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function get_database_statistics

Maturity: 44

Retrieves statistical information about a Neo4j graph database, including counts of nodes grouped by label and counts of relationships grouped by type.

File:
/tf/active/vicechatdev/neo4j_schema/neo4j_python_snippets.py
Lines:
2294 - 2314
Complexity:
simple

Purpose

This function provides a comprehensive overview of the database structure by querying and aggregating node and relationship counts. It's useful for database monitoring, understanding data distribution, generating reports, validating data imports, and getting a quick snapshot of the graph database's contents. The function executes two Cypher queries: one to count nodes by their labels and another to count relationships by their types.

Source Code

def get_database_statistics():
    """Get general statistics about the database"""
    stats = {}
    
    # Node counts by label
    query = """
    MATCH (n)
    RETURN labels(n) AS label, count(*) AS count
    """
    label_counts = run_query(query)
    stats["node_counts"] = {record["label"][0]: record["count"] for record in label_counts if record["label"]}
    
    # Relationship counts by type
    query = """
    MATCH ()-[r]->()
    RETURN type(r) AS type, count(*) AS count
    """
    rel_counts = run_query(query)
    stats["relationship_counts"] = {record["type"]: record["count"] for record in rel_counts}
    
    return stats

Return Value

Returns a dictionary with two keys: 'node_counts' containing a mapping of node labels (strings) to their counts (integers), and 'relationship_counts' containing a mapping of relationship types (strings) to their counts (integers). Example: {'node_counts': {'Person': 100, 'Company': 50}, 'relationship_counts': {'WORKS_AT': 75, 'KNOWS': 200}}

Dependencies

  • neo4j

Required Imports

from neo4j import GraphDatabase

Usage Example

# Assuming run_query() is already defined and configured
# Example run_query implementation:
from neo4j import GraphDatabase

driver = GraphDatabase.driver('bolt://localhost:7687', auth=('neo4j', 'password'))

def run_query(query, parameters=None):
    with driver.session() as session:
        result = session.run(query, parameters or {})
        return [record.data() for record in result]

# Use the function
stats = get_database_statistics()
print(f"Node counts: {stats['node_counts']}")
print(f"Relationship counts: {stats['relationship_counts']}")

# Example output:
# Node counts: {'Person': 150, 'Company': 25, 'Product': 300}
# Relationship counts: {'WORKS_AT': 120, 'PURCHASED': 450, 'KNOWS': 200}

Best Practices

  • Ensure the run_query() function is properly implemented with error handling and connection management
  • This function may be slow on large databases as it scans all nodes and relationships; consider caching results or running during off-peak hours
  • The function assumes nodes have at least one label; nodes without labels are filtered out by the 'if record["label"]' condition
  • For very large databases, consider adding LIMIT clauses or using database metadata queries instead of full scans
  • Close the Neo4j driver connection properly when done to avoid resource leaks
  • Consider adding error handling for database connection failures or query execution errors
  • The function returns empty dictionaries for node_counts and relationship_counts if the database is empty

Tags

neo4j graph-database statistics database-monitoring cypher-query data-analysis node-count relationship-count database-metrics

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