kglite 0.5.4

A high-performance graph database library with Python bindings written in Rust

KGLite

An embedded knowledge graph engine for Python.

Use it for: local analytics, ETL pipelines, notebooks, embedding in apps, fast prototyping. Not for: multi-user server deployments, cross-call transactions, HA/replication.

Embedded, in-process	No server, no network; import and go
In-memory	Persistence via save()/load() snapshots
Cypher subset	Querying + mutations; returns dict or DataFrame
Single-label nodes	Each node has exactly one type
Single-threaded	Designed for single-threaded use (see Threading)

Requirements: Python 3.10+ (CPython) | macOS (ARM/Intel), Linux (x86_64/aarch64), Windows (x86_64) | pandas >= 1.5

pip install kglite

Feature Matrix

Feature	Status
Embedded / in-process	Yes
Cypher queries (MATCH, CREATE, SET, DELETE, MERGE, ...)	Yes
DataFrame output (to_df=True)	Yes
Graph algorithms (shortest path, centrality, communities)	Yes
Persistence (binary snapshots)	Yes
Multi-label nodes	No
Transactions across calls	No
Concurrency / thread safety	No
Server mode	No

---

Quick Start

import kglite

graph = kglite.KnowledgeGraph()

# Create nodes and relationships
graph.cypher("CREATE (:Person {name: 'Alice', age: 28, city: 'Oslo'})")
graph.cypher("CREATE (:Person {name: 'Bob', age: 35, city: 'Bergen'})")
graph.cypher("CREATE (:Person {name: 'Charlie', age: 42, city: 'Oslo'})")
graph.cypher("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS]->(b)
""")

# Query — returns list[dict]
result = graph.cypher("""
    MATCH (p:Person) WHERE p.age > 30
    RETURN p.name AS name, p.city AS city
    ORDER BY p.age DESC
""")
for row in result:
    print(row['name'], row['city'])

# Or get a pandas DataFrame
df = graph.cypher("MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age", to_df=True)

# Mutations return stats
result = graph.cypher("CREATE (:Person {name: 'Dave', age: 22})")
print(result['stats'])  # {'nodes_created': 1, 'relationships_created': 0, ...}

# Check graph size
print(graph.graph_info())  # {'node_count': 4, 'edge_count': 1, ...}

# Persist to disk and reload
graph.save("my_graph.kgl")
loaded = kglite.load("my_graph.kgl")

Loading Data from DataFrames

For bulk loading (thousands of rows), use the fluent API:

import pandas as pd

users_df = pd.DataFrame({
    'user_id': [1001, 1002, 1003],
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [28, 35, 42]
})

graph.add_nodes(data=users_df, node_type='User', unique_id_field='user_id', node_title_field='name')

edges_df = pd.DataFrame({'source_id': [1001, 1002], 'target_id': [1002, 1003]})
graph.add_connections(data=edges_df, connection_type='KNOWS', source_type='User',
                      source_id_field='source_id', target_type='User', target_id_field='target_id')

graph.cypher("MATCH (u:User) WHERE u.age > 30 RETURN u.name, u.age")

---

Core Concepts

Nodes have four built-in fields: id (unique within type), title (display name), type (label), plus arbitrary properties. Each node has exactly one type — labels(n) returns a string, not a list.

Relationships connect two nodes with a type (e.g., :KNOWS) and optional properties. The Cypher API calls them "relationships"; the fluent API calls them "connections" — they're the same thing.

Return shape. Read queries return list[dict] — each dict is one row keyed by column alias. Mutation queries (CREATE, SET, DELETE, REMOVE, MERGE) return {'stats': {...}} with counts like nodes_created, properties_set, etc. Mutations with a RETURN clause return {'rows': [...], 'stats': {...}}. Pass to_df=True to get a pandas DataFrame instead (read queries only).

Atomicity. Each cypher() call is atomic at the statement level — if any clause fails, the graph remains unchanged (copy-on-write internally). There are no multi-statement transactions: two separate cypher() calls are independent. Durability only via explicit save().

Selections (fluent API only) are lightweight views — a set of node indices that flow through chained operations like type_filter().filter().traverse(). They don't copy data. Use explain() to see the pipeline.

Tombstones. DELETE leaves empty slots in the internal graph storage. After heavy deletion, check graph.graph_info()['fragmentation_ratio'] and call graph.vacuum() if it exceeds 0.3 (see Graph Maintenance).

