Spatial SQL app

The page outlines the steps to manage spatial data using SedonaSQL.

Note

Since v1.5.0, Sedona assumes geographic coordinates to be in longitude/latitude order. If your data is lat/lon order, please use ST_FlipCoordinates to swap X and Y.

SedonaSQL supports SQL/MM Part3 Spatial SQL Standard. It includes four kinds of SQL operators as follows. All these operators can be directly called through:

ScalaJavaPython

var myDataFrame = sedona.sql("YOUR_SQL")
myDataFrame.createOrReplaceTempView("spatialDf")

Dataset<Row> myDataFrame = sedona.sql("YOUR_SQL")
myDataFrame.createOrReplaceTempView("spatialDf")

myDataFrame = sedona.sql("YOUR_SQL")
myDataFrame.createOrReplaceTempView("spatialDf")

Detailed SedonaSQL APIs are available here: SedonaSQL API. You can find example county data (i.e., county_small.tsv) in Sedona GitHub repo.

Set up dependencies

Scala/JavaPython

1. Read Sedona Maven Central coordinates and add Sedona dependencies in build.sbt or pom.xml.
2. Add Apache Spark core, Apache SparkSQL in build.sbt or pom.xml.
3. Please see SQL example project

1. Please read Quick start to install Sedona Python.
2. This tutorial is based on Sedona SQL Jupyter Notebook example. You can interact with Sedona Python Jupyter notebook immediately on Binder. Click to interact with Sedona Python Jupyter notebook immediately on Binder.

Create Sedona config

Use the following code to create your Sedona config at the beginning. If you already have a SparkSession (usually named spark) created by Wherobots/AWS EMR/Databricks, please skip this step and can use spark directly.

Sedona >= 1.4.1

You can add additional Spark runtime config to the config builder. For example, SedonaContext.builder().config("spark.sql.autoBroadcastJoinThreshold", "10485760")

ScalaJavaPython

import org.apache.sedona.spark.SedonaContext

val config = SedonaContext.builder()
.master("local[*]") // Delete this if run in cluster mode
.appName("readTestScala") // Change this to a proper name
.getOrCreate()

If you use SedonaViz together with SedonaSQL, please add the following line after SedonaContext.builder() to enable Sedona Kryo serializer:

.config("spark.kryo.registrator", classOf[SedonaVizKryoRegistrator].getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

import org.apache.sedona.spark.SedonaContext;

SparkSession config = SedonaContext.builder()
.master("local[*]") // Delete this if run in cluster mode
.appName("readTestScala") // Change this to a proper name
.getOrCreate()

If you use SedonaViz together with SedonaSQL, please add the following line after SedonaContext.builder() to enable Sedona Kryo serializer:

.config("spark.kryo.registrator", SedonaVizKryoRegistrator.class.getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

from sedona.spark import *

config = SedonaContext.builder() .\
    config('spark.jars.packages',
           'org.apache.sedona:sedona-spark-shaded-3.0_2.12:1.5.0,'
           'org.datasyslab:geotools-wrapper:1.5.0-28.2'). \
    getOrCreate()

If you are using Spark versions >= 3.4, please replace the 3.0 in package name of sedona-spark-shaded with the corresponding major.minor version of Spark, such as sedona-spark-shaded-3.4_2.12:1.5.0.

Sedona < 1.4.1

The following method has been deprecated since Sedona 1.4.1. Please use the method above to create your Sedona config.

ScalaJavaPython

var sparkSession = SparkSession.builder()
.master("local[*]") // Delete this if run in cluster mode
.appName("readTestScala") // Change this to a proper name
// Enable Sedona custom Kryo serializer
.config("spark.serializer", classOf[KryoSerializer].getName) // org.apache.spark.serializer.KryoSerializer
.config("spark.kryo.registrator", classOf[SedonaKryoRegistrator].getName)
.getOrCreate() // org.apache.sedona.core.serde.SedonaKryoRegistrator

If you use SedonaViz together with SedonaSQL, please use the following two lines to enable Sedona Kryo serializer instead:

