### Install SedonaDB via pip and connect

Source: https://sedona.apache.org/latest/blog/2026/06/19/sedonadb-040-release

Install SedonaDB using pip and establish a connection with interactive mode enabled. This is the primary way to get started with the Python API.

```python
# pip install --upgrade "apache-sedona[db]"
import sedona.db

sd = sedona.db.connect()
sd.options.interactive = True
```

--------------------------------

### Run Sedona container with default memory (Example 2)

Source: https://sedona.apache.org/latest/setup/docker?q=

Starts a container with default 4GB driver and executor memory using the 1.9.1 image.

```bash
docker run -d -e -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 apache/sedona:1.9.1
```

--------------------------------

### Run Sedona container with custom memory (Example 1)

Source: https://sedona.apache.org/latest/setup/docker?q=

Starts a container with 6GB driver and 8GB executor memory using the latest image, in detached mode.

```bash
docker run -d -e DRIVER_MEM=6g -e EXECUTOR_MEM=8g -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 apache/sedona:latest
```

--------------------------------

### SQL Example: ParseAddress with Output

Source: https://sedona.apache.org/latest/api/sql/Address-Functions/ParseAddress?q=

This example demonstrates the ParseAddress SQL function, returning an array of label-value pairs for the input address. The output shows the parsed components such as house number, road, city, state, and postal code. Requires at least 2 GB of free disk space for libpostal data, and on Windows you must manually install libpostal and libjpostal.

```sql
SELECT ParseAddress("100 W 1st St, Los Angeles, CA 90012");

```

```text
[{house_number, 100}, {road, w 1st st}, {city, los angeles}, {state, ca}, {postcode, 90012}]

```

--------------------------------

### Example Glue notebook first cell with Sedona config

Source: https://sedona.apache.org/latest/setup/glue

If using the example notebook from Glue, the first cell should look like this, including the Sedona config and the standard Glue imports and context setup.

```python
%idle_timeout 2880
%glue_version 5.0
%worker_type G.1X
%number_of_workers 5

# Sedona Config
%extra_jars https://repo1.maven.org/maven2/org/apache/sedona/sedona-spark-shaded-3.5_2.12/1.9.1/sedona-spark-shaded-3.5_2.12-1.9.1.jar, https://repo1.maven.org/maven2/org/datasyslab/geotools-wrapper/1.9.1-33.5/geotools-wrapper-1.9.1-33.5.jar
%additional_python_modules apache-sedona==1.9.1


import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job

sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
```

--------------------------------

### Run Sedona container with persistent /opt volume (Example 3)

Source: https://sedona.apache.org/latest/setup/docker?q=

Starts a container with a Docker volume mounted at /opt to persist Jupyter and Zeppelin data.

```bash
docker run -d -e DRIVER_MEM=6g -e EXECUTOR_MEM=8g \
    -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 \
    -v sedona_opt:/opt \
    apache/sedona:latest
```

--------------------------------

### Run Sedona container with default memory (Example 2)

Source: https://sedona.apache.org/latest/setup/docker

Starts a container with default 4GB driver and executor memory using the Sedona 1.9.1 image. Note: the command includes an extra '-e' flag before the port mappings.

```bash
docker run -d -e -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 apache/sedona:1.9.1

```

--------------------------------

### Run Sedona container with custom memory (Example 1)

Source: https://sedona.apache.org/latest/setup/docker

Starts a container with 6GB driver memory and 8GB executor memory using the latest image. Runs in detached mode and binds the specified ports.

```bash
docker run -d -e DRIVER_MEM=6g -e EXECUTOR_MEM=8g -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 apache/sedona:latest

```

--------------------------------

### ST_X example output

Source: https://sedona.apache.org/latest/api/flink/Geometry-Accessors/ST_X?q=

The output of the example query is 0.0.

```text
0.0
```

--------------------------------

### Run Sedona container with persistent /opt volume (Example 3)

Source: https://sedona.apache.org/latest/setup/docker

Starts a container with custom memory and mounts a Docker volume named 'sedona_opt' to /opt to persist Jupyter and Zeppelin data.

```bash
docker run -d -e DRIVER_MEM=6g -e EXECUTOR_MEM=8g \
    -p 8888:8888 -p 8080:8080 -p 8081:8081 -p 4040:4040 -p 8085:8085 \
    -v sedona_opt:/opt \
    apache/sedona:latest

```

--------------------------------

### ST_BingTileAt example output

Source: https://sedona.apache.org/latest/api/flink/Spatial-Indexing/ST_BingTileAt?q=

The output of the example query is the quadkey string '123030123010121'.

```text
123030123010121

