https://github.com/graphframes/graphframes
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
https://github.com/graphframes/graphframes
apache-spark big-data connected-components dataframe dataframes graphs network-motif network-motifs networks spark
Last synced: 5 days ago
JSON representation
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
- Host: GitHub
- URL: https://github.com/graphframes/graphframes
- Owner: graphframes
- License: apache-2.0
- Created: 2016-01-20T23:17:56.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2025-04-11T04:28:54.000Z (5 days ago)
- Last Synced: 2025-04-11T04:57:44.800Z (5 days ago)
- Topics: apache-spark, big-data, connected-components, dataframe, dataframes, graphs, network-motif, network-motifs, networks, spark
- Language: Scala
- Homepage: http://graphframes.github.io/graphframes
- Size: 7.6 MB
- Stars: 1,039
- Watchers: 55
- Forks: 247
- Open Issues: 170
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-spark - GraphFrames - commit/graphframes/graphframes.svg"> - Data frame based graph API. (Packages / Graph Processing)
README
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# GraphFrames: DataFrame-based Graphs
This is a package for graphs processing and analytics on scale. It is built on top of Apache Spark and relies 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
## GraphFrames is Back!
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.
## Installation and Quick-Start
The easiest way to start using GraphFrames is through the [Spark Packages system](https://spark-packages.org/package/graphframes/graphframes). Just run the following command:
```bash
# 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:
```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!
```python
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}|
# +------------+--------------+----------------+-------------+
```
## Learn GraphFrames
To learn more about GraphFrames, check out these resources:
* [GraphFrames Documentation](https://graphframes.github.io/graphframes)
* [GraphFrames Network Motif Finding Tutorial](https://graphframes.github.io/graphframes/docs/_site/motif-tutorial.html)
* [Introducing GraphFrames](https://databricks.com/blog/2016/03/03/introducing-graphframes.html)
* [On-Time Flight Performance with GraphFrames for Apache Spark](https://databricks.com/blog/2016/03/16/on-time-flight-performance-with-graphframes-for-apache-spark.html)
## Community Resources
* [GraphFrames Google Group](https://groups.google.com/forum/#!forum/graphframes)
* [#graphframes Discord Channel on GraphGeeks](https://discord.com/channels/1162999022819225631/1326257052368113674)
## `graphframes-py` is our Official PyPi Package
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](https://pypi.org/project/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](#installation-and-quick-start).
```bash
pip install graphframes-py
```
This project does not own or control the [graphframes PyPI package](https://pypi.org/project/graphframes/) (installs 0.6.0) or [graphframes-latest PyPI package](https://pypi.org/project/graphframes-latest/) (installs 0.8.4).
## GraphFrames and sbt
If you use the sbt-spark-package plugin, in your sbt build file, add the following, pulled from [GraphFrames on Spark Packages](https://spark-packages.org/package/graphframes/graphframes):
```
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 and Maven
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](https://spark-packages.org/package/graphframes/graphframes).
```xml
graphframes
graphframes
0.8.4-spark3.5-s_2.12
SparkPackagesRepo
https://repos.spark-packages.org/
```
## GraphFrames Internals
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](https://people.eecs.berkeley.edu/~matei/papers/2016/grades_graphframes.pdf).
## Building and running unit tests
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.
## Release new version
Please see guide `dev/release_guide.md`.
## Spark version compatibility
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.
## Contributing
GraphFrames is collaborative effort among UC Berkeley, MIT, Databricks and the open source community. We welcome open source contributions as well!
## Releases:
See [release notes](https://github.com/graphframes/graphframes/releases).