This is a package for graphs processing and analytics on scale. It is build on top of Apache Spark and relying on DataFrame abstraction. Users can write highly expressive queries by leveraging the DataFrame API, combined with a new API for network motif finding. The user also benefits from DataFrame performance optimizations within the Spark SQL engine. GraphFrames works in Java, Scala, and Python.
You can find user guide and API docs at https://graphframes.github.io/graphframes
This projects was in maintenance mode for some time, but we are happy to announce that it is now back in active development! We are working on a new release with many bug fixes and improvements. We are also working on a new website and documentation.
The easiest way to start using GraphFrames is through the Spark Packages system. Just run the following command:
# Interactive Scala/Java $ spark-shell --packages graphframes:graphframes:0.8.4-spark3.5-s_2.12 # Interactive Python $ pyspark --packages graphframes:graphframes:0.8.4-spark3.5-s_2.12 # Submit a script in Scala/Java/Python $ spark-submit --packages graphframes:graphframes:0.8.4-spark3.5-s_2.12 script.py
Now you can create a GraphFrame as follows.
In Python:
from pyspark.sql import SparkSession from graphframes import GraphFrame spark = SparkSession.builder.getOrCreate() nodes = [ (1, "Alice", 30), (2, "Bob", 25), (3, "Charlie", 35) ] nodes_df = spark.createDataFrame(nodes, ["id", "name", "age"]) edges = [ (1, 2, "friend"), (2, 1, "friend"), (2, 3, "friend"), (3, 2, "enemy") # eek! ] edges_df = spark.createDataFrame(edges, ["src", "dst", "relationship"]) g = GraphFrame(nodes_df, edges_df)
Now let's run some graph algorithms at scale!
g.inDegrees.show() # +---+--------+ # | id|inDegree| # +---+--------+ # | 2| 2| # | 1| 1| # | 3| 1| # +---+--------+ g.outDegrees.show() # +---+---------+ # | id|outDegree| # +---+---------+ # | 1| 1| # | 2| 2| # | 3| 1| # +---+---------+ g.degrees.show() # +---+------+ # | id|degree| # +---+------+ # | 1| 2| # | 2| 4| # | 3| 2| # +---+------+ g2 = g.pageRank(resetProbability=0.15, tol=0.01) g2.vertices.show() # +---+-----+---+------------------+ # | id| name|age| pagerank| # +---+-----+---+------------------+ # | 1| John| 30|0.7758750474847483| # | 2|Alice| 25|1.4482499050305027| # | 3| Bob| 35|0.7758750474847483| # +---+-----+---+------------------+ # GraphFrames' most used feature... # Connected components can do big data entity resolution on billions or even trillions of records! # First connect records with a similarity metric, then run connectedComponents. # This gives you groups of identical records, which you then link by same_as edges or merge into list-based master records. sc.setCheckpointDir("/tmp/graphframes-example-connected-components") # required by GraphFrames.connectedComponents g.connectedComponents().show() # +---+-----+---+---------+ # | id| name|age|component| # +---+-----+---+---------+ # | 1| John| 30| 1| # | 2|Alice| 25| 1| # | 3| Bob| 35| 1| # +---+-----+---+---------+ # Find frenemies with network motif finding! See how graph and relational queries are combined? ( g.find("(a)-[e]->(b); (b)-[e2]->(a)") .filter("e.relationship = 'friend' and e2.relationship = 'enemy'") .show() ) # These are paths, which you can aggregate and count to find complex patterns. # +------------+--------------+----------------+-------------+ # | a| e| b| e2| # +------------+--------------+----------------+-------------+ # |{2, Bob, 25}|{2, 3, friend}|{3, Charlie, 35}|{3, 2, enemy}| # +------------+--------------+----------------+-------------+
To learn more about GraphFrames, check out these resources:
- GraphFrames Documentation
- GraphFrames Network Motif Finding Tutorial
- Introducing GraphFrames
- On-Time Flight Performance with GraphFrames for Apache Spark
We recommend using the Spark Packages system to install the latest version of GraphFrames, but now publish a build of our Python package to PyPi in the graphframes-py package. It can be used to provide type hints in IDEs, but does not load the java-side of GraphFrames so will not work without loading the GraphFrames package. See Installation and Quick-Start.
pip install graphframes-py
This project does not own or control the graphframes PyPI package (installs 0.6.0) or graphframes-latest PyPI package (installs 0.8.4).
If you use the sbt-spark-package plugin, in your sbt build file, add the following, pulled from GraphFrames on Spark Packages:
spDependencies += "graphframes/graphframes:0.8.4-spark3.5-s_2.12"
Otherwise,
resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/"
libraryDependencies += "graphframes" % "graphframes" % "0.8.4-spark3.5-s_2.12"
GraphFrames is not on Maven Central Repository but we are going to restore it soon. For now use Spark Packages system to install the package: https://spark-packages.org/package/graphframes/graphframes.
<dependencies> <!-- list of dependencies --> <dependency> <groupId>graphframes</groupId> <artifactId>graphframes</artifactId> <version>0.8.4-spark3.5-s_2.12</version> </dependency> </dependencies> <repositories> <!-- list of other repositories --> <repository> <id>SparkPackagesRepo</id> <url>https://repos.spark-packages.org/</url> </repository> </repositories>
To learn how GraphFrames works internally to combine graph and relational queries, check out the paper GraphFrames: An Integrated API for Mixing Graph and Relational Queries, Dave et al. 2016.
To compile this project, run build/sbt assembly from the project home directory. This will also run the Scala unit tests.
To run the Python unit tests, run the run-tests.sh script from the python/ directory. You will need to set SPARK_HOME to your local Spark installation directory.
Please see guide dev/release_guide.md.
This project is compatible with Spark 3.4+. Significant speed improvements have been made to DataFrames in recent versions of Spark, so you may see speedups from using the latest Spark version.
GraphFrames is collaborative effort among UC Berkeley, MIT, Databricks and the open source community. We welcome open source contributions as well!
See release notes.