Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/18055#discussion_r117666471
--- Diff:
core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala ---
@@ -54,7 +54,7 @@ import org.apache.spark.util.io.{ChunkedByteBuffer
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97783672
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,25 +95,101 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97701247
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,25 +95,100 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97700863
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,25 +95,100 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97700723
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,25 +95,100 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97700670
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,25 +95,100 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16677#discussion_r97700568
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
---
@@ -230,6 +230,21 @@ case object SinglePartition
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
@viirya i suggest fix the 2 in this pr, let's wait some comment on 1. /cc
@rxin and @wzhfy who may comment on the first case.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
For 1, my idea is not use the proposal in this PR,
1. how you determine `total rows in all partitions are (much) more than
limit number.` and then go into this code path and how to decide
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
all partitions after local limit are about/nearly 100,000,000 rows
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
Again, to clean, I am against the performance regression in flowing case
0. limit num is 100,000,000
1. the original table rows is very big, much larger than 100,000,000 rows
2. after
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
I think shuffle is ok, but shuffle to one partition leads to the
performance issue.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
Assume local limit output 100,000,000 rows, then in global limit it will
be take in a single partition, so it is very slow and can not use other free
cores to improve the parallelism.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
@viirya my team member post the mail list, actually we mean the case i
listed above, the main issue is the single partition issue in global limit,
if in that case you fall back to old global limit
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
I think the local limit cost is important, we assume recompute partions
number: m, all the partitions: n
m = 1, n =100 is a positive case, but there also cases that m very close to
n(even m = n
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
Your proposal avoid the cost of all partitions compute and shuffle for
local limit but introduce some partitions recompute for local limit stage.
We can not decide which cost is cheaper
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
I think before compare our proposals , we should first make sure our
proposal will not bring performance regression.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
Not get you, but let me explain more,
If we use map output statistics to decide each global limit should take how
many element.
1. local limit shuffle with the maillist partitioner and return
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
need define a new map output statistics to do this
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
Yes, you are right, we can not ensure the uniform distribution for global
limit.
An idea is not use a special partitioner, after the shuffle we should get
the mapoutput statistics for row num
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
refer to the maillist
>One issue left is how to decide shuffle partition number.
We can have a config of the maximum number of elements for each GlobalLimit
task to process,
then
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
To clear, now we have these issues:
1. local limit compute all partitions, that means it launch many tasks
but actually maybe very small tasks is enough.
2. global limit single partition
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96784321
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/16633
@viirya @rxin i support the idea of @wzhfy in the maillist
http://apache-spark-developers-list.1001551.n3.nabble.com/Limit-Query-Performance-Suggestion-td20570.html,
it solved the single partition
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96782626
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96782094
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96781278
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96780810
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96780571
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96779648
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96773557
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96773174
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/16633#discussion_r96673145
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala ---
@@ -90,21 +94,74 @@ trait BaseLimitExec extends UnaryExecNode
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15240
retest this please
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Github user scwf commented on the issue:
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retest this please
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15240
retest this please
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15297
@YuhuWang2002
We should limit the use case for outer join:
For left outer join, such as A left join B, this implementation now can not
handle the case of skew of table B. That's because
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15481
`CoarseGrainedSchedulerBackend.removeExecutor` also use ask, but it does
not matter right? because it just send msg once and log the error if failure
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15481
Updated, can you review again?
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15481
ok, i will revert to the initial commit.
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Github user scwf commented on the issue:
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retest this please.
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Github user scwf commented on the issue:
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retest this please.
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GitHub user scwf opened a pull request:
https://github.com/apache/spark/pull/15481
[SPARK-17929] [CORE] Fix deadlock when CoarseGrainedSchedulerBackend reset
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-17929
Now
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15297
retest this please
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15297
retest this please.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15240
/cc @rxin can you help review this?
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
retest this please
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
@kayousterhout Thanks for your comment, i have updated based on all your
comment.
.
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Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/15213#discussion_r80865465
--- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
---
@@ -1256,11 +1257,13 @@ class DAGScheduler
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
@markhamstra in my fix i just want to make the minor changes for the
dagscheduer, and your fix is also ok to me, i can update this according your
comment. Thanks:)
/cc @zsxwing may also have
GitHub user scwf opened a pull request:
https://github.com/apache/spark/pull/15240
[SPARK-17556] Executor side broadcast for broadcast joins
## What changes were proposed in this pull request?
Design doc :
https://issues.apache.org/jira/secure/attachment/12830286/executor
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
> actual problem is not in abortStage but rather in improper additions to
failedStages
correct, i think a more accurate description for this issue is "do not add
`failedStag
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
Actually the failedStages only added here in spark.
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Github user scwf commented on the issue:
https://github.com/apache/spark/pull/15213
Thanks @zsxwing to explain this.
