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FYI, I believe the test failures are from SPARK-22595, which is fixed now.
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cc @kiszk @viirya @gatorsmile
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--- Diff:
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@@ -413,16 +399,12 @@ class ColumnarBatchSuite
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[SPARK-22604][SQL] remove the get address methods from ColumnVector
## What changes were proposed in this pull request?
`nullsNativeAddress` and `valuesNativeAddress` are only used in
Github user HyukjinKwon commented on the issue:
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I think we should document this in read side too if we are going to do and
actually I think it's not intended as the thirdparty one does not allow empty
string IIRC. Will take a closer look
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I expect 10 ~ 15 minutes decrease. Let me wait the results before cc'ing
someone to review.
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Build started: [SparkR] `ALL`
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[SPARK-22603][SQL] Fix 64KB JVM bytecode limit problem with FormatString
## What changes were proposed in this pull request?
This PR changes `FormatString` code generation to place generated
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--- Diff: R/pkg/tests/fulltests/test_sparkSQL.R ---
@@ -3078,101 +3091,117 @@ test_that("coalesce, repartition, numPartitions",
{
})
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--- Diff: R/pkg/tests/fulltests/test_sparkSQL.R ---
@@ -3021,41 +3021,54 @@ test_that("dapplyCollect() on DataFrame with a
binary column", {
})
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--- Diff: R/pkg/tests/fulltests/test_sparkSQL.R ---
@@ -3078,101 +3091,117 @@ test_that("coalesce, repartition, numPartitions",
{
})
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--- Diff: R/pkg/tests/fulltests/test_sparkSQL.R ---
@@ -3021,41 +3021,54 @@ test_that("dapplyCollect() on DataFrame with a
binary column", {
})
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This one is the last where I am seeing kind of hanging in few specific
tests. I think I need AppVeyor tests to see the actual elapsed time diff.
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[WIP][SPARK-21693][FOLLOWUP][R] Reduce shuffle partitions running R worker
in few tests to speed up
## What changes were proposed in this pull request?
This is a followup to reduce
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cc @michal-databricks @kiszk @gatorsmile
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[SPARK-22602][SQL] remove ColumnVector#loadBytes
## What changes were proposed in this pull request?
`ColumnVector#loadBytes` is only used as an optimization for reading
UTF8String in
Github user gaborgsomogyi commented on the issue:
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As I've seen the univocity library does not support explicit quote turn off
feature on the write side.
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Github user kiszk commented on the issue:
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In int case, the followings are the number of java byte code length for a
value
1 byte: -1, 0, 1, 2, 3, 4, 5 (iconst_?)
2 byte: -128 ~ -2, 6 ~ 127 (bipush)
3 bytes: -32768 ~ -129, 128
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The PR looks good.
Although it can solve the constant pool pressure, however, I have a
question. Does it mean every time a constant falling in short range is used in
codes, we increase 1
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Github user mgaido91 commented on the issue:
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@gatorsmile I added a test case to check that the execution plan is
`WholeStageCodegenExec` as expected. I also made some performance test using
almost the same code, ie.:
```
val N = 30
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Can one of the admins verify this patch?
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[SPARK-22484][DOC] Document PySpark DataFrame csv writer behavior wheâ¦
## What changes were proposed in this pull request?
In PySpark API Document, DataFrame.write.csv() says that
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Github user kiszk commented on the issue:
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Here is [a PR](https://github.com/janino-compiler/janino/pull/34) to fix a
problem regarding `sipush`.
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Do you mean we manually run `ReuseExchange` after the registration of
exchanges?
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think about shuffle as a server-client framework, does your change need to
update the server side? i.e. do users need to upgrade their external shuffle
service for new spark version with this
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--- Diff: core/src/main/scala/org/apache/spark/storage/BlockId.scala ---
@@ -52,8 +52,9 @@ case class RDDBlockId(rddId: Int, splitIndex: Int)
extends
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Also cc @cloud-fan
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@@ -110,6 +110,13 @@ public
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@@ -165,13 +165,23 @@ public
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@@ -52,8 +52,9 @@ case class RDDBlockId(rddId: Int, splitIndex: Int)
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To explain better the intentions, it doesn't try to solve of somehow
provide a compatibility layer between old and new versions of Spark, all it
does is slightly improving the UX, because
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thanks, merging to master!
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cc @andrewor14 since I believe you know this part of spark pretty well,
maybe you could help me integrating this.
Any idea why Jenkins didn't start the testing?
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thanks, merging to master!
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can we run `ReuseExchange` twice?
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ping @cloud-fan
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Next, I analyzed usage of constant pool entries and java bytecode ops using
`janinoc`. The summary is as follows:
```
array[4] : 6 + 0 * n entries, 6-8 java bytecode ops / read access
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retest this please
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First of all, I have to share sad news with you. `janino` does not use
`sipush` for values from 128 to 32767. Current `janino` 3.0.7 uses `iconst...`,
`bipush`, or `ldc`. `javac` uses `sipush` for
Github user viirya commented on the issue:
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The exchanges only register with `ExchangeCoordinator` after query
transformation. So during transformation, we don't know if two
`ExchangeCoordinator` have the same exchanges.
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> Can you please explain more, and how to reproduce this issue?
Sure,
1. Start master and worker (in version 2.2.0 for instance):
```bash
cat ./python/pyspark/version.py
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--- Diff: bin/spark-sql.cmd ---
@@ -0,0 +1,25 @@
+@echo off
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+rem
+rem Licensed to the Apache Software Foundation (ASF) under one or
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cc @zsxwing
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@@ -211,8 +211,8 @@ private[spark] class KafkaRDD[K, V](
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https://github.com/apache/spark/pull/19808
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