[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
[
https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:37 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
*doing extra.cache() before the filtering will fix the issue* but obviously
isn't a solution.
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
*doing extra.cache() before the filtering will fix the issue.*
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sq
[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
[
https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:29 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
*doing extra.cache() before the filtering will fix the issue.*
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
doing extra.cache() before the filtering will fix the issue.
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun
[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
[
https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:22 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
doing extra.cache() before the filtering will fix the issue.
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apach
[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
[
https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:24 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
doing extra.cache() before the filtering will fix the issue.
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
doing extra.cache() before the filtering will fix the issue.
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$an
[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
[
https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:21 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > java.lang.RuntimeException:
Invalid PythonUDF (b#1L, c#6L), requires attributes from more than one
child.
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf >
`java.lang.RuntimeException: Invalid PythonUDF (b#1L, c#6L), requires
attributes from more than one child.`
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNod
[jira] [Comment Edited] (SPARK-18589) persist() resolves "java.lang.RuntimeException: Invalid PythonUDF (...), requires attributes from more than one child"
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https://issues.apache.org/jira/browse/SPARK-18589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761741#comment-15761741
]
Franklyn Dsouza edited comment on SPARK-18589 at 12/19/16 5:20 PM:
---
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf >
`java.lang.RuntimeException: Invalid PythonUDF (b#1L, c#6L), requires
attributes from more than one child.`
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
was (Author: franklyndsouza):
The sequence of steps that causes this are:
{code}
join two dataframes A and B > make a udf that uses one column from A and
another from B > filter on column produced by udf > :nuke:
{code}
Here are some minimum steps to reproduce this issue in pyspark
{code:python}
from pyspark.sql import types
from pyspark.sql import functions as F
df1 = sqlCtx.createDataFrame([types.Row(a=1, b=2), types.Row(a=1, b=4)])
df2 = sqlCtx.createDataFrame([types.Row(a=1, c=12)])
joined = df1.join(df2, df1['a'] == df2['a'])
extra = joined.withColumn('sum', F.udf(lambda a,b : a+b,
types.IntegerType())(joined['b'], joined['c']))
filtered = extra.where(extra['sum'] < F.lit(10)).collect()
{code}
> persist() resolves "java.lang.RuntimeException: Invalid PythonUDF
> (...), requires attributes from more than one child"
> --
>
> Key: SPARK-18589
> URL: https://issues.apache.org/jira/browse/SPARK-18589
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
>Affects Versions: 2.0.2, 2.1.0
> Environment: Python 3.5, Java 8
>Reporter: Nicholas Chammas
>Priority: Minor
>
> Smells like another optimizer bug that's similar to SPARK-17100 and
> SPARK-18254. I'm seeing this on 2.0.2 and on master at commit
> {{fb07bbe575aabe68422fd3a31865101fb7fa1722}}.
> I don't have a minimal repro for this yet, but the error I'm seeing is:
> {code}
> py4j.protocol.Py4JJavaError: An error occurred while calling o247.count.
> : java.lang.RuntimeException: Invalid PythonUDF <...>(...), requires
> attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:149)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:149)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
> at
> org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala
