heirarchy should be rootTag and att should be rowTag as

df = spark.read \
    .format("com.databricks.spark.xml") \
    .option("rootTag", "hierarchy") \
    .option("rowTag", "att") \
    .load("test.xml")

and you should get

+-----+------+----------------------------+
|Order|attval|children                    |
+-----+------+----------------------------+
|1    |Data  |[[[1, Studyval], [2, Site]]]|
|2    |Info  |[[[1, age], [2, gender]]]   |
+-----+------+----------------------------+

and schema

root
 |-- Order: long (nullable = true)
 |-- attval: string (nullable = true)
 |-- children: struct (nullable = true)
 |    |-- att: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- Order: long (nullable = true)
 |    |    |    |-- attval: string (nullable = true)

find more information on databricks xml

@jxc's answer in the comments to the question is the best solution:

df = spark.read.format("com.databricks.spark.xml")\
               .option("rowTag", "head")\
               .load(','.join(s3_paths))

Here is an example using a toy dataset:

fnames = ['books_part1.xml','books_part2.xml'] # part1 -> ids bk101-bk106, part2 -> ids bk107-bk112

df = spark.read.format('xml') \
              .option('rowTag','book')\
              .load(','.join(fnames))

df.show()

# +-----+--------------------+--------------------+---------------+-----+------------+--------------------+
# |  _id|              author|         description|          genre|price|publish_date|               title|
# +-----+--------------------+--------------------+---------------+-----+------------+--------------------+
# |bk101|Gambardella, Matthew|An in-depth look ...|       Computer|44.95|  2000-10-01|XML Developer's G...|
# |bk102|          Ralls, Kim|A former architec...|        Fantasy| 5.95|  2000-12-16|       Midnight Rain|
# |bk103|         Corets, Eva|After the collaps...|        Fantasy| 5.95|  2000-11-17|     Maeve Ascendant|
# |bk104|         Corets, Eva|In post-apocalyps...|        Fantasy| 5.95|  2001-03-10|     Oberon's Legacy|
# |bk105|         Corets, Eva|The two daughters...|        Fantasy| 5.95|  2001-09-10|  The Sundered Grail|
# |bk106|    Randall, Cynthia|When Carla meets ...|        Romance| 4.95|  2000-09-02|         Lover Birds|
# |bk107|      Thurman, Paula|A deep sea diver ...|        Romance| 4.95|  2000-11-02|       Splish Splash|
# |bk108|       Knorr, Stefan|An anthology of h...|         Horror| 4.95|  2000-12-06|     Creepy Crawlies|
# |bk109|        Kress, Peter|After an inadvert...|Science Fiction| 6.95|  2000-11-02|        Paradox Lost|
# |bk110|        O'Brien, Tim|Microsoft's .NET ...|       Computer|36.95|  2000-12-09|Microsoft .NET: T...|
# |bk111|        O'Brien, Tim|The Microsoft MSX...|       Computer|36.95|  2000-12-01|MSXML3: A Compreh...|
# |bk112|         Galos, Mike|Microsoft Visual ...|       Computer|49.95|  2001-04-16|Visual Studio 7: ...|
# +-----+--------------------+--------------------+---------------+-----+------------+--------------------+

One way is to use the databricks spark-xml library :

1. Import the spark-xml library into your workspace https://docs.databricks.com/user-guide/libraries.html#create-a-library (search spark-xml in the maven/spark package section and import it)
2. Attach the library to your cluster https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster
3. Use the following code in your notebook to read the xml file, where "note" is the root of my xml file.

xmldata = spark.read.format('xml').option("rootTag","note").load('dbfs:/mnt/mydatafolder/xmls/note.xml')

Example :

Parquet format contains information about the schema, XML doesn't. You can't just read the schema without inferring it from the data.

Since I don't have information about your XML file I'll use this sample: XML Sample File

Save that XML sample to sample.xml and you'll have to specify the Spark XML package in order to parse the XML file.

Here's the example:

from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession \
        .builder \
        .appName("Test") \
        .config("spark.jars.packages", "com.databricks:spark-xml_2.12:0.13.0") \
        .getOrCreate()

    df = spark.read.format('xml').options(rowTag='catalog').load('sample.xml')
    df.printSchema()

The result is:

root
 |-- book: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- _id: string (nullable = true)
 |    |    |-- author: string (nullable = true)
 |    |    |-- description: string (nullable = true)
 |    |    |-- genre: string (nullable = true)
 |    |    |-- price: double (nullable = true)
 |    |    |-- publish_date: date (nullable = true)
 |    |    |-- title: string (nullable = true)