```

--------------------------------

### Example for creating GeoDataFrame with Point and LineString

Source: https://sedona.apache.org/latest/api/pydocs/_modules/sedona/spark/geopandas/geodataframe.html

Shows the initial imports and GeoDataFrame creation for a second example, using Point and LineString geometries. This is the beginning of an example that is not fully shown on the page.

```python
>>> from shapely.geometry import Point, LineString
>>> from sedona.spark.geopandas import GeoDataFrame
```

--------------------------------

### Python: Raster width query with RS_Width and RS_Example

Source: https://sedona.apache.org/latest/blog/2025/12/01/sedonadb-020-release

Shows how to connect to SedonaDB and run a SQL query to get the width of the example raster. The output displays a table with a single row and column, showing the width value 64 as uint64.

```python
import sedona.db

sd = sedona.db.connect()

sd.sql("SELECT RS_Width(RS_Example()) as width").show()
# > ┌────────┐
# > │  width │
# > │ uint64 │
# > ╞════════╡
# > │     64 │
# > └────────┘
```

--------------------------------

### with_basic_auth example usage

Source: https://sedona.apache.org/latest/api/pydocs/_modules/sedona/spark/stac/client.html

Shows example usage of the with_basic_auth method, including standard basic auth, API key as username, and method chaining. These examples are from the docstring of the with_basic_auth method.

```python
# Standard basic auth
client = Client.open("https://example.com/stac/v1")
client.with_basic_auth("user", "pass")

# API key as username (common pattern)
client.with_basic_auth("api_key_xyz", "")

# Method chaining
df = Client.open(url).with_basic_auth(api_key, "").search(collection_id="test")
```

--------------------------------

### Build and serve documentation via Makefile

Source: https://sedona.apache.org/latest/setup/compile

Use the provided Makefile to automatically set up the environment, sync dependencies, build assets, and compile the documentation.

```bash
make docsbuild

```

--------------------------------

### GeoSeries.distance example setup

Source: https://sedona.apache.org/latest/api/pydocs/sedona.spark.geopandas.api.html

Defines the GeoSeries s and s2 used in the distance examples, including imports and index specification.

```python
>>> from sedona.spark.geopandas import GeoSeries
>>> from shapely.geometry import Polygon, LineString, Point
>>> s = GeoSeries(
...     [
...         Polygon([(0, 0), (1, 0), (1, 1)]),
...         Polygon([(0, 0), (-1, 0), (-1, 1)]),
...         LineString([(1, 1), (0, 0)]),
...         Point(0, 0),
...     ],
... )
>>> s2 = GeoSeries(
...     [
...         Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]),
...         Point(3, 1),
...         LineString([(1, 0), (2, 0)]),
...         Point(0, 1),
...     ],
...     index=range(1, 5),
... )
```

--------------------------------

### SQL ST_Buffer point examples

Source: https://sedona.apache.org/latest/api/flink/Geometry-Processing/ST_Buffer?q=

SQL examples demonstrating planar buffer with default and custom quad_segs style parameter.

```sql
SELECT ST_Buffer(ST_GeomFromWKT('POINT(0 0)'), 10)

```

```sql
SELECT ST_Buffer(ST_GeomFromWKT('POINT(0 0)'), 10, false, 'quad_segs=2')

```

--------------------------------

### onApplicationStart

Source: https://sedona.apache.org/latest/api/scaladoc/spark/org/apache/sedona/core/monitoring/Listener.html

Callback invoked when the application starts.

```APIDOC
## onApplicationStart

### Description
Called when the application starts.

### Method
`def onApplicationStart(applicationStart: SparkListenerApplicationStart): Unit`

### Parameters
- **applicationStart** (SparkListenerApplicationStart) - Required - The event containing application start information.

### Returns
`Unit`
```

--------------------------------

### ST_YMin example query

Source: https://sedona.apache.org/latest/api/flink/Bounding-Box-Functions/ST_YMin

Returns the minimum Y coordinate of the given geometry. The example uses ST_GeomFromText to create a polygon and ST_YMin to get its minimum Y value.

```sql
SELECT ST_YMin(ST_GeomFromText('POLYGON((0 0 1, 1 1 1, 1 2 1, 1 1 1, 0 0 1))'))

```

--------------------------------

### Install ipykernel and enter Pipenv shell

Source: https://sedona.apache.org/latest/tutorial/jupyter-notebook

Installs the ipykernel package and activates the Pipenv shell, preparing the environment for Jupyter notebook kernel setup.

```bash
pipenv install ipykernel
pipenv shell
```

--------------------------------

### RS_CRS WKT2 format

Source: https://sedona.apache.org/latest/api/sql/Raster-Operators/RS_CRS

Example of getting the CRS in WKT2 format.

```sql
SELECT RS_CRS(raster, 'wkt2') FROM raster_table
```