.config("spark.serializer", classOf[KryoSerializer].getName) // org.apache.spark.serializer.KryoSerializer
.config("spark.kryo.registrator", classOf[SedonaVizKryoRegistrator].getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

SparkSession sparkSession = SparkSession.builder()
.master("local[*]") // Delete this if run in cluster mode
.appName("readTestScala") // Change this to a proper name
// Enable Sedona custom Kryo serializer
.config("spark.serializer", KryoSerializer.class.getName) // org.apache.spark.serializer.KryoSerializer
.config("spark.kryo.registrator", SedonaKryoRegistrator.class.getName)
.getOrCreate() // org.apache.sedona.core.serde.SedonaKryoRegistrator

If you use SedonaViz together with SedonaSQL, please use the following two lines to enable Sedona Kryo serializer instead:

.config("spark.serializer", KryoSerializer.class.getName) // org.apache.spark.serializer.KryoSerializer
.config("spark.kryo.registrator", SedonaVizKryoRegistrator.class.getName) // org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator

sparkSession = SparkSession. \
    builder. \
    appName('appName'). \
    config("spark.serializer", KryoSerializer.getName). \
    config("spark.kryo.registrator", SedonaKryoRegistrator.getName). \
    config('spark.jars.packages',
           'org.apache.sedona:sedona-spark-shaded-3.0_2.12:1.5.0,'
           'org.datasyslab:geotools-wrapper:1.5.0-28.2'). \
    getOrCreate()

If you are using Spark versions >= 3.4, please replace the 3.0 in package name of sedona-spark-shaded with the corresponding major.minor version of Spark, such as sedona-spark-shaded-3.4_2.12:1.5.0.

Initiate SedonaContext

Add the following line after creating Sedona config. If you already have a SparkSession (usually named spark) created by Wherobots/AWS EMR/Databricks, please call SedonaContext.create(spark) instead.

Sedona >= 1.4.1

ScalaJavaPython

import org.apache.sedona.spark.SedonaContext

val sedona = SedonaContext.create(config)

import org.apache.sedona.spark.SedonaContext;

SparkSession sedona = SedonaContext.create(config)

from sedona.spark import *

sedona = SedonaContext.create(config)

Sedona < 1.4.1

The following method has been deprecated since Sedona 1.4.1. Please use the method above to create your SedonaContext.

ScalaJavaPython

SedonaSQLRegistrator.registerAll(sparkSession)

SedonaSQLRegistrator.registerAll(sparkSession)

from sedona.register import SedonaRegistrator

SedonaRegistrator.registerAll(spark)

You can also register everything by passing --conf spark.sql.extensions=org.apache.sedona.sql.SedonaSqlExtensions to spark-submit or spark-shell.

Load data from files

Assume we have a WKT file, namely usa-county.tsv, at Path /Download/usa-county.tsv as follows:

POLYGON (..., ...)  Cuming County   
POLYGON (..., ...)  Wahkiakum County
POLYGON (..., ...)  De Baca County
POLYGON (..., ...)  Lancaster County

The file may have many other columns.

Use the following code to load the data and create a raw DataFrame:

ScalaJavaPython

var rawDf = sedona.read.format("csv").option("delimiter", "\t").option("header", "false").load("/Download/usa-county.tsv")
rawDf.createOrReplaceTempView("rawdf")
rawDf.show()

Dataset<Row> rawDf = sedona.read.format("csv").option("delimiter", "\t").option("header", "false").load("/Download/usa-county.tsv")
rawDf.createOrReplaceTempView("rawdf")
rawDf.show()

rawDf = sedona.read.format("csv").option("delimiter", "\t").option("header", "false").load("/Download/usa-county.tsv")
rawDf.createOrReplaceTempView("rawdf")
rawDf.show()

