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Github user jose-torres commented on the issue:
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I split off PackedRowWriterFactory with the intent to refactor MemorySinkV2
to use it later, but I just realized no refactoring is actually needed. So I've
slotted it in.
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Github user cloud-fan commented on a diff in the pull request:
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@@ -52,6 +54,10 @@ private[spark] class BroadcastManager(
Github user cloud-fan commented on the issue:
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To be consistent with Java/Scala API, I think we should only add this new
API to `UDFRegistration`. We should also move the `registerFunction` to there
too, and deprecate it in `Catalog`.
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[SPARK-23052] Migrate ConsoleSink to data source V2 api.
## What changes were proposed in this pull request?
Migrate ConsoleSink to data source V2 api.
Note that this includes
Github user ho3rexqj commented on a diff in the pull request:
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@@ -52,6 +54,10 @@ private[spark] class BroadcastManager(
Github user dongjoon-hyun commented on the issue:
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Thank you for review and approval, @HyukjinKwon .
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Github user HyukjinKwon commented on the issue:
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Yea, @gatorsmile's
https://github.com/apache/spark/pull/20217#issuecomment-357131129 this was
exactly what was on my mind.
I have few arguments to
Github user ho3rexqj commented on a diff in the pull request:
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Github user gatorsmile commented on the issue:
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Deprecating `registerFunction(name, f, returnType)` is not acceptable in
Spark 2.x releases.
They still call `registerFunction(name, f)` when `f` is a UDF based on my
above comment.
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Github user gatorsmile commented on the issue:
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Another way is to change the default value of `returnType` of
`registerFunction` to None. To avoid the behavior change, we can set
`returnType` for Python functions to `StringType` internally.
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cc @srowen , @HyukjinKwon , @sameeragarwal .
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[MINOR][BUILD] Fix Java linter errors
## What changes were proposed in this pull request?
This PR cleans up the java-lint errors (for v2.3.0-rc1 tag). Hopefully,
this will be the
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Github user icexelloss commented on the issue:
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My two cents:
I kind of like the PySpark API `registerUDF`. I think the API is simple and
clear, comparing to the alternative (merging this API to `registerFunction` and
throwing an
Github user maropu commented on a diff in the pull request:
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -237,13 +237,18 @@ class Dataset[T] private[sql](
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Retest this please.
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Also, I am fine with having a discussion later within 2.3.0 timeline and
doing that in a followup if needed.
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Jenkins retest this please
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Just wanted to make sure and check other possibilities (which is usually
not quite good to check so late like this ... ). I am not against this
implementation and fine as is too.
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--- Diff:
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@@ -141,7 +141,7 @@ statement
(LIKE?
Github user HyukjinKwon commented on the issue:
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Yup, I am looking at it. `def register(name: String, udf:
UserDefinedFunction)` and `def register[RT: TypeTag](name: String, func:
Function0[RT])`, etc.
The usual way to resemble
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--- Diff: python/pyspark/sql/context.py ---
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returnType=StringType()):
>>> _ =
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Github user gatorsmile commented on the issue:
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`registerUDF` is more consistent with the Scala APIs defined in
`UDFRegistration`. We do not need to provide `returnType`.
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Hi, @gatorsmile and @cloud-fan .
Could you review this, too?
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--- Diff: python/pyspark/sql/context.py ---
@@ -203,18 +203,48 @@ def registerFunction(self, name, f,
returnType=StringType()):
>>> _ =
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ok to test
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@squito by now, the variable `droppedEventsCounter ` is unused. So, I want
to reuse it.
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Any more comment? @icexelloss @HyukjinKwon @ueshin @viirya
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The problem in the test runs above was also a test issue. The new code
changed the synchronization such that the row wasn't written when the test
expected; I verified manually that the failing
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
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--- Diff: python/pyspark/sql/group.py ---
@@ -233,6 +233,27 @@ def apply(self, udf):
| 2| 1.1094003924504583|
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Github user rednaxelafx commented on the issue:
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Thanks for your comments, @viirya !
I'd say only having (1) and (2) makes it much less useful than having all
3, but it's still useful in its own for helping people understand exactly
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/20121
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Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/20241#discussion_r161116898
--- Diff: python/pyspark/ml/feature.py ---
@@ -1577,6 +1577,8 @@ class OneHotEncoder(JavaTransformer, HasInputCol,
HasOutputCol, JavaMLReadable,
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