--------------------------------

### Initialize SedonaContext in notebook

Source: https://sedona.apache.org/latest/setup/azure-synapse-analytics?q=

Starts a Spark session with Sedona dependencies and required configurations. Must be run at the beginning of the notebook.

```python
from sedona.spark import SedonaContext

config = (
    SedonaContext.builder()
    .config(
        "spark.jars.packages",
        "org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,"
        "org.datasyslab:geotools-wrapper-1.6.1-28.2",
    )
    .config("spark.serializer", "org.apache.sedona.core.serde.SedonaKryoRegistrator")
    .config(
        "spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator"
    )
    .config(
        "spark.sql.extensions",
        "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions",
    )
    .getOrCreate()
)

sedona = SedonaContext.create(config)
```

--------------------------------

### ST_AsBinary example

Source: https://sedona.apache.org/latest/api/flink/Geometry-Output/ST_AsBinary

Converts a WKT geometry to its binary representation. The example uses ST_GeomFromWKT to create a point geometry and then applies ST_AsBinary to get the WKB output.

```sql
SELECT ST_AsBinary(ST_GeomFromWKT('POINT (1 1)'))
```

```text
0101000000000000000000f87f000000000000f87f
```

--------------------------------

### Setup Pipenv Python version

Source: https://sedona.apache.org/latest/tutorial/jupyter-notebook?q=

Navigate to the docs/usecases directory and initialize a Pipenv environment with Python 3.8.

```bash
cd docs/usecases
pipenv --python 3.8

```

--------------------------------

### Example output of downloaded packages

Source: https://sedona.apache.org/latest/setup/azure-synapse-analytics

Example output showing the packages that pip downloaded, including apache_sedona, shapely, rasterio, affine, cligj, and click_plugins.

```text
Downloading apache_sedona-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (177 kB)
Downloading shapely-2.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.5 MB)
Downloading rasterio-1.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (22.2 MB)
Downloading affine-2.4.0-py3-none-any.whl (15 kB)
Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)
Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)
```

--------------------------------

### ST_YMax example query

Source: https://sedona.apache.org/latest/api/flink/Bounding-Box-Functions/ST_YMax?q=

Returns the maximum Y coordinate of the given polygon geometry. The example uses ST_GeomFromText to create a polygon and ST_YMax to get its maximum Y coordinate.

```sql
SELECT ST_YMax(ST_GeomFromText('POLYGON((0 0 1, 1 1 1, 1 2 1, 1 1 1, 0 0 1))'))

```

```text
2

```

--------------------------------

### Example: Using approx_count with a shapefile

Source: https://sedona.apache.org/latest/api/rdocs/reference/approx_count.html

Complete example that loads a shapefile into a typed RDD and calls `approx_count` to get the approximate record count. The example connects to Spark, reads a shapefile as polygons, and computes the approximate count.

```r
library(sparklyr)
#> 
#> Attaching package: ‘sparklyr’
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
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 <- sedona_read_shapefile_to_typed_rdd(
    sc,
    location = input_location, type = "polygon"
  )
  approx_cnt <- approx_count(rdd)
}
```

--------------------------------

### Build assets and serve documentation with mike

Source: https://sedona.apache.org/latest/setup/compile

After installing MkDocs and MkDocs-Material, run these commands in the Sedona root folder to build the docs-overrides assets and serve the website locally using mike.

```bash
cd docs-overrides && npm ci && npx gulp build
cd ..
uv run mike deploy --update-aliases latest-snapshot -b website -p
uv run mike serve -b website

```

--------------------------------

### Initialize SedonaContext with configuration

Source: https://sedona.apache.org/latest/setup/azure-synapse-analytics

Starts a SedonaContext with required Spark configurations for Sedona 1.6.1, including the shaded jar, geotools wrapper, Kryo serializer, SedonaViz/SedonaSql extensions, and creates the Sedona instance.

```python
from sedona.spark import SedonaContext

config = (
    SedonaContext.builder()
    .config(
        "spark.jars.packages",
        "org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,"
        "org.datasyslab:geotools-wrapper-1.6.1-28.2",
    )
    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    .config(
        "spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator"
    )
    .config(
        "spark.sql.extensions",
        "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions",
    )
    .getOrCreate()
)

sedona = SedonaContext.create(config)
```

--------------------------------

### SQL Example: ST_MMin

Source: https://sedona.apache.org/latest/api/flink/Bounding-Box-Functions/ST_MMin?q=

