graphframes/graphframes

Health v2 · maintenance only

Adoption not part of the score

1203 stars · 274 forks observed · 2026-09-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity stable

machine-learning data-science graph-processing big-data data-science microservices jvm python cross-platform apache-spark dataframes graph-algorithms pagerank connected-components motif-finding pregel pyspark graphx spark-connect graph-processing

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