The output will be like this:

|                 _c0|_c1|_c2|     _c3|  _c4|        _c5|                 _c6|_c7|_c8|  _c9|_c10| _c11|_c12|_c13|      _c14|    _c15|       _c16|        _c17|
+--------------------+---+---+--------+-----+-----------+--------------------+---+---+-----+----+-----+----+----+----------+--------+-----------+------------+
|POLYGON ((-97.019...| 31|039|00835841|31039|     Cuming|       Cuming County| 06| H1|G4020|null| null|null|   A|1477895811|10447360|+41.9158651|-096.7885168|
|POLYGON ((-123.43...| 53|069|01513275|53069|  Wahkiakum|    Wahkiakum County| 06| H1|G4020|null| null|null|   A| 682138871|61658258|+46.2946377|-123.4244583|
|POLYGON ((-104.56...| 35|011|00933054|35011|    De Baca|      De Baca County| 06| H1|G4020|null| null|null|   A|6015539696|29159492|+34.3592729|-104.3686961|
|POLYGON ((-96.910...| 31|109|00835876|31109|  Lancaster|    Lancaster County| 06| H1|G4020| 339|30700|null|   A|2169240202|22877180|+40.7835474|-096.6886584|

Create a Geometry type column

All geometrical operations in SedonaSQL are on Geometry type objects. Therefore, before any kind of queries, you need to create a Geometry type column on a DataFrame.

SELECT ST_GeomFromWKT(_c0) AS countyshape, _c1, _c2

You can select many other attributes to compose this spatialdDf. The output will be something like this:

|                 countyshape|_c1|_c2|     _c3|  _c4|        _c5|                 _c6|_c7|_c8|  _c9|_c10| _c11|_c12|_c13|      _c14|    _c15|       _c16|        _c17|
+--------------------+---+---+--------+-----+-----------+--------------------+---+---+-----+----+-----+----+----+----------+--------+-----------+------------+
|POLYGON ((-97.019...| 31|039|00835841|31039|     Cuming|       Cuming County| 06| H1|G4020|null| null|null|   A|1477895811|10447360|+41.9158651|-096.7885168|
|POLYGON ((-123.43...| 53|069|01513275|53069|  Wahkiakum|    Wahkiakum County| 06| H1|G4020|null| null|null|   A| 682138871|61658258|+46.2946377|-123.4244583|
|POLYGON ((-104.56...| 35|011|00933054|35011|    De Baca|      De Baca County| 06| H1|G4020|null| null|null|   A|6015539696|29159492|+34.3592729|-104.3686961|
|POLYGON ((-96.910...| 31|109|00835876|31109|  Lancaster|    Lancaster County| 06| H1|G4020| 339|30700|null|   A|2169240202|22877180|+40.7835474|-096.6886584|

Although it looks same with the input, but actually the type of column countyshape has been changed to Geometry type.

To verify this, use the following code to print the schema of the DataFrame:

spatialDf.printSchema()

The output will be like this:

root
 |-- countyshape: geometry (nullable = false)
 |-- _c1: string (nullable = true)
 |-- _c2: string (nullable = true)
 |-- _c3: string (nullable = true)
 |-- _c4: string (nullable = true)
 |-- _c5: string (nullable = true)
 |-- _c6: string (nullable = true)
 |-- _c7: string (nullable = true)

Note

SedonaSQL provides lots of functions to create a Geometry column, please read SedonaSQL constructor API.

Load GeoJSON using Spark JSON Data Source

Spark SQL's built-in JSON data source supports reading GeoJSON data. To ensure proper parsing of the geometry property, we can define a schema with the geometry property set to type 'string'. This prevents Spark from interpreting the property and allows us to use the ST_GeomFromGeoJSON function for accurate geometry parsing.