---

Table of Contents

• Cypher Queries — MATCH, CREATE, SET, DELETE, MERGE, aggregation, shortestPath
• Common Recipes — Quick copy/paste snippets
• Advanced API: Data Management | Querying | Pattern Matching
• Graph Algorithms | Spatial Operations | Analytics
• Schema and Indexes | Import and Export | Performance | Graph Maintenance

---

When to Use What

Interface	Best For	Key Benefits
Cypher (recommended)	Ad-hoc queries, exploration, analytics, mutations	Standard syntax, declarative, familiar to Neo4j users
Fluent API (advanced)	Bulk loading from DataFrames, multi-step pipelines	Chainable operations, explain(), computed properties
Pattern matching (specialized)	Quick structural checks without full Cypher overhead	Lightweight, minimal parsing

Start with Cypher for most tasks. Use the fluent API when bulk-loading from pandas DataFrames or building pipelines that store intermediate computed properties. Use pattern matching for simple structural queries where you don't need WHERE/RETURN clauses.

---

Common Recipes

Upsert with MERGE

graph.cypher("""
    MERGE (p:Person {email: '[e-mail]'})
    ON CREATE SET p.created = '2024-01-01', p.name = 'Alice'
    ON MATCH SET p.last_seen = '2024-01-15'
""")

Top-K Nodes by Centrality

top_nodes = graph.pagerank(top_k=10)
for node in top_nodes:
    print(f"{node['title']}: {node['score']:.3f}")

2-Hop Neighborhood

graph.cypher("""
    MATCH (me:Person {name: 'Alice'})-[:KNOWS*2]-(fof:Person)
    WHERE fof <> me
    RETURN DISTINCT fof.name
""")

Export Subgraph

subgraph = (
    graph.type_filter('Person')
    .filter({'name': 'Alice'})
    .expand(hops=2)
    .to_subgraph()
)
subgraph.export('alice_network.graphml', format='graphml')

Create Index for Speed

graph.create_index('Product', 'category')

# ~3x faster on 100k+ node graphs (equality only; depends on selectivity)
result = graph.cypher("MATCH (p:Product) WHERE p.category = 'Electronics' RETURN p.name")

Parameterized Queries

graph.cypher(
    "MATCH (p:Person) WHERE p.city = $city AND p.age > $min_age RETURN p.name",
    params={'city': 'Oslo', 'min_age': 25}
)

Delete Subgraph

graph.cypher("""
    MATCH (u:User) WHERE u.status = 'inactive'
    DETACH DELETE u
""")

Aggregation with Relationship Properties

graph.cypher("""
    MATCH (p:Person)-[r:RATED]->(m:Movie)
    RETURN p.name, avg(r.score) AS avg_rating, count(m) AS movies_rated
    ORDER BY avg_rating DESC
""")

---

Cypher Queries

A substantial Cypher subset. See the Supported Cypher Subset table for exact coverage.

Single-label note: Each node has exactly one type. labels(n) returns a string, not a list. SET n:OtherLabel is not supported.

result = graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WHERE p.age > 30 AND f.city = 'Oslo'
    RETURN p.name AS person, f.name AS friend, p.age AS age
    ORDER BY p.age DESC
    LIMIT 10
""")

# Read queries → list[dict]
for row in result:
    print(f"{row['person']} knows {row['friend']}")

# Pass to_df=True for a DataFrame
df = graph.cypher("MATCH (n:Person) RETURN n.name, n.age ORDER BY n.age", to_df=True)

WHERE Clause

# Comparisons: =, <>, <, >, <=, >=
graph.cypher("MATCH (n:Product) WHERE n.price >= 500 RETURN n.title, n.price")

# Boolean operators: AND, OR, NOT
graph.cypher("MATCH (n:Person) WHERE n.age > 25 AND NOT n.city = 'Oslo' RETURN n.name")

# Null checks
graph.cypher("MATCH (n:Person) WHERE n.email IS NOT NULL RETURN n.name")

# String predicates: CONTAINS, STARTS WITH, ENDS WITH
graph.cypher("MATCH (n:Person) WHERE n.name CONTAINS 'ali' RETURN n.name")

