[jira] [Comment Edited] (SPARK-17867) Dataset.dropDuplicates (i.e. distinct) should consider the columns with same column name
[
https://issues.apache.org/jira/browse/SPARK-17867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015772#comment-16015772
]
Mitesh edited comment on SPARK-17867 at 5/18/17 1:48 PM:
-
I'm seeing a regression from this change, the last `del <> 'hi'` filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
was (Author: masterddt):
I'm seeing a regression from this change, the last {del <> 'hi'} filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
> Dataset.dropDuplicates (i.e. distinct) should consider the columns with same
> column name
>
>
> Key: SPARK-17867
> URL: https://issues.apache.org/jira/browse/SPARK-17867
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Reporter: Liang-Chi Hsieh
>Assignee: Liang-Chi Hsieh
> Fix For: 2.1.0
>
>
> We find and get the first resolved attribute from output with the given
> column name in Dataset.dropDuplicates. When we have the more than one columns
> with the same name. Other columns are put into aggregation columns, instead
> of grouping columns. We should fix this.
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[jira] [Comment Edited] (SPARK-17867) Dataset.dropDuplicates (i.e. distinct) should consider the columns with same column name
[
https://issues.apache.org/jira/browse/SPARK-17867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015772#comment-16015772
]
Mitesh edited comment on SPARK-17867 at 5/18/17 1:48 PM:
-
I'm seeing a regression from this change, the last "del <> 'hi'" filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
was (Author: masterddt):
I'm seeing a regression from this change, the last `del <> 'hi'` filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
> Dataset.dropDuplicates (i.e. distinct) should consider the columns with same
> column name
>
>
> Key: SPARK-17867
> URL: https://issues.apache.org/jira/browse/SPARK-17867
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Reporter: Liang-Chi Hsieh
>Assignee: Liang-Chi Hsieh
> Fix For: 2.1.0
>
>
> We find and get the first resolved attribute from output with the given
> column name in Dataset.dropDuplicates. When we have the more than one columns
> with the same name. Other columns are put into aggregation columns, instead
> of grouping columns. We should fix this.
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[jira] [Comment Edited] (SPARK-17867) Dataset.dropDuplicates (i.e. distinct) should consider the columns with same column name
[
https://issues.apache.org/jira/browse/SPARK-17867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015772#comment-16015772
]
Mitesh edited comment on SPARK-17867 at 5/18/17 1:48 PM:
-
I'm seeing a regression from this change, the last {del <> 'hi'} filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
was (Author: masterddt):
I'm seeing a regression from this change, the last filter gets pushed down past
the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
> Dataset.dropDuplicates (i.e. distinct) should consider the columns with same
> column name
>
>
> Key: SPARK-17867
> URL: https://issues.apache.org/jira/browse/SPARK-17867
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Reporter: Liang-Chi Hsieh
>Assignee: Liang-Chi Hsieh
> Fix For: 2.1.0
>
>
> We find and get the first resolved attribute from output with the given
> column name in Dataset.dropDuplicates. When we have the more than one columns
> with the same name. Other columns are put into aggregation columns, instead
> of grouping columns. We should fix this.
--
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[jira] [Comment Edited] (SPARK-17867) Dataset.dropDuplicates (i.e. distinct) should consider the columns with same column name
[
https://issues.apache.org/jira/browse/SPARK-17867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015772#comment-16015772
]
Mitesh edited comment on SPARK-17867 at 5/18/17 1:49 PM:
-
I'm seeing a regression from this change, the last "del <> 'hi'" filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
was (Author: masterddt):
I'm seeing a regression from this change, the last "del <> 'hi'" filter gets
pushed down past the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
> Dataset.dropDuplicates (i.e. distinct) should consider the columns with same
> column name
>
>
> Key: SPARK-17867
> URL: https://issues.apache.org/jira/browse/SPARK-17867
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Reporter: Liang-Chi Hsieh
>Assignee: Liang-Chi Hsieh
> Fix For: 2.1.0
>
>
> We find and get the first resolved attribute from output with the given
> column name in Dataset.dropDuplicates. When we have the more than one columns
> with the same name. Other columns are put into aggregation columns, instead
> of grouping columns. We should fix this.
--
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(v6.3.15#6346)
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[jira] [Comment Edited] (SPARK-17867) Dataset.dropDuplicates (i.e. distinct) should consider the columns with same column name
[
https://issues.apache.org/jira/browse/SPARK-17867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015772#comment-16015772
]
Mitesh edited comment on SPARK-17867 at 5/18/17 1:47 PM:
-
I'm seeing a regression from this change, the last filter gets pushed down past
the dropDuplicates aggregation. cc [~cloud_fan]
{code:none}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
was (Author: masterddt):
I'm seeing a regression from this change, the last filter gets pushed down past
the dropDuplicates aggregation. cc [~cloud_fan]
{code:scala}
val df = Seq((1,2,3,"hi"), (1,2,4,"hi"))
.toDF("userid", "eventid", "vk", "del")
.filter("userid is not null and eventid is not null and vk is not null")
.repartitionByColumns(Seq("userid"))
.sortWithinPartitions(asc("userid"), asc("eventid"), desc("vk"))
.dropDuplicates("eventid")
.filter("userid is not null")
.repartitionByColumns(Seq("userid")).
sortWithinPartitions(asc("userid"))
.filter("del <> 'hi'")
// filter should not be pushed down to the local table scan
df.queryExecution.sparkPlan.collect {
case f @ FilterExec(_, t @ LocalTableScanExec(_, _)) =>
assert(false, s"$f was pushed down to $t")
{code}
> Dataset.dropDuplicates (i.e. distinct) should consider the columns with same
> column name
>
>
> Key: SPARK-17867
> URL: https://issues.apache.org/jira/browse/SPARK-17867
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Reporter: Liang-Chi Hsieh
>Assignee: Liang-Chi Hsieh
> Fix For: 2.1.0
>
>
> We find and get the first resolved attribute from output with the given
> column name in Dataset.dropDuplicates. When we have the more than one columns
> with the same name. Other columns are put into aggregation columns, instead
> of grouping columns. We should fix this.
--
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