only me with this mental issue)
Unless I'm mistaken, -Pmesos won't get included in 2.0.x releases
unless someone adds it to branch-2.0. Correct?
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Hi Sean,
So, another question would be when is the change going to be released
then? What's the version for the master? The next release's 2.0.2 so
it's not for mesos profile either :(
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Hi Sean,
Sure, but then the question is why it's not a part of 2.0.1? I thought
it was considered ready for prime time and so should be shipped in
2.0.1.
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Hi,
That's even more interesting. How's so since the profile got added a
week ago or later and RC2 was cut two/three days ago? Anyone know?
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Hi,
I keep asking myself why are you guys not including -Pmesos in your
builds? Is this on purpose or have you overlooked it?
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that
there are places where you check whether an expression is
deterministic by the method not the trait. If so, there's this
ambiguity I'm talking about.
Anyway, I'm glad to learn from you guys! Thanks.
Pozdrawiam,
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Mastering Apache Spark 2.0 http://bit.ly
On Sat, Sep 24, 2016 at 5:27 AM, Hyukjin Kwon wrote:
> Then, are we going to submit a PR and fix this maybe?
https://issues.apache.org/jira/browse/SPARK-17656
Thanks Hyukjin! Unless someone beats me to it, I'm going to have a PR
over the weekend.
Jacek
]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala#L80
[3]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala#L271
Pozdrawiam,
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+1
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On Fri, Sep 23, 2016 at 8:01 AM, Reynold Xin <r...@databricks.com> wrote:
> Please vote on
r and I'll
remember longer! :-)
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-
To unsubs
(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
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On Sat, Sep 17, 2016 at 11:08 PM, Xiang Gao
Hi Sean,
Thanks a lot for help understanding the different jars.
Do you think there's anything that should be reported as an
enhancement/issue/task in JIRA?
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-runtime-4.5.3.jar
Even if that does not cause any class mismatches, it might be worth to
exclude them to minimize the size of the Spark distro.
What do you think?
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,
Jacek Laskowski
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and @scala.annotation.varargs only. WDYT?
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way of thinking?
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On Thu, Sep 8, 2016 at 11:20 AM, Steve Loughran <ste...@hortonworks.com> wrote:
>
On Thu, Sep 8, 2016 at 9:03 AM, Fred Reiss wrote:
> I suppose the type-inference-time check for the presence of the input
> directory could be moved to the FileStreamSource's initialization. But if
> the directory isn't there when the source is being created, it probably
>
scala:153)
... 48 elided
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with to fix/reproduce the issue, let me know. I wish I knew how to
write a unit test for this. Where in the code to look for inspiration?
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lang.OutOfMemoryError: Unable to acquire 262144 bytes of memory, got 0
...
Please see
https://gist.github.com/jaceklaskowski/906d62b830f6c967a7eee5f8eb6e9237
and let me know if I should file an issue. I don't think 10^3 elements
and groupBy should kill spark-shell.
Pozdrawiam,
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Hi,
Definitely well deserved. Don't check your emails for the 2 weeks. Not even
for a minute :-)
Jacek
On 30 Aug 2016 10:21 a.m., "Reynold Xin" wrote:
> A lot of people have been pinging me on github and email directly and
> expect instant reply. Just FYI I'm on vacation
? I'm tempted to say that for some data sources
DataFrames are faster than Datasets...always. True? What am I missing?
https://twitter.com/jaceklaskowski/status/770554918419755008
Thanks a lot, Reynold, for helping me out to get the gist of it all!
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eScan parquet [id#196L,name#197] Batched: true, Format:
ParquetFormat, InputPaths:
file:/Users/jacek/dev/oss/spark/cities.parquet, PartitionFilters: [],
PushedFilters: [], ReadSchema: struct<id:bigint,name:string>
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Mastering A
Hi Michael,
Congrats!
BTW What I like about the change the most is that it uses the
pluggable interface for TaskScheduler and SchedulerBackend (as
introduced by YARN). Think Standalone should follow the steps. WDYT?
