schema_of_xml

Parses a XML string and infers its schema in DDL format.

Syntax

from pyspark.sql import functions as sf

sf.schema_of_xml(xml, options=None)

Parameters

Parameter	Type	Description
xml	pyspark.sql.Column or str	A XML string or a foldable string column containing a XML string.
options	dict, optional	Options to control parsing. Accepts the same options as the XML datasource.

Returns

pyspark.sql.Column: a string representation of a StructType parsed from given XML.

Examples

Example 1: Parsing a simple XML with a single element

from pyspark.sql import functions as sf
df = spark.range(1)
df.select(sf.schema_of_xml(sf.lit('<p><a>1</a></p>')).alias("xml")).collect()

[Row(xml='STRUCT<a: BIGINT>')]

Example 2: Parsing an XML with multiple elements in an array

from pyspark.sql import functions as sf
df.select(sf.schema_of_xml(sf.lit('<p><a>1</a><a>2</a></p>')).alias("xml")).collect()

[Row(xml='STRUCT<a: ARRAY<BIGINT>>')]

Example 3: Parsing XML with options to exclude attributes

from pyspark.sql import functions as sf
schema = sf.schema_of_xml('<p><a attr="2">1</a></p>', {'excludeAttribute':'true'})
df.select(schema.alias("xml")).collect()

[Row(xml='STRUCT<a: BIGINT>')]

Example 4: Parsing XML with complex structure

from pyspark.sql import functions as sf
df.select(
    sf.schema_of_xml(
        sf.lit('<root><person><name>Alice</name><age>30</age></person></root>')
    ).alias("xml")
).collect()

[Row(xml='STRUCT<person: STRUCT<age: BIGINT, name: STRING>>')]

Example 5: Parsing XML with nested arrays

from pyspark.sql import functions as sf
df.select(
    sf.schema_of_xml(
        sf.lit('<data><values><value>1</value><value>2</value></values></data>')
    ).alias("xml")
).collect()

[Row(xml='STRUCT<values: STRUCT<value: ARRAY<BIGINT>>>')]