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--- Diff: docs/sql-programming-guide.md ---
@@ -1812,6 +1812,7 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
-
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--- Diff: docs/running-on-kubernetes.md ---
@@ -561,6 +561,13 @@ specific to Spark on Kubernetes.
This is distinct from spark.executor.cores:
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--- Diff: R/pkg/R/functions.R ---
@@ -818,6 +818,7 @@ setMethod("factorial",
#' first(df$c, TRUE)
#' }
#' @note first(characterOrColumn)
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--- Diff: R/pkg/R/functions.R ---
@@ -963,6 +964,7 @@ setMethod("kurtosis",
#' last(df$c, TRUE)
#' }
#' @note last since 1.4.0
+#'
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--- Diff: R/pkg/R/functions.R ---
@@ -679,6 +679,19 @@ setMethod("hash",
column(jc)
})
+#' @details
+#'
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"current_date",
"current_timestamp",
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})
+#' @details
+#'
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> one question: no impact on compilation time for a large expression tree?
The string interpolation in `CodeBlock` is not different to default
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> To keep all the previous behaviour, SQL_OPTIONS_REDACTION_PATTERN can
include user
User names, unlike passwords, are useful for debugging. And they're not
meant to be secret. They're
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I also like this structural approach.
one question: no impact on compilation time for a large expression tree?
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GitHub user vanzin opened a pull request:
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[SPARK-24188][core] Restore "/version" API endpoint.
It was missing the jax-rs annotation.
You can merge this pull request into a Git repository by running:
$ git pull
Github user kiszk commented on a diff in the pull request:
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--- Diff:
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@@ -413,6 +413,25 @@ class DataFrameFunctionsSuite extends QueryTest
Github user gatorsmile commented on the issue:
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Yeah. That is fine. Will try to review the relevant PRs ASAP. Please ping
me. Thanks again!
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> To ensure the PR and review quality, we normally avoid doing everything
in a single huge PR. It would be much better if you can cut it to a few smaller
PRs.
I'll have a go at it. Of
Github user vanzin commented on the issue:
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I'm fine with the current patch, will leave it open for comments during the
weekend.
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ping
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Ah, ok.
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https://issues.apache.org/jira/browse/HIVE-14768 did the same thing.
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ok, give me more time to do so.
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Github user maropu commented on the issue:
https://github.com/apache/spark/pull/21240
Also, ISTM this function is less useful for uses.
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Github user maropu commented on the issue:
https://github.com/apache/spark/pull/21240
Why we need this? I thinks it's ok to add a new rewriting rule for `EXCEPT
ALL` and `INTERSECT ALL` in analyzer?
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21173
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GitHub user huaxingao opened a pull request:
https://github.com/apache/spark/pull/21244
[SPARK-24815]add flatten function to SparkR
## What changes were proposed in this pull request?
add array flatten function to SparkR
## How was this patch tested?
Unit
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