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many thanks @gatorsmile
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Thanks! Merged to master.
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Github user ConeyLiu commented on the issue:
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Hi @jerryshao thanks for your reviewing.
>So it somehow reflects that CPU core contention is the main issue for
memory pre-occupation
I have modified the code, now it will not request
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
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@@ -993,7 +996,10 @@ class JDBCSuite extends SparkFunSuite
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--- Diff: python/pyspark/sql/functions.py ---
@@ -1921,10 +1921,12 @@ def from_json(col, schema, options={}):
@since(2.1)
def to_json(col,
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{"time":"26/08/2015"}
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--- Diff: R/pkg/R/functions.R ---
@@ -1715,7 +1717,15 @@ setMethod("to_date",
#'
#' # Converts an array of structs into a JSON array
#' df2
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@@ -99,73 +100,18 @@ public ArrayData copy() {
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--- Diff: docs/sql-programming-guide.md ---
@@ -1333,7 +1333,7 @@ the following case-insensitive options:
customSchema
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--- Diff: python/pyspark/serializers.py ---
@@ -343,6 +343,8 @@ def _load_stream_without_unbatching(self, stream):
key_batch_stream =
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@@ -29,7 +33,11 @@ class
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--- Diff: python/pyspark/tests.py ---
@@ -644,6 +644,18 @@ def test_cartesian_chaining(self):
set([(x, (y, y)) for x in range(10) for y
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@zhengruifeng Yeah, it is better. Actually the difference between running
multiple `withColumn` and one `withColumns` is mainly in the cost of query
analysis and plan/dataset initialization. I will
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LGTM
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Merging to master.
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In the test code, should we use `model.transform(df).count` instead?
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Add a test for it?
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[SPARK-22003][SQL] support array column in vectorized reader with UDF
## What changes were proposed in this pull request?
The UDF needs to deserialize the `UnsafeRow`. When the column
GitHub user wangyum opened a pull request:
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[SPARK-22002][SQL] Read JDBC table use custom schema support specify
partial fields.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18266 add a
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FYI, the `withColumns` API was proposed in #17819.
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Ran the similar benchmark as
https://github.com/apache/spark/pull/18902#issuecomment-321727416:
numColums | Old Mean | Old Median | New Mean | New Median
-- | -- | -- | -- | --
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cc @MLnick @zhengruifeng @yanboliang
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[SPARK-22001][ML][SQL] ImputerModel can do withColumn for all input columns
at one pass
## What changes were proposed in this pull request?
SPARK-21690 makes one-pass `Imputer` by
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--- Diff: python/pyspark/ml/tests.py ---
@@ -1464,20 +1464,79 @@ def test_logistic_regression_summary(self):
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ok to test
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Sure.
ok to test
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--- Diff: python/pyspark/serializers.py ---
@@ -343,9 +346,6 @@ def _load_stream_without_unbatching(self, stream):
key_batch_stream =
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Doh, sorry @holdenk and @aray, I didn't know the PR was open and was in
progress. Although the approach looks different with
https://github.com/apache/spark/pull/19226, let me close mine and
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[SPARK-21985][PYTHON] Fix zip-chained RDD to work
## What changes were proposed in this pull request?
This PR proposes to return an iterator of lists (batches) of objects in
Github user jerryshao commented on the issue:
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@HyukjinKwon would you please help to trigger the Jenkins? Thanks!
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Github user jerryshao commented on a diff in the pull request:
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--- Diff:
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@@ -81,7 +83,8 @@ private[v1] class OneStageResource(ui: SparkUI)
Github user holdenk commented on the issue:
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Sure, no worries. I think we should keep the test for now and we can hope
this goes into RC2 (I assume something will be missing from RC1 or I'll screw
up its packaging in some way). Otherwise the
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--- Diff: core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala ---
@@ -97,6 +97,7 @@ private[spark] object UIData {
var memoryBytesSpilled:
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LGTM Thanks for this catch!
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Github user viirya commented on the issue:
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@MLnick Thanks for pinging me.
I go through this quickly. The basic idea is the same, performing the
operations on multiple inputs columns at one single Dataset/DataFrame operation.
Github user aray commented on the issue:
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@holdenk I'm not going to be able to solve this tonight (short of just
removing the failing test).
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@srowen -- may I close this pull request?
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[SPARK-21915][ML][PySpark] Model 1 and Model 2 ParamMaps Missing
@dongjoon-hyun @HyukjinKwon
Error in PySpark example code:
Github user marktab closed the pull request at:
https://github.com/apache/spark/pull/19152
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Github user jinxing64 commented on the issue:
https://github.com/apache/spark/pull/19219
cc @cloud-fan @jiangxb1987
Could you please take a look at this ?
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