GFQL: The Dataframe-Native Graph Query Language

GFQL is a graph query language that runs directly on Python dataframes. It needs no graph database. The same query runs on pandas, Polars, cuDF (GPU), or Polars-GPU, and on a remote Graphistry server. Install it with pip install graphistry.

GFQL accepts two syntaxes. The Python chain syntax composes n() and e() steps. The Cypher syntax, g.gfql("MATCH (a)-[e]->(b) ..."), covers a bounded subset of Cypher, the graph query language popularized by Neo4j. Both compile to vectorized dataframe operations, so one machine handles graphs of 100M+ edges in interactive time (see GFQL vs Apache Spark GraphFrames on One Node).

Where to start

• New to GFQL: 10 Minutes to GFQL (10 minutes), Overview of GFQL, then the speedup case study.

• Coming from Cypher: Cypher Syntax In GFQL and GFQL Quick Reference, then Cypher to GFQL Python & Wire Protocol Mapping.

• Need speed: Choosing a GFQL Engine: pandas, Polars, cuDF, Polars-GPU picks the engine. engine='polars' is the one-keyword CPU speedup; GFQL Performance: Measured Against Graph Databases covers GPU and remote GPU.

• Start from known nodes: Indexing Guide: Build Once, Query Faster and Adjacency Index: Fast Lookups from Known Nodes make lookups from known nodes cost O(degree) instead of O(E).

Start Here

• 10 Minutes to GFQL
• Overview of GFQL
• Speedup Case Study: vs Neo4j

Guides

• Performance and Benchmarks
• Language Reference

Developer Resources

• GFQL Specifications
  • GFQL Language Specification
  • GFQL Python Embedding
  • GFQL Wire Protocol Specification
  • Cypher to GFQL Python & Wire Protocol Mapping
  • GFQL JSON Generation Guide for LLMs
  • Overview
• GFQL Validation Guide
  • GFQL Validation Fundamentals
  • GFQL Validation for LLMs
  • GFQL Validation in Production
  • Overview