Thanks Judit, Ayan
Judit,
You almost got it. The explode might help here.
But when I tried I see load() doesn’t like to read from xmlcomment column on
oracle_data.
scala> val xmlDF = sqlContext.sql("SELECT * FROM oracle_data")
17/06/29 18:31:58 INFO parse.ParseDriver: Parsing command: SELECT * FROM
oracle_data
17/06/29 18:31:58 INFO parse.ParseDriver: Parse Completed
…
scala> val xmlDF_flattened =
xmlDF.withColumn("xmlcomment",explode(sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load($"xmlcomment")))
<console>:22: error: overloaded method value load with alternatives:
()org.apache.spark.sql.DataFrame <and>
(path: String)org.apache.spark.sql.DataFrame
cannot be applied to (org.apache.spark.sql.ColumnName)
val xmlDF_flattened =
xmlDF.withColumn("xmlcomment",explode(sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load($"xmlcomment")))
Ayan,
Output of books_inexp.show was as below
title, author
Midnight Rain, Ralls, Kim
Maeve Ascendant, Corets, Eva
Regards,
Amol
From: Judit Planas [mailto:judit.pla...@epfl.ch]
Sent: Thursday, June 29, 2017 3:46 AM
To: user@spark.apache.org
Subject: Re: SparkSQL to read XML Blob data to create multiple rows
Hi Amol,
Not sure I understand completely your question, but the SQL function "explode"
may help you:
http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.functions.explode
Here you can find a nice example:
https://stackoverflow.com/questions/38210507/explode-in-pyspark
HTH,
Judit
On 29/06/17 09:05, ayan guha wrote:
Hi
Not sure if I follow your issue. Can you please post output of
books_inexp.show()?
On Thu, Jun 29, 2017 at 2:30 PM, Talap, Amol
<amol.ta...@capgemini.com<mailto:amol.ta...@capgemini.com>> wrote:
Hi:
We are trying to parse XML data to get below output from given input sample.
Can someone suggest a way to pass one DFrames output into load() function or
any other alternative to get this output.
Input Data from Oracle Table XMLBlob:
SequenceID
Name
City
XMLComment
1
Amol
Kolhapur
<books><Comments><Comment><Title>Title1.1</Title><Description>Description_1.1</Description><Comment><Title>Title1.2</Title><Description>Description_1.2</Description><Comment><Title>Title1.3</Title><Description>Description_1.3</Description></Comment></Comments></books>
2
Suresh
Mumbai
<books><Comments><Comment><Title>Title2</Title><Description>Description_2</Description></Comment></Comments></books>
3
Vishal
Delhi
<books><Comments><Comment><Title>Title3</Title><Description>Description_3</Description></Comment></Comments></books>
4
Swastik
Bangalore
<books><Comments><Comment><Title>Title4</Title><Description>Description_4</Description></Comment></Comments></books>
Output Data Expected using Spark SQL:
SequenceID
Name
City
Title
Description
1
Amol
Kolhapur
Title1.1
Description_1.1
1
Amol
Kolhapur
Title1.1
Description_1.2
1
Amol
Kolhapur
Title1.3
Description_1.3
2
Suresh
Mumbai
Title2
Description_2
3
Vishal
Delhi
Title3.1
Description_3.1
4
Swastik
Bangalore
Title4
Description_4
I am able to parse single XML using below approach in spark-shell using example
below but how do we apply the same recursively for all rows ?
https://community.hortonworks.com/questions/71538/parsing-xml-in-spark-rdd.html.
val dfX =
sqlContext.read.format("com.databricks.spark.xml").option("rowTag","book").load("books.xml")
val xData = dfX.registerTempTable("books")
dfX.printSchema()
val books_inexp =sqlContext.sql("select title,author from books where price<10")
books_inexp.show
Regards,
Amol
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Best Regards,
Ayan Guha
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