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SPARK-1151: Update dev merge script to use spark.git instead of incubator-spark #47

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SPARK-1151: Update dev merge script to use spark.git instead of incubator-spark #47

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Refer to this link for build results: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/12930/

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pwendell commented Mar 1, 2014

Thanks tom - merged.

@asfgit asfgit closed this in 4ba3f70 Mar 1, 2014
jhartlaub referenced this pull request in jhartlaub/spark May 27, 2014
@mateiz
Merge pull request alteryx#47 from xiliu82/branch-0.8
64fae16 
add Function3 and WrappedFunction3

I need these two to add more API to spark streaming
JasonMWhite pushed a commit to JasonMWhite/spark that referenced this pull request Dec 2, 2015
@orenmazor
Merge pull request apache#47 from Shopify/orens_problematic_bump
a970f6e 
Orens problematic bump
JasonMWhite pushed a commit to JasonMWhite/spark that referenced this pull request Dec 2, 2015
@orenmazor
Revert "Merge pull request apache#47 from Shopify/orens_problematic_b…
03aa82b 
…ump"

This reverts commit a970f6e, reversing
changes made to c6ac972.
JasonMWhite pushed a commit to JasonMWhite/spark that referenced this pull request Dec 2, 2015
@orenmazor
Revert "Revert "Merge pull request apache#47 from Shopify/orens_probl…
f37de6c 
…ematic_bump""

This reverts commit 03aa82b.
JasonMWhite pushed a commit to JasonMWhite/spark that referenced this pull request Dec 2, 2015
@orenmazor
Revert "Revert "Revert "Merge pull request apache#47 from Shopify/ore…
b089305 
…ns_problematic_bump"""

This reverts commit f37de6c.
asfgit pushed a commit that referenced this pull request Apr 27, 2016
@dongjoon-hyun @hvanhovell
[SPARK-14664][SQL] Implement DecimalAggregates optimization for Windo…
af92299 
…w queries

## What changes were proposed in this pull request?

This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer.

**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1)
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [sum(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
:     +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
   +- Exchange SinglePartition, None
      +- Generate explode([1.0,2.0]), false, false, [a#21]
         +- Scan OneRowRelation[]
```

**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5))
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [avg(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
:     +- INPUT
+- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
   +- Exchange SinglePartition, None
      +- Generate explode([1.0,2.0]), false, false, [a#42]
         +- Scan OneRowRelation[]
```

After this PR, those queries work fine and new optimized physical plans look like the followings.

**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [sum(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
:     +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
   +- Exchange SinglePartition, None
      +- Generate explode([1.0,2.0]), false, false, [a#33]
         +- Scan OneRowRelation[]
```

**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
:  +- Project [avg(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
:     +- INPUT
+- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER (  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
   +- Exchange SinglePartition, None
      +- Generate explode([1.0,2.0]), false, false, [a#45]
         +- Scan OneRowRelation[]
```

In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did.

## How was this patch tested?

Pass the Jenkins tests (with newly added testcases)

Author: Dongjoon Hyun <[email protected]>

Closes #12421 from dongjoon-hyun/SPARK-14664.
vlad17 pushed a commit to vlad17/spark that referenced this pull request Aug 23, 2016
@davies @rxin
add dynamic partition pruning for Spark 2.0
765add8 
## What changes were proposed in this pull request?
This patch was originally authored by davies. The patch implements dynamic partition pruning for branch-2.0.

## How was this patch tested?
An end-to-end test was added in SQLQuerySuite.

Author: Davies Liu <[email protected]>

Closes apache#47 from rxin/rxin-dynamic-partition-pruning.
jzhuge added a commit to jzhuge/spark that referenced this pull request Mar 7, 2019
@jzhuge
Merge pull request apache#47 in BDP/apache-spark from spark-2.3.2-loa…
6c7f339 
…dv2table to netflix/2.3.2-unstable

Squashed commit of the following:

commit fec21fd4abab2ef27ae1f4199e3c75217b6a3e8e
Author: Vinitha Gankidi <[email protected]>
Date:   Tue Feb 26 14:58:13 2019 -0800

