See https://issues.apache.org/jira/browse/SPARK-16390
On Sat, Jul 2, 2016 at 6:35 PM, Reynold Xin wrote:
> Thanks, Koert, for the great email. They are all great points.
>
> We should probably create an umbrella JIRA for easier tracking.
>
>
> On Saturday, July 2, 2016, Koert Kuipers wrote:
>
>
Please vote on releasing the following candidate as Apache Spark version
2.0.0. The vote is open until Friday, July 8, 2016 at 23:00 PDT and passes
if a majority of at least 3 +1 PMC votes are cast.
[ ] +1 Release this package as Apache Spark 2.0.0
[ ] -1 Do not release this package because ...
I asked this question in Scala user group two years ago:
https://groups.google.com/forum/#!topic/scala-user/W4f0d8xK1nk
Take a look if you are interested in.
On Tue, Jul 5, 2016 at 1:31 PM, Reynold Xin wrote:
> You can file it here: https://issues.scala-lang.org/secure/Dashboard.jspa
>
> Perhap
You can file it here: https://issues.scala-lang.org/secure/Dashboard.jspa
Perhaps "bug" is not the right word, but "limitation". println accepts a
single argument of type Any and returns Unit, and it appears that Scala
fails to infer the correct overloaded method in this case.
def println() = C
I don't think that's a scala compiler bug.
println is a valid expression that returns unit.
Unit is not a single-argument function, and does not match any of the
overloads of foreachPartition
You may be used to a conversion taking place when println is passed to
method expecting a function, but
Jacek,
This is definitely not necessary, but I wouldn't waste cycles "fixing"
things like this when they have virtually zero impact. Perhaps next time we
update this code we can "fix" it.
Also can you comment on the pull request directly?
On Tue, Jul 5, 2016 at 1:07 PM, Jacek Laskowski wrote:
oh you mean instead of:
assert(ds3.select(NameAgg.toColumn).schema.head.nullable === true)
just do:
assert(ds3.select(NameAgg.toColumn).schema.head.nullable)
i did mostly === true because i also had === false, and i liked the
symmetry, but sure this can be fixed if its not the norm
On Tue, Jul 5,
On Mon, Jul 4, 2016 at 6:14 AM, wrote:
> Repository: spark
> Updated Branches:
> refs/heads/master 88134e736 -> 8cdb81fa8
>
>
> [SPARK-15204][SQL] improve nullability inference for Aggregator
>
> ## What changes were proposed in this pull request?
>
> TypedAggregateExpression sets nullable base
Hi Reynold,
Is this already reported and tracked somewhere. I'm quite sure that
people will be asking about the reasons Spark does this. Where are
such issues reported usually?
Pozdrawiam,
Jacek Laskowski
https://medium.com/@jaceklaskowski/
Mastering Apache Spark http://bit.ly/mastering-apac
Please consider this vote canceled and I will work on another RC soon.
On Tue, Jun 21, 2016 at 6:26 PM, Reynold Xin wrote:
> Please vote on releasing the following candidate as Apache Spark version
> 2.0.0. The vote is open until Friday, June 24, 2016 at 19:00 PDT and passes
> if a majority of a
-sparkr-dev@googlegroups +dev@spark.apache.org
[Please send SparkR development questions to the Spark user / dev
mailing lists. Replies inline]
> From:
> Date: Tue, Jul 5, 2016 at 3:30 AM
> Subject: Call to new JObject sometimes returns an empty R environment
> To: SparkR Developers
>
>
>
> H
This seems like a Scala compiler bug.
On Tuesday, July 5, 2016, Jacek Laskowski wrote:
> Well, there is foreach for Java and another foreach for Scala. That's
> what I can understand. But while supporting two language-specific APIs
> -- Scala and Java -- Dataset API lost support for such simple
These topics have been included in the documentation for recent builds of Spark
2.0.
Michael
> On Jul 5, 2016, at 3:49 AM, Romi Kuntsman wrote:
>
> You can also claim that there's a whole section of "Migrating from 1.6 to
> 2.0" missing there:
> https://spark.apache.org/docs/2.0.0-preview/sql
Well, there is foreach for Java and another foreach for Scala. That's
what I can understand. But while supporting two language-specific APIs
-- Scala and Java -- Dataset API lost support for such simple calls
without type annotations so you have to be explicit about the variant
(since I'm using Sca
Right, should have noticed that in your second mail. But foreach
already does what you want, right? it would be identical here.
How these two methods do conceptually different things on different
arguments. I don't think I'd expect them to accept the same functions.
On Tue, Jul 5, 2016 at 3:18 PM
ds is Dataset and the problem is that println (or any other
one-element function) would not work here (and perhaps other methods
with two variants - Java's and Scala's).
Pozdrawiam,
Jacek Laskowski
https://medium.com/@jaceklaskowski/
Mastering Apache Spark http://bit.ly/mastering-apache-spark
A DStream is a sequence of RDDs, not of elements. I don't think I'd
expect to express an operation on a DStream as if it were elements.
On Tue, Jul 5, 2016 at 2:47 PM, Jacek Laskowski wrote:
> Sort of. Your example works, but could you do a mere
> ds.foreachPartition(println)? Why not? What shoul
Sort of. Your example works, but could you do a mere
ds.foreachPartition(println)? Why not? What should I even see the Java
version?
scala> val ds = spark.range(10)
ds: org.apache.spark.sql.Dataset[Long] = [id: bigint]
scala> ds.foreachPartition(println)
:26: error: overloaded method value foreac
Do you not mean ds.foreachPartition(_.foreach(println)) or similar?
On Tue, Jul 5, 2016 at 2:22 PM, Jacek Laskowski wrote:
> Hi,
>
> It's with the master built today. Why can't I call
> ds.foreachPartition(println)? Is using type annotation the only way to
> go forward? I'd be so sad if that's th
Hi,
It's with the master built today. Why can't I call
ds.foreachPartition(println)? Is using type annotation the only way to
go forward? I'd be so sad if that's the case.
scala> ds.foreachPartition(println)
:28: error: overloaded method value foreachPartition with alternatives:
(func:
org.apa
You can also claim that there's a whole section of "Migrating from 1.6 to
2.0" missing there:
https://spark.apache.org/docs/2.0.0-preview/sql-programming-guide.html#migration-guide
*Romi Kuntsman*, *Big Data Engineer*
http://www.totango.com
On Tue, Jul 5, 2016 at 12:24 PM, nihed mbarek wrote:
>
Hi,
I just discover that that SparkSession will replace SQLContext for spark
2.0
JavaDoc is clear
https://spark.apache.org/docs/2.0.0-preview/api/java/org/apache/spark/sql/SparkSession.html
but there is no mention in sql programming guide
https://spark.apache.org/docs/2.0.0-preview/sql-programming
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