[jira] [Assigned] (SPARK-6511) Publish hadoop provided build with instructions for different distros

2015-06-09 Thread Patrick Wendell (JIRA)

 [ 
https://issues.apache.org/jira/browse/SPARK-6511?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Patrick Wendell reassigned SPARK-6511:
--

Assignee: Patrick Wendell

 Publish hadoop provided build with instructions for different distros
 ---

 Key: SPARK-6511
 URL: https://issues.apache.org/jira/browse/SPARK-6511
 Project: Spark
  Issue Type: Improvement
  Components: Build
Reporter: Patrick Wendell
Assignee: Patrick Wendell

 Currently we publish a series of binaries with different Hadoop client jars. 
 This mostly works, but some users have reported compatibility issues with 
 different distributions.
 One improvement moving forward might be to publish a binary build that simply 
 asks you to set HADOOP_HOME to pick up the Hadoop client location. That way 
 it would work across multiple distributions, even if they have subtle 
 incompatibilities with upstream Hadoop.
 I think a first step for this would be to produce such a build for the 
 community and see how well it works. One potential issue is that our fancy 
 excludes and dependency re-writing won't work with the simpler append 
 Hadoop's classpath to Spark. Also, how we deal with the Hive dependency is 
 unclear, i.e. should we continue to bundle Spark's Hive (which has some fixes 
 for dependency conflicts) or do we allow for linking against vanilla Hive at 
 runtime.



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[jira] [Assigned] (SPARK-6511) Publish hadoop provided build with instructions for different distros

2015-06-09 Thread Apache Spark (JIRA)

 [ 
https://issues.apache.org/jira/browse/SPARK-6511?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-6511:
---

Assignee: Apache Spark  (was: Patrick Wendell)

 Publish hadoop provided build with instructions for different distros
 ---

 Key: SPARK-6511
 URL: https://issues.apache.org/jira/browse/SPARK-6511
 Project: Spark
  Issue Type: Improvement
  Components: Build
Reporter: Patrick Wendell
Assignee: Apache Spark

 Currently we publish a series of binaries with different Hadoop client jars. 
 This mostly works, but some users have reported compatibility issues with 
 different distributions.
 One improvement moving forward might be to publish a binary build that simply 
 asks you to set HADOOP_HOME to pick up the Hadoop client location. That way 
 it would work across multiple distributions, even if they have subtle 
 incompatibilities with upstream Hadoop.
 I think a first step for this would be to produce such a build for the 
 community and see how well it works. One potential issue is that our fancy 
 excludes and dependency re-writing won't work with the simpler append 
 Hadoop's classpath to Spark. Also, how we deal with the Hive dependency is 
 unclear, i.e. should we continue to bundle Spark's Hive (which has some fixes 
 for dependency conflicts) or do we allow for linking against vanilla Hive at 
 runtime.



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[jira] [Assigned] (SPARK-6511) Publish hadoop provided build with instructions for different distros

2015-06-09 Thread Apache Spark (JIRA)

 [ 
https://issues.apache.org/jira/browse/SPARK-6511?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-6511:
---

Assignee: Patrick Wendell  (was: Apache Spark)

 Publish hadoop provided build with instructions for different distros
 ---

 Key: SPARK-6511
 URL: https://issues.apache.org/jira/browse/SPARK-6511
 Project: Spark
  Issue Type: Improvement
  Components: Build
Reporter: Patrick Wendell
Assignee: Patrick Wendell

 Currently we publish a series of binaries with different Hadoop client jars. 
 This mostly works, but some users have reported compatibility issues with 
 different distributions.
 One improvement moving forward might be to publish a binary build that simply 
 asks you to set HADOOP_HOME to pick up the Hadoop client location. That way 
 it would work across multiple distributions, even if they have subtle 
 incompatibilities with upstream Hadoop.
 I think a first step for this would be to produce such a build for the 
 community and see how well it works. One potential issue is that our fancy 
 excludes and dependency re-writing won't work with the simpler append 
 Hadoop's classpath to Spark. Also, how we deal with the Hive dependency is 
 unclear, i.e. should we continue to bundle Spark's Hive (which has some fixes 
 for dependency conflicts) or do we allow for linking against vanilla Hive at 
 runtime.



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