[jira] [Commented] (SPARK-19136) Aggregator with case class as output type fails with ClassCastException

2017-03-22 Thread Hyukjin Kwon (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15937680#comment-15937680
 ] 

Hyukjin Kwon commented on SPARK-19136:
--

[~a1ray], do you think this JIRA is resolvable?

> Aggregator with case class as output type fails with ClassCastException
> ---
>
> Key: SPARK-19136
> URL: https://issues.apache.org/jira/browse/SPARK-19136
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.2, 2.1.0
>Reporter: Mathieu D
>Priority: Minor
>
> {{Aggregator}} with a case-class as output type returns a Row that cannot be 
> cast back to this type, it fails with {{ClassCastException}}.
> Here is a dummy example to reproduce the problem 
> {code}
> import org.apache.spark.sql._
> import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
> import org.apache.spark.sql.expressions.Aggregator
> import spark.implicits._
> case class MinMax(min: Int, max: Int)
> case class MinMaxAgg() extends Aggregator[Row, (Int, Int), MinMax] with 
> Serializable {
>   def zero: (Int, Int) = (Int.MaxValue, Int.MinValue)
>   def reduce(b: (Int, Int), a: Row): (Int, Int) = (Math.min(b._1, 
> a.getAs[Int](0)), Math.max(b._2, a.getAs[Int](0)))
>   def finish(r: (Int, Int)): MinMax = MinMax(r._1, r._2)
>   def merge(b1: (Int, Int), b2: (Int, Int)): (Int, Int) = (Math.min(b1._1, 
> b2._1), Math.max(b1._2, b2._2))
>   def bufferEncoder: Encoder[(Int, Int)] = ExpressionEncoder()
>   def outputEncoder: Encoder[MinMax] = ExpressionEncoder()
> }
> val ds = Seq(1, 2, 3, 4).toDF("col1")
> val agg = ds.agg(MinMaxAgg().toColumn.alias("minmax"))
> {code}
> bq. {code}
> ds: org.apache.spark.sql.DataFrame = [col1: int]
> agg: org.apache.spark.sql.DataFrame = [minmax: struct]
> {code}
> {code}agg.printSchema(){code}
> bq. {code}
> root
>  |-- minmax: struct (nullable = true)
>  ||-- min: integer (nullable = false)
>  ||-- max: integer (nullable = false)
> {code}
> {code}agg.head(){code}
> bq. {code}
> res1: org.apache.spark.sql.Row = [[1,4]]
> {code}
> {code}agg.head().getAs[MinMax](0){code}
> bq. {code}
> java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to line4c81e18af34342cda654c381ee91139525.$read$$iw$$iw$$iw$$iw$MinMax
> [...]
> {code}



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[jira] [Commented] (SPARK-19136) Aggregator with case class as output type fails with ClassCastException

2017-01-13 Thread Andrew Ray (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15821868#comment-15821868
 ] 

Andrew Ray commented on SPARK-19136:


I forgot you can also just do:
{code}
ds.select(MinMaxAgg().toColumn)
{code}

> Aggregator with case class as output type fails with ClassCastException
> ---
>
> Key: SPARK-19136
> URL: https://issues.apache.org/jira/browse/SPARK-19136
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.2, 2.1.0
>Reporter: Mathieu D
>Priority: Minor
>
> {{Aggregator}} with a case-class as output type returns a Row that cannot be 
> cast back to this type, it fails with {{ClassCastException}}.
> Here is a dummy example to reproduce the problem 
> {code}
> import org.apache.spark.sql._
> import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
> import org.apache.spark.sql.expressions.Aggregator
> import spark.implicits._
> case class MinMax(min: Int, max: Int)
> case class MinMaxAgg() extends Aggregator[Row, (Int, Int), MinMax] with 
> Serializable {
>   def zero: (Int, Int) = (Int.MaxValue, Int.MinValue)
>   def reduce(b: (Int, Int), a: Row): (Int, Int) = (Math.min(b._1, 
> a.getAs[Int](0)), Math.max(b._2, a.getAs[Int](0)))
>   def finish(r: (Int, Int)): MinMax = MinMax(r._1, r._2)
>   def merge(b1: (Int, Int), b2: (Int, Int)): (Int, Int) = (Math.min(b1._1, 
> b2._1), Math.max(b1._2, b2._2))
>   def bufferEncoder: Encoder[(Int, Int)] = ExpressionEncoder()
>   def outputEncoder: Encoder[MinMax] = ExpressionEncoder()
> }
> val ds = Seq(1, 2, 3, 4).toDF("col1")
> val agg = ds.agg(MinMaxAgg().toColumn.alias("minmax"))
> {code}
> bq. {code}
> ds: org.apache.spark.sql.DataFrame = [col1: int]
> agg: org.apache.spark.sql.DataFrame = [minmax: struct]
> {code}
> {code}agg.printSchema(){code}
> bq. {code}
> root
>  |-- minmax: struct (nullable = true)
>  ||-- min: integer (nullable = false)
>  ||-- max: integer (nullable = false)
> {code}
> {code}agg.head(){code}
> bq. {code}
> res1: org.apache.spark.sql.Row = [[1,4]]
> {code}
> {code}agg.head().getAs[MinMax](0){code}
> bq. {code}
> java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to line4c81e18af34342cda654c381ee91139525.$read$$iw$$iw$$iw$$iw$MinMax
> [...]
> {code}



