Well I've tried with 1.5.2, 1.6.2 and 2.0.1
FYI, I have created https://issues.apache.org/jira/browse/SPARK-18388
On Wed, Nov 9, 2016 at 3:08 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> Which version of Spark? Does seem like a bug.
>
> On Wed, Nov 9, 2016 at 10:06 AM, Raviteja Lokineni <
> raviteja.lokin...@gmail.com> wrote:
>
>> Does this stacktrace look like a bug guys? Definitely seems like one to
>> me.
>>
>> Caused by: java.lang.StackOverflowError
>> at org.apache.spark.sql.execution.SparkPlan.prepare(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>> at org.apache.spark.sql.execution.SparkPlan.prepare(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>> at org.apache.spark.sql.execution.SparkPlan.prepare(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>> at org.apache.spark.sql.execution.SparkPlan.prepare(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$prepare$1.apply(SparkPlan.scala:195)
>> at scala.collection.immutable.List.foreach(List.scala:381)
>>
>>
>> On Wed, Nov 9, 2016 at 10:48 AM, Raviteja Lokineni <
>> raviteja.lokin...@gmail.com> wrote:
>>
>>> Hi all,
>>>
>>> I am not sure if this is a bug or not. Basically I am generating weekly
>>> aggregates of every column of data.
>>>
>>> Adding source code here (also attached):
>>>
>>> from pyspark.sql.window import Window
>>> from pyspark.sql.functions import *
>>>
>>> timeSeries = sqlContext.read.option("header",
>>> "true").format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat").load("file:///tmp/spark-bug.csv")
>>>
>>> # Hive timestamp is interpreted as UNIX timestamp in seconds*
>>> days = lambda i: i * 86400
>>>
>>> w = (Window()
>>> .partitionBy("id")
>>> .orderBy(col("dt").cast("timestamp").cast("long"))
>>> .rangeBetween(-days(6), 0))
>>>
>>> cols = ["id", "dt"]
>>> skipCols = ["id", "dt"]
>>>
>>> for col in timeSeries.columns:
>>> if col in skipCols:
>>> continue
>>> cols.append(mean(col).over(w).alias("mean_7_"+col))
>>> cols.append(count(col).over(w).alias("count_7_"+col))
>>> cols.append(sum(col).over(w).alias("sum_7_"+col))
>>> cols.append(min(col).over(w).alias("min_7_"+col))
>>> cols.append(max(col).over(w).alias("max_7_"+col))
>>>
>>> df = timeSeries.select(cols)
>>> df.orderBy('id', 'dt').write\
>>> .format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat")\
>>> .save("file:///tmp/spark-bug-out.csv")
>>>
>>>
>>> Thanks,
>>> --
>>> *Raviteja Lokineni* | Business Intelligence Developer
>>> TD Ameritrade
>>>
>>> E: raviteja.lokin...@gmail.com
>>>
>>> [image: View Raviteja Lokineni's profile on LinkedIn]
>>> <http://in.linkedin.com/in/ravitejalokineni>
>>>
>>>
>>
>>
>> --
>> *Raviteja Lokineni* | Business Intelligence Developer
>> TD Ameritrade
>>
>> E: raviteja.lokin...@gmail.com
>>
>> [image: View Raviteja Lokineni's profile on LinkedIn]
>> <http://in.linkedin.com/in/ravitejalokineni>
>>
>>
>
--
*Raviteja Lokineni* | Business Intelligence Developer
TD Ameritrade
E: raviteja.lokin...@gmail.com
[image: View Raviteja Lokineni's profile on LinkedIn]
<http://in.linkedin.com/in/ravitejalokineni>
