# graphframes/graphframes

GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs

Repository: https://github.com/graphframes/graphframes
Canonical: https://ross.abutalabs.com/products/graphframes
Homepage: https://graphframes.io/
Language: Scala
License: Apache-2.0
License Family: permissive
Topics: apache-spark, big-data, connected-components, dataframe, dataframes, graphs, network-motif, network-motifs, networks, spark, pyspark, graph-algorithms, graph-analysis, graph-theory
Last push: 2026-08-31T18:51:56+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 87, longevity 100
- inputs: {"age_days": 3878, "days_push": 2, "days_rel": 5, "gap_med": 53, "n_releases_24m": 10}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1203, forks 274 (observed 2026-09-01T02:14:02.678422+00:00)

## What it is
GraphFrames is a package for Apache Spark that provides DataFrame-based graph processing with distributed graph algorithms like PageRank, connected components, and label propagation. It offers APIs in Scala, Java, and Python, including motif finding and Pregel-style custom graph computation.

## Use cases
- run PageRank on a billion-edge graph in Spark
- find connected components for identity resolution at scale
- detect fraud cycles in large transaction networks
- compute shortest paths for compliance analytics
- cluster huge graphs with label propagation
- find network motifs with expressive DataFrame queries
- build a knowledge graph with a property graph model
- replace deprecated GraphX with a maintained DataFrame-based alternative

## When to choose
- you already run Apache Spark and need distributed graph analytics
- you want graph algorithms that scale with your Spark cluster
- you prefer DataFrame APIs over RDD-based GraphX
- you need PySpark or Spark Connect support for graph processing

## When to avoid
- your graphs fit in memory on a single machine (NetworkX or igraph is simpler)
- you don't use Apache Spark or a JVM/Python Spark environment
- you need interactive single-node graph visualization

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science
- domain: graph-processing, big-data, data-science, microservices
- platform: jvm, python, cross-platform
- tags: apache-spark, dataframes, graph-algorithms, pagerank, connected-components, motif-finding, pregel, pyspark, graphx, spark-connect, graph-processing

## Member repositories
- graphframes/graphframes (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:14:02.678422+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:20:36.096629+00:00, confidence not recorded.
  - readme: https://github.com/graphframes/graphframes (fetched 2026-09-01T02:14:02.678422+00:00, sha b44432937072)
  - homepage: https://graphframes.io/ (fetched 2026-08-29T12:27:38.526699+00:00, sha dc6d0a9eaed9)
  - site_page: https://graphframes.io/01-about/01-index.html (fetched 2026-08-29T12:27:38.529383+00:00, sha cebc91bbf972)
  - site_page: https://graphframes.io/02-quick-start/01-installation.html (fetched 2026-08-29T12:27:38.531827+00:00, sha d5e91a9a5f87)
- Data as of 2026-08-30T08:39:29.467469+00:00.