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Mastering
On Wed, Aug 24, 2016 at 2:32 PM, Steve Loughran wrote:
> no reason; the key thing is : not in cluster mode, as there your work happens
> elsewhere
Right! Anything but cluster mode should make it easy (that leaves us
with local).
Jacek
On Wed, Aug 24, 2016 at 11:13 AM, Steve Loughran wrote:
> I'd recommend
...which I mostly agree to with some exceptions :)
> -stark spark standalone from there
Why spark standalone since the OP asked about "learning how query
execution flow occurs in Spark SQL"? How
fixes).
[1]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L67-L73
[2]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystConf.scala#L43-L45
Pozdrawiam,
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ke to hear the real reason).
Thanks.
[1]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala#L46
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class that I could
extend to assert the exception's message? How to run the test? Is sbt
catalyst/testOnly [testName] enough?
Please guide. Thanks.
[1]
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala#L249
Pozdrawiam,
Ja
!
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On Tue, Aug 16, 2016 at 10:51 PM, Yin Huai wrote:
> Do you want to try it?
Yes, indeed! I'd be more than happy. Guide me if you don't mind. Thanks.
Should I create a JIRA for this?
Jacek
-
To
891)
...
res1: org.apache.spark.sql.Dataset[A] = [id: int]
scala> spark.version
res2: String = 2.1.0-SNAPSHOT
See the complete stack trace at
https://gist.github.com/jaceklaskowski/a969fdd5c2c9cdb736bf647b01257a3e.
I'm quite positive that it didn't happen a day or two ago.
Pozdrawiam,
Hi Tim,
AWESOME. Thanks a lot for releasing it. That makes me even more eager
to see it in Spark's codebase (and replacing the current RDD-based
API)!
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Thanks Sean. That reflects my sentiments so well!
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On Mon, Aug 15, 2016 at 1:08 AM, Sean Owen <so...@cloudera.
s and PMCs should do not
users:
"Do not include any links on the project website that might encourage
non-developers to download and use nightly builds, snapshots, release
candidates, or any other similar package."
Pozdrawiam,
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Master
Hi Olivier,
I don't know either, but am curious what you've tried already.
Jacek
On 3 Aug 2016 10:50 a.m., "Olivier Girardot" <
o.girar...@lateral-thoughts.com> wrote:
> Hi everyone,
> I'm currently to use Spark 2.0.0 and making Dataframes work with kryo.
> registrationRequired=true
> Is it
la#L355
[3]
https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala#L360
[4]
https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala#L200
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om/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala#L205
[4]
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala#L146-L147
Pozdrawiam,
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Kill 'em all -- one by one slowly yet gradually! :)
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On Wed, Jul 27, 2016 at 9:11 PM, Holden Karau <
hts before filling an JIRA issue.
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Hi,
Fixed now. git pull and start over.
https://github.com/apache/spark/commit/e1bd70f44b11141b000821e9754efeabc14f24a5
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Hi,
It seems that the current master is broken twice. I've just sent a PR
for the first one. Please review and merge.
https://github.com/apache/spark/pull/14315
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+1000
Thanks Ismael for bringing this up! I meant to have send it earlier too
since I've been struggling with a sbt-based Scala project for a Spark
package myself this week and haven't yet found out how to do local
publishing.
If such a guide existed for Maven I could use it for sbt easily too
And the reason is that not all Spark installations are for YARN as the
cluster manager.
Jacek
On 13 Jul 2016 9:23 a.m., "Sean Owen" wrote:
> It's activated by a profile called 'yarn', like several other modules.
>
> On Wed, Jul 13, 2016 at 5:15 AM, Niranda Perera
>
Hi,
Read the doc http://spark.apache.org/docs/latest/spark-standalone.html
which seems to be the cluster manager the OP uses.
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Hi,
It appears you're running local mode (local[*] assumed) so killing
spark-shell *will* kill the one and only executor -- the driver :)
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Hi,
Use jps -lm and see the processes on the machine(s) to kill.
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On Wed, Jul 6, 2016 at 9:49 PM, Mr rty ff <y
Thanks Cody, Reynold, and Ryan! Learnt a lot and feel "corrected".
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On Wed, Jul 6, 2016 at 2:46 AM, Shixiong
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?
