Read geospatial data into a Spark DataFrame.

These functions are deprecated and will be removed in a future release. Sedona has been implementing readers as spark DataFrame sources, so you can use spark_read_source with the right sources ("shapefile", "geojson", "geoparquet") to read geospatial data.

Functions to read geospatial data from a variety of formats into Spark DataFrames.

• spark_read_shapefile: from a shapefile

• spark_read_geojson: from a geojson file

• spark_read_geoparquet: from a geoparquet file

Usage

spark_read_shapefile(sc, name = NULL, path = name, options = list(), ...)

spark_read_geojson(
  sc,
  name = NULL,
  path = name,
  options = list(),
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE
)

spark_read_geoparquet(
  sc,
  name = NULL,
  path = name,
  options = list(),
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE
)

Arguments

sc

A spark_connection.

name

The name to assign to the newly generated table.

path

The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.

options

A list of strings with additional options. See https://spark.apache.org/docs/latest/sql-programming-guide.html#configuration.

...

Optional arguments; currently unused.

repartition

The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.

memory

Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)

overwrite

Boolean; overwrite the table with the given name if it already exists?

Value

A tbl

See also

Other Sedona DF data interface functions: spark_write_geojson()

Examples

library(sparklyr)
library(apache.sedona)

sc <- spark_connect(master = "spark://HOST:PORT")

if (!inherits(sc, "test_connection")) {
  input_location <- "/dev/null" # replace it with the path to your input file
  rdd <- spark_read_shapefile(sc, location = input_location)
}