GFQL: The Dataframe-Native Graph Query Language#

Welcome to GFQL, the first dataframe-native graph query language with GPU support. GFQL is part of the PyGraphistry ecosystem and is designed to make graph analytics easier and faster without the need for complex external infrastructure such as databases. Whether you’re working with CPUs or leveraging GPU acceleration for massive datasets, GFQL integrates seamlessly with your data science 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 your favorite high-performance graph queries directly on your dataframes. It’s built to be familiar to users of Cypher, other graph query languages, and popular dataframe libraries. By being native to accelerated Python datascience dataframe technologies such as Apache Arrow, Numpy, Nvidia RAPIDS, and Graphistry, it can already do workloads like 100M+ edges in interactive time on a single machine.

For Cypher-style MATCH … RETURN workflows, start with GFQL Quick Reference, GFQL WHERE, GFQL RETURN, and Cypher to GFQL Mapping.

Recommended paths:

New to GFQL: Overview of GFQL -> GFQL Quick Reference -> GFQL WHERE (Same-Path Constraints) -> GFQL RETURN (Row Pipelines)
Performance path (intro -> GPU -> remote GPU): 10 Minutes to GFQL -> GFQL Performance: Unleashing Vectorization and GPU Power for Scalable Graph Analytics -> GFQL Remote Mode
Translating Cypher: Cypher to GFQL Python & Wire Protocol Mapping
Building agents/integrations: GFQL Language Specification + GFQL Python Embedding + GFQL Wire Protocol Specification