Github user jiangxb1987 commented on the issue:
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> but the thread killing left the shared SparkContext sometimes in a state
where further jobs can't be submitted.
Just curious how this happened?
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@cloud-fan, the change seems fine but would there be any clever trick to
test this? Seems we could very likely do the similar thing by mistake.
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--- Diff:
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}
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@maropu Thanks! Didn't know about creating a literal this way.
Don't you feel that the suggested change is way more elegant?
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ok to test
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--- Diff: python/pyspark/sql/functions.py ---
@@ -151,13 +151,15 @@ def _():
_collect_list_doc = """
Aggregate function: returns a
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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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}
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/functions.scala ---
@@ -561,6 +571,7 @@ object functions {
* The function by default
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--- Diff:
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Github user maropu commented on the issue:
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Like this?
```
scala> val structTy = StructType.fromDDL("a ARRAY")
structTy: org.apache.spark.sql.types.StructType =
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Can one of the admins verify this patch?
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@juliuszsompolski please, look at it.
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[SPARK-24171] Adding a note for non-deterministic functions
## What changes were proposed in this pull request?
I propose to add a clear statement for functions like `collect_list()`
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@maropu That would work if you had scala case classes for all the types. In
our case, we're working on a generic framework, where we only have Spark
schemas (and I'd rather not generate case classes
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thanks, merging to master/2.3!
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Simple scan benchmarks:
code:
https://github.com/apache/spark/compare/master...maropu:DataSourceReadBenchmark
master: https://gist.github.com/maropu/a767d21ed1dd047ec2bdca92915dc5c5
this
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retest this please
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@gatorsmile
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[SPARK-24133][SQL] Check for integer overflows when resizing
WritableColumnVectors
`ColumnVector`s store string data in one big byte array. Since the array
size is capped at just under
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retest this please
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How about this?
```
scala> val df = Seq(Outer(Seq.empty[Inner]),
Outer(Seq.empty[Inner])).toDF("a")
df: org.apache.spark.sql.DataFrame = [a: array>]
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@vanzin yes, in the example I am providing the hdfs URI, without using
ViewFS. Actually viewFS was not even configured in that case. In my cluster
test there were just two different namespaces and
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Github user mgaido91 commented on a diff in the pull request:
https://github.com/apache/spark/pull/21028#discussion_r185753083
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
---
@@ -378,6 +405,125 @@ case class
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/20973
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21163
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