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--- Diff:
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@@ -51,10 +51,12 @@ case class
Github user ueshin commented on a diff in the pull request:
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Github user viirya commented on the issue:
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Most looks pretty good. Only main question I have is about the empty
partition issue.
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--- Diff: python/pyspark/sql/tests.py ---
@@ -3344,6 +3342,22 @@ def test_vectorized_udf_wrong_return_type(self):
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--- Diff: python/pyspark/worker.py ---
@@ -80,14 +77,12 @@ def wrap_pandas_udf(f, return_type):
arrow_return_type = toArrowType(return_type)
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--- Diff: python/pyspark/sql/tests.py ---
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[SPARK-22110][SQL][Documentation] Add usage and improve documentation with
arguments and examples for trim function
## What changes were proposed in this pull request?
This PR proposes
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Github user viirya commented on the issue:
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Why you close it? You can just edit the PR title.
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Github user kevinyu98 commented on the issue:
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I am so sorry that I made mistake on the jira number, I create a new jira
SPARK-22110, but I used the wrong number, let me close this PR, then put
correct jira number.
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Github user WeichenXu123 commented on the issue:
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Oh I get your point. This is different from `RDD.aggregate`, it directly
return Map and avoid shuffling. it seems useful when numKeys is small.
But, I check the final `reduce` step, it
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Github user ArtRand commented on the issue:
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Hello @devaraj-kavali. Yes. I've been playing around with this because it's
inconvenient to clean up ZK whenever you uninstall/reinstall the Dispatcher.
The problem is that the only signal of a
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--- Diff:
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+/*
+
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How does it relate to https://github.com/apache/spark/pull/19283?
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ping @cloud-fan @gatorsmile
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To me, I am willing to merge this one soon.
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--- Diff: python/pyspark/serializers.py ---
@@ -246,15 +243,9 @@ def cast_series(s, t):
def loads(self, obj):
"""
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--- Diff: python/pyspark/worker.py ---
@@ -80,14 +77,12 @@ def wrap_pandas_udf(f, return_type):
arrow_return_type = toArrowType(return_type)
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2183,14 +2183,29 @@ def pandas_udf(f=None, returnType=StringType()):
:param f: python
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2183,14 +2183,29 @@ def pandas_udf(f=None, returnType=StringType()):
:param f: python
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--- Diff: python/pyspark/sql/tests.py ---
@@ -3256,11 +3256,9 @@ def test_vectorized_udf_null_string(self):
def
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--- Diff: python/pyspark/sql/tests.py ---
@@ -3308,12 +3306,12 @@ def test_vectorized_udf_invalid_length(self):
from pyspark.sql.functions
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--- Diff: python/pyspark/sql/tests.py ---
@@ -3308,12 +3306,12 @@ def test_vectorized_udf_invalid_length(self):
from pyspark.sql.functions
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--- Diff:
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@@ -28,12 +28,18 @@ class SparkOptimizer(
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test this please
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--- Diff:
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@@ -78,8 +82,14 @@ class SQLContextSuite extends SparkFunSuite with
Github user ConeyLiu commented on the issue:
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@jiangxb1987 ,@WeichenXu123, thanks for your reviewing. This change is
inspired by the `TODO List`. You can see the follow code snippet:
```scala
// TODO: Calling aggregateByKey and
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/18936
Hi Sean, sorry for late reply. Yeah, actually we do have some performance
data on F2J vs. OpenBLAS. It seems there is no performance gain from openblas,
not even on the unit test level. We are
Github user WeichenXu123 commented on the issue:
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Yes. I guess the perf gain is because, this PR use local hashmap which can
use unlimited memory, but current spark aggregation impl, will auto spill local
hashmap when exceeding a threshold.
Github user VinceShieh commented on the issue:
https://github.com/apache/spark/pull/19317
Nice catch. thanks. the perf gain is truly narrow.
I believe this impl just tried to align with the impl of
'reduceByKeyLocally'.
@ConeyLiu maybe we should revisit the code, along with
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Jenkins, retest this please
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2183,14 +2183,29 @@ def pandas_udf(f=None, returnType=StringType()):
:param f: python
Github user WeichenXu123 commented on the issue:
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And I have to point out that your impl have high risk causing OOM. The
current impl will auto spill when local hashmap is too large and can take
advantage of spark auto memory management
Github user viirya commented on a diff in the pull request:
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2183,14 +2183,29 @@ def pandas_udf(f=None, returnType=StringType()):
:param f: python function if
Github user HyukjinKwon commented on the issue:
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Yea .. let's open a separate JIRA.
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@viirya Yeah the perf gap I only focus on `mean` which can take advantage
of codegen.
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Github user viirya commented on the issue:
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Yeah, I think that fix should work for the strategy `Imputer.mean` because
`Imputer.mean` aggregates many columns at once now and that can be a too large
gen'd code for aggregation.
For the
Github user jkbradley commented on the issue:
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LGTM
Merging with master
Thanks @WeichenXu123 for the fix and for testing for backwards
compatibility!
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--- Diff:
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ping @gatorsmile
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I think you should create a new JIRA, instead of using SPARK-22088 which is
for the wrong style issue.
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ok to test
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[SPARK-22088][SQL][Documentation] Add usage and improve documentation with
arguments and examples for trim function
## What changes were proposed in this pull request?
This PR proposes
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--- Diff:
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@@ -146,7 +146,13 @@ case class
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@@ -324,17 +367,34 @@ case class
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@@ -329,6 +392,27 @@ class
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/19327#discussion_r140612202
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala
---
@@ -89,61 +89,124 @@ class
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/19327#discussion_r140605588
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
---
@@ -324,17 +367,34 @@ case class
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/19327#discussion_r140614816
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala
---
@@ -89,61 +89,124 @@ class
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/19327#discussion_r140617841
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
---
@@ -216,22 +229,51 @@ case class
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