[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-25 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
if they chain like that then i think i know how to do the optimization.

but do they? look for example at dataset.groupByKey(...).mapValues(...)

Dataset[T].groupByKey[K] uses function T => K and creates
KeyValueGroupedDataset[K, T]

KeyValueGroupedDataset[K, T].mapValues[W] uses function T => W and creates
KeyValueGroupedDataset[K, W]

so i have T => K and then T => W


On Thu, Oct 20, 2016 at 8:26 PM, Wenchen Fan 
wrote:

> 2 chained AppendColumns will have 2 functions: T => U and U => W, so we
> can combine them this way:
> convert UnsafeRow to T
> apply func to T to generate U
> apply func to U to generate W
> convert W to UnsafeRow
> append the new UnsafeRow to the original one
>
> —
> You are receiving this because you were mentioned.
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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-24 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
@cloud-fan that makes sense to me, but its definitely not a quick win to 
create that optimization.
let me think about it some more


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-21 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
That's a good point, let's focus on `ds.groupBy(...).mapValues(...)` then. 
One thought, in `mapValues`, we will project away the previous value 
attributes, so the workflow should be:
```
convert UnsafeRow to T
apply func1 to T to generate U
convert U to UnsafeRow
apply func2 to T to generate  W
convert W to UnsafeRow
join the two new UnsafeRows, without the original row.
```


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-21 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
@cloud-fan i can try to optimize 
```grouped.mapValues(...).mapValues(...)``` but its a bit of an anti-pattern 
(there should be no need to do mapValues twice) so i dont think there is much 
gain in optimizing this. what do you think?


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-21 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
To optimize `ds.groupBy(...).mapValues(...)`, yea it's not trivial as you 
explained above. But for `grouped.mapValues(...).mapValues(...)`, I think it 
should not be that hard, as it's a pattern like two `AppendColumns` with 
functions func1: `T => U` and func2: `U => W`, if we treat 
`Project(AppendColumns, ...)` as one operator.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-20 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
2 chained `AppendColumns` will have 2 functions: T => U and U => W, so we 
can combine them this way:
convert UnsafeRow to T
apply func to T to generate U
apply func to U to generate W
convert W to UnsafeRow
append the new UnsafeRow to the original one


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-20 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
@rxin i can give it a try (the optimizer rule)


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-20 Thread rxin 
Github user rxin commented on the issue:

https://github.com/apache/spark/pull/13526
  
Alright merging in master. Thanks.

@koertkuipers would you be able to add the optimizer rule?


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-20 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
retest this please


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-20 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
it lacks an optimizer rule to collapse `AppendColumns`, but seems ok to 
merge it first and add the rule in follow-up.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-10-18 Thread rxin 
Github user rxin commented on the issue:

https://github.com/apache/spark/pull/13526
  
cc @cloud-fan 

This looks good to me -- but I don't remember why we didn't merge it 
earlier already.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-07 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
could we "rewind"/undo the append for the key and change it to a map that 
inserts new values and key? so remove one append and replace it with another 
operation?


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-07 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
the tricky part with that is that (ds: Dataset[(K,
V)]).groupBy(_._1).mapValues(_._2) should return a
KeyValueGroupedDataset[K, V]

On Tue, Jun 7, 2016 at 8:22 PM, Wenchen Fan 
wrote:

> A possible approach maybe just keep the function given by mapValues, and
> apply it before calling the function given by mapGroups. By doing this,
> we at least won't make the performance worse, as the underlying plan
> doesn't change.
>
> —
> You are receiving this because you authored the thread.
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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-07 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
A possible approach maybe just keep the function given by `mapValues`, and 
apply it before calling the function given by `mapGroups`. By doing this, we at 
least won't make the performance worse, as the underlying plan doesn't change.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-07 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
```
scala> val x = Seq(("a", 1), ("b", 2)).toDS
x: org.apache.spark.sql.Dataset[(String, Int)] = [_1: string, _2: int]