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[jira] [Commented] (SPARK-19136) Aggregator with case class as output type fails with ClassCastException

2017-01-10 Thread Mathieu D (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15816422#comment-15816422
 ] 

Mathieu D commented on SPARK-19136:
---

And... a RDD version based on treeAggregate is even quicker :-/
At least for that dummy min/max.


> Aggregator with case class as output type fails with ClassCastException
> ---
>
> Key: SPARK-19136
> URL: https://issues.apache.org/jira/browse/SPARK-19136
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.2, 2.1.0
>Reporter: Mathieu D
>
> {{Aggregator}} with a case-class as output type returns a Row that cannot be 
> cast back to this type, it fails with {{ClassCastException}}.
> Here is a dummy example to reproduce the problem 
> {code}
> import org.apache.spark.sql._
> import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
> import org.apache.spark.sql.expressions.Aggregator
> import spark.implicits._
> case class MinMax(min: Int, max: Int)
> case class MinMaxAgg() extends Aggregator[Row, (Int, Int), MinMax] with 
> Serializable {
>   def zero: (Int, Int) = (Int.MaxValue, Int.MinValue)
>   def reduce(b: (Int, Int), a: Row): (Int, Int) = (Math.min(b._1, 
> a.getAs[Int](0)), Math.max(b._2, a.getAs[Int](0)))
>   def finish(r: (Int, Int)): MinMax = MinMax(r._1, r._2)
>   def merge(b1: (Int, Int), b2: (Int, Int)): (Int, Int) = (Math.min(b1._1, 
> b2._1), Math.max(b1._2, b2._2))
>   def bufferEncoder: Encoder[(Int, Int)] = ExpressionEncoder()
>   def outputEncoder: Encoder[MinMax] = ExpressionEncoder()
> }
> val ds = Seq(1, 2, 3, 4).toDF("col1")
> val agg = ds.agg(MinMaxAgg().toColumn.alias("minmax"))
> {code}
> bq. {code}
> ds: org.apache.spark.sql.DataFrame = [col1: int]
> agg: org.apache.spark.sql.DataFrame = [minmax: struct]
> {code}
> {code}agg.printSchema(){code}
> bq. {code}
> root
>  |-- minmax: struct (nullable = true)
>  ||-- min: integer (nullable = false)
>  ||-- max: integer (nullable = false)
> {code}
> {code}agg.head(){code}
> bq. {code}
> res1: org.apache.spark.sql.Row = [[1,4]]
> {code}
> {code}agg.head().getAs[MinMax](0){code}
> bq. {code}
> java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to line4c81e18af34342cda654c381ee91139525.$read$$iw$$iw$$iw$$iw$MinMax
> [...]
> {code}



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[jira] [Commented] (SPARK-19136) Aggregator with case class as output type fails with ClassCastException

2017-01-10 Thread Mathieu D (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15816366#comment-15816366
 ] 

Mathieu D commented on SPARK-19136:
---

Both queries are not equivalent, the dummy group generate a shuffle + 
repartition.
12s vs almost 2mn for 1M rows on my laptop. 
So the 2nd one is definitely not acceptable.

So, I'll go with the first one, thanks for the hint !