# IN lists
graph.cypher("MATCH (n:Person) WHERE n.city IN ['Oslo', 'Bergen'] RETURN n.name")

# Regex matching with =~
graph.cypher("MATCH (n:Person) WHERE n.name =~ '(?i)^ali.*' RETURN n.name")
graph.cypher("MATCH (n:Person) WHERE n.email =~ '.*@example\\.com$' RETURN n.name")

Relationship Properties

Relationships can have properties. Access them with r.property syntax:

# Create relationships with properties
graph.cypher("""
    MATCH (p:Person {name: 'Alice'}), (m:Movie {title: 'Inception'})
    CREATE (p)-[:RATED {score: 5, comment: 'Excellent'}]->(m)
""")

# Access, filter, aggregate, sort by relationship properties
graph.cypher("MATCH (p)-[r:RATED]->(m) RETURN p.name, r.score, r.comment, type(r)")
graph.cypher("MATCH (p)-[r:RATED]->(m) WHERE r.score >= 4 RETURN p.name, m.title")
graph.cypher("MATCH (p)-[r:RATED]->(m) RETURN avg(r.score) AS avg_rating")
graph.cypher("MATCH ()-[r:RATED]->(m) RETURN m.title, r.score ORDER BY r.score DESC")

Aggregation

graph.cypher("MATCH (n:Person) RETURN n.city, count(*) AS population ORDER BY population DESC")
graph.cypher("MATCH (n:Person) RETURN avg(n.age) AS avg_age, min(n.age), max(n.age)")

# DISTINCT
graph.cypher("MATCH (n:Person) RETURN DISTINCT n.city")
graph.cypher("MATCH (n:Person) RETURN count(DISTINCT n.city) AS unique_cities")

WITH Clause

graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friend_count
    WHERE friend_count > 3
    RETURN p.name, friend_count
    ORDER BY friend_count DESC
""")

OPTIONAL MATCH

Left outer join — keeps rows even when no match:

graph.cypher("""
    MATCH (p:Person)
    OPTIONAL MATCH (p)-[:KNOWS]->(f:Person)
    RETURN p.name, count(f) AS friends
""")

Built-in Functions

Function	Description
toUpper(expr)	Convert to uppercase
toLower(expr)	Convert to lowercase
toString(expr)	Convert to string
toInteger(expr)	Convert to integer
toFloat(expr)	Convert to float
size(expr)	Length of string or list
type(r)	Relationship type
id(n)	Node ID
labels(n)	Node type (string, not list — single-label)
coalesce(a, b, ...)	First non-null argument
length(p)	Path hop count
nodes(p)	Nodes in a path
relationships(p)	Relationships in a path

Arithmetic

graph.cypher("MATCH (n:Product) RETURN n.title, n.price * 1.25 AS price_with_tax")

CASE Expressions

# Generic form
graph.cypher("""
    MATCH (n:Person)
    RETURN n.name,
           CASE WHEN n.age >= 18 THEN 'adult' ELSE 'minor' END AS category
""")

# Simple form
graph.cypher("""
    MATCH (n:Person)
    RETURN n.name,
           CASE n.city WHEN 'Oslo' THEN 'capital' WHEN 'Bergen' THEN 'west coast' ELSE 'other' END AS region
""")

List Comprehensions

[x IN list WHERE predicate | expression] syntax:

# Map: double each number
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] | x * 2] AS doubled")
# [2, 4, 6, 8, 10]

# Filter only
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] WHERE x > 3] AS filtered")
# [4, 5]

# Filter + map
graph.cypher("UNWIND [1] AS _ RETURN [x IN [1, 2, 3, 4, 5] WHERE x > 3 | x * 2] AS result")
# [8, 10]

# With collect() — transform aggregated values
graph.cypher("""
    MATCH (p:Person)
    WITH collect(p.name) AS names
    RETURN [x IN names | toUpper(x)] AS upper_names
""")

Note: List comprehensions require at least one row in the pipeline. Use UNWIND [1] AS _ or a preceding MATCH/WITH to provide the row context.

Map Projections

n {.prop1, .prop2, alias: expr} syntax — select specific properties from a node:

# Select only name and age (returns a dict per row)
graph.cypher("MATCH (p:Person) RETURN p {.name, .age} AS info")
# [{'info': {'name': 'Alice', 'age': 30}}, {'info': {'name': 'Bob', 'age': 25}}]

# Mix shorthand properties with computed values
graph.cypher("""
    MATCH (p:Person)-[:WORKS_AT]->(c:Company)
    RETURN p {.name, .age, company: c.name} AS info
""")
# [{'info': {'name': 'Alice', 'age': 30, 'company': 'Acme'}}, ...]