ScalaJavaPython

val schema = "type string, crs string, totalFeatures long, features array<struct<type string, geometry string, properties map<string, string>>>"
sedona.read.schema(schema).json(geojson_path)
    .selectExpr("explode(features) as features") // Explode the envelope to get one feature per row.
    .select("features.*") // Unpack the features struct.
    .withColumn("geometry", expr("ST_GeomFromGeoJSON(geometry)")) // Convert the geometry string.
    .printSchema()

String schema = "type string, crs string, totalFeatures long, features array<struct<type string, geometry string, properties map<string, string>>>";
sedona.read.schema(schema).json(geojson_path)
    .selectExpr("explode(features) as features") // Explode the envelope to get one feature per row.
    .select("features.*") // Unpack the features struct.
    .withColumn("geometry", expr("ST_GeomFromGeoJSON(geometry)")) // Convert the geometry string.
    .printSchema();

schema = "type string, crs string, totalFeatures long, features array<struct<type string, geometry string, properties map<string, string>>>";
(sedona.read.json(geojson_path, schema=schema) 
    .selectExpr("explode(features) as features") # Explode the envelope to get one feature per row.
    .select("features.*") # Unpack the features struct.
    .withColumn("geometry", f.expr("ST_GeomFromGeoJSON(geometry)")) # Convert the geometry string.
    .printSchema())

Load Shapefile and GeoJSON using SpatialRDD

Shapefile and GeoJSON can be loaded by SpatialRDD and converted to DataFrame using Adapter. Please read Load SpatialRDD and DataFrame <-> RDD.

Load GeoParquet

Since v1.3.0, Sedona natively supports loading GeoParquet file. Sedona will infer geometry fields using the "geo" metadata in GeoParquet files.

Scala/JavaJavaPython

val df = sedona.read.format("geoparquet").load(geoparquetdatalocation1)
df.printSchema()

Dataset<Row> df = sedona.read.format("geoparquet").load(geoparquetdatalocation1)
df.printSchema()

df = sedona.read.format("geoparquet").load(geoparquetdatalocation1)
df.printSchema()

The output will be as follows:

root
 |-- pop_est: long (nullable = true)
 |-- continent: string (nullable = true)
 |-- name: string (nullable = true)
 |-- iso_a3: string (nullable = true)
 |-- gdp_md_est: double (nullable = true)
 |-- geometry: geometry (nullable = true)

Sedona supports spatial predicate push-down for GeoParquet files, please refer to the SedonaSQL query optimizer documentation for details.

Load data from JDBC data sources

The 'query' option in Spark SQL's JDBC data source can be used to convert geometry columns to a format that Sedona can interpret. This should work for most spatial JDBC data sources. For Postgis there is no need to add a query to convert geometry types since it's already using EWKB as it's wire format.

ScalaJavaPython

// For any JDBC data source, including Postgis.
val df = sedona.read.format("jdbc")
    // Other options.
    .option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

// This is a simplified version that works for Postgis.
val df = sedona.read.format("jdbc")
    // Other options.
    .option("dbtable", "my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

// For any JDBC data source, including Postgis.
Dataset<Row> df = sedona.read().format("jdbc")
    // Other options.
    .option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

// This is a simplified version that works for Postgis.
Dataset<Row> df = sedona.read().format("jdbc")
    // Other options.
    .option("dbtable", "my_table")
    .load()
    .withColumn("geom", expr("ST_GeomFromWKB(geom)"))

# For any JDBC data source, including Postgis.
df = (sedona.read.format("jdbc")
    # Other options.
    .option("query", "SELECT id, ST_AsBinary(geom) as geom FROM my_table")
    .load()
    .withColumn("geom", f.expr("ST_GeomFromWKB(geom)")))

# This is a simplified version that works for Postgis.
df = (sedona.read.format("jdbc")
    # Other options.
    .option("dbtable", "my_table")
    .load()
    .withColumn("geom", f.expr("ST_GeomFromWKB(geom)")))

Transform the Coordinate Reference System

Sedona doesn't control the coordinate unit (degree-based or meter-based) of all geometries in a Geometry column. The unit of all related distances in SedonaSQL is same as the unit of all geometries in a Geometry column.

By default, this function uses lon/lat order since v1.5.0. Before, it used lat/lon order. You can use ST_FlipCoordinates to swap X and Y.

For more details, please read the ST_Transform section in Sedona API References.

