Github user srowen commented on the issue:
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Hm, that's a bug isn't it? any time is ambiguous without a timezone. These
things can make sense for local display but not for output of data.
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Github user HyukjinKwon commented on the issue:
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Thank you for your comment @srowen !
I did this for two reasons. Firstly, it can't write and read back the dates
or timestamps but it might have to be converted manually by users if it writes
Github user kiszk commented on the issue:
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@cloud-fan and @hvanhovell thank you for your comments. Based on your
comments, I implemented ```UnsafeArrayData``` by using one implementation with
explicit clearing ```null bits``` by ```Arrays.fill
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
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@@ -151,31 +151,23 @@ class GradientBoostedTreesModel @Since("1
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--- Diff:
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@@ -205,31 +205,27 @@ private[spark] object GradientBoostedTrees
Github user srowen commented on the issue:
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@yanghaogn since you already made a JIRA, connect it in the title please.
It is not resolved
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Github user srowen commented on the issue:
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Nah @HyukjinKwon I think this is a simple fix that's self-contained here.
Either the iterations or the estimate formula is wrong here and that's all
there is to it. `(0 until n)` would be fine too.
Github user srowen commented on the issue:
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This has a big problem though -- the times are now wrong, because there is
no timezone.
Why would we write formatted dates in a data file though? it's for human
consumption. Timestamp is an appro
Github user adrian-wang closed the pull request at:
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Closing, thx!
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+1 large / infinitely growing event log files are quite a problem for long
running streaming jobs
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[SPARK-16216][SQL] CSV data source does not write date and timestamp
correctly
## What changes were proposed in this pull request?
This PR corrects CSV data sources in order to write `
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[SPARK-16215][SQL] Reduce runtime overhead of a program that writes an
primitive array in Dataframe/Dataset
## What changes were proposed in this pull request?
This PR optimize generate code
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Github user gatorsmile commented on the issue:
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@cloud-fan Submitted a PR for converting CTAS in parquet to data source
tables without hive support. Could you also review that PR? Thanks!
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Just realized this PR introduced the original changes. Could you also
review my PR: https://github.com/apache/spark/pull/13907?
When users create table as query with `STORED AS` or `ROW F
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As the denominator is n, the iteration time should also be n
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GitHub user yanghaogn opened a pull request:
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make rdd count be n
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain
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[SPARK-16213][SQL] Reduce runtime overhead of a program that creates an
primitive array in DataFrame
## What changes were proposed in this pull request?
This PR reduces runtime overhead of a
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[SPARK-16212][STREAMING][KAFKA] code cleanup from review feedback
## What changes were proposed in this pull request?
code cleanup in kafka-0-8 to match suggested changes for kafka-0-10 branch
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@vectorijk It seems difficult to handle this JIRA for me. Could you help me
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Thanks so much for taking care!
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--- Diff: R/pkg/R/SQLContext.R ---
@@ -110,11 +110,46 @@ infer_type <- function(x) {
}
}
-getDefaultSqlSource <- function() {
+#
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Hi, @rxin .
For this `OptimizeIn` PR, please let me know if we need further
optimization.
Thank you always.
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[SPARK-16209] [SQL] Convert Hive Tables to Data Source Tables for CREATE
TABLE AS SELECT
What changes were proposed in this pull request?
Currently, the following created table is Hive
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GitHub user dongjoon-hyun opened a pull request:
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[SPARK-16208][SQL] Add `CollapseEmptyPlan` optimizer
## What changes were proposed in this pull request?
This PR adds a new logical optimizer, `CollapseEmptyPlan`, to collapse a
logi
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