# System properties (id, type) work too
graph.cypher("MATCH (p:Person) RETURN p {.name, .type, .id} AS info LIMIT 1")
# [{'info': {'name': 'Alice', 'type': 'Person', 'id': 1}}]

Parameters

graph.cypher(
    "MATCH (n:Person) WHERE n.age > $min_age RETURN n.name, n.age",
    params={'min_age': 25}
)

# Parameters in inline pattern properties
graph.cypher(
    "MATCH (n:Person {name: $name}) RETURN n.age",
    params={'name': 'Alice'}
)

# Parameters with DataFrame output
df = graph.cypher(
    "MATCH (n:Person) WHERE n.age > $min_age RETURN n.name, n.age ORDER BY n.age",
    params={'min_age': 20}, to_df=True
)

UNWIND

Expand a list into rows:

graph.cypher("UNWIND [1, 2, 3] AS x RETURN x, x * 2 AS doubled")

UNION

graph.cypher("""
    MATCH (n:Person) WHERE n.city = 'Oslo' RETURN n.name AS name
    UNION
    MATCH (n:Person) WHERE n.age > 30 RETURN n.name AS name
""")

Variable-Length Paths

# 1 to 3 hops
graph.cypher("MATCH (a:Person)-[:KNOWS*1..3]->(b:Person) WHERE a.name = 'Alice' RETURN b.name")

# Exact 2 hops
graph.cypher("MATCH (a:Person)-[:KNOWS*2]->(b:Person) RETURN a.name, b.name")

WHERE EXISTS

Check for subpattern existence. The outer variable (p) is bound from MATCH. Both brace { } and parenthesis (( )) syntax are supported:

# Brace syntax
graph.cypher("MATCH (p:Person) WHERE EXISTS { (p)-[:KNOWS]->(:Person) } RETURN p.name")

# Parenthesis syntax (equivalent)
graph.cypher("MATCH (p:Person) WHERE EXISTS((p)-[:KNOWS]->(:Person)) RETURN p.name")

# Negation
graph.cypher("""
    MATCH (p:Person)
    WHERE NOT EXISTS { (p)-[:PURCHASED]->(:Product) }
    RETURN p.name
""")

# With property filter in inner pattern
graph.cypher("""
    MATCH (p:Person)
    WHERE EXISTS { (p)-[:KNOWS]->(:Person {city: 'Oslo'}) }
    RETURN p.name
""")

shortestPath()

BFS shortest path between two nodes. Supports directed (->) and undirected (-) syntax:

# Directed — only follows edges in their defined direction
result = graph.cypher("""
    MATCH p = shortestPath((a:Person {name: 'Alice'})-[:KNOWS*..10]->(b:Person {name: 'Dave'}))
    RETURN length(p), nodes(p), relationships(p), a.name, b.name
""")

# Undirected — traverses edges in both directions (same as fluent API)
result = graph.cypher("""
    MATCH p = shortestPath((a:Person {name: 'Alice'})-[:KNOWS*..10]-(b:Person {name: 'Dave'}))
    RETURN length(p), nodes(p), relationships(p)
""")

# No path → empty list (not an error)

Path functions: length(p) returns hop count, nodes(p) returns node list, relationships(p) returns edge type list.

CREATE / SET / DELETE / REMOVE / MERGE

# CREATE — returns stats
result = graph.cypher("CREATE (n:Person {name: 'Alice', age: 30, city: 'Oslo'})")
print(result['stats']['nodes_created'])  # 1

# CREATE relationship between existing nodes
graph.cypher("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS]->(b)
""")

# SET — update properties
result = graph.cypher("MATCH (n:Person {name: 'Bob'}) SET n.age = 26, n.city = 'Stavanger'")
print(result['stats']['properties_set'])  # 2

# DELETE — plain DELETE errors if node has relationships; DETACH removes all
graph.cypher("MATCH (n:Person {name: 'Alice'}) DETACH DELETE n")

# REMOVE — remove properties (id/type are immutable)
graph.cypher("MATCH (n:Person {name: 'Alice'}) REMOVE n.city")

# MERGE — match or create
graph.cypher("""
    MERGE (n:Person {name: 'Alice'})
    ON CREATE SET n.created = 'today'
    ON MATCH SET n.updated = 'today'
""")

Error example: DELETE on a node with relationships returns: "Cannot delete node with existing relationships. Use DETACH DELETE to remove the node and all its relationships."

