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Apache Sedona is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines.

The apache.sedona R package exposes an interface to Apache Sedona through sparklyr enabling higher-level access through a dplyr backend and familiar R functions.


To use Apache Sedona from R, you just need to install the apache.sedona package; Spark dependencies are managed directly by the package.

# Install released version from CRAN

Development version

To use the development version, you will need both the latest version of the package and of the Apache Sedona jars.

To get the latest R package from GtiHub:

# Install development version from GitHub

To get the latest Sedona jars you can:

The path to the sedona-spark-shaded and sedona-viz jars needs to be put in the SEDONA_JAR_FILES environment variables (see below).


spark_read_* functions will read geospatial data into Spark Dataframes. The resulting Spark dataframe object can then be modified using dplyr verbs familiar to many R users. In addition, spatial UDFs supported by Sedona can inter-operate seamlessly with other functions supported in sparklyr’s dbplyr SQL translation env. For example, the code below finds the average area of all polygons in polygon_sdf:

The first time you load Sedona, Spark will download all the dependent jars, which can take a few minutes and cause the connection to timeout. You can either retry (some jars will already be downloaded and cached) or increase the "sparklyr.connect.timeout" parameter in the sparklyr config.


## Only if using development version:
Sys.setenv("SEDONA_JAR_FILES" = "<path to sedona-spark-shaded jar>:<path to sedona-viz jar>")

sc <- spark_connect(master = "local")
polygon_sdf <- spark_read_geojson(sc, location = "/tmp/polygon.json")
mean_area_sdf <- polygon_sdf %>%
  dplyr::summarize(mean_area = mean(ST_Area(geometry)))

Notice that all of the above can open up many interesting possibilities. For example, one can extract ML features from geospatial data in Spark dataframes, build a ML pipeline using ml_* family of functions in sparklyr to work with such features, and if the output of a ML model happens to be a geospatial object as well, one can even apply visualization routines in apache.sedona to visualize the difference between any predicted geometry and the corresponding ground truth.