> Aggregator with case class as output type fails with ClassCastException
> ---
>
> Key: SPARK-19136
> URL: https://issues.apache.org/jira/browse/SPARK-19136
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.2, 2.1.0
>Reporter: Mathieu D
>
> {{Aggregator}} with a case-class as output type returns a Row that cannot be 
> cast back to this type, it fails with {{ClassCastException}}.
> Here is a dummy example to reproduce the problem 
> {code}
> import org.apache.spark.sql._
> import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
> import org.apache.spark.sql.expressions.Aggregator
> import spark.implicits._
> case class MinMax(min: Int, max: Int)
> case class MinMaxAgg() extends Aggregator[Row, (Int, Int), MinMax] with 
> Serializable {
>   def zero: (Int, Int) = (Int.MaxValue, Int.MinValue)
>   def reduce(b: (Int, Int), a: Row): (Int, Int) = (Math.min(b._1, 
> a.getAs[Int](0)), Math.max(b._2, a.getAs[Int](0)))
>   def finish(r: (Int, Int)): MinMax = MinMax(r._1, r._2)
>   def merge(b1: (Int, Int), b2: (Int, Int)): (Int, Int) = (Math.min(b1._1, 
> b2._1), Math.max(b1._2, b2._2))
>   def bufferEncoder: Encoder[(Int, Int)] = ExpressionEncoder()
>   def outputEncoder: Encoder[MinMax] = ExpressionEncoder()
> }
> val ds = Seq(1, 2, 3, 4).toDF("col1")
> val agg = ds.agg(MinMaxAgg().toColumn.alias("minmax"))
> {code}
> bq. {code}
> ds: org.apache.spark.sql.DataFrame = [col1: int]
> agg: org.apache.spark.sql.DataFrame = [minmax: struct]
> {code}
> {code}agg.printSchema(){code}
> bq. {code}
> root
>  |-- minmax: struct (nullable = true)
>  ||-- min: integer (nullable = false)
>  ||-- max: integer (nullable = false)
> {code}
> {code}agg.head(){code}
> bq. {code}
> res1: org.apache.spark.sql.Row = [[1,4]]
> {code}
> {code}agg.head().getAs[MinMax](0){code}
> bq. {code}
> java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to line4c81e18af34342cda654c381ee91139525.$read$$iw$$iw$$iw$$iw$MinMax
> [...]
> {code}



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[jira] [Commented] (SPARK-19136) Aggregator with case class as output type fails with ClassCastException

2017-01-10 Thread Andrew Ray (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-19136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15815460#comment-15815460
 ] 

Andrew Ray commented on SPARK-19136:


You did not to a _typed_ aggregation so your result is a dataframe. It can be 
manipulated back into a dataset like:
{code}
agg.select("minmax.*").as[MinMax]
{code}

Or you can do a typed aggregation to get back a typed result, although there is 
no method for a global typed aggregate currently so the creation of a dummy 
group is needed: 
{code}
ds.groupByKey(_ => 1).agg(MinMaxAgg().toColumn)
{code}

> Aggregator with case class as output type fails with ClassCastException
> ---
>
> Key: SPARK-19136
> URL: https://issues.apache.org/jira/browse/SPARK-19136
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.2, 2.1.0
>Reporter: Mathieu D
>
> {{Aggregator}} with a case-class as output type returns a Row that cannot be 
> cast back to this type, it fails with {{ClassCastException}}.
> Here is a dummy example to reproduce the problem 
> {code}
> import org.apache.spark.sql._
> import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
> import org.apache.spark.sql.expressions.Aggregator
> import spark.implicits._
> case class MinMax(min: Int, max: Int)
> case class MinMaxAgg() extends Aggregator[Row, (Int, Int), MinMax] with 
> Serializable {
>   def zero: (Int, Int) = (Int.MaxValue, Int.MinValue)
>   def reduce(b: (Int, Int), a: Row): (Int, Int) = (Math.min(b._1, 
> a.getAs[Int](0)), Math.max(b._2, a.getAs[Int](0)))
>   def finish(r: (Int, Int)): MinMax = MinMax(r._1, r._2)
>   def merge(b1: (Int, Int), b2: (Int, Int)): (Int, Int) = (Math.min(b1._1, 
> b2._1), Math.max(b1._2, b2._2))
>   def bufferEncoder: Encoder[(Int, Int)] = ExpressionEncoder()
>   def outputEncoder: Encoder[MinMax] = ExpressionEncoder()
> }
> val ds = Seq(1, 2, 3, 4).toDF("col1")
> val agg = ds.agg(MinMaxAgg().toColumn.alias("minmax"))
> {code}
> bq. {code}
> ds: org.apache.spark.sql.DataFrame = [col1: int]
> agg: org.apache.spark.sql.DataFrame = [minmax: struct]
> {code}
> {code}agg.printSchema(){code}
> bq. {code}
> root
>  |-- minmax: struct (nullable = true)
>  ||-- min: integer (nullable = false)
>  ||-- max: integer (nullable = false)
> {code}
> {code}agg.head(){code}
> bq. {code}
> res1: org.apache.spark.sql.Row = [[1,4]]
> {code}
> {code}agg.head().getAs[MinMax](0){code}
> bq. {code}
> java.lang.ClassCastException: 
> org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast 
> to line4c81e18af34342cda654c381ee91139525.$read$$iw$$iw$$iw$$iw$MinMax
> [...]
> {code}



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