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https://issues.apache.org/jira/browse/SPARK-12635?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15093987#comment-15093987
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Dmitriy Selivanov edited comment on SPARK-12635 at 1/12/16 2:48 PM:
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Hi!
First, thanks to all SparkR and Spark developers.
I just start to evaluate SparkR. I tried it several times (since it was in
AMPLab), but before 1.6 there were too many rough edges. So I used Scala API.
For now I see two main limiting issues (and they are interconnected):
1. Lack of UDF in R interface. I saw SPARK-6817.
2. And I think more important - lack of fast serialization / deserialization. I
believe it is impossible to develop useful R UDF interface without fast
serialization / deserialization.
cc [~sunrui]
Consider following example. I have tiny cached spark DF with nrow=300k, ncol=25
and I want to collect it to local R session:
{code:R}
df_local <- collect(df)
{code}
Resulting R data.frame is only ~ 70mb!!, but it takes **120sec** to collect it
to R. (compared to **7sec** of df.toPandas() in pyspark).
I made some profiling. Almost all time is spent at this calls collect ->
callJStatic -> invokeJava -> readObject. readObject make a lot of read* calls
from
[deserialize.R](https://github.com/apache/spark/blob/c3d505602de2fd2361633f90e4fff7e041849e28/R/pkg/R/deserialize.R).
So for now it **much** faster to write spark data.frame to simple plain
csv/json and then read it R. I didn't read python serialization. Is it diffrent
from R? Why so dramatic difference between R and Python?
was (Author: dselivanov):
Hi!
First, thanks to all SparkR and Spark developers.
I just start to evaluate SparkR. I tried it several times (since it was in
AMPLab), but before 1.6 there were too many rough edges. So I used Scala API.
For now I see two main limiting issues (and they are interconnected):
1. Lack of UDF in R interface. I saw SPARK-6817.
2. And I think more important - lack of fast serialization / deserialization. I
believe it is impossible to develop useful R UDF interface without fast
serialization / deserialization.
Consider following example. I have tiny cached spark DF with nrow=300k, ncol=25
and I want to collect it to local R session:
{code:R}
df_local <- collect(df)
{code}
Resulting R data.frame is only ~ 70mb!!, but it takes **120sec** to collect it
to R. (compared to **7sec** of df.toPandas() in pyspark).
I made some profiling. Almost all time is spent at this calls collect ->
callJStatic -> invokeJava -> readObject. readObject make a lot of read* calls
from
[deserialize.R](https://github.com/apache/spark/blob/c3d505602de2fd2361633f90e4fff7e041849e28/R/pkg/R/deserialize.R).
So for now it **much** faster to write spark data.frame to simple plain
csv/json and then read it R. I didn't read python serialization. Is it diffrent
from R? Why so dramatic difference between R and Python?
> More efficient (column batch) serialization for Python/R
> --------------------------------------------------------
>
> Key: SPARK-12635
> URL: https://issues.apache.org/jira/browse/SPARK-12635
> Project: Spark
> Issue Type: New Feature
> Components: PySpark, SparkR, SQL
> Reporter: Reynold Xin
>
> Serialization between Scala / Python / R is pretty slow. Python and R both
> work pretty well with column batch interface (e.g. numpy arrays). Technically
> we should be able to just pass column batches around with minimal
> serialization (maybe even zero copy memory).
> Note that this depends on some internal refactoring to use a column batch
> interface in Spark SQL.
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