GFQL: The Dataframe-Native Graph Query Language

GFQL: The Dataframe-Native Graph Query Language#

Welcome to GFQL, the first fully vectorized dataframe-native graph query language with an open-source GPU runtime. GFQL is part of the PyGraphistry ecosystem and is designed to make graph analytics easier and faster without requiring a graph database as the execution layer. Whether you’re working with CPUs or leveraging GPU acceleration for massive datasets, GFQL integrates directly into Python dataframe workflows through a simple pip install graphistry.

GFQL bridges the gap between traditional storage-tier graph databases and the modern compute tier, allowing you to perform high-performance graph queries directly on your dataframes. It is built to feel familiar to users of Cypher, other graph query languages, and popular dataframe libraries. By being native to accelerated Python data-science technologies such as Apache Arrow, NumPy, NVIDIA RAPIDS, and Graphistry, it can already handle workloads like 100M+ edges in interactive time on a single machine.

If you are new to Cypher: Cypher is a graph query language popularized by Neo4j and related tools. It uses ASCII-art graph patterns such as (n1)-[e1]->(n2) to describe traversals from one node to another across an edge. GFQL supports a bounded Cypher surface directly through g.gfql("MATCH ..."), so Cypher users can keep familiar MATCH / WHERE / RETURN patterns while moving execution onto GFQL’s vectorized columnar engine and open-source GPU runtime. Use g.gfql_remote([...]) when you want the same GFQL model executed remotely.

For Cypher syntax through g.gfql("MATCH ..."), start with Cypher Syntax In GFQL, GFQL Quick Reference, GFQL RETURN, and Cypher to GFQL Mapping.

Recommended paths:

See also:

User Guide

Developer Resources