{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"GraphFrames provides distributed graph processing and analytics on Apache Spark using DataFrame abstractions, with built-in algorithms and flexible APIs like Pregel and AggregateMessages for custom graph computation.","skillfed_tags":["distributed-computing","graph-algorithms","spark"],"use_cases":["Entity resolution at scale by connecting similar records and running connected components to group duplicates","Fraud detection in large transaction networks using cycle detection and K-Core algorithm","Social network analysis such as ranking search results with distributed PageRank or finding independent sets for marketing campaigns","Compliance analytics using shortest-path algorithms and motif analysis to detect suspicious patterns","Knowledge graph construction and querying with property graph models and relational joins","Graph clustering and community detection on massive networks using label propagation"],"what_it_does":"GraphFrames is a Python library that brings graph processing to Apache Spark's distributed computing framework. It sits on top of Spark's DataFrame API, letting you represent graphs as vertex and edge DataFrames, then run graph algorithms\u2014like PageRank, connected components, shortest paths, and motif finding\u2014across a cluster. The package combines relational queries with graph traversals, so you can filter and join graph data using SQL-like syntax while leveraging Spark's optimizer for performance.\n\nTypical use involves creating vertex and edge DataFrames, constructing a GraphFrame object, then calling built-in algorithms or writing custom logic with Pregel and message-passing APIs. It's designed for scenarios where your graph is too large for a single machine and you need both graph-specific operations and the flexibility to combine them with relational transformations.","worth_installing":"Yes, if you have Apache Spark and need to run graph algorithms on data too large for a single machine. The library is actively maintained, has low install friction, and carries a permissive MIT license. However, verify that your Spark version and Python environment are compatible, since the latest release date and classifier information suggest the package may not have been updated for very recent versions."},"id":"graphframes","links":{"html":"https://skillfed.io/packages/graphframes","md":"https://skillfed.io/packages/graphframes.md","pypi":"https://pypi.org/project/graphframes/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2018-12-05","license_spdx":null,"license_treatment":"permissive","name":"graphframes","python_support":"unspecified","summary":"GraphFrames: DataFrame-based Graphs"},"popularity":{"monthly_downloads":2683394,"position":2940,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.6"}