Mutation Semantics

Atomicity: Each cypher() call is atomic at the statement level — if any clause fails, the graph remains unchanged (copy-on-write internally). There are no multi-statement transactions; two separate cypher() calls are independent. Durability only via explicit save().

Index maintenance: Property and composite indexes are updated automatically by all mutation operations (CREATE, SET, DELETE, REMOVE, MERGE).

DataFrame Output

df = graph.cypher("""
    MATCH (p:Person)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friends
    RETURN p.name, p.city, friends
    ORDER BY friends DESC
""", to_df=True)

EXPLAIN

Prefix any Cypher query with EXPLAIN to see the query plan without executing it:

plan = graph.cypher("""
    EXPLAIN
    MATCH (p:Person)
    OPTIONAL MATCH (p)-[:KNOWS]->(f:Person)
    WITH p, count(f) AS friends
    RETURN p.name, friends
""")
print(plan)
# Query Plan:
#   1. NodeScan (MATCH) :Person
#   2. FusedOptionalMatchAggregate (optimized OPTIONAL MATCH + count)
#   3. Projection (RETURN) [p.name, friends]
# Optimizations: optional_match_fusion=1

Returns a string (not data). Mutation queries with EXPLAIN are not executed.

Supported Cypher Subset

Category	Supported
Clauses	MATCH, OPTIONAL MATCH, WHERE, RETURN, WITH, ORDER BY, SKIP, LIMIT, UNWIND, UNION/UNION ALL, CREATE, SET, DELETE, DETACH DELETE, REMOVE, MERGE, EXPLAIN
Patterns	Node (n:Type), relationship -[:REL]->, variable-length *1..3, undirected -[:REL]-, properties {key: val}, p = shortestPath(...)
WHERE	=, <>, <, >, <=, >=, =~ (regex), AND, OR, NOT, IS NULL, IS NOT NULL, IN [...], CONTAINS, STARTS WITH, ENDS WITH, EXISTS { pattern }, EXISTS(( pattern ))
RETURN	n.prop, r.prop, AS aliases, DISTINCT, arithmetic +/-/*//, map projections n {.prop1, .prop2}
Aggregation	count(*), count(expr), sum, avg/mean, min, max, collect, std
Expressions	CASE WHEN...THEN...ELSE...END, $param, [x IN list WHERE ... | expr]
Functions	toUpper, toLower, toString, toInteger, toFloat, size, length, type, id, labels, coalesce, nodes(p), relationships(p)
Mutations	CREATE (n:Label {props}), CREATE (a)-[:TYPE]->(b), SET n.prop = expr, DELETE, DETACH DELETE, REMOVE n.prop, MERGE ... ON CREATE SET ... ON MATCH SET
Not supported	CALL/stored procedures, FOREACH, subqueries, SET n:Label (label mutation), REMOVE n:Label, multi-label

---

Advanced API: Data Management

Expand section

For most use cases, use Cypher queries. The fluent API below is for bulk operations from DataFrames or complex data pipelines.

Adding Nodes

products_df = pd.DataFrame({
    'product_id': [101, 102, 103],
    'title': ['Laptop', 'Phone', 'Tablet'],
    'price': [999.99, 699.99, 349.99],
    'stock': [45, 120, 30]
})

graph.add_nodes(
    data=products_df,
    node_type='Product',
    unique_id_field='product_id',
    node_title_field='title',
    columns=['product_id', 'title', 'price', 'stock'],
    column_types={'launch_date': 'datetime'},  # explicit type hints (see Working with Dates)
    conflict_handling='update'  # 'update' | 'replace' | 'skip' | 'preserve'
)

Property Mapping

When adding nodes, unique_id_field and node_title_field are renamed to id and title. The original column names no longer exist as properties.

