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
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
Github user rxin commented on the issue:
https://github.com/apache/spark/pull/13439
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
Github user squito closed the pull request at:
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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
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 ==
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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:
https://github.com/apache/spark/pull/13439
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
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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Can one of the admins verify this patch?
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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
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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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Github user dongjoon-hyun commented on the issue:
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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
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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
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
Github user zhonghaihua commented on the issue:
https://github.com/apache/spark/pull/12258
@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
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
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Github user felixcheung commented on a diff in the pull request:
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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
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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:
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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
Github user WeichenXu123 commented on the issue:
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@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
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Github user MLnick commented on a diff in the pull request:
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--- 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:
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Seems it is fine to not have metrics when we use hiveResultString.
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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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Github user cloud-fan commented on the issue:
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A possible approach maybe just keep the function given by `mapValues`, and
apply it before calling the function given by `mapGroups`. By doing this, we at
least won't make the performance worse,
Github user vanzin commented on the issue:
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@tgravescs the problem is this code in Client.scala:
sparkConf.set(TOKEN_RENEWAL_INTERVAL, renewalInterval)
That will write the value to the config with the `ms` suffix. I think
Github user viirya commented on the issue:
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@rxin I've updated this to more simple approach that doesn't introduce new
classes. The main change is to check if the current vector is constant or not
and do suitable data access. Please take a
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Github user cloud-fan commented on the issue:
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LGTM, merging to master and 2.0, thanks!
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Github user cloud-fan commented on the issue:
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`QueryExecution.hiveResultString` will call `SparkPlan.executeCollect`
without setting an execution id. This method is only used in test, should we
just stop reporting metrics for this case, or
Github user viirya commented on a diff in the pull request:
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--- Diff:
sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OnHeapColumnVector.java
---
@@ -70,26 +71,106 @@ public long nullsNativeAddress()
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Github user viirya commented on the issue:
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The latest benchmark is run individually for each type of column vector. As
stated in `ColumnarBatchBenchmark`, it is hard to reason about the JIT. If we
put these 4 cases together to run benchmark,
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Github user viirya commented on the issue:
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Benchmark again on new change:
Environment:
Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux
3.19.0-25-generic
Intel(R) Core(TM) i7-5557U CPU @ 3.10GHz
Github user zjffdu commented on the issue:
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\cc @yanboliang
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Github user vanzin commented on the issue:
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@dhruve could you close the PR? The bot doesn't do it automatically for
backports. thx
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Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/9207
So I guess I'm wondering what our plans for PMML look like - I'm happy to
update this or go in the direction @MLnick suggested if thats what we want?
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/12938
**[Test build #60139 has
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/60139/consoleFull)**
for PR 12938 at commit
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/13335
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