Re: Using flatMap on Dataframes with Spark 2.0
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"
À: "Sun Rui"
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"
À: "Sun Rui"
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"
À: "Julien Nauroy"
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?
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
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"
À: "Sun Rui"
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"
À: "Julien Nauroy"
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?
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
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 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
>
> <http://stackoverflow.com/questions/36648128/how-to-store-custom-objects-in-a-dataset-in-spark-1-6>
>
> How did you setup your encoder?
>
>
> De: "Sun Rui"
> À: "Julien Nauroy"
> 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?
> On Jul 23, 2016, at 20:36, Julien Nauroy <mailto: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
>
>
>
>
Re: Using flatMap on Dataframes with Spark 2.0
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" À: "Julien Nauroy" 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? 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
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? > On Jul 23, 2016, at 20:36, Julien Nauroy 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 > >