>
>
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?
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Scala I want to use Scala API right). It appears that
any single-argument-function operators in Datasets are affected :(
My question was to know whether there are works to fix it (if possible
-- I don't know if it is).
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Mastering
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).
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unc:
org.apache.spark.api.java.function.ForeachPartitionFunction[Record])Unit
(f: Iterator[Record] => Unit)Unit
cannot be applied to (Unit)
ds.foreachPartition(println)
^
scala> sc.version
res9: String = 2.0.0-SNAPSHOT
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On Sun, Jul 3, 2016 at 3:49 PM, Sean Owen <so...@cloudera.com> wrote:
> On Sun, Jul 3, 2016 at 2:42 PM, Jacek Laskowski <ja...@japila.pl> wrote:
>> 2. Add new features to master (versions - master: 2.0.0-SNAPSHOT
>> branch: 2.0.0-RC1)
>
> Either:
> a) you proh
g? I must be missing something, but can't see it.
You're right, it has nothing to do with pace of release but the
project needs frequent releases say quarterly.
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k
On 3 Jul 2016 2:59 a.m., "Reynold Xin" <r...@databricks.com> wrote:
> Because in that case you cannot merge anything meant for 2.1 until 2.0 is
> released.
>
> On Saturday, July 2, 2016, Jacek Laskowski <ja...@japila.pl> wrote:
>
>> Hi,
>>
>&
Hi,
Always release from master. What could be the gotchas?
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On Sat, Jul 2, 2016 at 11:36 PM, Sean Owen <
Hi,
Thanks Sean! It makes sense.
I'm not fully convinced that's how it should be, so I apologize if I
ever ask about the version management in Spark again :)
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Hi,
That makes sense. Thanks Dongjoon for the very prompt response!
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On Tue, Jun 28, 2016 at 6:58 PM, Dongjoon Hyun
Hi,
While reviewing the release notes for 1.6.2 I stumbled upon
https://issues.apache.org/jira/browse/SPARK-13522. It's got Target
Version/s: 2.0.0 with Fix Version/s: 1.6.2, 2.0.0.
What's the meaning of Target Version/s in Spark?
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Thanks Sean. I'm going to create a JIRA for it and start the work under it.
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On Mon, Jun 27, 2016 at 9:19 AM, Sean Owen
/mesos/MesosCoarseGrainedSchedulerBackend.scala#L71
[3]
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L73-L74
[4]
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/Utils.scala#L748
Pozdrawiam,
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Spark application for execution
both -- memory and cores -- can be specified explicitly.
Would you agree? Do I miss anything important?
I was very surprised when I found it out as I thought that memory
would also have been a limiting factor.
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`backend.reviveOffers()`?
p.s. I understand that it's somehow related to how Mesos manages
resources where it offers resources, but can't find anything related
to `reviving offers` in Mesos docs :(
Please guide. Thanks!
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+1
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On Sat, Jun 18, 2016 at 9:13 AM, Reynold Xin <r...@databricks.com> wrote:
> Looks like that's resolved n
On Sat, Jun 18, 2016 at 6:13 AM, Pedro Rodriguez
wrote:
> using Datasets (eg using $ to select columns).
Or even my favourite one - the tick ` :-)
Jacek
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That's be awesome to have another 2.0 RC! I know many people who'd
consider it as a call to action to play with 2.0.
+1000
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. And
only then I could work on
https://issues.apache.org/jira/browse/YARN-5247. Is this about
changing the annotation(s) only?
Thanks for your support!
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]
https://github.com/apache/spark/blob/master/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L1298
[2] Path.SEPARATOR
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Hi,
Congrats Yanbo!
p.s. It should go to user@, too.
Jacek
On 4 Jun 2016 4:49 a.m., "Matei Zaharia" wrote:
Hi all,
The PMC recently voted to add Yanbo Liang as a committer. Yanbo has been a
super active contributor in many areas of MLlib. Please join me in
welcoming
nt (and yarn-cluster) are no longer in use, I'm pretty
sure it's of no use and could be safely removed. If not, we should do
something with it anyway.
Please guide before I file a JIRA issue. Thanks.
p.s. On to hunting SPARK_YARN_MODE...
