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Samik R commented on SPARK-20310:
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Hi Sean,
Thanks for your comments. You are probably thinking that I
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Apache Spark commented on SPARK-20316:
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User 'ouyangxiaochen' has created a pull request for this
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Apache Spark reassigned SPARK-20316:
Assignee: Apache Spark
> In SparkSQLCLIDriver, val and var should strictly follow the
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Apache Spark reassigned SPARK-20316:
Assignee: (was: Apache Spark)
> In SparkSQLCLIDriver, val and var should strictly
Xiaochen Ouyang created SPARK-20316:
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Summary: In SparkSQLCLIDriver, val and var should strictly follow
the Scala syntax
Key: SPARK-20316
URL: https://issues.apache.org/jira/browse/SPARK-20316
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Apache Spark commented on SPARK-19924:
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User 'gatorsmile' has created a pull request for this issue:
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Stephane Maarek commented on SPARK-20287:
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[~c...@koeninger.org]
How about using the subscribe
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Shixiong Zhu resolved SPARK-20131.
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Resolution: Fixed
Assignee: Shixiong Zhu
Fix Version/s: 2.2.0
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Daniel Nuriyev commented on SPARK-20036:
Thank you, Cody. I will do as you say and report what
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Apache Spark reassigned SPARK-20315:
Assignee: Apache Spark (was: Xiao Li)
> Set ScalaUDF's deterministic to true
>
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Apache Spark reassigned SPARK-20315:
Assignee: Xiao Li (was: Apache Spark)
> Set ScalaUDF's deterministic to true
>
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Apache Spark commented on SPARK-20315:
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User 'gatorsmile' has created a pull request for this issue:
Xiao Li created SPARK-20315:
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Summary: Set ScalaUDF's deterministic to true
Key: SPARK-20315
URL: https://issues.apache.org/jira/browse/SPARK-20315
Project: Spark
Issue Type: Bug
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Yan Facai (颜发才) commented on SPARK-20199:
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It's not hard, and I can work on it.
However, there
Eric Wasserman created SPARK-20314:
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Summary: Inconsistent error handling in JSON parsing SQL functions
Key: SPARK-20314
URL: https://issues.apache.org/jira/browse/SPARK-20314
Project: Spark
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Andrew Ash edited comment on SPARK-1809 at 4/12/17 11:00 PM:
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I'm not using
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Andrew Ash closed SPARK-1809.
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Resolution: Unresolved
Not using Mesos anymore, so closing
> Mesos backend doesn't respect
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Apache Spark commented on SPARK-9103:
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User 'jsoltren' has created a pull request for this issue:
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Jacques Nadeau commented on SPARK-13534:
Great, thanks [~holdenk]!
> Implement Apache Arrow
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holdenk commented on SPARK-13534:
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So I'm following along with the progress on this, I'll try and take a
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Tathagata Das resolved SPARK-20301.
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Resolution: Fixed
Fix Version/s: 2.2.0
> Flakiness in StreamingAggregationSuite
>
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Tejas Patil commented on SPARK-20184:
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Out of curiosity, I tried out a query with ~20 columns
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holdenk resolved SPARK-19570.
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Resolution: Fixed
Fix Version/s: 2.2.0
> Allow to disable hive in pyspark shell
>
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holdenk reassigned SPARK-19570:
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Assignee: Jeff Zhang
> Allow to disable hive in pyspark shell
>
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Jacques Nadeau commented on SPARK-13534:
Anybody know some committers we can get to look at this?
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Cody Koeninger commented on SPARK-19976:
What would your expected behavior be when you delete
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Apache Spark commented on SPARK-15354:
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User 'cloud-fan' has created a pull request for this issue:
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holdenk commented on SPARK-20202:
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Oh right, sorry I was misreading the intent of Affects Version/s.
>
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Cody Koeninger commented on SPARK-20037:
I'd be inclined to say this is a duplicate of the issue
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Cody Koeninger commented on SPARK-20036:
fixKafkaParams is related to executor consumers, not the
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Cody Koeninger commented on SPARK-20287:
The issue here is that the underlying new Kafka consumer
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Xiao Li updated SPARK-20291:
Fix Version/s: 2.0.3
> NaNvl(FloatType, NullType) should not be cast to NaNvl(DoubleType,
> DoubleType)
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Albert Meltzer updated SPARK-20313:
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Description:
Given two tables T1 and T2, partitioned on column part1, the following have
Albert Meltzer created SPARK-20313:
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Summary: Possible lack of join optimization when partitions are in
the join condition
Key: SPARK-20313
URL: https://issues.apache.org/jira/browse/SPARK-20313
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Albert Meltzer updated SPARK-20313:
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Description:
Given two tables T1 and T2, partitioned on column part1, the following have
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Xiao Li resolved SPARK-20304.