Your DataFrame Column	Stored As	Why
unique_id_field (e.g., user_id)	id	Canonical identifier
node_title_field (e.g., name)	title	Display/label field
All other columns	Same name	Preserved as-is

# After adding with unique_id_field='user_id', node_title_field='name':
graph.type_filter('User').filter({'user_id': 1001})  # WRONG — field was renamed
graph.type_filter('User').filter({'id': 1001})        # CORRECT

Use explain() to verify node counts at each step:

result = graph.type_filter('User').filter({'id': 1001})
print(result.explain())
# TYPE_FILTER User (1000 nodes) -> FILTER (1 nodes)

Retrieving Nodes

products = graph.type_filter('Product')
products.get_nodes()                       # all properties
products.get_properties(['price', 'stock'])  # specific properties
products.get_titles()                       # just titles

Working with Dates

graph.add_nodes(
    data=estimates_df,
    node_type='Estimate',
    unique_id_field='estimate_id',
    node_title_field='name',
    column_types={'valid_from': 'datetime', 'valid_to': 'datetime'}
)

graph.type_filter('Estimate').filter({'valid_from': {'>=': '2020-06-01'}})
graph.type_filter('Estimate').valid_at('2020-06-15')
graph.type_filter('Estimate').valid_during('2020-01-01', '2020-06-30')

Creating Connections

purchases_df = pd.DataFrame({
    'user_id': [1001, 1001, 1002],
    'product_id': [101, 103, 102],
    'date': ['2023-01-15', '2023-02-10', '2023-01-20'],
    'quantity': [1, 2, 1]
})

graph.add_connections(
    data=purchases_df,
    connection_type='PURCHASED',
    source_type='User',
    source_id_field='user_id',
    target_type='Product',
    target_id_field='product_id',
    columns=['date', 'quantity']
)

Note: source_type and target_type each refer to a single node type. To connect nodes of the same type, set both to the same value (e.g., source_type='Person', target_type='Person').

Batch Property Updates

result = graph.type_filter('Prospect').filter({'status': 'Inactive'}).update({
    'is_active': False,
    'deactivation_reason': 'status_inactive'
})

updated_graph = result['graph']
print(f"Updated {result['nodes_updated']} nodes")

---

Advanced API: Querying

Expand section

For most queries, prefer Cypher. The fluent API below is for building reusable query chains or when you need explain() and selection-based workflows.

Filtering

graph.type_filter('Product').filter({'price': 999.99})
graph.type_filter('Product').filter({'price': {'<': 500.0}, 'stock': {'>': 50}})
graph.type_filter('Product').filter({'id': {'in': [101, 103]}})
graph.type_filter('Product').filter({'category': {'is_null': True}})

# Orphan nodes (no connections)
graph.filter_orphans(include_orphans=True)

Sorting

graph.type_filter('Product').sort('price')
graph.type_filter('Product').sort('price', ascending=False)
graph.type_filter('Product').sort([('stock', False), ('price', True)])

Traversing the Graph

alice = graph.type_filter('User').filter({'title': 'Alice'})
alice_products = alice.traverse(connection_type='PURCHASED', direction='outgoing')

# Filter and sort traversal targets
expensive = alice.traverse(
    connection_type='PURCHASED',
    filter_target={'price': {'>=': 500.0}},
    sort_target='price',
    max_nodes=10
)

# Get connection information
alice.get_connections(include_node_properties=True)

Set Operations

n3 = graph.type_filter('Prospect').filter({'geoprovince': 'N3'})
m3 = graph.type_filter('Prospect').filter({'geoprovince': 'M3'})

n3.union(m3)                    # all nodes from both (OR)
n3.intersection(m3)             # nodes in both (AND)
n3.difference(m3)               # nodes in n3 but not m3
n3.symmetric_difference(m3)     # nodes in exactly one (XOR)

---

Pattern Matching

Expand section

For simpler pattern-based queries without full Cypher clause support:

results = graph.match_pattern(
    '(p:Play)-[:HAS_PROSPECT]->(pr:Prospect)-[:BECAME_DISCOVERY]->(d:Discovery)'
)

for match in results:
    print(f"Play: {match['p']['title']}, Discovery: {match['d']['title']}")

# With property conditions
graph.match_pattern('(u:User)-[:PURCHASED]->(p:Product {category: "Electronics"})')

# Limit results for large graphs
graph.match_pattern('(a:Person)-[:KNOWS]->(b:Person)', max_matches=100)

---

Graph Algorithms

Expand section

Shortest Path

result = graph.shortest_path(source_type='Person', source_id=1, target_type='Person', target_id=100)
if result:
    for node in result["path"]:
        print(f"{node['type']}: {node['title']}")
    print(f"Connections: {result['connections']}")
    print(f"Path length: {result['length']}")

