Just a follow-up on my last question: the RowEncoder has to be defined AFTER
declaring the columns, or else the new columns won't be serialized and will
disappear after the flatMap.
So the code should look like:
var df1 = spark.read.parquet(fileName)
df1 = df1.withColumn("newCol", df1.col("anyExistingCol"))
df1.printSchema() // here newCol exists
implicit val encoder: ExpressionEncoder[Row] = RowEncoder(df1.schema)
df1 = df1.flatMap(x => List(x))
df1.printSchema() // newCol still exists!
Julien
----- Mail original -----
De: "Julien Nauroy" <julien.nau...@u-psud.fr>
À: "Sun Rui" <sunrise_...@163.com>
Cc: user@spark.apache.org
Envoyé: Dimanche 24 Juillet 2016 12:43:42
Objet: Re: Using flatMap on Dataframes with Spark 2.0
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.nau...@u-psud.fr>
À: "Sun Rui" <sunrise_...@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_...@163.com>
À: "Julien Nauroy" <julien.nau...@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.nau...@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_...@163.com >
À: "Julien Nauroy" < julien.nau...@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.nau...@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>
