns.

Properties

• columns: list[str] - List of column names

Methods

• __len__() -> int - Number of rows
• __getitem__(index: int) -> dict - Get row by index
• __iter__() - Iterate over rows
• to_list() -> list[dict] - Convert all rows to a list of dictionaries

Example

result = g.execute("MATCH (n) RETURN n, n.name LIMIT 5")

print(f"Columns: {result.columns}")  # ['n', 'n.name']
print(f"Rows: {len(result)}")        # 5

# Access by index
first_row = result[0]
print(first_row['n.name'])

# Iterate
for row in result:
    print(row['n.name'])

# Convert to list
all_data = result.to_list()

Supported Data Types

Python types are automatically converted to/from Cypher types:

Python Type	Cypher Type	Notes
None	NULL	-
bool	BOOLEAN	-
int	INTEGER	-
float	FLOAT	-
str	STRING	-
list	LIST	Nested lists supported
dict	MAP	-

Graph types (Node, Relationship, Path) are returned as dictionaries with a _type field:

result = g.execute("MATCH (n:Person {name: 'Alice'}) RETURN n")
node = result[0]['n']

print(node['_type'])        # 'Node'
print(node['id'])           # Node ID
print(node['labels'])       # ['Person']
print(node['properties'])   # {'name': 'Alice', 'age': 30}

Advanced Examples

Parameterized Queries

# Create with parameters
g.execute(
    "CREATE (p:Person {name: $name, age: $age})",
    {"name": "Charlie", "age": 35}
)

# Query with parameters
result = g.execute(
    "MATCH (p:Person) WHERE p.age BETWEEN $min AND $max RETURN p",
    {"min": 25, "max": 35}
)

Working with Relationships

# Create relationship with properties
g.execute("""
    MATCH (a:Person {name: 'Alice'})
    MATCH (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS {since: 2020, strength: 0.8}]->(b)
""")

# Query relationships
result = g.execute("""
    MATCH (a:Person)-[r:KNOWS]->(b:Person)
    RETURN a.name, r.since, r.strength, b.name
""")

for row in result:
    print(f"{row['a.name']} knows {row['b.name']} since {row['r.since']}")

Aggregations

result = g.execute("""
    MATCH (p:Person)
    RETURN
        count(p) as total,
        avg(p.age) as avg_age,
        min(p.age) as min_age,
        max(p.age) as max_age
""")

stats = result[0]
print(f"Total: {stats['total']}, Avg Age: {stats['avg_age']:.1f}")

Path Queries

# Find shortest path
result = g.execute("""
    MATCH path = shortestPath(
        (a:Person {name: 'Alice'})-[:KNOWS*]-(b:Person {name: 'Charlie'})
    )
    RETURN path
""")

path = result[0]['path']
print(f"Path length: {len(path['nodes']) - 1}")

Bulk Loading for High Performance 🚀

For loading large amounts of data, use the native bulk loader API instead of Cypher queries.

Performance Comparison

Benchmark: Creating 1,000 nodes

Method	Time	Throughput	Speedup
Bulk Loader	0.0018s	555K nodes/sec	99x faster
Cypher Queries	0.178s	5.6K nodes/sec	baseline

Bulk Loading Example

import ocpg
import time

g = ocpg.Graph()

# Traditional approach (slow)
start = time.time()
for i in range(1000):
    g.execute(f"CREATE (p:Person {{name: 'Person{i}', age: {20 + i}}})")
print(f"Cypher: {time.time() - start:.2f}s")

# Bulk loader approach (99x faster!)
g2 = ocpg.Graph()
start = time.time()
node_ids = []
for i in range(1000):
    node_id = g2.create_node(["Person"], {"name": f"Person{i}", "age": 20 + i})
    node_ids.append(node_id)
print(f"Bulk: {time.time() - start:.2f}s")

# Create relationships between nodes
for i in range(len(node_ids) - 1):
    g2.create_relationship(node_ids[i], node_ids[i + 1], "KNOWS", {})

When to Use Bulk Loading

✅ Use bulk loaders for:

• Initial data import
• Migrating from other databases
• Loading CSV/JSON files
• Creating large test datasets
• Any time you create >100 nodes/relationships

❌ Use Cypher for:

• Complex pattern matching and creation
• Conditional logic (MERGE, conditional SET)
• When you need to match existing nodes first
• One-off operations
• When query readability matters more than performance

Performance Tips

1. Use bulk loaders (create_node, create_relationship) for loading data - 99x faster
2. Use parameters instead of string formatting for better performance and security
3. Batch operations when creating many nodes/relationships
4. Use LIMIT for large result sets
5. Create indexes for frequently queried properties (when index support is added)

Development

Building from source:

# Install maturin
pip install maturin

# Build and install in development mode
cd python-bindings
maturin develop

# Build release wheel
maturin build --release

# Run tests
python -m pytest

License

Apache 2.0 - See LICENSE for details.

Links

• GitHub Repository
• openCypher Specification
• Issue Tracker