All Paths

paths = graph.all_paths(
    source_type='Play', source_id=1,
    target_type='Wellbore', target_id=100,
    max_hops=4,
    max_results=100  # Prevent OOM on dense graphs
)

Connected Components

components = graph.connected_components()
# Returns list of lists: [[node_indices...], [node_indices...], ...]
print(f"Found {len(components)} connected components")
print(f"Largest component: {len(components[0])} nodes")

graph.are_connected(source_type='Person', source_id=1, target_type='Person', target_id=100)

Centrality Algorithms

All centrality methods return a list of dicts with type, title, id, and score keys, sorted by score descending.

graph.betweenness_centrality(top_k=10)
graph.betweenness_centrality(normalized=True, sample_size=500)
graph.pagerank(top_k=10, damping_factor=0.85)
graph.degree_centrality(top_k=10)
graph.closeness_centrality(top_k=10)

Community Detection

Identify clusters of densely connected nodes.

# Louvain modularity optimization (recommended)
result = graph.louvain_communities()
# {'communities': {0: [{title, type, id}, ...], 1: [...]},
#  'modularity': 0.45, 'num_communities': 2}

for comm_id, members in result['communities'].items():
    names = [m['title'] for m in members]
    print(f"Community {comm_id}: {names}")

# With edge weights and resolution tuning
result = graph.louvain_communities(weight_property='strength', resolution=1.5)

# Label propagation (faster, less precise)
result = graph.label_propagation(max_iterations=100)

Node Degrees

degrees = graph.type_filter('Person').get_degrees()
# Returns: {'Alice': 5, 'Bob': 3, ...}

---

Spatial Operations

Expand section

Bounding Box

graph.type_filter('Discovery').within_bounds(
    lat_field='latitude', lon_field='longitude',
    min_lat=58.0, max_lat=62.0, min_lon=1.0, max_lon=5.0
)

Distance Queries (Haversine)

graph.type_filter('Wellbore').near_point_km(
    center_lat=60.5, center_lon=3.2, max_distance_km=50.0,
    lat_field='latitude', lon_field='longitude'
)

WKT Geometry Intersection

graph.type_filter('Field').intersects_geometry(
    'POLYGON((1 58, 5 58, 5 62, 1 62, 1 58))',
    geometry_field='wkt_geometry'
)

Point-in-Polygon

graph.type_filter('Block').contains_point(lat=60.5, lon=3.2, geometry_field='wkt_geometry')

---

Analytics

Expand section

Statistics

price_stats = graph.type_filter('Product').statistics('price')
unique_cats = graph.type_filter('Product').unique_values(property='category', max_length=10)

Calculations

graph.type_filter('Product').calculate(expression='price * 1.1', store_as='price_with_tax')

graph.type_filter('User').traverse('PURCHASED').calculate(
    expression='sum(price * quantity)', store_as='total_spent'
)

graph.type_filter('User').traverse('PURCHASED').count(store_as='product_count', group_by_parent=True)

Connection Aggregation

graph.type_filter('Discovery').traverse('EXTENDS_INTO').calculate(
    expression='sum(share_pct)',
    aggregate_connections=True
)

Supported: sum, avg/mean, min, max, count, std.

---

Schema and Indexes

Expand section

Schema Definition

graph.define_schema({
    'nodes': {
        'Prospect': {
            'required': ['npdid_prospect', 'prospect_name'],
            'optional': ['prospect_status'],
            'types': {'npdid_prospect': 'integer', 'prospect_name': 'string'}
        }
    },
    'connections': {
        'HAS_ESTIMATE': {'source': 'Prospect', 'target': 'ProspectEstimate'}
    }
})

errors = graph.validate_schema()
schema = graph.get_schema()

Indexes

Indexes accelerate equality lookups only (WHERE n.prop = value). Range conditions (<, >, <=, >=) always scan.

graph.create_index('Prospect', 'prospect_geoprovince')
graph.create_composite_index('Person', ['city', 'age'])

graph.list_indexes()
graph.drop_index('Prospect', 'prospect_geoprovince')

Indexes are maintained automatically by all mutation operations.

