Spark Integration

GraphLab Create has the ability to convert Apache Spark's Resilient Distributed Datasets (RDD) to an SFrame and back.

Setup the Environment

To use GraphLab Create within PySpark, you need to set the $SPARK_HOME and $PYTHONPATH environment variables on the driver. A common usage:

export SPARK_HOME =

Run from the PySpark Python Shell


Run from a standard Python Shell

Make sure you have exported the PYTHONPATH and SPARK_HOME environment variables. Then run (for example):


Then you need to start spark:

from pyspark import SparkContext
from pyspark.sql import SQLContext
# Launch spark by creating a spark context
sc = SparkContext()
# Create a SparkSQL context to manage dataframe schema information.
sql = SQLContext(sc)

Make an SFrame from an RDD

from graphlab import SFrame
rdd = sc.parallelize([(x,str(x), "hello") for x in range(0,5)])
sframe = SFrame.from_rdd(rdd, sc)
print sframe
|       X1      |
| [0, 0, hello] |
| [1, 1, hello] |
| [2, 2, hello] |
| [3, 3, hello] |
| [4, 4, hello] |
[5 rows x 1 columns]

Make an SFrame from a Dataframe (preferred)

from graphlab import SFrame
rdd = sc.parallelize([(x,str(x), "hello") for x in range(0,5)])
df = sql.createDataFrame(rdd)
sframe = SFrame.from_rdd(df, sc)
print sframe
| _1 | _2 |   _3  |
| 0  | 0  | hello |
| 1  | 1  | hello |
| 2  | 2  | hello |
| 3  | 3  | hello |
| 4  | 4  | hello |
[5 rows x 3 columns]

Make an RDD from an SFrame

from graphlab import SFrame
sf = gl.SFrame({'x': [1,2,3], 'y': ['fish', 'chips', 'salad']})
rdd = sf.to_rdd(sc)
[(0, '0', 'hello'),
 (1, '1', 'hello'),
 (2, '2', 'hello'),
 (3, '3', 'hello'),
 (4, '4', 'hello')]

Make a DataFrame from an SFrame (preferred)

from graphlab import SFrame
sf = gl.SFrame({'x': [1,2,3], 'y': ['fish', 'chips', 'salad']})
df = sf.to_spark_dataframe(sc,sql)
|  x|    y|
|  1| fish|
|  2|chips|
|  3|salad|

Requirements and Caveats

  • The currently release requires Python 2.7, Spark 1.3 or later, and the hadoop binary must be within the PATH of the driver when running on a cluster or interacting with Hadoop (e.g., you should be able to run hadoop classpath).

  • We also currently only support Mac and Linux platforms but will have Windows support soon.

  • The GraphLab integration with Spark supports Spark execution modes local,yarn-client, and standalone spark://<hostname:port>. ("yarn-cluster" is not available through PySpark)

Recommended Settings for Spark Installation on a Cluster

We recommend downloading Pre-built for Hadoop 2.4 and later version of Apache Spark.


  1. RDD conversion works with GraphLab Create right out of the box. No additional Spark setup is required. When you install GraphLab Create, it comes with a JAR that enables this feature. To find the location of the JAR file, execute this command:
  2. GraphLab Create can only convert to types it supports. This means that if you have an RDD with Python types other than int, long, str, list, dict, array.array, or datetime.datetime (image is not supported for conversion currently), your conversion may fail (when using Spark locally, you may get lucky and successfully convert an unsupported type, but it will probably fail on a YARN cluster).

  3. SFrames fit most naturally with DataFrame. Both have strict column types and a they have a similar approach to storing data. This is why we also have a graphlab.SFrame.to_spark_dataframe method. The graphlab.SFrame.from_rdd method works with both DataFrame and any other rdd, so there is no from_dataframe method.