@markhamstra the issue happens in the case of my PR description. It usually
depends on muti-thread submitting jobs cases and the order of fetch failure, so
i said
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/15213#discussion_r80274817
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala ---
@@ -2105,6 +2109,54 @@ class DAGSchedulerSuite extends SparkFunSuite
GitHub user scwf opened a pull request:
https://github.com/apache/spark/pull/15213
[SPARK-17644] [CORE] Fix the race condition when DAGScheduler handle the
FetchFailed event
## What changes were proposed in this pull request?
| Time|Thread 1 , Job1 | Thread 2
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/14712#discussion_r77014850
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala ---
@@ -168,6 +169,107 @@ class StatisticsSuite extends QueryTest
Github user scwf commented on the issue:
https://github.com/apache/spark/pull/14712
/cc @cloud-fan @rxin
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-173435459
@yhuai thanks
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Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/7336#discussion_r50269169
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala ---
@@ -198,33 +241,99 @@ private[spark] class
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-173241662
retest this please
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5827#issuecomment-172144781
@rxin Our parser is a extended version of the `SqlParser`, the main
difference is that we add the support for subquery(both correlated and
uncorrelated ),exists
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5827#issuecomment-172151467
Actually we were trying to contribute this improvements, unfortunately the
community do not want them for maintain(or compatibility with hive ql) reason
in the past
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-172144917
Ping @rxin
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5827#issuecomment-171667688
@rxin, yes we used this and we implements a new sqlparser based on this
interface to support ANSI tpcds sql.
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Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/10682#discussion_r49404076
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/LogicalPlanToSQLSuite.scala
---
@@ -24,6 +24,9 @@ class LogicalPlanToSQLSuite extends
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-170359121
Back to update, @marmbrus @rxin please help review this when you have time.
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GitHub user scwf opened a pull request:
https://github.com/apache/spark/pull/10682
[SPARK-12742] [SQL] org.apache.spark.sql.hive.LogicalPlanToSQLSuite failure
due to Table already exists exception
```
[info] Exception encountered when attempting to run a suite with class
name
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-170412209
@rxin, yes, This PR try to fix the same issue on the Hive support side.
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5400#issuecomment-170037893
>>The cached size cannot be greater than 2GB.
@rxin how to understand the `cached size`? the partition size of a cached
rdd?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/5400#issuecomment-169517123
hi @squito, can you explain in which situation users will hit the 2g limit?
will a job of processing very large data(such as PB level data) reach this
limit?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/10311#issuecomment-166772051
Get it thanks @marmbrus :)
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/10311#issuecomment-166611132
Hi @cloud-fan can you explain in which cases we can use this feature or the
motivation for this?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/10253#issuecomment-163590407
LGTM
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/9748#issuecomment-162860246
@davies here are some problems when deserialize for RoaringBitmap. see the
examples below:
run this piece of code
```
import com.esotericsoftware.kryo.io.{Input
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/9748#issuecomment-163074120
ok, should i send pr to master and branch-1.6 both?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/10213#issuecomment-163089761
/cc @davies
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GitHub user scwf opened a pull request:
https://github.com/apache/spark/pull/10213
[SPARK-1] [Core] Deserialize RoaringBitmap using Kryo serializer throw
Buffer underflow exception
Deserialize RoaringBitmap using Kryo serializer throw Buffer underflow
exception
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/9215#issuecomment-151041137
should this merged to branch-1.5?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7642#issuecomment-147906001
hi @davies seems this is not compatible with hiveql, HiveQl still parse
float number as double.
https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/9055#issuecomment-147273499
ok, does this support multi exists and in in where clause?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/9055#issuecomment-147272550
what's the difference with #4812?
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Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/4436#discussion_r40289134
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
---
@@ -67,6 +68,17 @@ abstract class Expression extends
Github user scwf commented on a diff in the pull request:
https://github.com/apache/spark/pull/4436#discussion_r40283225
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
---
@@ -67,6 +68,17 @@ abstract class Expression extends
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-141853176
@litao-buptsse, i will update this soon thanks.
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7417#issuecomment-138544481
@Sephiroth-Lin can you rebase this?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7417#issuecomment-138037270
@zsxwing it is definitely putting the small table in the left side of
'RDD.cartesian` improve the performance. you can have a simple test that do
cartesian with a big data
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/8125#issuecomment-131669839
@liancheng we have this cases:
The production system produce small text/csv files every five minute, and
we use spark sql to do some ETL work(such as agg) on this small
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-130185516
retest this please
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https://github.com/apache/spark/pull/7336#issuecomment-130316086
retest this please
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https://github.com/apache/spark/pull/7336#issuecomment-130312583
retest this please
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-130153626
Retest this please
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-130127052
/cc @marmbrus can you take a look at this?
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-130152470
yes, since we upgrade the hive version to 1.2.1, we should adapt the token
tree in hiveql, the old one is not correct in 1.2.1. Updated
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7336#issuecomment-129266361
/cc @marmbrus
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7197#issuecomment-128555369
@davies https://issues.apache.org/jira/browse/SPARK-9725
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/7197#issuecomment-128352195
@davies here is a bug when this PR is in, that is
when set executor memory = 32g, all the queries for string field will
have problem. seems it return empty/garbled
Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-125853720
retest this please
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-125565512
retest this please
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Github user scwf commented on the pull request:
https://github.com/apache/spark/pull/4380#issuecomment-124285788
/cc @marmbrus
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