---

Import and Export

Expand section

Saving and Loading

graph.save("my_graph.kgl")
loaded_graph = kglite.load("my_graph.kgl")

Portability: Save files use bincode serialization and are not guaranteed portable across OS, CPU architecture, or library versions. Always re-export via a portable format (GraphML, CSV) when sharing across machines. Each file includes a format version and the library version that wrote it — check with graph_info()['format_version'] and graph_info()['library_version'] after loading. If the internal data structures change between releases, loading will fail with a clear version mismatch error rather than silent corruption.

Export Formats

graph.export('my_graph.graphml', format='graphml')  # Gephi, yEd
graph.export('my_graph.gexf', format='gexf')        # Gephi native
graph.export('my_graph.json', format='d3')           # D3.js
graph.export('my_graph.csv', format='csv')           # creates _nodes.csv + _edges.csv

graphml_string = graph.export_string(format='graphml')

Subgraph Extraction

subgraph = (
    graph.type_filter('Company')
    .filter({'title': 'Acme Corp'})
    .expand(hops=2)
    .to_subgraph()
)
subgraph.export('acme_network.graphml', format='graphml')

---

Performance

Expand section

Tips

1. Batch operations — add nodes/connections in batches, not individually
2. Specify columns — only include columns you need to reduce memory
3. Filter by type first — type_filter() before filter() for narrower scans
4. Create indexes — on frequently filtered equality conditions (~3x on 100k+ nodes; depends on selectivity)
5. Use lightweight methods — node_count(), indices(), get_node_by_id() skip property materialization
6. Cypher LIMIT — use LIMIT to avoid scanning entire result sets

Lightweight Methods

Method	Returns	Speed
node_count()	Integer count (total graph if no filter, selection count after filter)	Fastest
indices()	List of node indices	Fast
id_values()	List of ID values	Fast
get_ids()	List of {id, title, type} dicts	Medium
get_nodes()	List of full node dicts	Slowest

Lightweight path methods: shortest_path_length(), shortest_path_indices(), shortest_path_ids().

Performance Model

kglite is optimized for knowledge graph workloads — complex multi-step queries on heterogeneous, property-rich graphs. Operations have overhead compared to raw graph algorithms because they build selections, materialize Python dicts, and support the full query API.

Speed claims caveat: The "~3x" index speedup was measured on equality-filtered queries over 100k+ node graphs. Actual improvement depends on graph size, selectivity, and property cardinality. On small graphs (<1k nodes) the overhead of index lookup may not be noticeable. Always benchmark on your own data.

Threading

Designed for single-threaded use. The Rust code does not release the Python GIL during operations. If you share a graph instance across threads, guard access with your own lock.

---

Graph Maintenance

Expand section

After heavy mutation workloads (DELETE, REMOVE), the internal graph storage accumulates tombstones. Use graph_info() to monitor storage health.

Diagnostics

info = graph.graph_info()
# {'node_count': 950, 'node_capacity': 1000, 'node_tombstones': 50,
#  'edge_count': 2800, 'fragmentation_ratio': 0.05,
#  'type_count': 3, 'property_index_count': 2, 'composite_index_count': 0}

Vacuum — Compact Storage

if info['fragmentation_ratio'] > 0.3:
    result = graph.vacuum()
    print(f"Reclaimed {result['tombstones_removed']} slots, remapped {result['nodes_remapped']} nodes")

vacuum() rebuilds the graph with contiguous indices and rebuilds all indexes. Resets the current selection — call between query chains.

Reindex — Rebuild Indexes

graph.reindex()

Recovery tool, not routine maintenance. Indexes are maintained automatically by all mutations. Use reindex() only if you suspect corruption (e.g., after a crash during save()).

Recommended Workflow

info = graph.graph_info()
if info['fragmentation_ratio'] > 0.3:
    graph.vacuum()

---

Operation Reports

Expand section

Operations that modify the graph return detailed reports:

report = graph.add_nodes(data=df, node_type='Product', unique_id_field='product_id')
print(f"Created {report['nodes_created']} nodes in {report['processing_time_ms']}ms")

if report['has_errors']:
    print(f"Errors: {report['errors']}")

Node report fields: operation, timestamp, nodes_created, nodes_updated, nodes_skipped, processing_time_ms, has_errors, errors.

Connection report fields: connections_created, connections_skipped, property_fields_tracked.

graph.get_last_report()
graph.get_operation_index()
graph.get_report_history()