+1
Tested HiveThriftServer2 against Hive 0.12.0 on Mac OS X. Known issues
are fixed. Hive version inspection works as expected.
On 11/15/14 8:25 AM, Zach Fry wrote:
+0
I expect to start testing on Monday but won't have enough results to change
my vote from +0
until Monday night or Tuesday mo
+0
I expect to start testing on Monday but won't have enough results to change
my vote from +0
until Monday night or Tuesday morning.
Thanks,
Zach
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S
I think in this case we can probably just drop that dependency, so
there is a simpler fix. But mostly I'm curious whether anyone else has
observed this.
On Fri, Nov 14, 2014 at 12:24 PM, Hari Shreedharan
wrote:
> Seems like a comment on that page mentions a fix, which would add yet
> another prof
Seems like a comment on that page mentions a fix, which would add yet another
profile though — specifically telling mvn that if it is an apple jdk, use the
classes.jar as the tools.jar as well, since Apple-packaged JDK 6 bundled them
together.
Link: http://permalink.gmane.org/gmane.comp.java
A work around for this fix is identified here:
http://dbknickerbocker.blogspot.com/2013/04/simple-fix-to-missing-toolsjar-in-jdk.html
However, if this affects more users I'd prefer to just fix it properly
in our build.
On Fri, Nov 14, 2014 at 12:17 PM, Patrick Wendell wrote:
> A recent patch bro
A recent patch broke clean builds for me, I am trying to see how
widespread this issue is and whether we need to revert the patch.
The error I've seen is this when building the examples project:
spark-examples_2.10: Could not resolve dependencies for project
org.apache.spark:spark-examples_2.10:j
+1
Tested on Mac OS X, and verified that sort-based shuffle bug is fixed.
Matei
> On Nov 14, 2014, at 10:45 AM, Andrew Or wrote:
>
> Hi all, since the vote ends on a Sunday, please let me know if you would
> like to extend the deadline to allow more time for testing.
>
> 2014-11-13 12:10 GMT-
Hi all, since the vote ends on a Sunday, please let me know if you would
like to extend the deadline to allow more time for testing.
2014-11-13 12:10 GMT-08:00 Sean Owen :
> Ah right. This is because I'm running Java 8. This was fixed in
> SPARK-3329 (
> https://github.com/apache/spark/commit/2b7
Yeah I think someone even just suggested that today in a separate
thread? couldn't hurt to just add an example.
On Fri, Nov 14, 2014 at 4:48 PM, Corey Nolet wrote:
> In the past, I've built it by providing -Dhadoop.version=2.5.1 exactly like
> you've mentioned. What prompted me to write this emai
You're the second person to request this today. Planning to include this in my
PR for Spark-4338.
-Sandy
> On Nov 14, 2014, at 8:48 AM, Corey Nolet wrote:
>
> In the past, I've built it by providing -Dhadoop.version=2.5.1 exactly like
> you've mentioned. What prompted me to write this email wa
In the past, I've built it by providing -Dhadoop.version=2.5.1 exactly like
you've mentioned. What prompted me to write this email was that I did not
see any documentation that told me Hadoop 2.5.1 was officially supported by
Spark (i.e. community has been using it, any bugs are being fixed, etc...
I don't think it's necessary. You're looking at the hadoop-2.4
profile, which works with anything >= 2.4. AFAIK there is no further
specialization needed beyond that. The profile sets hadoop.version to
2.4.0 by default, but this can be overridden.
On Fri, Nov 14, 2014 at 3:43 PM, Corey Nolet wrot
I noticed Spark 1.2.0-SNAPSHOT still has 2.4.x in the pom. Since 2.5.x is
the current stable Hadoop 2.x, would it make sense for us to update the
poms?
Hi Quizhuang - you have two options:
1) Within the map step define a validation function that will be executed
on every record.
2) Use the filter function to create a filtered dataset prior to
processing.
On 11/14/14, 10:28 AM, "Qiuzhuang Lian" wrote:
>Hi,
>
>MapReduce has the feature of skippi
Hi,
MapReduce has the feature of skipping bad records. Is there any equivalent
in Spark? Should I use filter API to do this?
Thanks,
Qiuzhuang
It shows nullPointerException, your data could be corrupted? Try putting a
try catch inside the operation that you are doing, Are you running the
worker process on the master node also? If not, then only 1 node will be
doing the processing. If yes, then try setting the level of parallelism and
numb
Hi All,
We have set up 2 node cluster (NODE-DSRV05 and NODE-DSRV02) each is
having 32gb RAM and 1 TB hard disk capacity and 8 cores of cpu. We have set
up hdfs which has 2 TB capacity and the block size is 256 mb When we try
to process 1 gb file on spark, we see the following exception
14/11/
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