Returns the minimum M coordinate of the given geometry. The example uses ST_GeomFromWKT to create a LINESTRING ZM and then ST_MMin to get the minimum M value, which is -1.0.

```sql
SELECT ST_MMin(
        ST_GeomFromWKT('LINESTRING ZM(1 1 1 1, 2 2 2 2, 3 3 3 3, -1 -1 -1 -1)')
)

```

--------------------------------

### Output of UDF bufferLength example

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Shows the output of the UDF example, displaying the original geometry, distance, buffered geometry, and length columns.

```text
+------------------------------+--------+--------------------------------------------------+-----------------+
|                          geom|distance|                                        buffedGeom|           length|
+------------------------------+--------+--------------------------------------------------+-----------------+
|POLYGON ((1 1, 1 2, 2 1, 1 1))|    10.0|POLYGON ((1 -9, -0.9509032201612866 -8.80785280...|66.14518337329191|
+------------------------------+--------+--------------------------------------------------+-----------------+
```

--------------------------------

### RS_CRS WKT1 format

Source: https://sedona.apache.org/latest/api/sql/Raster-Operators/RS_CRS

Example of getting the CRS in WKT1 format, with sample output.

```sql
SELECT RS_CRS(raster, 'wkt1') FROM raster_table
```

```text
GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]
```

--------------------------------

### Install and connect to SedonaDB with Python

Source: https://sedona.apache.org/latest/blog/2026/06/19/sedonadb-040-release?q=

Install the apache-sedona package with the db extra using pip, then import sedona.db and create a connection. Enabling interactive mode is shown.

```python
# pip install --upgrade "apache-sedona[db]"
import sedona.db

sd = sedona.db.connect()
sd.options.interactive = True

```

--------------------------------

### Install SedonaPyDeck with pydeck-map extra

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Installs apache-sedona with the 'pydeck-map' extra to enable SedonaPyDeck functionality. This is required before using SedonaPyDeck APIs.

```bash
pip install apache-sedona[pydeck-map]

```

--------------------------------

### Example OSM PBF DataFrame for ways

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Example output of the DataFrame for ways, showing columns id, kind, location, tags, refs, ref_roles, and ref_types.

```text
+-------+----+--------+--------------------+--------------------+---------+---------+
|     id|kind|location|                tags|                refs|ref_roles|ref_types|
+-------+----+--------+--------------------+--------------------+---------+---------+
|4307329| way|    NULL|{junction -> roun...|[2448759046, 7093...|     NULL|     NULL|
|4307330| way|    NULL|{surface -> aspha...|[26063923, 260639...|     NULL|     NULL|
|4308966| way|    NULL|{sidewalk -> sepa...|[3387797238, 9252...|     NULL|     NULL|
|4308968| way|    NULL|{surface -> pavin...|[26083890, 744724...|     NULL|     NULL|
|4308969| way|    NULL|{cycleway:both ->...|[9526831176, 1218...|     NULL|     NULL|
+-------+----+--------+--------------------+--------------------+---------+---------+
```

--------------------------------

### GeoSeries.is_valid_reason example

Source: https://sedona.apache.org/latest/api/pydocs/sedona.spark.geopandas.api.html

Shows how to get the reason for invalidity of each geometry, including for missing geometries.

```python
>>> from sedona.spark.geopandas import GeoSeries
>>> from shapely.geometry import Polygon
>>> s = GeoSeries(
...     [
...         Polygon([(0, 0), (1, 1), (0, 1)]),
...         Polygon([(0,0), (1, 1), (1, 0), (0, 1)]),  # bowtie geometry
...         Polygon([(0, 0), (2, 2), (2, 0)]),
...         Polygon([(0, 0), (2, 0), (1, 1), (2, 2), (0, 2), (1, 1), (0, 0)]),
...         None
...     ]
... )
>>> s
0         POLYGON ((0 0, 1 1, 0 1, 0 0))
1    POLYGON ((0 0, 1 1, 1 0, 0 1, 0 0))
2         POLYGON ((0 0, 2 2, 2 0, 0 0))
3                                   None
dtype: geometry
```

```python
>>> s.is_valid_reason()
0    Valid Geometry
1    Self-intersection at or near point (0.5, 0.5, NaN)
2    Valid Geometry
3    Ring Self-intersection at or near point (1.0, 1.0)
4    None
dtype: object
```

--------------------------------

### Client.open with basic auth and method chaining

Source: https://sedona.apache.org/latest/api/pydocs/_modules/sedona/spark/stac/client.html

Shows example usage of Client.open with and without authentication, including basic auth and method chaining. The examples are from the docstring of the open method.

```python
# Without authentication
client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")

# With custom headers
client = Client.open(
    "https://example.com/stac/v1",
    headers={"Authorization": "Bearer token123"}
)

# Using convenience methods
client = Client.open("https://example.com/stac/v1")
client.with_basic_auth("username", "password")
```

