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Github user gatorsmile commented on the issue:
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So far, the best workaround is that predicate-based JDBC API; otherwise, as
I mentioned above, we need to do it using sampling to find the boundary of each
block.
> In one embodiment, a
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re-ping @jkbradley
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Github user felixcheung commented on a diff in the pull request:
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--- Diff: R/pkg/inst/tests/testthat/test_Windows.R ---
@@ -20,7 +20,7 @@ test_that("sparkJars tag in SparkContext", {
if (.Platform$OS.type !=
Github user dongjoon-hyun commented on the issue:
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Hi, @gatorsmile .
This is the original PR which has two fixes together now.
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Github user dongjoon-hyun commented on the issue:
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Hi, @gatorsmile .
I tested here and applied to #16579 .
PR #16579 has two fixes. After merging #16579 , I'm going to close this one.
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Github user felixcheung commented on a diff in the pull request:
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -172,11 +172,23 @@ class Dataset[T] private[sql](
Github user felixcheung commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/dataframe.py ---
@@ -85,17 +85,20 @@ def rdd(self):
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc,
Github user felixcheung commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/dataframe.py ---
@@ -85,17 +85,20 @@ def rdd(self):
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc,
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--- Diff:
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@@ -206,18 +209,22 @@ private[spark] class DiskBlockObjectWriter(
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--- Diff: R/pkg/inst/tests/testthat/test_Windows.R ---
@@ -20,7 +20,7 @@ test_that("sparkJars tag in SparkContext", {
if (.Platform$OS.type !=
Github user tejasapatil closed the pull request at:
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Github user SparkQA commented on the issue:
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Github user djvulee commented on the issue:
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Using the *predicates* parameters to split the table seems reasonable, but
it just put some work should be done by Spark to users in my personal opinion.
Users need know how to split the table
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jenkins, retest this please
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Github user djvulee commented on the issue:
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Yes, this solution is not suitable for large table, but I can not find a
better solution, this is the best optimisation I can find.
So just add it as a choose, let the users know what he is
Github user zhengruifeng commented on the issue:
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I think now clustering metrics are not that general, comparing with
classification/regression metrics:
WSSSE only apply to `KMeans` and `BiKMeans`
Loglikelihood only apply to `GMM`
Github user xwu0226 commented on a diff in the pull request:
https://github.com/apache/spark/pull/16626#discussion_r97213829
--- Diff:
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@@ -168,6 +168,43 @@ case class AlterTableRenameCommand(
}
Github user gatorsmile commented on the issue:
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Please update the PR description.
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--- Diff:
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@@ -584,14 +593,18 @@ private[spark] class
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Github user asfgit closed the pull request at:
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retest this please
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Github user yanboliang commented on the issue:
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Merged into master. If there are new comments about the model persistence
compatibility issue, we can address them in follow-up work. Thanks for all your
reviewing.
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Github user dongjoon-hyun commented on the issue:
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For `SET -v` without sorting, please refer #16624 , too.
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Github user asfgit closed the pull request at:
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Merged into master, branch-2.1 and branch-2.0. Thanks for all your
reviewing.
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Github user djvulee commented on the issue:
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@gatorsmile can you take a look at?
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Github user djvulee commented on the issue:
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Table2 with about 5M rows, 200partition by SparkSQL.
(The table using the MySQL sharding, and every partition will return 10K
rows at most)
old partition result(elements in
Github user sethah commented on the issue:
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ping @yanboliang
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Github user djvulee commented on the issue:
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Here is the real data test result:
Table with 1.2Million rows, 50partition by SparkSQL.
old partition result(elements in each partition)
Github user AmplabJenkins commented on the issue:
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Can one of the admins verify this patch?
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GitHub user djvulee opened a pull request:
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[SparkSQL] a better balance partition method for jdbc API
## What changes were proposed in this pull request?
The partition method in` jdbc` using the equal
step, this can lead to skew
Github user gatorsmile commented on a diff in the pull request:
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--- Diff:
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@@ -649,6 +649,14 @@ object SQLConf {
.doubleConf
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--- Diff: R/pkg/inst/tests/testthat/test_Windows.R ---
@@ -20,7 +20,7 @@ test_that("sparkJars tag in SparkContext", {
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--- Diff:
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@@ -736,6 +736,22 @@ class SparkSqlAstBuilder(conf: SQLConf)
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--- Diff:
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@@ -67,28 +67,33 @@ class
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LGTM, merging to master!
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Github user witgo commented on the issue:
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@squito
My understanding is that the TaskSchedulerImpl class contains many
synchronized statements (synchronized the methods). If a synchronized
statements execution time is very long, it will
Github user witgo commented on a diff in the pull request:
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--- Diff:
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@@ -602,6 +619,20 @@ class
Github user viirya commented on the issue:
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It is true of course you can construct a combination of complex string
operations and compare it with a simple Scala UDF. But as you said, the
previous claim is true in most of time. I also think
Github user chenghao-intel commented on a diff in the pull request:
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Github user chenghao-intel commented on the issue:
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I think that's true in most of time for`Scala UDF needs extra conversion
between internal format and external format on input and out`, not all of the
time, for example, some built-in string
Github user viirya commented on a diff in the pull request:
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@@ -514,6 +514,34 @@ case class OptimizeCodegen(conf:
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Github user viirya commented on the issue:
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I think most of time it should be as Scala UDF needs extra conversion
between internal format and external format on input and out.
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Github user chenghao-intel commented on the issue:
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Actually I doubt this is really an optimization, as the assumption of Scala
UDF is slower than the non-SCALA UDF probably not always true.
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Github user viirya commented on a diff in the pull request:
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Github user felixcheung commented on the issue:
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I've found the root cause, from investigations, but need to test cross
platform for the fix.
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Github user chenghao-intel commented on a diff in the pull request:
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@@ -514,6 +514,34 @@ case class
Github user dongjoon-hyun commented on the issue:
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The final failure, `HiveSparkSubmitSuite.dir` is irrelevant to this PR.
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Github user dongjoon-hyun commented on the issue:
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Hi, @srowen and @gatorsmile .
Finally, this PR resolved all issues.
Could you review this again?
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Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16670#discussion_r97211236
--- Diff: R/pkg/R/utils.R ---
@@ -756,12 +756,12 @@ varargsToJProperties <- function(...) {
props
}
-launchScript <-
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/16624
Retest this please
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/16670
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Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16670#discussion_r97211194
--- Diff: R/pkg/R/utils.R ---
@@ -756,12 +756,12 @@ varargsToJProperties <- function(...) {
props
}
-launchScript <-
GitHub user felixcheung opened a pull request:
https://github.com/apache/spark/pull/16670
[SPARK-19324][SPARKR] Spark VJM stdout output is getting dropped in SparkR
## What changes were proposed in this pull request?
This affects mostly running job from the driver in client
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