[GitHub] spark pull request: [SPARK-11517][SQL]Calc partitions in parallel ...

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2016-02-29 Thread zhichao-li
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2016-02-26 Thread chenghao-intel
Github user chenghao-intel commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-189470296 LGTM except some minor suggestions. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your projec

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2016-02-26 Thread chenghao-intel
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[GitHub] spark pull request: [SPARK-11517][SQL]Calc partitions in parallel ...

2016-02-26 Thread chenghao-intel
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2016-02-26 Thread chenghao-intel
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2016-02-24 Thread zhichao-li
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2016-02-16 Thread chenghao-intel
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2016-02-16 Thread chenghao-intel
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2016-02-16 Thread chenghao-intel
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2016-02-16 Thread chenghao-intel
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2016-01-17 Thread zhichao-li
Github user zhichao-li commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-172447842 @yhuai @rxin , any thoughts or concerns for this PR? It's common that one table contains tons of partitions(i.e every 15mins a partition for clicking data). --- If

[GitHub] spark pull request: [SPARK-11517][SQL]Calc partitions in parallel ...

2015-11-18 Thread zhichao-li
Github user zhichao-li commented on a diff in the pull request: https://github.com/apache/spark/pull/9483#discussion_r45285408 --- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala --- @@ -211,7 +211,7 @@ abstract class RDD[T: ClassTag]( // Our dependencies and partiti

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2015-11-18 Thread zhichao-li
Github user zhichao-li commented on a diff in the pull request: https://github.com/apache/spark/pull/9483#discussion_r45285157 --- Diff: sql/hive/src/main/scala/org/apache/spark/sql/hive/ParallelUnionRDD.scala --- @@ -0,0 +1,52 @@ +/* + * Licensed to the Apache Software Fou

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2015-11-18 Thread yhuai
Github user yhuai commented on a diff in the pull request: https://github.com/apache/spark/pull/9483#discussion_r45269601 --- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala --- @@ -211,7 +211,7 @@ abstract class RDD[T: ClassTag]( // Our dependencies and partitions w

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2015-11-18 Thread yhuai
Github user yhuai commented on a diff in the pull request: https://github.com/apache/spark/pull/9483#discussion_r45269521 --- Diff: sql/hive/src/main/scala/org/apache/spark/sql/hive/ParallelUnionRDD.scala --- @@ -0,0 +1,52 @@ +/* + * Licensed to the Apache Software Foundati

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2015-11-15 Thread zhonghaihua
Github user zhonghaihua commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-156810714 Hi @zhichao-li ,thanks for doing this.I got a problem of scanning partitions slowly,and I apply this patch to my spark version.In my case: * Before I apply this p

[GitHub] spark pull request: [SPARK-11517][SQL]Calc partitions in parallel ...

2015-11-15 Thread zhonghaihua
Github user zhonghaihua commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-156810307 Hi @zhichao-li ,thanks for doing this.I got a problem of scanning partitions slowly,and I apply this patch to my spark version.In my case: * Before I apply this

[GitHub] spark pull request: [SPARK-11517][SQL]Calc partitions in parallel ...

2015-11-04 Thread chenghao-intel
Github user chenghao-intel commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-153974427 cc/ @scwf @Sephiroth-Lin, not sure if you guys get time for benchmarking this with the real world cases. --- If your project is set up for it, you can reply to t

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2015-11-04 Thread zhichao-li
Github user zhichao-li commented on the pull request: https://github.com/apache/spark/pull/9483#issuecomment-153930848 cc @chenghao-intel --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this

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2015-11-04 Thread zhichao-li
GitHub user zhichao-li opened a pull request: https://github.com/apache/spark/pull/9483 [SPARK-11517][SQL]Calc partitions in parallel for multiple partitions table Currently we calculate the getPartitions for each "hive partition" in sequence way, it would be faster if we can parall