--------------------------------

### Example OSM PBF DataFrame for nodes

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Example output of the DataFrame for nodes, showing columns id, kind, location, tags, refs, ref_roles, and ref_types.

```text
+---------+----+--------------------+--------------------+----+---------+---------+
|       id|kind|            location|                tags|refs|ref_roles|ref_types|
+---------+----+--------------------+--------------------+----+---------+---------+
|248675410|node|{21.0884952545166...|{tactile_paving -...|NULL|     NULL|     NULL|
|260821820|node|{21.0195023191553...|{created_by -> JOSM}|NULL|     NULL|     NULL|
|349665123|node|{22.1437530517578...|{source -> http:/...|NULL|     NULL|     NULL|
|353366899|node|{22.9787712097167...|{source -> http:/...|NULL|     NULL|     NULL|
|359460224|node|{22.4816703796386...|{source -> http:/...|NULL|     NULL|     NULL|
+---------+----+--------------------+--------------------+----+---------+---------+
only showing top 5 rows
```

--------------------------------

### def init(configuration: Configuration): WriteContext

Source: https://sedona.apache.org/latest/api/scaladoc/spark/org/apache/spark/sql/execution/datasources/geoparquet/GeoParquetWriteSupport.html

Initializes the write support with the given configuration and returns a WriteContext.

```APIDOC
## def init(configuration: Configuration): WriteContext

### Description
Initializes the write support with the given configuration and returns a WriteContext.

### Parameters
- **configuration** (Configuration) - Required - The configuration for the write operation.

### Returns
- **WriteContext** - The write context for the operation.
```

--------------------------------

### ST_EndPoint example

Source: https://sedona.apache.org/latest/api/flink/Geometry-Accessors/ST_EndPoint

Returns the last point of the linestring geometry. The example uses ST_GeomFromText to create a linestring and ST_EndPoint to get its last point, producing the point POINT(160 170).

```sql
SELECT ST_EndPoint(ST_GeomFromText('LINESTRING(100 150,50 60, 70 80, 160 170)'))
```

```text
POINT(160 170)
```

--------------------------------

### Sample output of gLocal

Source: https://sedona.apache.org/latest/tutorial/sql

Example output table showing the resulting columns: geometry, val, weights, G, EG, VG, Z, and P for each point.

```text
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+
|   geometry|val|             weights|                  G|                 EG|                  VG|                   Z|                   P|
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+
|POINT (2 2)|0.9|[{{POINT (2 3), 1...| 0.4488188976377953|0.45454545454545453| 0.00356321373799772|-0.09593402008347063|  0.4617864875295957|
|POINT (2 3)|1.2|[{{POINT (2 2), 0...|0.35433070866141736|0.36363636363636365|0.003325666155464539|-0.16136436037034918|  0.4359032175415549|
|POINT (3 3)|1.2|[{{POINT (2 3), 1...|0.28346456692913385| 0.2727272727272727|0.002850570990398176| 0.20110780337013057| 0.42030714022155924|
|POINT (3 2)|1.2|[{{POINT (2 2), 0...| 0.4488188976377953|0.45454545454545453| 0.00356321373799772|-0.09593402008347063|  0.4617864875295957|
|POINT (3 1)|1.2|[{{POINT (3 2), 3...| 0.3622047244094489| 0.2727272727272727|0.002850570990398176|  1.6758983614177538| 0.04687905137429871|
|POINT (2 1)|2.2|[{{POINT (2 2), 0...| 0.4330708661417323|0.36363636363636365|0.003325666155464539|  1.2040263812249166| 0.11428969105925013|
|POINT (1 1)|1.2|[{{POINT (2 1), 5...| 0.2834645669291339| 0.2727272727272727|0.002850570990398176|  0.20110780337013057| 0.42030714022155924|
|POINT (1 2)|0.2|[{{POINT (2 2), 0...|0.35433070866141736|0.45454545454545453| 0.00356321373799772|   -1.67884535146075|0.046591093685710794|
|POINT (1 3)|1.2|[{{POINT (2 3), 1...| 0.2047244094488189| 0.2727272727272727|0.002850570990398176| -1.2736827546774914| 0.10138793530151635|
|POINT (0 2)|1.0|[{{POINT (1 2), 7...|0.09448818897637795|0.18181818181818182|0.002137928242798632| -1.8887168824332323|0.029464887612748458|
|POINT (4 2)|1.2|[{{POINT (3 2), 3...| 0.1889763779527559|0.18181818181818182|0.002137928242798632| 0.15481285921583854| 0.43848442662481324|
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+

