graflo 1.6.5

A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph). Features automatic PostgreSQL schema inference.

GraFlo

A Graph Schema & Transformation Language (GSTL) for Labeled Property Graphs (LPG).

GraFlo provides a declarative, database-agnostic specification for mapping heterogeneous data sources — tabular (CSV, SQL), hierarchical (JSON, XML), and RDF/SPARQL — to a unified LPG representation and ingesting it into ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, or NebulaGraph.

Package Renamed: This package was formerly known as graphcast.

Overview

GraFlo separates what the graph looks like from where data comes from and which database stores it.

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flowchart LR
    SI["<b>Source Instance</b><br/>File · SQL · SPARQL · API"]
    R["<b>Resource</b><br/>Actor Pipeline"]
    GS["<b>Logical Graph Schema</b><br/>Vertex/Edge Definitions<br/>Identities · Transforms"]
    DBA["<b>DB-aware Projection</b><br/>DatabaseFeatures<br/>VertexConfigDBAware · EdgeConfigDBAware"]
    GC["<b>GraphContainer</b><br/>Covariant Graph Representation"]
    DB["<b>Graph DB (LPG)</b><br/>ArangoDB · Neo4j · TigerGraph · Others"]

    SI --> R --> GS --> GC --> DBA --> DB

Source Instance → Resource → Logical Graph Schema → Covariant Graph Representation → DB-aware Projection → Graph DB

Stage	Role	Code
Source Instance	A concrete data artifact — a CSV file, a PostgreSQL table, a SPARQL endpoint, a .ttl file.	AbstractDataSource subclasses (FileDataSource, SQLDataSource, SparqlEndpointDataSource, …) with a DataSourceType.
Resource	A reusable transformation pipeline — actor steps (descend, transform, vertex, edge) that map raw records to graph elements. Data sources bind to Resources by name via the DataSourceRegistry.	Resource (part of Schema).
Graph Schema	Declarative logical vertex/edge definitions, identities, typed fields, and named transforms — defined in YAML or Python.	Schema, VertexConfig, EdgeConfig.
Covariant Graph Representation	A database-independent collection of vertices and edges.	GraphContainer.
DB-aware Projection	Resolves DB-specific naming/default/index behavior from logical schema + DatabaseFeatures.	Schema.resolve_db_aware(), VertexConfigDBAware, EdgeConfigDBAware.
Graph DB	The target LPG store — same API for all supported databases.	ConnectionManager, DBWriter, DB connectors.

Supported source types (DataSourceType)

DataSourceType	Pattern	DataSource	Schema inference
FILE — CSV / JSON / JSONL / Parquet	FilePattern	FileDataSource	manual
SQL — PostgreSQL tables	TablePattern	SQLDataSource	automatic (3NF with PK/FK)
SPARQL — RDF files (.ttl, .rdf, .n3)	SparqlPattern	RdfFileDataSource	automatic (OWL/RDFS ontology)
SPARQL — SPARQL endpoints (Fuseki, …)	SparqlPattern	SparqlEndpointDataSource	automatic (OWL/RDFS ontology)
API — REST APIs	—	APIDataSource	manual
IN_MEMORY — list / DataFrame	—	InMemoryDataSource	manual

Supported targets

ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph — same API for all.

Features

• Declarative LPG schema — Define vertices, edges, vertex identity, secondary DB indexes, weights, and transforms in YAML or Python. The Schema is the single source of truth, independent of source or target.
• Database abstraction — One logical schema, one API. Target ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, or NebulaGraph without rewriting pipelines. DB idiosyncrasies are handled in DB-aware projection (Schema.resolve_db_aware(...)) and connector/writer stages.
• Resource abstraction — Each Resource defines a reusable actor pipeline (descend → transform → vertex → edge) that maps raw records to graph elements. Data sources bind to Resources by name via the DataSourceRegistry, decoupling transformation logic from data retrieval.
• SPARQL & RDF support — Query SPARQL endpoints (e.g. Apache Fuseki), read .ttl/.rdf/.n3 files, and auto-infer schemas from OWL/RDFS ontologies. Install with pip install graflo[sparql].
• Schema inference — Generate graph schemas from PostgreSQL 3NF databases (PK/FK heuristics) or from OWL/RDFS ontologies (owl:Class → vertices, owl:ObjectProperty → edges, owl:DatatypeProperty → vertex fields).
• Typed fields — Vertex fields and edge weights carry types (INT, FLOAT, STRING, DATETIME, BOOL) for validation and database-specific optimisation.
• Parallel batch processing — Configurable batch sizes and multi-core execution.

Documentation

Full documentation is available at: growgraph.github.io/graflo

Installation

pip install graflo

# With RDF / SPARQL support (adds rdflib + SPARQLWrapper)
pip install graflo[sparql]

Usage Examples

Simple ingest

from suthing import FileHandle

from graflo import Schema, Caster, Patterns
from graflo.db.connection.onto import ArangoConfig

schema = Schema.from_dict(FileHandle.load("schema.yaml"))

# Option 1: Load config from docker/arango/.env (recommended)
conn_conf = ArangoConfig.from_docker_env()

# Option 2: Load from environment variables
# Set: ARANGO_URI, ARANGO_USERNAME, ARANGO_PASSWORD, ARANGO_DATABASE
conn_conf = ArangoConfig.from_env()

# Option 3: Load with custom prefix (for multiple configs)
# Set: USER_ARANGO_URI, USER_ARANGO_USERNAME, USER_ARANGO_PASSWORD, USER_ARANGO_DATABASE
user_conn_conf = ArangoConfig.from_env(prefix="USER")