To convert Coordinate Reference System of the Geometry column created before, use the following code:

SELECT ST_Transform(countyshape, "epsg:4326", "epsg:3857") AS newcountyshape, _c1, _c2, _c3, _c4, _c5, _c6, _c7
FROM spatialdf

The first EPSG code EPSG:4326 in ST_Transform is the source CRS of the geometries. It is WGS84, the most common degree-based CRS.

The second EPSG code EPSG:3857 in ST_Transform is the target CRS of the geometries. It is the most common meter-based CRS.

This ST_Transform transform the CRS of these geometries from EPSG:4326 to EPSG:3857. The details CRS information can be found on EPSG.io

The coordinates of polygons have been changed. The output will be like this:

+--------------------+---+---+--------+-----+-----------+--------------------+---+
|      newcountyshape|_c1|_c2|     _c3|  _c4|        _c5|                 _c6|_c7|
+--------------------+---+---+--------+-----+-----------+--------------------+---+
|POLYGON ((-108001...| 31|039|00835841|31039|     Cuming|       Cuming County| 06|
|POLYGON ((-137408...| 53|069|01513275|53069|  Wahkiakum|    Wahkiakum County| 06|
|POLYGON ((-116403...| 35|011|00933054|35011|    De Baca|      De Baca County| 06|
|POLYGON ((-107880...| 31|109|00835876|31109|  Lancaster|    Lancaster County| 06|

Run spatial queries

After creating a Geometry type column, you are able to run spatial queries.

Range query

Use ST_Contains, ST_Intersects, ST_Within to run a range query over a single column.

The following example finds all counties that are within the given polygon:

SELECT *
FROM spatialdf
WHERE ST_Contains (ST_PolygonFromEnvelope(1.0,100.0,1000.0,1100.0), newcountyshape)

Note

Read SedonaSQL constructor API to learn how to create a Geometry type query window

KNN query

Use ST_Distance to calculate the distance and rank the distance.

The following code returns the 5 nearest neighbor of the given polygon.

SELECT countyname, ST_Distance(ST_PolygonFromEnvelope(1.0,100.0,1000.0,1100.0), newcountyshape) AS distance
FROM spatialdf
ORDER BY distance DESC
LIMIT 5

Join query

The details of a join query is available here Join query.

Other queries

There are lots of other functions can be combined with these queries. Please read SedonaSQL functions and SedonaSQL aggregate functions.

Visualize query results

Sedona provides SedonaPyDeck and SedonaKepler wrappers, both of which expose APIs to create interactive map visualizations from SedonaDataFrames in a Jupyter environment.

Note

Both SedonaPyDeck and SedonaKepler expect the default geometry order to be lon-lat. If your dataframe has geometries in the lat-lon order, please check out ST_FlipCoordinates

Note

Both SedonaPyDeck and SedonaKepler are designed to work with SedonaDataFrames containing only 1 geometry column. Passing dataframes with multiple geometry columns will cause errors.

SedonaPyDeck

Spatial query results can be visualized in a Jupyter lab/notebook environment using SedonaPyDeck.

SedonaPyDeck exposes APIs to create interactive map visualizations using pydeck based on deck.gl

Note

To use SedonaPyDeck, GeoPandas and PyDeck must be installed. We recommend the following installation commands:

pip install 'pandas<=1.3.5'
pip install 'geopandas<=0.10.2'
pip install pydeck==0.8.0

The following tutorial showcases the various maps that can be created using SedonaPyDeck, the datasets used to create these maps are publicly available.

Each API exposed by SedonaPyDeck offers customization via optional arguments, details on all possible arguments can be found in the API docs of SedonaPyDeck.

Creating a Choropleth map using SedonaPyDeck

SedonaPyDeck exposes a create_choropleth_map API which can be used to visualize a choropleth map out of the passed SedonaDataFrame containing polygons with an observation:

Example (referenced from example notebook available via binder):

SedonaPyDeck.create_choropleth_map(df=groupedresult, plot_col='AirportCount')

Note

plot_col is a required argument informing SedonaPyDeck of the column name used to render the choropleth effect.