```

--------------------------------

### Example OSM PBF DataFrame for relations

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Example output of the DataFrame for relations, showing columns id, kind, location, tags, refs, ref_roles, and ref_types.

```text
+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
|   id|    kind|location|                tags|                refs|           ref_roles|           ref_types|
+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
|28124|relation|    NULL|{official_name ->...|[26382394, 26259985]|      [inner, outer]|          [WAY, WAY]|
|28488|relation|    NULL|  {type -> junction}|[26409253, 303249...|[roundabout, roun...|[WAY, WAY, WAY, WAY]|
|32939|relation|    NULL|{ref -> E 67, rou...|[140673970, 14067...|        [, , , , , ]|[WAY, WAY, RELATI...|
|34387|relation|    NULL|{note -> rząd III...|[209161000, 52154...|[main_stream, mai...|[WAY, WAY, WAY, W...|
|34392|relation|    NULL|{distance -> 1047...|[150033976, 25076...|[main_stream, mai...|[WAY, WAY, WAY, W...|
+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
```

--------------------------------

### ST_XMax example with ST_GeomFromText

Source: https://sedona.apache.org/latest/api/flink/Bounding-Box-Functions/ST_XMax

Returns the maximum X coordinate of a polygon geometry. The example uses ST_GeomFromText to create a polygon and then applies ST_XMax to get the maximum X coordinate, which is 2.

```sql
SELECT ST_XMax(ST_GeomFromText('POLYGON ((-1 -11, 0 10, 1 11, 2 12, -1 -11))'))

```

```text
2

```

--------------------------------

### def init(configuration: Configuration): WriteContext

Source: https://sedona.apache.org/latest/api/scaladoc/spark/org/apache/spark/sql/execution/datasources/geoparquet/GeoParquetWriteSupport.html

Initializes the write support with the given configuration and returns a WriteContext.

```APIDOC
## def init(configuration: Configuration): WriteContext

### Description
Initializes the write support with the given configuration and returns a WriteContext.

### Method
init

### Signature
```scala
def init(configuration: Configuration): WriteContext
```

### Parameters
- **configuration** (Configuration) - Required - The configuration for the write operation.

### Returns
- **WriteContext** - The write context for the operation.

### Definition Classes
GeoParquetWriteSupport → WriteSupport
```

--------------------------------

### Create map using SedonaKepler

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Creates an interactive map visualization using SedonaKepler's create_map API. The example passes a grouped SedonaDataFrame and a name for the map.

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

```

--------------------------------

### GeoSeries UDF example

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Example of a vectorized UDF that takes a GeoSeries and returns a buffered GeoSeries. Use this when you need to operate on entire GeoSeries for better performance.

```python
import geopandas as gpd
from sedona.spark import sedona_vectorized_udf, SedonaUDFType
from sedona.spark import GeometryType


@sedona_vectorized_udf(udf_type=SedonaUDFType.GEO_SERIES, return_type=GeometryType())
def vectorized_geo_series_buffer(series: gpd.GeoSeries) -> gpd.GeoSeries:
    buffered = series.buffer(0.1)

    return buffered
```

--------------------------------

### main(String[] args)

Source: https://sedona.apache.org/latest/api/javadoc/spark/org/apache/sedona/core/showcase/EarthdataMapperRunnableExample.html

The main method that serves as the entry point for the example.

```APIDOC
## main(String[] args)

### Description
The main method that serves as the entry point for the example.

### Method
main

### Parameters
- **args** (String[]) - Required - The arguments

### Returns
void
```

--------------------------------

### ST_Envelope example query

Source: https://sedona.apache.org/latest/api/flink/Bounding-Box-Functions/ST_Envelope

Returns the envelope boundary of the input geometry. The example uses ST_GeomFromWKT to create a LINESTRING and ST_Envelope to get its bounding box. The output is a POLYGON representing the envelope.

```sql
SELECT ST_Envelope(ST_GeomFromWKT('LINESTRING(0 0, 1 3)'))
```

--------------------------------

### Java: Load JDBC data with query and dbtable options

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Two Java examples for loading spatial data from JDBC. The first uses the 'query' option with ST_AsBinary for any JDBC source. The second uses 'dbtable' for PostGIS. Both convert the geometry column using ST_GeomFromWKB.

```java
// 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)"))

```

--------------------------------

### DBSCAN output example

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Sample output from the DBSCAN function showing the geometry, id, isCore flag, and cluster assignment. Points with cluster -1 are noise.

