[GitHub] spark issue #21156: [SPARK-24087][SQL] Avoid shuffle when join keys are a su...

2018-10-22 Thread maryannxue
Github user maryannxue commented on the issue:

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The idea is good. Is it possible to make it an optimization rule? Another 
suggestion is we need more test cases.


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[GitHub] spark issue #21156: [SPARK-24087][SQL] Avoid shuffle when join keys are a su...

2018-10-22 Thread maryannxue
Github user maryannxue commented on the issue:

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Sorry for the delay. I’ll take another look today.
On Mon, Oct 22, 2018 at 7:50 AM UCB AMPLab  wrote:

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[GitHub] spark issue #21156: [SPARK-24087][SQL] Avoid shuffle when join keys are a su...

2018-10-08 Thread wangshisan
Github user wangshisan commented on the issue:

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What is the status now? I think this is of great value, since this gives 
users more possibility to leverage bucket join, all joins which take the bucket 
key as the prefix of join keys will benefit from this. 
And we have a further optimization here:
1. Table A(a1, a2, a3) is bucketed by a1, a2
2. Table B(b1, b2, b3) is bucketed by b1.
3. A join B on (a1=b1, a2=b2, a3=b3)

In this case, only table B needs extra shuffle, and shuffle keys are (b1, 
b2).


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2018-07-26 Thread yucai
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closed by mistake, reopen it.


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2018-07-11 Thread yucai
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@maryannxue how about this way? Any better idea?


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[GitHub] spark issue #21156: [SPARK-24087][SQL] Avoid shuffle when join keys are a su...

2018-07-10 Thread maryannxue
Github user maryannxue commented on the issue:

https://github.com/apache/spark/pull/21156
  
IMHO, this ShuffledJoin is basically join + known distribution info. So 
instead of adding another join node (which doesn't map to any specific join 
algorithm), we should base this on 
https://issues.apache.org/jira/browse/SPARK-21784.


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2018-07-10 Thread yucai
Github user yucai commented on the issue:

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A classic scenario could be like below:
```
SELECT
  ...
FROM
  lstg_item item,
  lstg_item_vrtn v
WHERE 
  item.auct_end_dt = CAST(SUBSTR('2018-04-19 00:00:00',1,10) AS DATE)
  AND item.item_id = v.item_id
  AND item.auct_end_dt = v.auct_end_dt;
```
`lstg_item` is a really big table and `item_id` is its primary key.
If we bucket on its `item_id`:
- No data skew. Each partition will have the same data.
- Before this PR, the above query needs extra shuffle on big table. After 
this PR, we can save that shuffle.


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2018-07-10 Thread yucai
Github user yucai commented on the issue:

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@cloud-fan For bucket table, the user will do the bucket on the primary 
key, so in this case, they will not have the parallelism and data skew issue 
and we can see good benefit from avoiding shuffle.
Do you mean the performance regression in some more general cases?


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2018-07-10 Thread cloud-fan
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/21156
  
A high-level question: will we hit perf regression here? It seems to me 
that, using fewer join keys can lead to less parallelism or data skewness.


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2018-07-09 Thread gatorsmile
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cc @maryannxue 


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2018-07-09 Thread yucai
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@mgaido91 with this way, seems like we don't need `reorderJoinPredicates` 
anymore, any thoughts?


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2018-07-06 Thread yucai
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retest this please


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2018-07-06 Thread yucai
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retest this please 


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2018-07-06 Thread yucai
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@cloud-fan @gatorsmile @mgaido91 @viirya Could you help review this feature?



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[GitHub] spark issue #21156: [SPARK-24087][SQL] Avoid shuffle when join keys are a su...

2018-07-06 Thread yucai
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I run the failed commands locally, no issue, retest again.
```
info] SHA-1: 4e68a043f1e9aa75e4395aba7892196e90cca1f4
[info] Packaging 
/mnt/work/upstream/spark/external/kinesis-asl-assembly/target/scala-2.11/spark-streaming-kinesis-asl-assembly-2.4.0-SNAPSHOT.jar
 ...
[info] Done packaging.
[success] Total time: 29 s, completed Jul 6, 2018 12:32:58 AM
[yyu1@yucai-dev-1659859 spark]$ ./build/sbt -Phadoop-2.6 -Pkubernetes 
-Phive-thriftserver -Pflume -Pkinesis-asl -Pyarn -Pkafka-0-8 -Phive -Pmesos 
test:package streaming-kafka-0-8-assembly/assembly 
streaming-flume-assembly/assembly streaming-kinesis-asl-assembly/assembly
```


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2018-04-26 Thread yucai
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retest this please


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