scala> x.groupByKey(_._1).mapValues(_._2).reduceGroups(_ + _).explain
== Physical Plan ==
*SerializeFromObject [staticinvoke(class 
org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, 
scala.Tuple2, true]._1, true) AS value#36, input[0, scala.Tuple2, true]._2 AS 
value#37]
+- MapGroups , value#32.toString, value#34: int, [value#32], 
[value#34], obj#35: scala.Tuple2
   +- *Sort [value#32 ASC], false, 0
  +- Exchange hashpartitioning(value#32, 200)
 +- *Project [value#34, value#32]
+- AppendColumns , newInstance(class scala.Tuple2), 
[input[0, int, true] AS value#34]
   +- AppendColumns , newInstance(class 
scala.Tuple2), [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, 
StringType, fromString, input[0, java.lang.String, true], true) AS value#32]
  +- LocalTableScan [_1#28, _2#29]
```

it seems to AppendColumns are not collapsed


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-07 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
ok i will study the physical plans for both and try to understand why one 
would be slower


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
OK now I agree this is a useful API.

For performance, I would expect that 
`ds.groupByKey(_._1).mapValues(_._2).mapGroups { case (k, vs) => (k, vs.sum) }` 
should be at least as fast as `ds.groupByKey(_._1).mapGroups { case (k, vs) => 
(k, vs.map(_._2).sum) }`. But the current implementation looks not?

I'll take a closer look tomorrow, and let's discuss what's the best way to 
do it.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
can you explain a bit what is inefficient and would need an optimizer rule? 
is it mapValues being called twice? once for the key and then for the new 
values?
thanks!


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread koertkuipers 
Github user koertkuipers commented on the issue:

https://github.com/apache/spark/pull/13526
  
see this conversation:

https://mail-archives.apache.org/mod_mbox/spark-user/201602.mbox/%3ccaaswr-7kqfmxd_cpr-_wdygafh+rarecm9olm5jkxfk14fc...@mail.gmail.com%3E

mapGroups is not a very interesting API, since without support for 
secondary sort and hence no need for fold operations pushing all the value into 
the reducer never really makes sense. so the interesting APIs are reduce (when 
its fixed to be efficient and not use mapGroups) and agg.
how do you transform the values before they go into reduce? you can not do 
this currently, which is why we need something like mapValues. with Aggregators 
you can indeed do something similar inside the Aggregator (since the input type 
is not equal to the buffer type), but this leads to all Aggregators currently 
taking in some kind of input transform function, which hints at a suboptimal 
API and a pattern that should be generalized and extracted.

i am curious to know why appending a column is inefficient? i am open to 
different designs

about this being a rare case: i would argue the opposite. i expect to see a 
lot of key-value datasets (```Dataset[(K, V)]```) in our codebase, and on those 
a lot of operations like ```ds.groupByKey(_._1).mapValues.(_._2).reduce(...)```.
since this is the most natural translation of many RDD algos.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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https://github.com/apache/spark/pull/13526
  
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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread SparkQA 
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https://github.com/apache/spark/pull/13526
  
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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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https://github.com/apache/spark/pull/13526
  
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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread cloud-fan 
Github user cloud-fan commented on the issue:

https://github.com/apache/spark/pull/13526
  
I doubt if this feature is really useful? I think users can easily call 
`map` on the values during `mapGroups`, e.g.
```
val ds = Seq(("a", 10), ("a", 20), ("b", 1), ("b", 2), ("c", 1)).toDS()
ds.groupByKey(_._1).mapGroups { case (k, vs) => (k, vs.map(_._2).sum) }
```

On the other way, this implemetaion is kind of efficient, everytime we call 
`mapValues`, we append new columns to the underlying plan.


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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

2016-06-06 Thread rxin 
Github user rxin commented on the issue:

https://github.com/apache/spark/pull/13526
  
cc @cloud-fan  too



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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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[GitHub] spark issue #13526: [SPARK-15780][SQL] Support mapValues on KeyValueGroupedD...

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