```text
+----------------+---+------+-------+
|        geometry| id|isCore|cluster|
+----------------+---+------+-------+
|   POINT (2.5 4)|  3| false|      1|
|     POINT (3 4)|  2| false|      1|
|     POINT (3 5)|  5| false|      1|
|     POINT (1 3)|  9|  true|      0|
| POINT (2.5 4.5)|  7|  true|      1|
|     POINT (1 2)|  1|  true|      0|
| POINT (1.5 2.5)|  4|  true|      0|
| POINT (1.2 2.5)|  8|  true|      0|
|   POINT (1 2.5)| 11|  true|      0|
|     POINT (1 5)| 10| false|     -1|
|     POINT (5 6)| 12| false|     -1|
|POINT (12.8 4.5)|  6| false|     -1|
|     POINT (4 3)| 13| false|     -1|
+----------------+---+------+-------+

```

--------------------------------

### Python: Load JDBC data with query and dbtable options

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Two Python examples for loading spatial data from JDBC. The first uses the 'query' option with ST_AsBinary for any JDBC source. The second uses 'dbtable' for PostGIS. Both convert the geometry column using ST_GeomFromWKB.

```python
# 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)")))

```

--------------------------------

### RS_CRS PROJ string format

Source: https://sedona.apache.org/latest/api/sql/Raster-Operators/RS_CRS

Example of getting the CRS in PROJ string format, with sample output.

```sql
SELECT RS_CRS(raster, 'proj') FROM raster_table
```

```text
+proj=longlat +datum=WGS84 +no_defs +type=crs
```

--------------------------------

### Create heatmap using SedonaPyDeck

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Visualizes a heatmap from a SedonaDataFrame containing points using the create_heatmap API. The example uses the Chicago crimes dataset.

```python
SedonaPyDeck.create_heatmap(df=crimes_df)

```

--------------------------------

### RS_AsImage SQL example with output

Source: https://sedona.apache.org/latest/api/sql/Raster-Output/RS_AsImage?q=

SQL example showing RS_AsImage with and without the imageWidth parameter, followed by the HTML output string it returns.

```sql
SELECT RS_AsImage(raster, 500) from rasters
SELECT RS_AsImage(raster) from rasters
```

```text
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUAAAAECAAAAABjWKqcAAAAIElEQVR42mPgPfGfkYUhhfcBNw+DT1KihS6DqLKztjcATWMFp9rkkJgAAAAASUVORK5CYII=\" width=\"200\" />";
```

--------------------------------

### RS_CRS default PROJJSON format

Source: https://sedona.apache.org/latest/api/sql/Raster-Operators/RS_CRS

Example of getting the CRS in default PROJJSON format, with sample output.

```sql
SELECT RS_CRS(raster) FROM raster_table
```

```json
{
  "$schema": "https://proj.org/schemas/v0.7/projjson.schema.json",
  "type": "GeographicCRS",
  "name": "WGS 84",
  ...
}
```

--------------------------------

### Estimate UTM CRS example with geodatasets and geopandas

Source: https://sedona.apache.org/latest/api/pydocs/_modules/sedona/spark/geopandas/geoseries.html

Example usage of estimate_utm_crs() on a GeoSeries read from a geodatasets file. Requires 'pip install geodatasets'. The output shows the derived projected CRS (EPSG:32616) with details about the UTM zone and datum.

```python
>>> import geodatasets  # requires: pip install geodatasets
>>> import geopandas as gpd
>>> df = gpd.read_file(
...     geodatasets.get_path("geoda.chicago_commpop")
... )
>>> df.geometry.values.estimate_utm_crs()  # doctest: +SKIP
<Derived Projected CRS: EPSG:32616>
Name: WGS 84 / UTM zone 16N
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: Between 90°W and 84°W, northern hemisphere between equator and 84°N,...
- bounds: (-90.0, 0.0, -84.0, 84.0)
Coordinate Operation:
- name: UTM zone 16N
- method: Transverse Mercator
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
```

--------------------------------

### Create scatterplot map using SedonaPyDeck

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Visualizes a scatterplot from a SedonaDataFrame containing points using the create_scatterplot_map API. The example uses the Chicago crimes dataset.

```python
SedonaPyDeck.create_scatterplot_map(df=crimes_df)

```

--------------------------------

### Connect to SedonaDB

Source: https://sedona.apache.org/latest/blog/2026/03/01/sedonadb-030-release

Install the SedonaDB package and establish a connection. The interactive option enables a more interactive experience.

```python
# pip install "apache-sedona[db]"
import sedona.db

sd = sedona.db.connect()
sd.options.interactive = True
```

--------------------------------

### Get geometry type of a polygon (SQL)

Source: https://sedona.apache.org/latest/api/sql/geography/Geography-Functions/ST_GeometryType

Example SQL query using ST_GeometryType with a polygon expressed in WKT. It returns the string 'ST_Polygon'.

