Stream XML files using an auto-loader

Stream XML files on Databricks by combining the auto-loading features of the Spark batch API with the OSS library Spark-XML.

Written by Adam Pavlacka

Last published at: May 19th, 2022

Apache Spark does not include a streaming API for XML files. However, you can combine the auto-loader features of the Spark batch API with the OSS library, Spark-XML, to stream XML files.

In this article, we present a Scala based solution that parses XML data using an auto-loader.

Install Spark-XML library

You must install the Spark-XML OSS library on your Databricks cluster.

Review the install a library on a cluster (AWS | Azure) documentation for more details.



You must ensure that the version of Spark-XML you are installing matches the version of Spark on your cluster.

Create the XML file

Create the XML file and use DBUtils (AWS | Azure) to save it to your cluster.


val xml2="""<people>
    <age born="1990-02-24">25</age>
    <age born="1985-01-01">30</age>
    <age born="1980-01-01">30</age>


Define imports

Import the required functions.


import com.databricks.spark.xml.functions.from_xml
import com.databricks.spark.xml.schema_of_xml
import spark.implicits._
import com.databricks.spark.xml._
import org.apache.spark.sql.functions.{<input_file_name>}

Define a UDF to convert binary to string

The streaming DataFrame requires data to be in string format.

You should define a user defined function to convert binary data to string data.


val toStrUDF = udf((bytes: Array[Byte]) => new String(bytes, "UTF-8"))

Extract XML schema

You must extract the XML schema before you can implement the streaming DataFrame.

This can be inferred from the file using the schema_of_xml method from Spark-XML.

The XML string is passed as input, from the binary Spark data.


val df_schema ="binaryFile").load("/FileStore/tables/test/xml/data/age/").select(toStrUDF($"content").alias("text"))

val payloadSchema = schema_of_xml("text").as[String])

Implement the stream reader

At this point, all of the required dependencies have been met, so you can implement the stream reader.

Use readStream with binary and autoLoader listing mode options enabled.



Listing mode is used when working with small amounts of data. You can leverage fileNotificationMode if you need to scale up your application.

toStrUDF is used to convert binary data to string format (text).

from_xml is used to convert the string to a complex struct type, with the user-defined schema.


val df = spark.readStream.format("cloudFiles")
  .option("cloudFiles.useNotifications", "false") // Using listing mode, hence false is used
  .option("cloudFiles.format", "binaryFile")
  .select(toStrUDF($"content").alias("text")) // UDF to convert the binary to string
  .select(from_xml($"text", payloadSchema).alias("parsed")) // Function to convert string to complex types
  .withColumn("path",input_file_name) // input_file_name is used to extract the paths of input files

View output

Once everything is setup, view the output of display(df) in a notebook.

Sample code output in notebook.

Example notebook

This example notebook combines all of the steps into a single, functioning example.

Import it into your cluster to run the examples.

Streaming XML example notebook

Review the Streaming XML example notebook.

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