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Resolution: Fixed
Fix Version/s: 2.2.0
2.1.2
> AssertNotNull should not
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Xiao Li resolved SPARK-20303.
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Resolution: Fixed
Fix Version/s: 2.2.0
> Rename createTempFunction to registerFunction
>
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Albert Meltzer updated SPARK-20312:
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Description:
When optimizing an outer join, spark passes an empty row to both sides to see
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Albert Meltzer commented on SPARK-20312:
During query optimization, one of the subtrees becomes:
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Albert Meltzer commented on SPARK-20312:
Query plans are as follows:
{noformat}
== Parsed
Albert Meltzer created SPARK-20312:
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Summary: query optimizer calls udf with null values when it
doesn't expect them
Key: SPARK-20312
URL: https://issues.apache.org/jira/browse/SPARK-20312
Project:
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Apache Spark commented on SPARK-20292:
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User 'viirya' has created a pull request for this issue:
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Apache Spark reassigned SPARK-20292:
Assignee: Apache Spark
> string representation of TreeNode is messy
>
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Apache Spark reassigned SPARK-20292:
Assignee: (was: Apache Spark)
> string representation of TreeNode is messy
>
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Apache Spark commented on SPARK-20300:
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User 'MLnick' has created a pull request for this issue:
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Apache Spark reassigned SPARK-20300:
Assignee: (was: Apache Spark)
> Python API for ALSModel.recommendForAllUsers,Items
>
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Apache Spark reassigned SPARK-20300:
Assignee: Apache Spark
> Python API for ALSModel.recommendForAllUsers,Items
>
Juliusz Sompolski created SPARK-20311:
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Summary: SQL "range(N) as alias" or "range(N) alias" doesn't work
Key: SPARK-20311
URL: https://issues.apache.org/jira/browse/SPARK-20311
Project: Spark
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Omaiyma Popat commented on SPARK-19547:
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Hi Team, Can you please advise a resolution on the above
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Julien Champ commented on SPARK-19451:
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Any news on this bug / feature request ?
> Long values in
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Paul Zaczkieiwcz commented on SPARK-18055:
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Actually it seems it was this issue. I forgot to
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Sean Owen updated SPARK-20310:
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Issue Type: Improvement (was: Bug)
You may have a slightly earlier convergence problem in your build
Samik R created SPARK-20310:
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Summary: Dependency convergence error for scala-xml
Key: SPARK-20310
URL: https://issues.apache.org/jira/browse/SPARK-20310
Project: Spark
Issue Type: Bug
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Sean Owen reopened SPARK-20306:
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> beeline connect spark thrift server failure
>
>
>
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Sean Owen resolved SPARK-20306.
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Resolution: Not A Problem
> beeline connect spark thrift server failure
>
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Sean Owen resolved SPARK-20309.
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Resolution: Not A Problem
> Repartitioning - more than the default number of partitions
>
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Fei Wang commented on SPARK-20184:
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Also use the master branch to test my test case:
1. Java version
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sydt closed SPARK-20306.
Resolution: Fixed
> beeline connect spark thrift server failure
>
>
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balaji krishnan commented on SPARK-20309:
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thanks Sean. I will send that as an email to
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Sean Owen commented on SPARK-20309:
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It's not clear what you're asking, but this should go to
balaji krishnan created SPARK-20309:
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Summary: Repartitioning - more than the default number of
partitions
Key: SPARK-20309
URL: https://issues.apache.org/jira/browse/SPARK-20309
Project: Spark
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Artur Sukhenko resolved SPARK-20308.
Resolution: Duplicate
> org.apache.spark.shuffle.FetchFailedException: Too large frame
>
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Sean Owen reassigned SPARK-18692:
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Assignee: Hyukjin Kwon
> Test Java 8 unidoc build on Jenkins master builder
>
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Sean Owen resolved SPARK-18692.
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Resolution: Fixed
Fix Version/s: 2.2.0
Issue resolved by pull request 17477
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Herman van Hovell commented on SPARK-20184:
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I just tried your example using the master branch,
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xianyao jiang commented on SPARK-18791:
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when this feature will be provided?
is there any idea
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Sean Owen commented on SPARK-20306:
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This is some env or config issue, not Spark.
> beeline connect
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Sean Owen reassigned SPARK-20296:
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Assignee: Jason Tokayer
> UnsupportedOperationChecker text on distinct aggregations differs
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Sean Owen resolved SPARK-20296.
