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Install on AWS EMR

We recommend Sedona-1.3.1-incuabting and above for EMR. In the tutorial, we use AWS Elastic MapReduce (EMR) 6.9.0. It has the following applications installed: Hadoop 3.3.3, JupyterEnterpriseGateway 2.6.0, Livy 0.7.1, Spark 3.3.0.

This tutorial is tested on EMR on EC2 with EMR Studio (notebooks). EMR on EC2 uses YARN to manage resources.

Prepare initialization script

In your S3 bucket, add a script that has the following content:


# EMR clusters only have ephemeral local storage. It does not really matter where we store the jars.
sudo mkdir /jars

# Download Sedona jar
sudo curl -o /jars/sedona-spark-shaded-3.0_2.12-1.4.1.jar ""

# Download GeoTools jar
sudo curl -o /jars/geotools-wrapper-1.4.0-28.2.jar ""

# Install necessary python libraries
sudo python3 -m pip install pandas shapely==1.8.5
sudo python3 -m pip install pandas geopandas==0.10.2
sudo python3 -m pip install attrs matplotlib descartes apache-sedona==1.4.0

When you create a EMR cluster, in the bootstrap action, specify the location of this script.

Add software configuration

When you create a EMR cluster, in the software configuration, add the following content:

      "spark.yarn.dist.jars": "/jars/sedona-spark-shaded-3.0_2.12-1.4.1.jar,/jars/geotools-wrapper-1.4.0-28.2.jar",
      "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
      "spark.kryo.registrator": "org.apache.sedona.core.serde.SedonaKryoRegistrator",
      "spark.sql.extensions": "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions"


If you use Sedona 1.3.1-incubating, please use sedona-python-adpater-3.0_2.12 jar in the content above, instead of sedona-spark-shaded-3.0_2.12.

Last update: March 16, 2023 00:00:53