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--- Diff:
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@@ -311,22 +342,39 @@ class VectorIndexerModel private[ml] (
Github user srowen commented on the issue:
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Looks good though this would have to be opened vs master. How about just
not nulling fs? Rather than reopen it.
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--- Diff:
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@@ -1485,21 +1487,27 @@ class SparkSqlAstBuilder(conf: SQLConf)
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--- Diff: core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
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I am closing this because as @kiszk pointed out in his comment, there is no
reliable way to get `SQLConf` here.
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I think this should be linked to SPARK-22417.
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Github user yssharma commented on the issue:
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@brkyvz could you please have a look if it looks good. Would be great if
you're happy with the changes and we could merge it.
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Jenkins, test this please.
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--- Diff: python/pyspark/sql/session.py ---
@@ -416,6 +417,50 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row)
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--- Diff: python/pyspark/sql/session.py ---
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Github user viirya commented on the issue:
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To have a logical python runner, we may need to change some logic of
extracting python udfs. May require quite more change than this simple fix. If
you prefer it, I can do it. If it is just for
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--- Diff: python/pyspark/sql/tests.py ---
@@ -2592,6 +2592,16 @@ def test_create_dataframe_from_array_of_long(self):
df =
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Github user WeichenXu123 commented on the issue:
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ping @jkbradley Comments all addressed! Pls take a look again. Thanks!
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Github user mallman commented on the issue:
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> Yeah, I think with a config for this optimization is good.
I added a config switch, `spark.sql.nestedSchemaPruning.enabled`, which
disables the optimizations if set to `false`. By default
Github user guoxiaolongzte commented on the issue:
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retest this please
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GitHub user WeichenXu123 reopened a pull request:
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[SPARK-22126][ML] Fix model-specific optimization support for ML tuning
## What changes were proposed in this pull request?
Push down fitting parallelization code from
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GitHub user jinxing64 opened a pull request:
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[SPARK-22435][SQL] Support processing array and map type using script
## What changes were proposed in this pull request?
Currently, It is not supported to use script(e.g. python) to
Github user jinxing64 commented on a diff in the pull request:
https://github.com/apache/spark/pull/19652#discussion_r148749862
--- Diff:
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@@ -1485,21 +1487,27 @@ class SparkSqlAstBuilder(conf: SQLConf)
Github user wangyum commented on the issue:
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@srowen We can configure time zone by`spark.history.timeZone` now.
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--- Diff: python/pyspark/sql/session.py ---
@@ -416,6 +417,50 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row)
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--- Diff: python/pyspark/sql/session.py ---
@@ -416,6 +417,50 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row)
Github user cloud-fan commented on the issue:
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why is this patch so complicated? Based on the fact that data sources
accept a "timezone" option for read/writre, I'd expect it to be just:
* when `CreateTable`, set session local
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Github user cloud-fan commented on the issue:
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I see, is it better to introduce a logical python runner then we can do
column pruning correctly?
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Github user cloud-fan commented on the issue:
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You can call `SparkConf#registerKryoClasses` manually, maybe we can also
register these ml classes automatically in `KryoSerializer.newKryo` via
reflection.
cc @yanboliang @srowen
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+/*
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@hhbyyh comments addressed. Thanks!
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@@ -1454,22 +1454,24 @@ class SparkSqlAstBuilder(conf: SQLConf)
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Github user tmgstevens commented on the issue:
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@srowen, okay my issue around compilation issues, so I've changed the base
branch.
We can not null it, but I guess by re-open it we stand a better chance of
resolving any unexpected
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@@ -267,6 +268,33 @@ private class
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--- Diff: python/pyspark/sql/session.py ---
@@ -512,9 +557,7 @@ def createDataFrame(self, data, schema=None,
samplingRatio=None, verifySchema=Tr
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--- Diff: python/pyspark/sql/session.py ---
@@ -512,9 +557,7 @@ def createDataFrame(self, data, schema=None,
samplingRatio=None, verifySchema=Tr
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--- Diff: python/pyspark/rdd.py ---
@@ -56,6 +56,22 @@
__all__ = ["RDD"]
+class PythonEvalType(object):
+"""
+
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2049,133 +2049,18 @@ def map_values(col):
# User Defined
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cc @ueshin
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@@ -158,7 +173,13 @@ abstract class
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--- Diff: python/pyspark/sql/session.py ---
@@ -512,9 +557,7 @@ def createDataFrame(self, data, schema=None,
samplingRatio=None, verifySchema=Tr
Github user viirya commented on the issue:
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To have a python runner operator also has advantage like to work with
optimizer better, e.g. column pruning. I am not against this idea. However,
since it requires more change, I'd like to have more
Github user cloud-fan commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/group.py ---
@@ -214,11 +214,11 @@ def apply(self, udf):
:param udf: A function object returned by
GitHub user mgaido91 opened a pull request:
https://github.com/apache/spark/pull/19654
[SPARK-22437][PYSPARK] default mode for jdbc is wrongly set to None
## What changes were proposed in this pull request?
When writing using jdbc with python currently we are wrongly
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Github user HyukjinKwon commented on the issue:
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For the answer to
https://github.com/apache/spark/pull/19642#issuecomment-341688601,
> To have a logical python runner, we may need to change some logic of
extracting python udfs. May
Github user icexelloss commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2208,16 +2093,26 @@ def udf(f=None, returnType=StringType()):
| 8| JOHN DOE|
Github user cloud-fan commented on the issue:
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overall LGTM, but let's not add udf types that are not implemented yet,
like the AGGREGATE
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--- Diff: python/pyspark/sql/udf.py ---
@@ -0,0 +1,136 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+#
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--- Diff: python/pyspark/sql/group.py ---
@@ -214,11 +214,11 @@ def apply(self, udf):
:param udf: A function object returned by
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lgtm
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Thank you, @HyukjinKwon !
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why is column pruning execution details? Actually I feel it's werid to have
`ExtractPythonUDFs` rule applying on physical plans, is there a particular
reason?
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Github user hvanhovell commented on the issue:
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Looks good, one small question.
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I mean to have an individual python runner operator tells the way we
execute python udfs. Currently python udfs are just normal expressions. It
seems to me that logically they are just expressions.
Github user mgaido91 commented on the issue:
https://github.com/apache/spark/pull/19653
I think that the failure is related to the lack of changes in code outside
tests but I am not sure...
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Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/19649#discussion_r148810142
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/catalog/ExternalCatalogEventSuite.scala
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@@ -104,6 +109,8 @@ class
Github user mgaido91 commented on the issue:
https://github.com/apache/spark/pull/19654
I think that the failure is an infra issue..
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