Databricks XML


                Databricks XML

Databricks provides native support for working with XML data, allowing you to efficiently ingest, query, and process XML files within the platform. Here’s an overview of how you can use Databricks with XML:

Reading XML Files:

  • Auto Loader: You can use Auto Loader to automatically ingest XML files from cloud storage (like S3 or Azure Blob Storage) and incrementally process new files as they arrive.
  •"xml"): Use this method to read XML files directly into a Spark DataFrame. You can specify options like the rowTag (to identify the XML element that represents a row in the DataFrame) and schema inference.
  • schema_of_xml and from_xml functions: These SQL functions allow you to parse XML data within string columns in existing DataFrames.

Querying XML Data:

Once you have your XML data in a DataFrame, you can use standard Spark SQL queries to filter, aggregate, and transform the data.

Writing XML Files:

  • df.write.format("xml"): Use this method to write a DataFrame back to XML files.

Example (Reading and Querying):


from pyspark.sql.functions import col, from_xml, schema_of_xml

# Define the schema for the XML data
schema = schema_of_xml(“””

# Read XML file into DataFrame
df =“xml”).option(“rowTag”, “book”).load(“path/to/books.xml”)

# Parse XML using the schema
parsed_df = df.withColumn(“book”, from_xml(col(“value”), schema))

# Query the parsed DataFrame“book.title”, “”).show()

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