    NETFLIX-BUILD: loadV2Table should throw NoSuchTableException instead of returning None
hn5092 pushed a commit to hn5092/spark that referenced this pull request Nov 4, 2019
@7mming7
apache#47 ShuffleMapTask ignore spark.driver.maxResultSize
2e4637c
hn5092 pushed a commit to hn5092/spark that referenced this pull request Nov 4, 2019
@7mming7
Merge pull request apache#48 from shuiqing301/kyspark-2.4.1.x-4.x
cc48029 
apache#47 ShuffleMapTask ignore spark.driver.maxResultSize
@holdenk holdenk mentioned this pull request May 1, 2020
Closed
@maropu maropu mentioned this pull request Aug 7, 2020
Closed
cloud-fan pushed a commit that referenced this pull request Nov 10, 2020
@AngersZhuuuu @cloud-fan
[SPARK-33302][SQL] Push down filters through Expand
34f5e7c 
### What changes were proposed in this pull request?
Push down filter through expand.  For case below:
```
create table t1(pid int, uid int, sid int, dt date, suid int) using parquet;
create table t2(pid int, vs int, uid int, csid int) using parquet;

SELECT
       years,
       appversion,
       SUM(uusers) AS users
FROM   (SELECT
               Date_trunc('year', dt)          AS years,
               CASE
                 WHEN h.pid = 3 THEN 'iOS'
                 WHEN h.pid = 4 THEN 'Android'
                 ELSE 'Other'
               END                             AS viewport,
               h.vs                            AS appversion,
               Count(DISTINCT u.uid)           AS uusers
               ,Count(DISTINCT u.suid)         AS srcusers
        FROM   t1 u
               join t2 h
                 ON h.uid = u.uid
        GROUP  BY 1,
                  2,
                  3) AS a
WHERE  viewport = 'iOS'
GROUP  BY 1,
          2
```

Plan. before this pr:
```
== Physical Plan ==
*(5) HashAggregate(keys=[years#30, appversion#32], functions=[sum(uusers#33L)])
+- Exchange hashpartitioning(years#30, appversion#32, 200), true, [id=#251]
   +- *(4) HashAggregate(keys=[years#30, appversion#32], functions=[partial_sum(uusers#33L)])
      +- *(4) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[count(if ((gid#44 = 1)) u.`uid`#47 else null)])
         +- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, 200), true, [id=#246]
            +- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[partial_count(if ((gid#44 = 1)) u.`uid`#47 else null)])
               +- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
                  +- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44, 200), true, [id=#241]
                     +- *(2) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
                        +- *(2) Filter (CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46 = iOS)
                           +- *(2) Expand [ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, uid#7, null, 1), ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, null, suid#10, 2)], [date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44]
                              +- *(2) Project [uid#7, dt#9, suid#10, pid#11, vs#12]
                                 +- *(2) BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight
                                    :- *(2) Project [uid#7, dt#9, suid#10]
                                    :  +- *(2) Filter isnotnull(uid#7)
                                    :     +- *(2) ColumnarToRow
                                    :        +- FileScan parquet default.t1[uid#7,dt#9,suid#10] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t1], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date,suid:int>
                                    +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, true] as bigint))), [id=#233]
                                       +- *(1) Project [pid#11, vs#12, uid#13]
                                          +- *(1) Filter isnotnull(uid#13)
                                             +- *(1) ColumnarToRow
                                                +- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [isnotnull(uid#13)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t2], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```

Plan. after. this pr. :
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[years#0, appversion#2], functions=[sum(uusers#3L)], output=[years#0, appversion#2, users#5L])
   +- Exchange hashpartitioning(years#0, appversion#2, 5), true, [id=#71]
      +- HashAggregate(keys=[years#0, appversion#2], functions=[partial_sum(uusers#3L)], output=[years#0, appversion#2, sum#22L])
         +- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[count(distinct uid#7)], output=[years#0, appversion#2, uusers#3L])
            +- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, 5), true, [id=#67]
               +- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[partial_count(distinct uid#7)], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, count#27L])
                  +- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
                     +- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7, 5), true, [id=#63]
                        +- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles)) AS date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END AS CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
                           +- Project [uid#7, dt#9, pid#11, vs#12]
                              +- BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight, false
                                 :- Filter isnotnull(uid#7)
                                 :  +- FileScan parquet default.t1[uid#7,dt#9] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date>
                                 +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, false] as bigint)),false), [id=#58]
                                    +- Filter ((CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS) AND isnotnull(uid#13))
                                       +- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [(CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS), isnotnull..., Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>

```

### Why are the changes needed?
Improve  performance, filter more data.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Added UT

Closes #30278 from AngersZhuuuu/SPARK-33302.

Authored-by: angerszhu <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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3 participants
@tgravescs @AmplabJenkins @pwendell