```sql
SELECT ST_GeometryType(ST_GeogFromWKT('POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))'));

```

```plain text
ST_Polygon

```

--------------------------------

### ST_PointN(geometry, n)

Source: https://sedona.apache.org/latest/api/pydocs/_modules/sedona/spark/sql/st_functions.html

Gets the n-th point (1-based) from a geometry. Negative indices start from the end, and out-of-bounds indices return null.

```APIDOC
## ST_PointN(geometry, n)

### Description
Gets the n-th point (1-based) from a geometry. Negative indices start from the end, and out-of-bounds indices return null.

### Parameters
- **geometry** (ColumnOrName) - Required - Geometry column to get the point from.
- **n** (Union[ColumnOrName, int]) - Required - Index for the point to return, 1-based, negative values start from the end so -1 is the last point, values that are out of bounds return null.

### Returns
- **Column** - n-th point from the geometry as a point geometry column, or null if index is out of bounds.

```

--------------------------------

### Method: initPartition()

Source: https://sedona.apache.org/latest/api/javadoc/spark/org/apache/sedona/core/joinJudgement/RightIndexLookupJudgement.html

Initializes the partition by looking up the extent of the current partition.

```APIDOC
## Method: initPartition

### Description
Initializes the partition by looking up the extent of the current partition.

### Method
initPartition

### Signature
```java
protected void initPartition()
```

### Parameters
None

### Returns
void
```

--------------------------------

### Install SedonaDB with pip

Source: https://sedona.apache.org/latest/blog/2025/09/24/introducing-sedonadb-a-single-node-analytical-database-engine-with-geospatial-as-a-first-class-citizen?q=

Install the SedonaDB package with the 'db' extra using pip.

```bash
pip install "apache-sedona[db]"
```

--------------------------------

### Scala: Load JDBC data with query and dbtable options

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Two Scala examples for loading spatial data from JDBC. The first uses the 'query' option with ST_AsBinary to convert geometry for any JDBC source. The second uses 'dbtable' for PostGIS, which already uses EWKB. Both convert the geometry column using ST_GeomFromWKB.

```scala
// 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)"))

```

--------------------------------

### Shapely scalar UDF example

Source: https://sedona.apache.org/latest/tutorial/sql?q=

Example of a vectorized UDF that takes a Shapely BaseGeometry and returns a buffered geometry. Use this when you want to process individual geometries with Shapely operations.

```python
import shapely.geometry.base as b
from sedona.spark import sedona_vectorized_udf


@sedona_vectorized_udf(return_type=GeometryType())
def vectorized_buffer(geom: b.BaseGeometry) -> b.BaseGeometry:
    return geom.buffer(0.1)
```

--------------------------------

### Get total bounds with total_bounds

Source: https://sedona.apache.org/latest/api/pydocs/sedona.spark.geopandas.api.html

Computes the total bounds of a GeoDataFrame as a tuple of minx, miny, maxx, maxy. The example uses a GeoDataFrame with a CRS.

```python
>>> from shapely.geometry import Point, Polygon, LineString
>>> d = {'geometry': [Point(3, -1), Polygon([(0, 0), (1, 1), (1, 0)]),
... LineString([(0, 1), (1, 2)])]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
>>> gdf.total_bounds
array([ 0., -1.,  3.,  2.])
```

--------------------------------

### ST_BingTilesAround example output

Source: https://sedona.apache.org/latest/api/flink/Spatial-Indexing/ST_BingTilesAround

Output of the example query, showing the array of quadkey strings for the 3x3 neighborhood.

```text
[0, 2, 1, 3]

```

--------------------------------

### Start Spark cluster with start-all.sh

Source: https://sedona.apache.org/latest/setup/cluster?q=

Run this command from the root folder of the uncompressed Apache Spark distribution to start the cluster.

```bash
./sbin/start-all.sh

```

--------------------------------

### ST_NDims with x,y coordinate

Source: https://sedona.apache.org/latest/api/flink/Geometry-Accessors/ST_NDims

Returns 2 for a point with only x and y coordinates. The example uses ST_GeomFromText to create a 2D point and ST_NDims to get its dimension.

```sql
SELECT ST_NDims(ST_GeomFromText('POINT(1 1)'))

```

```text
2

```

--------------------------------

### Install uv and sync documentation dependencies

Source: https://sedona.apache.org/latest/setup/compile

Install the uv package manager and sync the documentation group dependencies, which include MkDocs and related tools.

```bash
python3 -m pip install uv
uv sync --group docs

```