Sedona is a powerful geospatial library for Apache Spark that allows you to perform spatial data processing and analysis. The latest version of Sedona, 1.4.1, is compatible with Spark 3.0, Scala 2.13, and requires the jts-core dependency. In this article, we will explore how to set up Sedona 1.4.1 with Spark 3.0, Scala 2.13, and the jts-core dependency.
Step 1: Installing Apache Spark 3.0
To use Sedona 1.4.1, you need to have Apache Spark 3.0 installed on your system. Here are the steps to install Apache Spark 3.0:
- Go to the Apache Spark website (https://spark.apache.org/downloads.html) and download the latest version of Apache Spark 3.0.
- Extract the downloaded file to a directory on your system.
- Set the SPARK_HOME environment variable to the directory where you extracted Apache Spark.
- Update your PATH environment variable to include the bin directory inside the SPARK_HOME directory.
Step 2: Installing Scala 2.13
Sedona 1.4.1 is compatible with Scala 2.13, so you need to have Scala 2.13 installed on your system. Here are the steps to install Scala 2.13:
- Go to the Scala website (https://www.scala-lang.org/download/) and download the latest version of Scala 2.13.
- Follow the installation instructions specific to your operating system to install Scala 2.13.
Step 3: Adding the jts-core Dependency
Sedona 1.4.1 requires the jts-core dependency, which is a Java Topology Suite library for spatial data models and operations. To add the jts-core dependency to your Sedona project, follow these steps:
- Open your Sedona project in your favorite IDE (Integrated Development Environment).
- Locate the build.sbt file in your project directory.
- Add the following line to the build.sbt file to include the jts-core dependency:
libraryDependencies += "org.locationtech.jts" % "jts-core" % "1.16.1" - Save the build.sbt file.
Step 4: Configuring Sedona 1.4.1
Now that you have installed Apache Spark 3.0, Scala 2.13, and added the jts-core dependency, you need to configure Sedona 1.4.1 in your project. Follow these steps:
- Open your Sedona project in your favorite IDE.
- Locate the SparkSession configuration file (usually named spark-defaults.conf) in your project directory.
- Add the following lines to the spark-defaults.conf file to configure Sedona:
spark.jars.packages org.apache.sedona:sedona-core-3.0_2.12:1.4.1,org.locationtech.jts:jts-core:1.16.1spark.serializer org.apache.spark.serializer.KryoSerializerspark.kryo.registrator org.apache.sedona.core.serde.SedonaKryoRegistrator - Save the spark-defaults.conf file.
Step 5: Using Sedona 1.4.1 in Your Project
Now that you have set up Sedona 1.4.1 with Spark 3.0, Scala 2.13, and the jts-core dependency, you can start using Sedona in your project. Here's a simple example to get you started:
import org.apache.sedona.core.{KryoSerializer, SedonaKryoRegistrator}
import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.SparkSession
object SedonaExample {
def main(args: Array[String]): Unit = {
// Create a SparkSession
val spark = SparkSession.builder()
.appName("SedonaExample")
.config("spark.serializer", classOf[KryoSerializer].getName)
.config("spark.kryo.registrator", classOf[SedonaKryoRegistrator].getName)
.getOrCreate()
// Your Sedona code goes here
// Stop the SparkSession
spark.stop()
}
}
Replace "Your Sedona code goes here" with your own Sedona code. You can now run your project and start using Sedona 1.4.1 with Spark 3.0, Scala 2.13, and the jts-core dependency.
In this article, we have learned how to set up Sedona 1.4.1 with Spark 3.0, Scala 2.13, and the jts-core dependency. By following the steps outlined in this article, you can start using Sedona to perform spatial data processing and analysis in your projects. Happy coding!
| Reference | Link |
|---|---|
| Apache Spark | https://spark.apache.org/downloads.html |
| Scala | https://www.scala-lang.org/download/ |