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VectorX Function Reference

VectorX provides a single conversion function for legacy DBLabs Mosaic geometry format.

SQL examples

Examples on this page use SQL (and Python where shown); in SQL, VectorX functions are prefixed with gbx_ (e.g. gbx_st_legacyaswkb). For more language-specific tips, see the Python, Scala, and SQL API pages.

Common setup​

Run this once before the examples below. It registers VectorX so you can use st_legacyaswkb in Python and gbx_st_legacyaswkb in SQL.

from databricks.labs.gbx.vectorx.jts.legacy import functions as vx
vx.register(spark)
Example output
VectorX registered. You can now use st_legacyaswkb in Python and gbx_st_legacyaswkb in SQL.

st_legacyaswkb​

Converts legacy Mosaic geometry to Well-Known Binary (WKB).

Parameters: legacyGeometry — Column containing legacy geometry string (e.g. {1, [[[x, y]]], [[]]}).

Returns: Binary WKB column.

Python:

from pyspark.sql import Row
from pyspark.sql.types import StructField, StructType

# Point (30, 10): typeId=1 (POINT), srid=0, boundaries=[[[30.0, 10.0]]], holes=[]
legacy_schema = _legacy_point_struct_schema()
schema = StructType([StructField("geom_legacy", legacy_schema)])
row = Row(geom_legacy=(1, 0, [[[30.0, 10.0]]], []))
shapes = spark.createDataFrame([row], schema)
shapes.select(vx.st_legacyaswkb("geom_legacy").alias("wkb")).show()
Example output
+-----------+
|wkb        |
+-----------+
|[BINARY]   |
+-----------+

SQL:

SELECT gbx_st_legacyaswkb(geom_legacy) AS wkb FROM legacy_table;
Example output
One row per input legacy geometry; wkb column contains binary WKB.

Next Steps​