I just published one using python.

https://github.com/blackrock/xml_to_parquet

Convert one or more XML files into Apache Parquet format. Only requires a XSD and XML file to get started.

It requires a XSD schema file to convert everything in your XML file into an equivalent parquet file with nested data structures that match XML paths.

Convert a small XML file to a Parquet file
python xml_to_parquet.py -x PurchaseOrder.xsd PurchaseOrder.xml

INFO - 2021-01-21 12:32:38 - Parsing XML Files..
INFO - 2021-01-21 12:32:38 - Processing 1 files
DEBUG - 2021-01-21 12:32:38 - Generating schema from PurchaseOrder.xsd
DEBUG - 2021-01-21 12:32:38 - Parsing PurchaseOrder.xml
DEBUG - 2021-01-21 12:32:38 - Saving to file PurchaseOrder.xml.parquet
DEBUG - 2021-01-21 12:32:38 - Completed PurchaseOrder.xml

Don't use SomeRoottag as a rowTag. It instructs Spark to use a whole document as a single row. Instead:

Copydf = (sqlContext.read.format('xml')
    .option("rowTag", "row")
    .load("/tmp/xmlfile.xml"))

Now there is no need to explode either:

Copydf.write.format("parquet").saveAsTable("xml_table")

Edit:

Considering your edit you are affected by a known bug. Please see Self-closing tags are not supported as top-level rows #92. It doesn't look like there is any current progress on solving that, so you may have to:

• Make a PR yourself to fix this thing.

• Parse file manually. If elements are always single line, it can be easily done with udf.

  Copyfrom pyspark.sql.functions import col, udf
  from lxml import etree
  
  @udf("struct<id: string, postTypeId: string>")
  def parse(s):
      try:
          attrib = etree.fromstring(s).attrib
          return attrib.get("Id"), attrib.get("PostTypeId")
      except:
          pass
  
  (spark.read.text("/tmp/someXML.xml")
      .where(col("value").rlike("^\\s*<row "))
      .select(parse("value").alias("value"))
      .select("value.*")
      .show())
  
  # +--------+----------+
  # |      id|postTypeId|
  # +--------+----------+
  # |47513849|         1|
  # | 4751323|         4|
  # |  475546|         1|
  # |   47597|         2|
  # +--------+----------+