The dataset used is available here and can also be found in the example notebook available here

Creating a Geometry map using SedonaPyDeck

SedonaPyDeck exposes a create_geometry_map API which can be used to visualize a passed SedonaDataFrame containing any type of geometries:

Example (referenced from overture notebook available via binder):

SedonaPyDeck.create_geometry_map(df_building, elevation_col='height')

Tip

elevation_col is an optional argument which can be used to render a 3D map. Pass the column with 'elevation' values for the geometries here.

Creating a Scatterplot map using SedonaPyDeck

SedonaPyDeck exposes a create_scatterplot_map API which can be used to visualize a scatterplot out of the passed SedonaDataFrame containing points:

Example:

SedonaPyDeck.create_scatterplot_map(df=crimes_df)

The dataset used here is the Chicago crimes dataset, available here

Creating a heatmap using SedonaPyDeck

SedonaPyDeck exposes a create_heatmap API which can be used to visualize a heatmap out of the passed SedonaDataFrame containing points:

Example:

SedonaPyDeck.create_heatmap(df=crimes_df)

The dataset used here is the Chicago crimes dataset, available here

SedonaKepler

Spatial query results can be visualized in a Jupyter lab/notebook environment using SedonaKepler.

SedonaKepler exposes APIs to create interactive and customizable map visualizations using KeplerGl.

Note

To use SedonaKepler, GeoPandas and KeplerGL must be installed. We recommend the following installation commands:

pip install 'pandas<=1.3.5'
pip install 'geopandas<=0.10.2'
pip install keplergl==0.3.2

This tutorial showcases how simple it is to instantly visualize geospatial data using SedonaKepler.

Example (referenced from an example notebook via the binder):

SedonaKepler.create_map(df=groupedresult, name="AirportCount")

The dataset used is available here and can also be found in the example notebook available here

Details on all the APIs available by SedonaKepler are listed in the SedonaKepler API docs

Save to permanent storage

To save a Spatial DataFrame to some permanent storage such as Hive tables and HDFS, you can simply convert each geometry in the Geometry type column back to a plain String and save the plain DataFrame to wherever you want.

Use the following code to convert the Geometry column in a DataFrame back to a WKT string column:

SELECT ST_AsText(countyshape)
FROM polygondf

Note

ST_AsGeoJSON is also available. We would like to invite you to contribute more functions

Save GeoParquet

Since v1.3.0, Sedona natively supports writing GeoParquet file. GeoParquet can be saved as follows:

df.write.format("geoparquet").save(geoparquetoutputlocation + "/GeoParquet_File_Name.parquet")

Sort then Save GeoParquet

To maximize the performance of Sedona GeoParquet filter pushdown, we suggest that you sort the data by their geohash values (see ST_GeoHash) and then save as a GeoParquet file. An example is as follows:

SELECT col1, col2, geom, ST_GeoHash(geom, 5) as geohash
FROM spatialDf
ORDER BY geohash

Save to Postgis

Unfortunately, the Spark SQL JDBC data source doesn't support creating geometry types in PostGIS using the 'createTableColumnTypes' option. Only the Spark built-in types are recognized. This means that you'll need to manage your PostGIS schema separately from Spark. One way to do this is to create the table with the correct geometry column before writing data to it with Spark. Alternatively, you can write your data to the table using Spark and then manually alter the column to be a geometry type afterward.

Postgis uses EWKB to serialize geometries. If you convert your geometries to EWKB format in Sedona you don't have to do any additional conversion in Postgis.

my_postgis_db# create table my_table (id int8, geom geometry);

df.withColumn("geom", expr("ST_AsEWKB(geom)")
    .write.format("jdbc")
    .option("truncate","true") // Don't let Spark recreate the table.
    // Other options.
    .save()

// If you didn't create the table before writing you can change the type afterward.
my_postgis_db# alter table my_table alter column geom type geometry;

Convert between DataFrame and SpatialRDD

DataFrame to SpatialRDD

Use SedonaSQL DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD. Please read Adapter Scaladoc

ScalaJavaPython

var spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

SpatialRDD spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

from sedona.utils.adapter import Adapter

spatialRDD = Adapter.toSpatialRdd(spatialDf, "usacounty")

"usacounty" is the name of the geometry column

Warning

Only one Geometry type column is allowed per DataFrame.