Pozdrawiam,
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On Sun, May 29, 2016 at 5:30 PM, jpivar...@gmail.com
wrote:
> If I find a way to provide
> access by modifying Spark source, can I just submit a pull request, or do I
> need to be a recognized Spark developer? If so, is there a process for
> becoming one?
Start a discussion
my limited understanding of the things (and I'm not even sure
how trustworthy it is). Use with extreme caution.
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On Sat, May 28, 20
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?
[1]
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala#L131-L142
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]
Thanks Reynold! :)
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On Tue, Apr 26, 2016 at 6:38 AM
]^
[error] one error found
[error] Compile failed at Apr 26, 2016 6:28:01 AM [0.449s]
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On Tue, Apr 26, 2016 at 6
Hi Yanbo,
https://issues.apache.org/jira/browse/SPARK-14730
Thanks!
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On Tue, Apr 19, 2016 at 8:55 AM, Yanbo Liang
ing it could have.
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When you say "in the future", do you have any specific timeframe in
mind? You got me curious :)
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On Mon, Ap
it's not a release feature I didn't mean to file an issue in
JIRA - please guide if needed).
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? It appears that we calls
the former to execute the latter (?) I'm confused. Please explain :)
I'd appreciate.
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/961M
[INFO]
Thank you so much for the prompt solution! And that's while I was
driving from Toronto to Mississauga. Thanks!
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ark-mllib-local_2.11
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OS X
➜ spark git:(master) ✗ java -version
java version "1.8.0_77"
Java(TM) SE Runtime Environment (build 1.8.0_77-b03)
Java HotSpot(TM) 64-Bit Server VM (build 25.77-b03, mixed mode)
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https://github.com/apache/spark/commit/c59abad052b7beec4ef550049413e95578e545be.
Is this a real issue with the build now or is this just me? I may have
seen a similar case before, but can't remember what the fix was.
Looking into it.
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Hi Ted,
Yeah, I saw the line, but forgot it's a test that may have been
testing that closures should not have return. More clear now. Thanks!
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it.
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implicits._ (52 terms, 31 are implicit)
2) import sqlContext.sql (1 terms)
scala> sc.version
res19: String = 2.0.0-SNAPSHOT
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Hi,
Looks related to the recent commit...
Repository: spark
Updated Branches:
refs/heads/master 2262a9335 -> 1f0c5dceb
[SPARK-14350][SQL] EXPLAIN output should be in a single cell
Jacek
03.04.2016 7:00 PM "Ted Yu" napisał(a):
> Hi,
> Based on master branch refreshed
g? Please guide.
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if at least some
> traits from org.apache.spark.ml.param.shared.sharedParams were
> public?HasInputCol(s) and HasOutputCol for example. These are useful
> pretty much every time you create custom Transformer.
>
> --
> Pozdrawiam,
> Maciej Szymkiewicz
>
>
> On 03
(and this discussion is a sign that the process has not been
conducted properly as people have concerns, me including).
Thanks Mridul!
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On Sat, Mar 26, 2016 at 3:23 AM, Joseph Bradley <jos...@databricks.com> wrote:
> There have been some comments ab
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Hi,
After few weeks with spark.ml now, I came to conclusion that
Transformer concept from Pipeline API (spark.ml/MLlib) should be part
of DataFrame (SQL) where they fit better. Are there any plans to
migrate Transformer API (ML) to DataFrame (SQL)?
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l` (`final`)
** `LogisticRegressionModel`
** `NaiveBayesModel`
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Hi Praveen,
I don't really know. I think TD or Michael should know as they
personally involved in the task (as far as I could figure it out from
the JIRA and the changes). Ping people on the JIRA so they notice your
question(s).
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Hi Praveen,
I've spent few hours on the changes related to streaming dataframes
(included in the SPARK-8360) and concluded that it's currently only
possible to read.stream(), but not write.stream() since there are no
streaming Sinks yet.
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Hi,
At first glance it appears the commit *yesterday* (Warsaw time) broke
the build :(
https://github.com/apache/spark/commit/0eea12a3d956b54bbbd73d21b296868852a04494
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