CODEWITHSUNDEEP
apache-sparkpysparkapache-spark-sqlapache-spark-xml
Kumar G
Kumar G

Reputation: 55

Reading XML File Through Dataframe

I have XML file like below format.

<nt:vars>
<nt:var id="1.3.0" type="TimeStamp"> 89:19:00.01</nt:var>
<nt:var id="1.3.1" type="OBJECT ">1.9.5.67.2</nt:var>
<nt:var id="1.3.9" type="STRING">AB-CD-EF</nt:var>
</nt:vars>

I built a dataframe on it using below code. Though the code is displaying 3 rows and retrieving id and type fields it'snot displaying actual value which is 89:19:00.01, 1.9.5.67.2, AB-CD-EF

spark.read.format("xml").option("rootTag","nt:vars").option("rowTag","nt:var").load("/FileStore/tables/POC_DB.xml").show()

Could you please help me if I have to add any other options to above line to bring the values as well please.

Upvotes: 0

Views: 329

Answers (1)

mck
mck

Reputation: 42422

You can instead specify rowTag as nt:vars:

df = spark.read.format("xml").option("rowTag","nt:vars").load("file.xml")

df.printSchema()
root
 |-- nt:var: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- _VALUE: string (nullable = true)
 |    |    |-- _id: string (nullable = true)
 |    |    |-- _type: string (nullable = true)

df.show(truncate=False)
+-------------------------------------------------------------------------------------------+
|nt:var                                                                                     |
+-------------------------------------------------------------------------------------------+
|[[ 89:19:00.01, 1.3.0, TimeStamp], [1.9.5.67.2, 1.3.1, OBJECT ], [AB-CD-EF, 1.3.9, STRING]]|
+-------------------------------------------------------------------------------------------+

And to get the values as separate rows, you can explode the array of structs:

df.select(F.explode('nt:var')).show(truncate=False)
+--------------------------------+
|col                             |
+--------------------------------+
|[ 89:19:00.01, 1.3.0, TimeStamp]|
|[1.9.5.67.2, 1.3.1, OBJECT ]    |
|[AB-CD-EF, 1.3.9, STRING]       |
+--------------------------------+

Or if you just want the values:

df.select(F.explode('nt:var._VALUE')).show()
+------------+
|         col|
+------------+
| 89:19:00.01|
|  1.9.5.67.2|
|    AB-CD-EF|
+------------+

Upvotes: 0

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