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predicate subquery (IN, EXISTS) in SELECT is not supported in 2.0, only
supported in WHERE/HAVING.
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GitHub user prabeesh opened a pull request:
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[Documentation] Fixed JAR path
## What changes were proposed in this pull request?
Spark-2.0.0 uses Scala-2.11
## How was this patch tested?
n/a
You can merge this
Github user rxin commented on the issue:
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Any reason why there isn't a jira ticket?
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Github user cloud-fan commented on the issue:
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LGTM except one minor comment, thanks for working on it!
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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@@ -452,6 +452,21 @@ class Analyzer(
def ap
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Github user cloud-fan commented on the issue:
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can we wait for https://github.com/apache/spark/pull/13553? thanks!
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ex
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Github user viirya commented on the issue:
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Ok. I got it. So I think the point is the memory usage reduction is not
worth doing this change. Let me close it now.
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cc @yhuai @liancheng @clockfly
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[SPARK-15814][SQL] Aggregator can return null result
## What changes were proposed in this pull request?
It's similar to the bug fixed in
https://github.com/apache/spark/pull/13425, we s
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Github user rxin commented on the issue:
https://github.com/apache/spark/pull/13439
Going back to my original question: what's the point of this complicated
pull request? How much memory would you save in practice? The column batches
are not for persistent memory storage yet, and they
Github user viirya commented on the issue:
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So will it be more practice to benchmark the case in which there are some
constant and some not constant column vectors are used together? And compare it
with the original case in which all columns a
Github user viirya commented on the issue:
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I see. My question is, as for example we create 2 column vectors, one is
constant and one is not. Because we will not re-use the column vectors, so
their constant flag is fixed and not changed. As th
Github user rxin commented on the issue:
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What I meant is that if in one process you have some invocation of the
function that would hit the true branch, and some other invocation of the
function that would hit the false branch, the performanc
Github user squito closed the pull request at:
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GitHub user squito reopened a pull request:
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[DO NOT MERGE] lots of blacklist testing
making jenkins run the scheduler tests a lot
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/squito/sp
Github user NarineK commented on the issue:
https://github.com/apache/spark/pull/12836
Thank you for the quick responses @sun-rui and @shivaram .
Here is how the `dataframe.queyExection.toString` printout starts with:
== Parsed Logical Plan ==
'SerializeFromObject
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Github user techaddict commented on the issue:
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@maropu Thanks for the review, addressed all the comments
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Github user techaddict commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/tests.py ---
@@ -1481,17 +1481,7 @@ def test_list_functions(self):
spark.sql("CREATE DATABASE some_db")
Github user viirya commented on the issue:
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Besides, I just wrote this test according to other tests in
`ColumnarBatchBenchmark` that benchmark on-heap, off-heap column vector access.
I was thinking it might be enough. If not, any else need to
Github user viirya commented on the issue:
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hmm, but as the flag is set, I think it will not be changed?
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Github user koertkuipers commented on the issue:
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could we "rewind"/undo the append for the key and change it to a map that
inserts new values and key? so remove one append and replace it with another
operation?
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Github user kamalcoursera commented on the issue:
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Hi Davies,
Could you please shed more light on the status of correlated but non-scalar
subquery in Spark 2.0 release. Appreciate if you can summarize any other
restrictions, if any.
Github user rxin commented on the issue:
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I am not sure if you are really testing it correctly -- your benchmark is
mostly likely just testing how well the CPU does branch prediction when the
flag is always true or false.
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Github user koertkuipers commented on the issue:
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the tricky part with that is that (ds: Dataset[(K,
V)]).groupBy(_._1).mapValues(_._2) should return a
KeyValueGroupedDataset[K, V]
On Tue, Jun 7, 2016 at 8:22 PM, Wenchen Fan
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[SPARK-15813] Use past tense for the cancel container request message
## What changes were proposed in this pull request?
Use past tense for the cancel container request message as it is log
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Github user hvanhovell commented on the issue:
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@marymwu this has been fixed in
https://github.com/apache/spark/commit/09b3c56c91831b3e8d909521b8f3ffbce4eb0395.
Could you close this PR?
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What do you think `dropDuplicates`?
1. ds.select("_1", "_2", "_3").dropDuplicates(Seq("_1",
"_2")).orderBy("_1", "_2").show()
2. ds.select("_1", "_2", "_3").dropDuplicates("_1", "_
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merge original repository
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please exp
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Github user HyukjinKwon commented on the issue:
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It would be nicer if this PR follows
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark and has
a test.
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Github user viirya commented on the issue:
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cc @rxin Can you also take a look of this? This is staying for a while too.
Thanks!
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SPARK-15755
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-15755
java.lang.NullPointerException when run spark 2.0 setting
spark.serializer=org.apache.spark.serializer.KryoSeriali
Github user viirya commented on the issue:
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@rxin hmm, I just think if we can improve it by just adding conditional
check, it might be worth doing.
For the performance hurt, this is benchmark for on-heap and off-heap column
vectors be
Github user zhonghaihua commented on the issue:
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@vanzin my JIRA username is `iward`. Thanks a lot.
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@marmbrus
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/UnsupportedOperationChecker.scala
---
@@ -123,27 +159,6 @@ object UnsupportedOpera
GitHub user tdas opened a pull request:
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Added support for sorting after streaming aggregation with complete mode
## What changes were proposed in this pull request?
When the output mode is complete, then the output of a streaming
a
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--- Diff: R/pkg/R/mllib.R ---
@@ -197,11 +197,10 @@ print.summary.GeneralizedLinearRegressionModel <-
function(x, ...) {
invisible(x)
}
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@pjfanning we are now focusing on bug fixes and stability fixes rather than
adding new features.
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Github user rxin commented on the issue:
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For API design it would be better to be very conservative, because we
cannot remove APIs. There is always value in adding something, but there is
also a cost to maintenance and user experience (too man
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@viirya this is still a pretty major change for unclear benefits. There
might be other more important things that need more eyes on...
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Github user rxin commented on a diff in the pull request:
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -2262,6 +2275,19 @@ class Dataset[T] private[sql](
def distinct(): Dataset[T]
Github user rxin commented on the issue:
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Wouldn't this hurt performance even more due to the extra branch?
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Github user zjffdu commented on the issue:
https://github.com/apache/spark/pull/13540
Thanks @BryanCutler @MechCoder @MLnick for the review. I just update the PR
to make it as property. Regarding the pyspark docs, I think there's umbrella
jira to parity scala mllib and pyspark mllib,
Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/13544
@rxin
a small problem:
in `HiveContext` there is a method `refreshTable` for refreshing metadata
of Hive table.
now using new SparkSession API with hive support, the method is remov
Github user SparkQA commented on the issue:
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Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12938#discussion_r66177599
--- Diff: python/pyspark/ml/classification.py ---
@@ -183,7 +191,7 @@ def getThresholds(self):
If :py:attr:`thresholds` is set, return its value.
Github user yhuai commented on the issue:
https://github.com/apache/spark/pull/13189
Seems it is fine to not have metrics when we use hiveResultString.
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