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Resolution: Fixed
Fix Version/s: 2.1.2
2.2.0
Issue resolved by pull
Stanislav Chernichkin created SPARK-20308:
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Summary: org.apache.spark.shuffle.FetchFailedException: Too large
frame
Key: SPARK-20308
URL: https://issues.apache.org/jira/browse/SPARK-20308
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pralabhkumar commented on SPARK-20199:
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For GBM its using Random Forest ,and to add randomness to
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Anne Rutten updated SPARK-20307:
Description:
when training a model in SparkR with string variables (tested with
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Anne Rutten updated SPARK-20307:
Description:
when training a model in SparkR with string variables (tested with
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pralabhkumar updated SPARK-20199:
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Priority: Major (was: Minor)
> GradientBoostedTreesModel doesn't have Column Sampling Rate
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Anne Rutten updated SPARK-20307:
Priority: Minor (was: Major)
> SparkR: pass on setHandleInvalid to spark.mllib functions that use
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Anne Rutten updated SPARK-20307:
Component/s: (was: MLlib)
> SparkR: pass on setHandleInvalid to spark.mllib functions that use
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Anne Rutten updated SPARK-20307:
Component/s: SparkR
> SparkR: pass on setHandleInvalid to spark.mllib functions that use
>
Anne Rutten created SPARK-20307:
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Summary: SparkR: pass on setHandleInvalid to spark.mllib functions
that use StringIndexer
Key: SPARK-20307
URL: https://issues.apache.org/jira/browse/SPARK-20307
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Apache Spark commented on SPARK-6227:
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User 'MLnick' has created a pull request for this issue:
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Hyukjin Kwon commented on SPARK-20294:
--
{quote}
If we now that the rdd is not empty (the function
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Fei Wang edited comment on SPARK-20184 at 4/12/17 9:21 AM:
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try this :
1.
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Fei Wang edited comment on SPARK-20184 at 4/12/17 9:21 AM:
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try this :
1.
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Fei Wang commented on SPARK-20184:
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try this :
1. create table
[code]
val df = (1 to 50).map(x =>
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João Pedro Jericó commented on SPARK-20294:
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Yes, I think that this would be a nice solution,
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sydt updated SPARK-20306:
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Description:
Beeline connect spark thrift server of spark-1.6.2 with kerberos failure and
error is :
Error
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sydt updated SPARK-20306:
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Description:
Beeline connect spark thrift server of spark-1.6.2 with kerberos failure and
error is :
ERROR
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Sean Owen commented on SPARK-20294:
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CC [~andrewor14] who might be able to comment better on this.
If
sydt created SPARK-20306:
Summary: beeline connect spark thrift server failure
Key: SPARK-20306
URL: https://issues.apache.org/jira/browse/SPARK-20306
Project: Spark
Issue Type: Bug
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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15965568#comment-15965568
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João Pedro Jericó edited comment on SPARK-20294 at 4/12/17 8:56 AM:
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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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João Pedro Jericó updated SPARK-20294:
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Description:
Currently the _inferSchema function on
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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15965568#comment-15965568
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João Pedro Jericó edited comment on SPARK-20294 at 4/12/17 8:57 AM:
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https://issues.apache.org/jira/browse/SPARK-20294?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15965568#comment-15965568
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João Pedro Jericó commented on SPARK-20294:
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Yes, if sampling ration is not given it infers for
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https://issues.apache.org/jira/browse/SPARK-20237?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-20237:
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Description:
In spark-1.6 and later versions, there is a problem with its memory management
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https://issues.apache.org/jira/browse/SPARK-20286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15965556#comment-15965556
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Miguel Pérez commented on SPARK-20286:
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I'm zero familiar with the code, but I'll try to send a
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https://issues.apache.org/jira/browse/SPARK-20302?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin resolved SPARK-20302.
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Resolution: Fixed
Fix Version/s: 2.2.0
> Short circuit cast when from and to types are
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https://issues.apache.org/jira/browse/SPARK-20305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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LvDongrong updated SPARK-20305:
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Attachment: failfetchresources.PNG
> Master may keep in the state of "COMPELETING_RECOVERY",then all
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https://issues.apache.org/jira/browse/SPARK-20305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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LvDongrong updated SPARK-20305:
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Attachment: (was: failfetchresources.PNG)
> Master may keep in the state of
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https://issues.apache.org/jira/browse/SPARK-20298?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-20298.
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Resolution: Fixed
Assignee: Brendan Dwyer
Fix Version/s: 2.2.0
Resolved by
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https://issues.apache.org/jira/browse/SPARK-20305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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LvDongrong updated SPARK-20305:
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Attachment: failfetchresources.PNG
> Master may keep in the state of "COMPELETING_RECOVERY",then all
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https://issues.apache.org/jira/browse/SPARK-20305?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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LvDongrong updated SPARK-20305:
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Description:
Master may keep in the state of "COMPELETING_RECOVERY",then all the application
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