SpatialRDD to DataFrame

Use SedonaSQL DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD. Please read Adapter Scaladoc

ScalaJavaPython

var spatialDf = Adapter.toDf(spatialRDD, sedona)

Dataset<Row> spatialDf = Adapter.toDf(spatialRDD, sedona)

from sedona.utils.adapter import Adapter

spatialDf = Adapter.toDf(spatialRDD, sedona)

All other attributes such as price and age will be also brought to the DataFrame as long as you specify carryOtherAttributes (see Read other attributes in an SpatialRDD).

You may also manually specify a schema for the resulting DataFrame in case you require different column names or data types. Note that string schemas and not all data types are supported—please check the Adapter Scaladoc to confirm what is supported for your use case. At least one column for the user data must be provided.

Scala

val schema = StructType(Array(
  StructField("county", GeometryUDT, nullable = true),
  StructField("name", StringType, nullable = true),
  StructField("price", DoubleType, nullable = true),
  StructField("age", IntegerType, nullable = true)
))
val spatialDf = Adapter.toDf(spatialRDD, schema, sedona)

SpatialPairRDD to DataFrame

PairRDD is the result of a spatial join query or distance join query. SedonaSQL DataFrame-RDD Adapter can convert the result to a DataFrame. But you need to provide the name of other attributes.

ScalaJavaPython

var joinResultDf = Adapter.toDf(joinResultPairRDD, Seq("left_attribute1", "left_attribute2"), Seq("right_attribute1", "right_attribute2"), sedona)

import scala.collection.JavaConverters; 

List leftFields = new ArrayList<>(Arrays.asList("c1", "c2", "c3"));
List rightFields = new ArrayList<>(Arrays.asList("c4", "c5", "c6"));
Dataset joinResultDf = Adapter.toDf(joinResultPairRDD, JavaConverters.asScalaBuffer(leftFields).toSeq(), JavaConverters.asScalaBuffer(rightFields).toSeq(), sedona);

from sedona.utils.adapter import Adapter

joinResultDf = Adapter.toDf(jvm_sedona_rdd, ["poi_from_id", "poi_from_name"], ["poi_to_id", "poi_to_name"], spark))

or you can use the attribute names directly from the input RDD

ScalaJavaPython

import scala.collection.JavaConversions._
var joinResultDf = Adapter.toDf(joinResultPairRDD, leftRdd.fieldNames, rightRdd.fieldNames, sedona)

import scala.collection.JavaConverters; 
Dataset joinResultDf = Adapter.toDf(joinResultPairRDD, JavaConverters.asScalaBuffer(leftRdd.fieldNames).toSeq(), JavaConverters.asScalaBuffer(rightRdd.fieldNames).toSeq(), sedona);

from sedona.utils.adapter import Adapter

joinResultDf = Adapter.toDf(result_pair_rdd, leftRdd.fieldNames, rightRdd.fieldNames, spark)

All other attributes such as price and age will be also brought to the DataFrame as long as you specify carryOtherAttributes (see Read other attributes in an SpatialRDD).

You may also manually specify a schema for the resulting DataFrame in case you require different column names or data types. Note that string schemas and not all data types are supported—please check the Adapter Scaladoc to confirm what is supported for your use case. Columns for the left and right user data must be provided.

Scala

val schema = StructType(Array(
  StructField("leftGeometry", GeometryUDT, nullable = true),
  StructField("name", StringType, nullable = true),
  StructField("price", DoubleType, nullable = true),
  StructField("age", IntegerType, nullable = true),
  StructField("rightGeometry", GeometryUDT, nullable = true),
  StructField("category", StringType, nullable = true)
))
val joinResultDf = Adapter.toDf(joinResultPairRDD, schema, sedona)

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Last update: November 8, 2023 05:57:21