GitHub user zhengruifeng opened a pull request:
https://github.com/apache/spark/pull/19288
[SPARK-22075][ML] unpersist datasets cached by PeriodicRDDCheckpointer
## What changes were proposed in this pull request?
PeriodicRDDCheckpointer will automatically persist the last 3
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Github user klion26 commented on the issue:
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@squito I agree with you that this should be handled by yarn.
In my opinion, this is some form of defensive programming. The Spark
Streaming and structured streaming will both request more
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--- Diff:
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@@ -0,0 +1,235 @@
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retest this please.
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Github user cloud-fan commented on the issue:
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> So we ended up not getting the correct outputOrdering during physical
planning stage before Sort nodes are added to the children.
What's the harm of this? I think only
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`signal 9`
retest this please
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--- Diff:
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@@ -351,11 +351,11 @@ private[spark] object EventLoggingListener
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@@ -351,11 +351,11 @@ private[spark] object EventLoggingListener
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Github user zhengruifeng commented on the issue:
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@srowen I check `LDA` : although `unpersistDataSet` is not called in it,
no intermediate cached rdds is generated after `fit()`.
Then I check `Pregel`, and find that each call of
Github user jerryshao commented on the issue:
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@klion26 , this is not a problem related to Spark Streaming and Structured
Streaming. For any Spark application it will run into this problem. This is
basically a YARN problem and looks hard to
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--- Diff: R/pkg/R/DataFrame.R ---
@@ -998,33 +998,44 @@ setMethod("unique",
#' sparkR.session()
#' path <- "path/to/file.json"
#' df <-
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--- Diff:
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@@ -0,0 +1,235 @@
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
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@@ -747,6 +747,19 @@ class JDBCSuite extends SparkFunSuite
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cc @liufengdb @gatorsmile @jiangxb1987
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[SPARK-22076][SQL] Expand.projections should not be a Stream
## What changes were proposed in this pull request?
Spark with Scala 2.10 fails with a group by cube:
```
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+/*
+ * Licensed to the
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retest this please
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--- Diff:
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@@ -0,0 +1,114 @@
+/*
+ *
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Github user zuotingbing commented on the issue:
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I am not sure. If we move this line `val in = new
BufferedInputStream(fs.open(log))` into try~catch, we should define `var in:
BufferedInputStream = null` before, and use `catch { case e:
Github user jerryshao commented on the issue:
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Strictly saying, this line `new BufferedInputStream(fs.open(log))` will
also throw exception, shouldn't you try-catch it?
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Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
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---
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache
Github user maryannxue commented on the issue:
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Thank you very much for the feedback, @tejasapatil, @gatorsmile! All the
suggestions/comments have been addressed by my latest check-in.
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Github user maryannxue commented on the issue:
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@cloud-fan Please refer to
https://issues.apache.org/jira/browse/SPARK-18591. The plan is to apply the
optimization during physical planning stage, and specifically, when creating
Aggregate
Github user LeoIV commented on the issue:
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The problem emerges in cases where you built a whole pipeline. You have a
set of documents you want to classify. These documents have some additional
features and they are preprocessed in the
Github user cloud-fan commented on a diff in the pull request:
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--- Diff:
core/src/main/scala/org/apache/spark/storage/memory/MemoryStore.scala ---
@@ -233,17 +235,13 @@ private[spark] class MemoryStore(
}
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--- Diff: core/src/main/scala/org/apache/spark/io/CompressionCodec.scala ---
@@ -216,3 +218,33 @@ private final class SnappyOutputStreamWrapper(os:
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@@ -351,11 +351,11 @@ private[spark] object
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@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software
Github user brkyvz commented on the issue:
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Thanks! Merging to master
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retest this please
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--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -279,7 +279,13 @@ class Analyzer(
* We
Github user zhengruifeng commented on the issue:
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@srowen In MLlib, `PeriodicRDDCheckpointer` is only used in
`GradientBoostedTrees`.
I just find that there is another checkpointer `PeriodicGraphCheckpointer`,
I will check it.
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retest this please
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Github user HyukjinKwon commented on the issue:
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Yea, thanks. That was a cool patch BTW.
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Github user maver1ck commented on the issue:
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@HyukjinKwon
I created this before https://github.com/apache/spark/pull/19249, which
greatly decrease function call.
I agree we can close it.
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Github user zuotingbing commented on the issue:
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@jerryshao if this line `new BufferedInputStream(fs.open(log))` throws
exception, it mean we do not need to close the object of BufferedInputStream
because it new failed.
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Github user jinxing64 commented on the issue:
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@yaooqinn
This change works well for me, thanks for fix !
After this change, hive client for execution(points to a dummy local
metastore) will never be used when running sql
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Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/15544#discussion_r139898372
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala
---
@@ -0,0 +1,235 @@
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/19243
Merged build finished. Test PASSed.
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