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From Julien Nauroy <julien.nau...@u-psud.fr>
Subject Re: Using flatMap on Dataframes with Spark 2.0
Date Sun, 24 Jul 2016 10:43:42 GMT
Hi again, 

Just another strange behavior I stumbled upon. Can anybody reproduce it? 
Here's the code snippet in scala: 
var df1 = spark.read.parquet(fileName) 


df1 = df1.withColumn("newCol", df1.col("anyExistingCol")) 
df1.printSchema() // here newCol exists 
df1 = df1.flatMap(x => List(x)) 
df1.printSchema() // newCol has disappeared 

Any idea what I could be doing wrong? Why would newCol disappear? 


Cheers, 
Julien 

	

----- Mail original -----

De: "Julien Nauroy" <julien.nauroy@u-psud.fr> 
À: "Sun Rui" <sunrise_win@163.com> 
Cc: user@spark.apache.org 
Envoyé: Samedi 23 Juillet 2016 23:39:08 
Objet: Re: Using flatMap on Dataframes with Spark 2.0 

Thanks, it works like a charm now! 

Not sure how I could have found it by myself though. 
Maybe the error message when you don't specify the encoder should point to RowEncoder. 


Cheers, 
Julien 

----- Mail original -----

De: "Sun Rui" <sunrise_win@163.com> 
À: "Julien Nauroy" <julien.nauroy@u-psud.fr> 
Cc: user@spark.apache.org 
Envoyé: Samedi 23 Juillet 2016 16:27:43 
Objet: Re: Using flatMap on Dataframes with Spark 2.0 

You should use : 
import org.apache.spark.sql.catalyst.encoders.RowEncoder 

val df = spark.read.parquet(fileName) 

implicit val encoder: ExpressionEncoder[Row] = RowEncoder(df.schema) 

val df1 = df.flatMap { x => List(x) } 



On Jul 23, 2016, at 22:01, Julien Nauroy < julien.nauroy@u-psud.fr > wrote: 

Thanks for your quick reply. 

I've tried with this encoder: 
implicit def RowEncoder: org.apache.spark.sql.Encoder[Row] = org.apache.spark.sql.Encoders.kryo[Row]

Using a suggestion from http://stackoverflow.com/questions/36648128/how-to-store-custom-objects-in-a-dataset-in-spark-1-6


How did you setup your encoder? 


----- Mail original -----

De: "Sun Rui" < sunrise_win@163.com > 
À: "Julien Nauroy" < julien.nauroy@u-psud.fr > 
Cc: user@spark.apache.org 
Envoyé: Samedi 23 Juillet 2016 15:55:21 
Objet: Re: Using flatMap on Dataframes with Spark 2.0 

I did a try. the schema after flatMap is the same, which is expected. 

What’s your Row encoder? 

<blockquote>

On Jul 23, 2016, at 20:36, Julien Nauroy < julien.nauroy@u-psud.fr > wrote: 

Hi, 

I'm trying to call flatMap on a Dataframe with Spark 2.0 (rc5). 
The code is the following: 
var data = spark.read.parquet(fileName).flatMap(x => List(x)) 

Of course it's an overly simplified example, but the result is the same. 
The dataframe schema goes from this: 
root 
|-- field1: double (nullable = true) 
|-- field2: integer (nullable = true) 
(etc) 

to this: 
root 
|-- value: binary (nullable = true) 

Plus I have to provide an encoder for Row. 
I expect to get the same schema after calling flatMap. 
Any idea what I could be doing wrong? 


Best regards, 
Julien 








</blockquote>





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