[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14326302#comment-14326302 ] Peter Lin commented on HIVE-7292: - Would love to use this production, is it going to release in hive 15? Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14326336#comment-14326336 ] Xuefu Zhang commented on HIVE-7292: --- Formerly 0.15, now 1.1 is going to be release soon. Release candidate is out. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14326335#comment-14326335 ] Xuefu Zhang commented on HIVE-7292: --- Formerly 0.15, now 1.1 is going to be release soon. Release candidate is out. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14326675#comment-14326675 ] Lefty Leverenz commented on HIVE-7292: -- Doc note: See comments on HIVE-9257 and HIVE-9448 for documentation issues. * [HIVE-9257 commit comment with doc notes | https://issues.apache.org/jira/browse/HIVE-9257?focusedCommentId=14273166page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14273166] * HIVE-9448 doc comments ** [list of configuration parameters | https://issues.apache.org/jira/browse/HIVE-9448?focusedCommentId=14292487page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14292487] ** [where documented | https://issues.apache.org/jira/browse/HIVE-9448?focusedCommentId=14298353page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14298353] Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14234264#comment-14234264 ] Xuefu Zhang commented on HIVE-7292: --- [~libing], I assume you assigned this JIRA to yourself by mistake. However, let me know if you plan to work on this. Thanks. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Bing Li Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14222599#comment-14222599 ] yuemeng commented on HIVE-7292: --- i am very interesting in hive on spark ,an try to use it,when i bulit it (download from https://github.com/apache/hive.git,and chose the spark branch)use maven with command: mvn package -DskipTests -Phadoop-2 -Pdist,but it give me some error like [ERROR] /home/ym/hive-on-spark/hive/ql/src/java/org/apache/hadoop/hive/ql/exec/spark/status/SparkJobStatus.java:[22,24] cannot find symbol [ERROR] symbol: class JobExecutionStatus [ERROR] location: package org.apache.spark [ERROR] /home/ym/hive-on-spark/hive/ql/src/java/org/apache/hadoop/hive/ql/exec/spark/status/SparkJobStatus.java:[33,10] cannot find symbol [ERROR] symbol: class JobExecutionStatus [ERROR] location: interface org.apache.hadoop.hive.ql.exec.spark.status.SparkJobStatus [ERROR] /home/ym/hive-on-spark/hive/ql/src/java/org/apache/hadoop/hive/ql/exec/spark/status/SparkJobMonitor.java:[31,24] cannot find symbol [ERROR] symbol: class JobExecutionStatus can you tell me why? Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14222600#comment-14222600 ] Xuefu Zhang commented on HIVE-7292: --- [~yuemeng], you can try removing org/apache/spark folder in your local maven repo to see if it fixes it. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14209307#comment-14209307 ] Kiran Lonikar commented on HIVE-7292: - Sorry, I have not looked at the code, but want to know how is the RDD structured? is it columnar? I am specifically interested for ORC, RC, Parquet files about how you preserve their columnar structure. RDD by nature is row wise and the SchemaRDD more specifically so. The spark sql component uses SchemaRDD which is row wise. Just to be clear, I am not reporting any problems with this JIRA. I am interested to know the implementation. I think columnar structure has its advantages and thats what hive vectorization did (https://issues.apache.org/jira/browse/HIVE-4160). The earlier SQL implementation shark also had some kind of columnar structure. I am not sure this spark on hive is preserving it. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14186862#comment-14186862 ] Paulo Motta commented on HIVE-7292: --- Is the branch already usable in production? Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14186891#comment-14186891 ] Xuefu Zhang commented on HIVE-7292: --- [~pauloricardomg], thanks for your interest. I think the branch is ready for propective users to try out, but I'd recommend for production you wait for a formal release. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5 Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14070044#comment-14070044 ] wangmeng commented on HIVE-7292: This is a very valuable project！ Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Components: Spark Reporter: Xuefu Zhang Assignee: Xuefu Zhang Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.2#6252)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14048936#comment-14048936 ] niraj rai commented on HIVE-7292: - I am in OOO, so, the replying to the email might get delayed. Please reach out to me at (408) 799-8605 if you need something urgent. Regards Niraj Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Reporter: Xuefu Zhang Assignee: Xuefu Zhang Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.2#6252)
[jira] [Commented] (HIVE-7292) Hive on Spark
[ https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14049138#comment-14049138 ] niraj rai commented on HIVE-7292: - I am in OOO, so, the replying to the email might get delayed. Hive on Spark - Key: HIVE-7292 URL: https://issues.apache.org/jira/browse/HIVE-7292 Project: Hive Issue Type: Improvement Reporter: Xuefu Zhang Assignee: Xuefu Zhang Attachments: Hive-on-Spark.pdf Spark as an open-source data analytics cluster computing framework has gained significant momentum recently. Many Hive users already have Spark installed as their computing backbone. To take advantages of Hive, they still need to have either MapReduce or Tez on their cluster. This initiative will provide user a new alternative so that those user can consolidate their backend. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. Finally, allowing Hive to run on Spark also has performance benefits. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. This is an umbrella JIRA which will cover many coming subtask. Design doc will be attached here shortly, and will be on the wiki as well. Feedback from the community is greatly appreciated! -- This message was sent by Atlassian JIRA (v6.2#6252)
