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new b97cd93db3 [docs] Simplify docs for GeometryType
b97cd93db3 is described below
commit b97cd93db3c0342f5523141bde28bfb51afeeb78
Author: JingsongLi <[email protected]>
AuthorDate: Mon Aug 17 09:04:19 2026 +0800
[docs] Simplify docs for GeometryType
---
docs/docs/concepts/data-types.md | 8 --------
docs/docs/flink/quick-start.mdx | 6 ------
docs/docs/iceberg/index.md | 1 -
docs/docs/program-api/java-api.mdx | 18 ------------------
docs/docs/spark/quick-start.mdx | 10 +---------
5 files changed, 1 insertion(+), 42 deletions(-)
diff --git a/docs/docs/concepts/data-types.md b/docs/docs/concepts/data-types.md
index a72ae3fe01..e1a100babc 100644
--- a/docs/docs/concepts/data-types.md
+++ b/docs/docs/concepts/data-types.md
@@ -206,11 +206,3 @@ All data types supported by Paimon are as follows:
</tr>
</tbody>
</table>
-
-:::note Geospatial type availability
-
-`GEOMETRY` and `GEOGRAPHY` columns require Parquet for data, per-level, and
changelog files. They cannot be used as primary, partition, bucket, sequence,
or clustering keys.
-
-The Paimon Java API supports geospatial columns. Spark 4.1 requires
`spark.sql.geospatial.enabled=true`, supports CRSs recognized by Spark, and
supports only the `spherical` geography edge algorithm. Flink SQL, Spark 3.x,
and Spark 4.0 reject geospatial columns instead of exposing them as binary and
losing the CRS or edge algorithm.
-
-:::
diff --git a/docs/docs/flink/quick-start.mdx b/docs/docs/flink/quick-start.mdx
index e39426c118..a9054a0673 100644
--- a/docs/docs/flink/quick-start.mdx
+++ b/docs/docs/flink/quick-start.mdx
@@ -31,12 +31,6 @@ under the License.
This documentation is a guide for using Paimon in Flink.
-:::warning
-
-Flink SQL does not currently support Paimon `GEOMETRY` or `GEOGRAPHY` columns.
Reading, writing, or copying a table whose schema contains either type fails
explicitly instead of exposing the column as `VARBINARY` and losing its CRS or
edge algorithm. Use the Paimon Java API or Spark 4.1 for geospatial columns.
-
-:::
-
## Jars
Paimon currently supports Flink 2.2, 2.1, 2.0, 1.20, 1.19, 1.18, 1.17, 1.16.
We recommend the latest Flink version for a better experience.
diff --git a/docs/docs/iceberg/index.md b/docs/docs/iceberg/index.md
index a3bc426312..f2878bcd68 100644
--- a/docs/docs/iceberg/index.md
+++ b/docs/docs/iceberg/index.md
@@ -116,6 +116,5 @@ Paimon Iceberg compatibility currently supports the
following data types.
- Spark SQL supports geospatial columns in Spark 4.1 when
`spark.sql.geospatial.enabled=true`, for CRSs recognized by Spark, with the
`spherical` geography edge algorithm. Spark 3.x, Spark 4.0, and Flink SQL
reject these columns instead of exposing them as binary and losing the CRS or
edge algorithm.
- When Iceberg metadata is enabled, a `GEOGRAPHY` CRS cannot contain a comma,
including in nested columns, because Iceberg's geospatial type grammar uses
commas to separate parameters.
- Iceberg REST catalog publication does not yet support geospatial columns.
Use `table-location`, `hadoop-catalog`, or `hive-catalog` metadata storage
instead.
-- Geospatial columns cannot be primary, partition, bucket, sequence, or
clustering keys. Paimon records null counts but does not publish byte-wise
lower or upper bounds for WKB values.
:::
diff --git a/docs/docs/program-api/java-api.mdx
b/docs/docs/program-api/java-api.mdx
index 6069503401..887f36c1bb 100644
--- a/docs/docs/program-api/java-api.mdx
+++ b/docs/docs/program-api/java-api.mdx
@@ -459,24 +459,6 @@ public class StreamWriteTable {
| map | org.apache.paimon.data.InternalMap |
| InternalRow | org.apache.paimon.data.InternalRow |
-### Geospatial Types
-
-Use the public type factories to declare geospatial columns:
-
-```java
-import org.apache.paimon.types.DataType;
-import org.apache.paimon.types.DataTypes;
-import org.apache.paimon.types.EdgeAlgorithm;
-
-DataType defaultGeometry = DataTypes.GEOMETRY();
-DataType projectedGeometry = DataTypes.GEOMETRY("EPSG:3857");
-DataType defaultGeography = DataTypes.GEOGRAPHY();
-DataType karneyGeography =
- DataTypes.GEOGRAPHY("OGC:CRS84", EdgeAlgorithm.KARNEY);
-```
-
-`GEOMETRY` and `GEOGRAPHY` values are represented as OGC Well-Known Binary
(WKB) byte arrays in Paimon's internal row API. The default CRS is `OGC:CRS84`,
and the default geography edge interpolation algorithm is
`EdgeAlgorithm.SPHERICAL`.
-
## Predicate Types
| SQL Predicate | Paimon Predicate
|
diff --git a/docs/docs/spark/quick-start.mdx b/docs/docs/spark/quick-start.mdx
index b552aa3b08..7a7432a63f 100644
--- a/docs/docs/spark/quick-start.mdx
+++ b/docs/docs/spark/quick-start.mdx
@@ -392,18 +392,10 @@ All Spark's data types are available in package
`org.apache.spark.sql.types`.
</tbody>
</table>
-:::warning
-
-Native `GeometryType` and `GeographyType` conversion is supported only in
Spark 4.1 and only for CRSs recognized by Spark. Enable it explicitly in
production with `--conf spark.sql.geospatial.enabled=true`; Spark enables it
automatically only in its test environment. Spark 4.1 supports only the
`spherical` geography edge algorithm, so Paimon geography types using
`vincenty`, `thomas`, `andoyer`, or `karney` cannot be converted. Spark 3.x and
Spark 4.0 reject Paimon geospatial columns ins [...]
-
-:::
-
-:::warning
-
Due to the previous design, in Spark3.3 and below, Paimon will map both
Paimon's TimestampType and LocalZonedTimestamp to Spark's TimestampType, and
only correctly handle with TimestampType.
Therefore, when using Spark3.3 and below, reads Paimon table with
LocalZonedTimestamp type written by other engines, such as Flink, the query
result of LocalZonedTimestamp type will have time zone offset, which needs to
be adjusted manually.
When using Spark3.4 and above, all timestamp types can be parsed correctly.
-:::
+Native `GeometryType` and `GeographyType` conversion is supported only in
Spark 4.1 and only for CRSs recognized by Spark. Enable it explicitly in
production with `--conf spark.sql.geospatial.enabled=true`; Spark enables it
automatically only in its test environment. Spark 4.1 supports only the
`spherical` geography edge algorithm, so Paimon geography types using
`vincenty`, `thomas`, `andoyer`, or `karney` cannot be converted. Spark 3.x and
Spark 4.0 reject Paimon geospatial columns ins [...]
