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>
>
>
