paleolimbot commented on code in PR #64:
URL: https://github.com/apache/sedona-db/pull/64#discussion_r2342140956
##########
docs/programming-guide.md:
##########
Review Comment:
Can you include this as the `.ipynb` instead of a `.md` file? (Then it is
easier to maintain and check in CI).
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+For most production applications, you will create SedonaDB DataFrames by
reading data from a file. Let’s see how to read GeoParquet files in AWS S3
into a SedonaDB DataFrame.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
Review Comment:
This can now be inlined into the `read_parquet()` call: `read_parquet(...,
options = {"aws.skip_signature": True, "aws.default_region": "us-west-2")`.
##########
docs/programming-guide.md:
##########
@@ -0,0 +1,239 @@
+# SedonaDB Guide
+
+This page explains how to process vector data with SedonaDB.
+
+You will learn how to create SedonaDB DataFrames, run spatial queries, and
perform I/O operations with various types of files.
+
+Let’s start by establishing a SedonaDB connection.
+
+## Establish SedonaDB connection
+
+Here’s how to create the SedonaDB connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Now let’s see how to create SedonaDB DataFrames.
+
+## Create SedonaDB DataFrame
+
+**Manually creating SedonaDB DataFrame**
+
+Here’s how to manually create a SedonaDB DataFrame:
+
+```python
+df = sd.sql("""
+SELECT * FROM (VALUES
+ ('one', ST_GeomFromWkt('POINT(1 2)')),
+ ('two', ST_GeomFromWkt('POLYGON((-74.0 40.7, -74.0 40.8, -73.9 40.8, -73.9
40.7, -74.0 40.7))')),
+ ('three', ST_GeomFromWkt('LINESTRING(-74.0060 40.7128, -73.9352 40.7306,
-73.8561 40.8484)')))
+AS t(val, point)""")
+```
+
+Check the contents of the DataFrame:
+
+```python
+df.show()
+```
+
+```
+┌───────┬───────────────────────────────────────────────────────────────┐
+│ val ┆ point │
+│ utf8 ┆ wkb │
+╞═══════╪═══════════════════════════════════════════════════════════════╡
+│ one ┆ POINT(1 2) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ two ┆ POLYGON((-74 40.7,-74 40.8,-73.9 40.8,-73.9 40.7,-74 40.7)) │
+├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ three ┆ LINESTRING(-74.006 40.7128,-73.9352 40.7306,-73.8561 40.8484) │
+└───────┴───────────────────────────────────────────────────────────────┘
+```
+
+Check the type of the DataFrame.
+
+```python
+type(df)
+
+sedonadb.dataframe.DataFrame
+```
+
+**Create SedonaDB DataFrame from files in S3**
+
+For most production applications, you will create SedonaDB DataFrames by
reading data from a file. Let’s see how to read GeoParquet files in AWS S3
into a SedonaDB DataFrame.
+
+Import the required libraries and set environment variables:
+
+```python
+import os
+
+os.environ["AWS_SKIP_SIGNATURE"] = "true"
+os.environ["AWS_DEFAULT_REGION"] = "us-west-2"
+```
+
+Read in the Overture divisions table into a SedonaDB DataFrame and create a
view:
+
+```python
+sd.read_parquet(
+
"s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
+).to_view("division_area")
+```
+
+Let’s now run some spatial queries.
+
+**Read from GeoPandas DataFrame**
+
+This section shows how to convert a GeoPandas DataFrame into a SedonaDB
DataFrame.
+
+Start by reading a FlatGeoBuf file into a GeoPandas DataFrame:
+
+```python
+path =
"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/natural-earth/files/natural-earth_cities.fgb";
+gdf = gpd.read_file(path)
+```
+
+Now convert the GeoPandas DataFrame to a SedonaDB DataFrame and view three
rows of content:
+
+```
+df = sd.create_data_frame(gdf)
+df.show(3)
+
+┌──────────────┬──────────────────────────────┐
+│ name ┆ geometry │
+│ utf8 ┆ geometry │
+╞══════════════╪══════════════════════════════╡
+│ Vatican City ┆ POINT(12.4533865 41.9032822) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ San Marino ┆ POINT(12.4417702 43.9360958) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ Vaduz ┆ POINT(9.5166695 47.1337238) │
+└──────────────┴──────────────────────────────┘
+```
+
+## Spatial queries
+
+Let’s see how to run spatial operations like filtering, joins, and clustering
algorithms.
+
+***Spatial filtering***
+
+Let’s run a spatial filtering operation to fetch all the objects in the
following polygon:
+
+```python
+nova_scotia_bbox_wkt = "POLYGON((-66.5 43.4, -66.5 47.1, -59.8 47.1, -59.8
43.4, -66.5 43.4))"
+
+ns = sd.sql(f"""
+SELECT country, region, names, geometry FROM division_area
+WHERE ST_Intersects(geometry,
ST_SetSRID(ST_GeomFromText('{nova_scotia_bbox_wkt}'), 4326))
+""")
+```
+
+Take a look at the data contained in the `ns` DataFrame:
+
+```
+ns.show(3)
+
+┌──────────┬──────────┬──────────────────────────────────────┬─────────────────────────────────────┐
+│ country ┆ region ┆ names ┆
geometry │
+│ utf8view ┆ utf8view ┆ struct(primary utf8, common map(fie… ┆
geometry │
+╞══════════╪══════════╪══════════════════════════════════════╪═════════════════════════════════════╡
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-117.8317675 49.000… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ ┆ {primary: Canada, common: {hy: Կան… ┆
MULTIPOLYGON(((-59.7502166 44.2338… │
+├╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ CA ┆ CA-NS ┆ {primary: Seal Island, common: , ru… ┆
POLYGON((-66.0528452 43.4531336,-6… │
+└──────────┴──────────┴──────────────────────────────────────┴─────────────────────────────────────┘
+```
+
+You can see it only includes the divisions in the Nova Scotia area. Skip to
the visualization section to see how this data can be graphed on a map.
+
+***K-nearest neighbors (KNN) joins***
+
+Create `restaurants` and `customers` tables so we can demonstrate the KNN join
functionality.
+
+```sql
+CREATE table restaurants AS (
+ SELECT * FROM (VALUES
+ (101, ST_Point(-74.01, 40.71), 'Pizza Palace'),
+ (102, ST_Point(-73.99, 40.69), 'Burger Barn'),
+ (103, ST_Point(-74.02, 40.72), 'Taco Town'),
+ (104, ST_Point(-73.98, 40.75), 'Sushi Spot'),
+ (105, ST_Point(-74.05, 40.68), 'Deli Direct')
+ ) AS t(id, location, name)
+)
Review Comment:
I don't think this will pass with the current version of SedonaDB because of
the DataFusion issue with VALUES. You should be able to use `SELECT location,
ST_Point(x, y) FROM (VALUES (1, -74.0, 40.7, 'Alice'), ...) AS t(id, lng, lat,
name)` to work around this.
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