# Option 4: Create config directly
# conn_conf = ArangoConfig(
#     uri="http://localhost:8535",
#     username="root",
#     password="123",
#     database="mygraph",  # For ArangoDB, 'database' maps to schema/graph
# )
# Note: If 'database' (or 'schema_name' for TigerGraph) is not set,
# Caster will automatically use Schema.general.name as fallback

from graflo.util.onto import FilePattern
import pathlib

# Create Patterns with file patterns
patterns = Patterns()
patterns.add_file_pattern(
    "work",
    FilePattern(regex="\Sjson$", sub_path=pathlib.Path("./data"), resource_name="work")
)

# Or use resource_mapping for simpler initialization
# patterns = Patterns(
#     _resource_mapping={
#         "work": "./data/work.json",
#     }
# )

schema.fetch_resource()

from graflo.hq.caster import IngestionParams
from graflo.hq import GraphEngine

# Option 1: Use GraphEngine for schema definition and ingestion (recommended)
engine = GraphEngine()
ingestion_params = IngestionParams(
    recreate_schema=False,  # Set to True to drop and redefine schema (script halts if schema exists)
    # max_items=1000,  # Optional: limit number of items to process
    # batch_size=10000,  # Optional: customize batch size
)

engine.define_and_ingest(
    schema=schema,
    target_db_config=conn_conf,  # Target database config
    patterns=patterns,  # Source data patterns
    ingestion_params=ingestion_params,
    recreate_schema=False,  # Set to True to drop and redefine schema (script halts if schema exists)
)

# Option 2: Use Caster directly (schema must be defined separately)
# from graflo.hq import GraphEngine
# engine = GraphEngine()
# engine.define_schema(schema=schema, target_db_config=conn_conf, recreate_schema=False)
# 
# caster = Caster(schema)
# caster.ingest(
#     target_db_config=conn_conf,
#     patterns=patterns,
#     ingestion_params=ingestion_params,
# )

PostgreSQL Schema Inference

from graflo.hq import GraphEngine
from graflo.db.connection.onto import PostgresConfig, ArangoConfig
from graflo import Caster
from graflo.onto import DBType

# Connect to PostgreSQL
postgres_config = PostgresConfig.from_docker_env()  # or PostgresConfig.from_env()

# Create GraphEngine and infer schema from PostgreSQL 3NF database
# Connection is automatically managed inside infer_schema()
engine = GraphEngine(target_db_flavor=DBType.ARANGO)
schema = engine.infer_schema(
    postgres_config,
    schema_name="public",  # PostgreSQL schema name
)

# Define schema in target database (optional, can also use define_and_ingest)
target_config = ArangoConfig.from_docker_env()
engine.define_schema(
    schema=schema,
    target_db_config=target_config,
    recreate_schema=False,
)

# Use the inferred schema with Caster for ingestion
caster = Caster(schema)
# ... continue with ingestion

RDF / SPARQL Ingestion

from pathlib import Path
from graflo.hq import GraphEngine
from graflo.db.connection.onto import ArangoConfig

engine = GraphEngine()

# Infer schema from an OWL/RDFS ontology file
ontology = Path("ontology.ttl")
schema = engine.infer_schema_from_rdf(source=ontology)

# Create data-source patterns (reads a local .ttl file per rdf:Class)
patterns = engine.create_patterns_from_rdf(source=ontology)

# Or point at a SPARQL endpoint instead:
# from graflo.db.connection.onto import SparqlEndpointConfig
# sparql_cfg = SparqlEndpointConfig(uri="http://localhost:3030", dataset="mydata")
# patterns = engine.create_patterns_from_rdf(
#     source=ontology,
#     endpoint_url=sparql_cfg.query_endpoint,
# )

target = ArangoConfig.from_docker_env()
engine.define_and_ingest(schema=schema, target_db_config=target, patterns=patterns)

Development

To install requirements

git clone [e-mail]:growgraph/graflo.git && cd graflo
uv sync --dev

Tests

Test databases

Quick Start: To start all test databases at once, use the convenience scripts from the docker folder:

cd docker
./start-all.sh    # Start all services
./stop-all.sh      # Stop all services
./cleanup-all.sh   # Remove containers and volumes

Individual Services: To start individual databases, navigate to each database folder and run:

Spin up Arango from arango docker folder by

docker-compose --env-file .env up arango

Neo4j from neo4j docker folder by

docker-compose --env-file .env up neo4j

TigerGraph from tigergraph docker folder by

docker-compose --env-file .env up tigergraph

FalkorDB from falkordb docker folder by

docker-compose --env-file .env up falkordb

Memgraph from memgraph docker folder by

docker-compose --env-file .env up memgraph

NebulaGraph from nebula docker folder by

docker-compose --env-file .env up

and Apache Fuseki from fuseki docker folder by

docker-compose --env-file .env up fuseki

To run unit tests

pytest test

Note: Tests require external database containers (ArangoDB, Neo4j, TigerGraph, FalkorDB, Memgraph, NebulaGraph, Fuseki) to be running. CI builds intentionally skip test execution. Tests must be run locally with the required database images started (see Test databases section above). NebulaGraph tests are gated behind pytest --run-nebula.

Requirements

• Python 3.11+ (Python 3.11 and 3.12 are officially supported)
• python-arango
• nebula3-python>=3.8.3 (NebulaGraph v3.x support)
• nebula5-python>=5.2.1 (NebulaGraph v5.x support)
• sqlalchemy>=2.0.0 (for PostgreSQL and SQL data sources)
• rdflib>=7.0.0 + SPARQLWrapper>=2.0.0 (optional, install with pip install graflo[sparql])

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.